ArabidopsisRootMorphology / geometry measurementSegmentationRoot system architecture
Introduction Plant parasitism by sedentary plant-parasitic nematodes is a dynamic and continuously evolving process, accompanied by profound remodelling of host root system architecture across distinct infection stages. However, the physiology and anisotropic growth of Arabidopsis thaliana roots under Heterodera schachtii infection, together with complex lateral root proliferation and increasingly dense, overlapping morphology, pose substantial challenges for accurate image segmentation. Methods Here, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features. These adaptations address the unique morphological complexity of the infected root system. Additionally, we integrated Root-TransUNet into a high-throughput phenotyping pipeline and applied it to an existing dataset of ~120,000 images of 362 A. thaliana MAGIC recombinant inbred lines collected over several months of infection. By extracting root system architecture traits, including root surface area and estimated root volume across infection stages, we enabled stage-specific association analyses between host root growth and nematode performance across these genotypes. Results Root-TransUNet achieved strong segmentation performance, demonstrating improved structural continuity and boundary precision compared with widely used CNN- and Transformer-based baselines, including UNet++. Stage-specific analyses revealed that the relationship between host root traits and nematode performance changed as infection progressed. During establishment, nematode number was largely independent of initial root size and varied strongly among genotypes, whereas during the reproductive phase (10-30 dpi), greater root expansion coincided with reduced estimated nematode volume accumulation. Notably, nematode burden was largely independent of host root size before infection, indicating that root quantity was generally not a limiting factor for infection in this experiment. Discussion These results demonstrate that Root-TransUNet can robustly segment infected root systems across a wide range of nematode infection densities, providing a scalable image-analysis framework for studying plant-parasitic nematode parasitism in combination with host root phenotyping.
Why it matches plant phenotyping methods感染根系の画像セグメンテーション手法を開発し、高スループット表現型解析パイプラインに統合して根系形態形質を抽出しており、表現型取得・抽出法が中心的である。
abstractHere, we introduce Root-TransUNet, an optimised segmentation architecture that extends TransUNet by incorporating a composite loss function to improve boundary precision and structural continuity, as well as dual-stage strip-pooling (SP) modules to enhance elongated and directional root features.
Reproduction assets foundThe paper analyzes a public BioImages dataset (S-BIAD2402) of ~400,000 RGB root/nematode infection images and provides authors' analysis code on GitHub; both are paper-specific, public, and actionable.Code · publicng molecular signatures, deepening our understanding of host-parasite resource allocation strategies, and establishing a foundation for the discovery of novel resistance mechanisms.
Code and data availability
Python-based source code for automating root analysis using the datasets above is accessible via our GitHub repository ( https://github.com/JieZhou1025/Root-nematode-interaction ).
Statements
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.ebi.ac.uk/biostudies/bioimages/studies/S-BIAD2402 .
Ethics statement
The manuscript presents research on animals that do not require ethical approval for their study.
AuthorOpen asset ↗JieZhou1025/Root-nematode-interactionlines:412-424Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Rice panicle blast detection is an important task in plant disease phenotyping. Field-based detection remains challenging because infected spike regions are often small, sparse, elongated, and affected by overlapping panicles, complex backgrounds, and variable illumination. In this study, we propose RPB-YOLO11, a lightweight YOLO11-based detector designed for rice panicle blast detection. The model uses a Lightweight Ghost Backbone (LGB) to reduce redundant computation. It uses Anisotropic Axial Stripe Attention (A2SA) to represent elongated panicle structures. It also uses Focal Multi-Scale Attention (FMSA) for multi-scale feature refinement and Adaptive Geometric Shape IoU (AGS-IoU) for geometry-aware localization. The model was trained and evaluated on a rice panicle image dataset containing 1,055 training images, 69 validation images, and 169 test images. On the test set, RPB-YOLO11 achieved 76.09% mAP50, 45.44% mAP50-95, 73.98% precision, and 72.75% recall with 6.21 GFLOPs. Compared with the YOLO11n baseline, it improved mAP50, mAP50-95, precision, and recall by 2.73, 2.12, 1.64, and 2.11 percentage points, respectively. An Android-oriented inference application supports local image inference, detection visualization, class counting, and diseased-panicle incidence estimation. These results suggest that RPB-YOLO11 provides a practical approach for image-based rice panicle blast survey.
Why it matches plant phenotyping methodsイネ穂いもちの画像検出モデルを開発・比較検証し、罹病穂率を推定する実用アプリまで構築しており、植物病害状態の画像ベース表現型取得が中心である。
abstractIn this study, we propose RPB-YOLO11, a lightweight YOLO11-based detector designed for rice panicle blast detection.
Reproduction assets foundThe paper links a public Hugging Face dataset used to establish the rice panicle blast detection dataset and a public GitHub release (data availability statement) containing the study's datasets/models.Dataset · publicsites, cultivars, growth stages, imaging conditions, and disease severities are still needed to evaluate generalization more fully.
Statements
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/XuzheYang2Doc/RPB-YOLO11/releases/tag/rpb-yolo11 .
Author contributions
XY: Data curation, Formal analysis, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review & editing. XZ: Conceptualization, Methodology, Visualization, Writing – original draft, Writing – review & editing. CX: Formal analysisOpen asset ↗XuzheYang2Doc/RPB-YOLO11 · rpb-yolo11lines:639-658Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Salt stress can markedly alter seedling architecture, creating a need for non-destructive three-dimensional (3D) phenotyping methods capable of resolving fine plant structures. However, organ-level segmentation of plant point clouds remains challenging because of leaf overlap, slender stems, ambiguous stem–leaf boundaries, and severe class imbalance. In this study, we developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress. PTV-SegCo integrates efficient channel attention, gated shallow–deep feature fusion, and a combined cross-entropy–Dice loss to improve representation of fine and minority organ structures. The dataset comprised 60 manually annotated 3D point-cloud samples from 12 cultivation trays repeatedly observed over five acquisition dates under six NaCl concentrations (0, 50, 100, 150, 200, and 250 mmol L −1 ). Because the earliest acquisition represented a particularly challenging developmental stage, these 12 samples were used as a fixed early-stage model-selection set, while samples from the remaining four dates were organized into four date-blocked training–validation configurations. Under this internal model-development protocol, PTV-SegCo achieved mean mAcc and mIoU values of 93.05% and 89.05%, respectively, and showed numerically higher performance than its direct backbone PTV-Seg50. These values should be interpreted as internal comparative results rather than as an unbiased estimate of generalization to unseen cultivation trays, and the present results should not be interpreted as establishing the broad competitiveness of PTV-SegCo against other point-based, convolution-based, graph-based, transformer-based, or plant-specific segmentation architectures. After semantic segmentation, reconstructed scenes were metrically calibrated using the known cultivation-tray dimensions, followed by individual-plant separation and quality control. Four reconstruction-derived structural descriptors—plant height, projected area, voxel occupancy volume, and leaf point ratio—were extracted to characterize temporal structural variation under different NaCl treatments. For treatment-level inference, individual-plant measurements were aggregated within each cultivation tray at each acquisition time, with the cultivation tray treated as the independent experimental unit. Independent manual validation showed close agreement for plant height and projected area, with R 2 values of 0.9969 and 0.986, respectively. Overall, the proposed workflow provides a feasible approach for organ-level segmentation and automated 3D structural analysis of small coriander seedlings under salt stress. The extracted descriptors primarily represent reconstruction-derived spatial characteristics and should not be interpreted as direct indicators of physiological status; voxel occupancy volume and leaf point ratio remain without direct external validation.
Why it matches plant phenotyping methods3D点群による器官セグメンテーションと構造形質抽出の手法開発・内部比較・手動検証が研究の中心であり、塩ストレスは適用対象である。
abstractwe developed PTV-SegCo, a task-adapted Point Transformer model for organ segmentation and structural phenotyping of coriander seedlings under salt stress.
Introduction Leaf shape is a genetically determined crop phenotype, and its accurate classification underpins soybean germplasm assessment and genetic improvement. Manual classification is highly subjective and struggles to distinguish morphologically similar leaves, while mainstream supervised classification demands large labeled datasets and incurs high development costs. Efficient feature frameworks for soybean leaf categorization are still insufficient. Methods In this study, 581 biologically replicated terminal leaflets sampled from 194 soybean varieties were analyzed at the single-leaflet level using traditional morphological indices and novel leaf contour angular features. Unsupervised K-means clustering was used to classify soybean leaflet morphological phenotypes; t-SNE was applied exclusively for dimensional reduction visualization, while Welch's ANOVA combined with Games-Howell post-hoc tests was adopted to detect inter-cluster phenotypic differences. Clustering stability and external consistency against manual visual labeling were further quantified via Adjusted Rand Index to comprehensively verify the reliability of grouping outputs. Results The results revealed no significant difference in leaflet edge complexity (p = 0.41) between two manually divided leaf groups distinguished by overall leaf outline similarity; these two morphologically similar leaf clusters failed to be fully separated even though the first two principal components accounted for 90.2% of total variance. For K-means clustering, k = 3 achieved better overall performance with a Calinski–Harabasz (CH) index of 395.55, Davies–Bouldin (DB) index of 1.03, and silhouette coefficient (SC) of 0.38, compared with k = 4. Nevertheless, the angular feature attained an F-value of 951.62 in driving sample reallocation across clusters, serving as the core indicator for fine subdivision at k = 4. Under k = 4 clustering, all six morphological indices differed significantly among the four groups (p < 0.05). Additionally, the number of cross-clustered samples increased from 66 to 119 as k rose from 3 to 4, with 96.6% of cross-clustering attributed to the leaflet contour angular feature. Discussion This research provides a novel reference and technical support for the automated identification and fine classification of soybean leaf morphology.
Why it matches plant phenotyping methods大豆小葉の形態表現型を角度特徴量とクラスタリングで自動分類する手法が研究の中心であり、検証指標も明示されているため。
abstractLeaf shape is a genetically determined crop phenotype, and its accurate classification underpins soybean germplasm assessment and genetic improvement.
Accurate quantification of rice aboveground biomass (AGB) is critical for crop monitoring but remains challenging due to the complex nonlinearity arising from the coupling of plant density, spatial structure, and internal dry matter distribution. To address the limitations of single-source remote sensing, this study proposed a Three-Dimensional Dry Matter Distribution Integration (3D-DMI) model, which establishes a physically interpretable framework decomposing AGB into dry matter density ( ρ ), horizontal projection distribution ( S ), and vertical cumulative distribution ( h d ) components. Guided by this framework, a core subset of six features (Red_650, MTCI, G_correlation, R_correlation, LPI, and HPA0_99) was extracted from UAV-based multispectral, RGB, and LiDAR data using a dual-step feature selection approach combining Maximum Information Coefficient (MIC) and Distance Correlation (dCor). A Random Forest (RF) regression model was then developed to estimate AGB across the entire growth season. The results demonstrated that the 3D-DMI model achieved excellent performance with an R 2 of 0.920, an RMSE of 0.184 kg/m², and an RPD of 3.544, significantly outperforming any single-sensor approach. Single-feature analysis revealed that while LiDAR-derived structural features provided the fundamental basis for biomass estimation, they encountered inherent saturation bottlenecks during late growth stages. Feature contribution analysis based on SHAP further quantified that LiDAR-derived features dominated the estimation process (68.5% contribution), providing the volumetric basis, whereas RGB textures (18.3%) and multispectral features (13.3%) provided indispensable supplements. Ultimately, this study established a robust, physically grounded computational paradigm for high-precision UAV-based rice biomass monitoring across the entire growth cycle.
Why it matches plant phenotyping methodsUAVマルチスペクトル・RGB・LiDARデータからイネの地上部バイオマスを推定する3D-DMI計算手法を開発・評価しており、植物形質の取得・抽出が研究の中心である。
abstractthis study proposed a Three-Dimensional Dry Matter Distribution Integration (3D-DMI) model
Plant disease and plant stress early warning systems have advanced through deep learning, remote sensing, digital phenotyping, disease forecasting, and sensor networks. Detection accuracy, precision, recall, F1-score, and area under the curve remain indispensable, but they are insufficient for judging whether a warning can support timely and proportionate phytoprotection under field variability. This Mini Review argues that intelligent plant health warning systems should be evaluated not only as prediction models, but also as safety-relevant decision-support systems embedded in biological, agronomic, and operational contexts. We first relate AI-based detection to established plant disease forecasting and decision-support traditions, including weather-based models, epidemiological forecasting, and integrated disease management. We then adapt selected safety-assurance concepts, including risk assessment, failure mode and effects analysis, Bow-tie reasoning, warning-threshold governance, reliability analysis, resilience thinking, and response closure, to host-pathogen-environment warning chains. The proposed framework links AI or sensor outputs with pathogen biology, host susceptibility, environmental conduciveness, inoculum pressure, uncertainty assessment, risk classification, threshold decisions, human or automated verification, intervention, and feedback learning. Illustrative crop-pathogen scenarios, including wheat rust, rice blast, potato late blight, grapevine downy mildew, and citrus greening, show how safety assurance can complement existing forecasting and decision-support systems rather than replace them. The framework remains conceptual, and whether these added assurance functions improve existing warning systems requires comparative evaluation under field conditions. Future systems should be evaluated through detection performance and response-oriented indicators such as lead time, calibration, false-alert burden, missed-warning rate, response completion, disease suppression, economic value, and learning after field action.
Why it matches plant phenotyping methods植物病害・ストレスの検出を含む知的警戒システムについて、AI・リモートセンシング・デジタルフェノタイピング・センサーネットワークの評価枠組みを体系的に論じる方法論レビューであり、方法論が中心です。
abstractPlant disease and plant stress early warning systems have advanced through deep learning, remote sensing, digital phenotyping, disease forecasting, and sensor networks.
Introduction In a complex orchard environment, canopy apples are obscured by various factors, making it hard for apple harvesting robots to accurately determine which apples can be directly harvested. Furthermore, the complex obstruction leads to difficulties in identifying keypoints on the apples and caculating the axis direction, directly affecting the robot's determination of grasping positions. Methods To solve these issues, a two-dimensional (2D) axis estimation method for pickable canopy apples based on a YOLO cascade network was proposed. Firstly, the study introduced SPDConv for lossless downsampling and adopts dynamic upsampling to improve segmentation boundary accuracy, constructing an instance segmentation network named the YOLO-SD model to select pickable apples according to different occlusion conditions and growth states. Secondly, the geometric center of the mask image was located using its minimum enclosing circle, and the region of interest was extracted through morphological dilation. Then, by integrating the RFAConv, SCSA attention, and MBConv modules, a keypoint detection network YOLO-RSM was constructed to extract keypoints of pickable apples. Finally, a 2D axis construction strategy was proposed, which adaptively constructs the growth axis based on the visibility of keypoints. Results Experimental results show that the overall average accuracy mAP50 of the YOLO-SD model for apple segmentation reached 95.2%, and the parameter quantity was reduced to 2.47 M. The average accuracy of the YOLO-RSM model for keypoint detection has reached 90.3%, which is 2.4%, 2.6%, and 4.7% higher than that of the YOLOv8n, YOLO11n, and YOLO12n models respectively. The 2D axis estimation algorithm has an average axis angular error of 7.23° ± 16.73°, and an axis estimation accuracy of 92.68%. Discussion The proposed method can achieve high-precision canopy apple segmentation, keypoint detection, and 2D axis estimation, thus offering technical support for the picking operations of apple harvesting robots.
Why it matches plant phenotyping methodsリンゴ果実のセグメンテーション、キーポイント抽出、成長軸（器官形態）の推定を中心に新規画像解析法を開発・検証しており、単なる収穫対象の検出を超える植物器官形質の推定に該当する。
abstracta two-dimensional (2D) axis estimation method for pickable canopy apples based on a YOLO cascade network was proposed.
To address the challenges posed by large lesion-scale variations, weak boundary cues, and high inter-class similarity in rice leaf disease detection, this study proposes an improved RT-DETRv2-R50-based rice disease detection model, termed CED-RTDETR. First, a Lightweight Contour-Guided Aggregation Backbone (LCGA-Backbone) is constructed. In the PResNet residual blocks, an InceptionDWConv2d-based direction-aware depthwise separable spatial mixing strategy is introduced to capture local, horizontal, and vertical lesion texture patterns with low computational overhead. Meanwhile, a Contour-guided Efficient Global Aggregation Block (CEGA Block) is embedded after the outputs of the C3, C4, and C5 stages. Through contour-difference enhancement, channel shuffle, group-wise efficient global aggregation, and bottleneck channel mixing, the proposed block strengthens the representation of the boundaries of small lesions, weak textures, and contextual semantics. Second, a Multi-scale Evidence Interaction Fusion Neck (MEIF-Neck) is used to perform multi-scale evidence interaction and salient-region competition after cross-scale feature concatenation. A lightweight feature reconstruction process is further implemented using Spatial-Evidence RepNCSPELAN (SE-RepNCSPELAN), which is developed from a YOLO-style feature fusion structure. Finally, a Direction-Amplitude Decoupled Deformable Attention (DAD-DA) mechanism is introduced to decompose sampling offsets into direction rotation residuals and radius gains while incorporating an aspect-ratio-aware geometric compression-restoration strategy, thereby improving the geometric stability of sampling locations during the decoding stage. Experimental results on the constructed rice leaf disease dataset show that CED-RTDETR achieves AP, AP50, and AP75 values of 29.5%, 75.2%, and 17.6%, respectively, outperforming RT-DETRv2-R50 by 4.1, 4.9, and 3.8 percentage points. To further evaluate the model on an additional public benchmark dataset, experiments were also conducted on the public Rice Disease Dataset. On this dataset, CED-RTDETR achieves AP, AP50, and AP75 values of 34.1%, 77.1%, and 23.2%, respectively, improving upon RT-DETRv2-R50 by 4.3, 5.6, and 3.4 percentage points. These results indicate that the proposed method achieves consistent overall performance improvements on both the constructed dataset and the public benchmark dataset.
Why it matches plant phenotyping methodsイネ葉の病斑・病害を画像から検出する深層学習モデルを開発し、構築データセットと公開ベンチマークで性能検証しているため、植物病害状態のフェノタイピング手法が中心です。
abstractTo address the challenges posed by large lesion-scale variations, weak boundary cues, and high inter-class similarity in rice leaf disease detection, this study proposes an improved RT-DETRv2-R50-based rice disease detection model, termed CED-RTDETR.
Introduction Accurate classification of unsound wheat kernels is important for automated grain quality assessment, but improved recognition performance often comes at the cost of increased model complexity. Methods This study presents ECA-ModNet, a parameter-efficient convolutional network derived from EfficientNetV2-S. The architecture replaces two early-stage Fused-MBConv blocks with Mod-FusedMBConv blocks to introduce input-dependent local contextual modulation and replaces the squeeze-and-excitation modules in later stages with efficient channel attention to model local cross-channel interactions using fewer attention-related parameters. Experiments were conducted on the seven-class G600 wheat subset of the GrainSpace dataset. Results Across three independent runs, ECA-ModNet achieved an accuracy of 90.17 ± 0.21% and a macro-F1 score of 90.23 ± 0.21%, improving upon EfficientNetV2-S by 3.80 and 3.84 percentage points, respectively. The parameter count decreased from 20.19M to 16.49M, while FLOPs increased marginally from 2.90G to 2.95G. ECA-ModNet achieved accuracy statistically comparable to that of ConvNeXt-Tiny and InceptionNeXt-T while using substantially fewer parameters, and obtained 3.03-4.55 percentage points higher mean accuracy than six lightweight baselines. Discussion Ablation experiments identified two Stage 1 Mod-FusedMBConv blocks with a 3×3 context kernel as the configuration with the highest mean accuracy among those evaluated. These results indicate that ECA-ModNet offers a favorable accuracy-parameter trade-off for image-based classification of unsound wheat kernels.
Why it matches plant phenotyping methods小麦粒の状態（unsound kernel）を画像から分類するためのCNNを開発・比較・アブレーション評価しており、植物器官の状態推定手法が研究の中心である。
abstractThis study presents ECA-ModNet, a parameter-efficient convolutional network derived from EfficientNetV2-S.
Reproduction assets foundThe paper analyzes the public GrainSpace dataset (G600 seven-class unsound wheat kernel subset) and provides an explicit data availability statement with a public GitHub URL. No author analysis code or trained model deposit is stated.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://github.com/hellodfan/GrainSpace .Open asset ↗hellodfan/GrainSpacelines:1035-1076Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Rice seed vigor is a key determinant of germination performance and final crop yield, making its rapid and non-destructive assessment essential for seed quality evaluation. Conventional vigor detection methods are often destructive, labor-intensive, and time-consuming. Hyperspectral imaging provides a promising non-destructive alternative, but hyperspectral data are typically high-dimensional, redundant, and susceptible to noise and scattering interference. Moreover, existing models still have limited ability to discriminate subtle spectral differences among seed vigor levels. To address these challenges, this study proposes a gated recurrent unit (GRU)-guided closed-loop CNN-Transformer network (GCT-BCLN) for accurate, non-destructive identification of rice seed vigor. The model establishes bidirectional information flow between CNN and Transformer via the GRU, enabling dynamic and synergistic optimization of local spectral features and global spectral representations. In addition, a combined preprocessing strategy integrating adaptive iteratively reweighted penalized least squares (AirPLS), Savitzky-Golay (SG) smoothing, and multiplicative scatter correction (MSC) was adopted to improve spectral quality. Experimental results showed that GCT-BCLN achieved a test accuracy of 0.9795 for hybrid indica rice, outperforming the CNN-Transformer fusion model by 1.37%. The model also achieved accuracies of 0.9793 and 0.9758 on conventional japonica rice and glutinous japonica rice, respectively, showing consistent performance across the three evaluated variety-specific datasets under the controlled experimental protocol. These results support the feasibility of GCT-BCLN for laboratory-scale, non-destructive discrimination of aging-induced rice seed categories under controlled conditions, while practical application requires further external validation.
Why it matches plant phenotyping methodsイネ種子の活力という植物状態を、ハイパースペクトル画像と新規深層学習モデルで非破壊推定する手法開発が研究の中心である。
abstractthis study proposes a gated recurrent unit (GRU)-guided closed-loop CNN-Transformer network (GCT-BCLN) for accurate, non-destructive identification of rice seed vigor.
Introduction Applying general-purpose vision-language models (VLMs) to crop disease diagnosis presents three critical bottlenecks: reliance on large-scale annotated data, the high computational cost of full finetuning, and existing adaptation methods designed mainly for discriminative classification without sufficient visual-linguistic interaction for generative diagnosis. Methods We propose BLAP, an adaptive multi-scale visual prompt fine-tuning framework built upon BLIP-2. BLAP introduces an adaptive visual prompt fusion module (APFM) with learnable prompt vectors and a gating mechanism, together with a multi-scale pyramid feature fusion module (PFM). All BLIP-2 backbone parameters are frozen, and only 0.11% of the model parameters are optimized. Results On a few-shot dataset comprising 990 images from 11 crops and 33 disease categories, BLAP achieved 92.78% recognition accuracy, outperforming the BLIP-2+LoRA baseline by 21.67 percentage points. BLEU-4 and ROUGE-L scores reached 0.6507 and 0.7184, respectively, while inference latency increased by only 2.15%. Discussion BLAP provides a lightweight solution that balances accuracy, efficiency, and interpretability for crop disease diagnosis in resource-constrained settings. The proposed dynamic prompt fusion and multiscale pyramid adaptation strategy may also be extended to parameter-efficient fine-tuning of visionlanguage models in other domain-specific applications.
Why it matches plant phenotyping methods作物病害画像から病徴・病害状態を推定する視覚モデル適応手法BLAPの開発と評価が中心であり、植物病害フェノタイピング手法に該当する。
abstractWe propose BLAP, an adaptive multi-scale visual prompt fine-tuning framework built upon BLIP-2.
Reproduction assets foundThe paper's few-shot crop disease dataset (990 images, 11 crops, 33 classes) is compiled entirely from four public Mendeley Data image repositories, each cited in Table 1 as the data source for specific crop/disease classes. These are the plant image inputs directly used for this paper's phenotyping/analysis. No authorDataset · publicncluding laboratory and field environments (Approximately 45% of them were captured in field environments), to enhance sample representativeness and model robustness.
Table 1
The number of collected diseases or healthy image data for each crop.
Crop
Disease
No. of images
Collection conditions
Data source
Apple
Apple scab
30
Lab
https://data.mendeley.com/datasets/tywbtsjrjv/1
Cedar apple rust
30
Lab
https://data.mendeley.com/datasets/tywbtsjrjv/1
Healthy
30
Lab
https://data.mendeley.com/datasets/tywbtsjrjv/1
Cashew
Healthy
30
Lab
https://data.mendeley.com/datasets/8fr7grr73p/1
Leaf miner
30
Lab
https://data.mendeley.com/datasets/8fr7grr73p/1
Red rust
30
Lab
https://data.mendeley.com/datasets/Open asset ↗lines:37-116Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Sustainable wheat farming is challenging. Real-time information on crop health, disease transmission, and anticipated yields is essential for farmers. However, they frequently use slow, expensive, or non-communicative tools. This project develops a workable solution. There is no need for massive server farms because the entire system operates on a single graphics card. It incorporates images of wheat fields, Indian farming notes, greenhouse records, harvest statistics, and NASA meteorological data. Consider them as various “eyes” for crop photo analysis, and we tried several lightweight computer vision models. ConvNeXt-Tiny was slower but could operate on older equipment with 75% accuracy; EfficientNetB0 recognised wheat heads with 92% accuracy; and AgroMark, a hybrid solution that merged photo analysis with agricultural metadata (soil type, rainfall, increased to 87%, etc. Combining picture analysis with attention mechanisms (CBAM) allowed us to anticipate the amount of wheat that a field will yield based on these photo insights, and the results showed that our predictions were accurate, with an R 2 score of 0.97. Additionally, we developed a versatile detector that simultaneously detects disease, stress, head count, and pests. It is adjusted to deal with training data that is unbalanced (some diseases are common, while others are rare). As we packed everything into a 16-GB graphics card, we spent real time determining which strategies smaller training sets, removing weak features, and adjusting loss functions, work. We encounter real-world obstacles along the road, such as photographs from different locations not always match, mislabeled photographs from different locations not always match, mislabeled diseases, and neglected rare pests. Our step-by-step instructions, charts, and code are available.
Why it matches plant phenotyping methods小麦画像から病害・ストレス・穂数・収量などの植物形質・状態を推定するマルチモーダル手法を開発し、複数モデルの精度比較と実装上の検証を行っており、表現型取得・推定が研究の中心である。
abstractThis project develops a workable solution.
Reproduction assets foundThe paper builds its multimodal wheat phenotyping analysis on several explicitly cited public data assets: the Kaggle Wheat Plant Diseases image dataset (used for disease classification, Tables 2 and 9), the Global Wheat Head Detection dataset (used for head detection, Tables 1 and 6), FAOSTAT and India Open GovernmentDataset · publicAvailable online at: https://www.fao.org/faostat/ . FAOSTAT statistical database.Open asset ↗lines:1110-1162Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotLeafClassificationStress / disease detectionDisease symptoms / severity
Automated plant disease recognition from field photographs remains challenging: models trained on laboratory datasets collapse on in-the-wild images, and the current state of the art on PlantWild-the largest open in-the-wild benchmark (18,542 images, 89 classes)-relies on text prototypes derived from a language model to reach 76.18% top 1 accuracy. We ask whether text supervision is truly necessary or whether the bottleneck is the diversity of the visual representation. Our central methodological finding is that backbone selection and fusion matter far more than classifier-head engineering : across 118 experiments, a simple fixed-weight linear-prototype combination on top of three complementary frozen backbones yields larger and more reproducible gains than any head-level adaptive routing mechanism we test. Specifically, through a systematic study of six frozen vision backbones (three CLIP, two DINOv3, and one DINOv2), three classifier heads, and four fusion configurations, completed in a single day on one consumer GPU, we establish three findings. (i) A single self-supervised backbone (DINOv2 ViT-L/14) already surpasses text-augmented MVPDR (77.56% vs. 76.18%). (ii) Concatenating three complementary backbones (DINOv2 + DINOv3 + CLIP) reaches 80.23% ± 0.41% (five seeds), exceeding the published MVPDR accuracy by +4.05 points and our own reproduction of MVPDR under an identical evaluation protocol by +7.96 points, without any language supervision. (The difference between the two deltas reflects evaluation-protocol differences-our split, model-selection criterion, and training schedule-rather than any discrepancy in the reported numbers; see Section 4.6.6 for a full reconciliation.) (iii) Every form of learned routing we test-per-class gating, backbone gating, sample-wise gating-is inessential; the gain is entirely attributable to backbone diversity and a simple linear-prototype scoring combination. On the smaller PlantDoc benchmark, the same principle transfers but with substantially higher seed variance: the best configuration reaches 80.09% at a favourable seed but 76.97% ± 1.48% over five seeds-a suggestive rather than robust gain. Beyond the accuracy headline, we provide a pathology-aware per-class analysis showing that DINOv2/v3 dominate on fine-texture lesion classes (rusts, mildews, and leaf spots) whilst CLIP's narrow advantage concentrates on organ/species-level identification (rice leaf and potato late blight). All primary claims are validated over five seeds, and all code, feature caches, and result files are released for full reproducibility.
Why it matches plant phenotyping methods植物病害の画像認識を対象に、複数の視覚基盤モデル融合と分類器を体系比較・検証しており、病害状態の推定手法が研究の中心である。
abstractOur central methodological finding is that backbone selection and fusion matter far more than classifier-head engineering
Reproduction assets foundThe paper states that its code, cached features, and result files will be released, but no authors' public URL or repository is provided in the supplied blocks (future-tense availability language only). The DINOv3 GitHub link and OpenReview link are third-party backbone resources, not paper-specific assets.Code · publicAll primary claims are validated over five seeds, and all code, feature caches, and result files are released for full reproducibility.Open asset ↗lines:332-335Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Accurate crop disease diagnosis is critical for agricultural productivity and food security, yet existing deep learning systems often struggle to generalize across visually similar diseases and varying environmental conditions. Recent Vision-Language Models (VLMs) have demonstrated promising zero-shot reasoning capabilities; however, most agricultural diagnostic systems still rely on isolated single-model predictions without collaborative reasoning or consensus mechanisms. In this work, we propose VIDA+PANDA, a multi-agent Vision-Language framework for zero-shot crop disease diagnosis. The framework consists of two stages: VIDA, where multiple VLM agents independently analyze crop leaf images to establish baseline performance, and the Peer-Anchored Named Deliberation Architecture (PANDA), which introduces a structured multi-round debate among a selected group of high-performing and architecturally diverse agents. During deliberation, agents exchange reasoning, critique peer predictions, and revise decisions through evidence-grounded discussion, while an anti-sycophancy mechanism discourages unsupported consensus shifts. Final predictions are generated through performance-weighted consensus voting. Experiments are conducted on the CDDM benchmark using seven heterogeneous VLMs from four independent providers, including two open-source models, under a fully zero-shot setting. A non-participant GPT-5 model serves as an independent judge to assess the final diagnostic predictions. Beyond conventional accuracy, the framework introduces three semantic measures: Semantic Label Similarity (SLS), which measures how semantically close a predicted crop-disease pair is to the ground truth and captures partial correctness overlooked by exact-match evaluation; Reasoning Specificity (RS), which measures how concretely an agent’s explanation references visual evidence such as lesion color, shape, texture, or margins; and Inter-Agent Reasoning Convergence (IRC), which measures the extent to which agents rely on similar visual evidence, capturing epistemic alignment independently of label correctness. Experimental results show that collaborative multiagent deliberation improves individual diagnostic performance and semantic alignment, with the largest gains observed among weaker participating agents. The findings also reveal important relationships between predictive accuracy, persuasive influence, and consensus formation in VLM-based agricultural diagnosis.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定するマルチエージェント画像・言語フレームワークを開発し、ベンチマークで性能評価しており、病害表現型の取得・推定手法が中心である。
abstractwe propose VIDA+PANDA, a multi-agent Vision-Language framework for zero-shot crop disease diagnosis.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Within the context of climate change, quinoa ( Chenopodium quinoa Willd.) is a climate-resilient crop with high nutritional value. The effects of deficit irrigation on quinoa growth and physiological performance under arid conditions remain insufficiently understood. This study evaluated ten quinoa genotypes (two commercial varieties and eight accessions) under two irrigation regimes to identify traits and spectral indices associated with water-stress tolerance. We combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively. Deficit irrigation reduced plant height (18%), specific leaf area (8%), yield (43%), harvest index (26%), relative water content (7%), and dry matter accumulation (36%), while relative chlorophyll content (SPAD, Soil Plant Analysis Development) and stomatal density increased by 16% and 13%, respectively; accession ACC_23 exhibited the highest water-use efficiency (5.9 g kg -1 ). A univariate analysis of 35 vegetation indices across 13 dates showed that: Health Index(HIV), Normalized Green-Red Difference Index (NGRD), Red-Green Ratio (RG) and Plant Senescence Reflectance Index (PSRI), were the most sensitive, detecting significant differences between irrigation treatments in up to 32 of the 130 possible genotype-by-date comparisons. Integrating remote sensing into crop phenotyping represented a significant methodological improvement by enhancing phenotyping efficiency, improving detection of deficit irrigation effects, and facilitating identification of tolerant quinoa genotypes for arid production systems.
Why it matches plant phenotyping methodsリモートセンシングと多時点の植 phenotyping を統合し、35の植生指数の感度比較によって水ストレス関連形質を抽出する方法適用が、研究の主要な技術的要素として明示されています。
abstractWe combined manual phenotyping of agromorphological and physiological traits with multispectral and spectroradiometer measurements to calculate 35 vegetation indices across 13 and 5 dates, respectively.
Accurate detection of small-target diseases on apple leaves is of great importance for optimizing orchard management and facilitating precision agriculture. Small-target disease detection remains challenging due to insufficient features and complex backgrounds. This work proposes an effective detector for apple leaf small-target diseases called ALSDet. The global context module is integrated into Stage2 to Stage4 of the ResNet-50 backbone, yielding a refinement of the feature-extraction architecture. In the bottleneck blocks of the Stage2 and Stage3, dilated convolution is used in place of the normal 3×3 convolution to enlarge the receptive field for small targets and strengthen feature extraction. During the model training phase, a multi-scale training strategy combined with the online hard example mining method is adopted to focus on learning hard samples and enhance adaptability for various scale targets. According to the experimental results, ALSDet obtains a mean average precision (mAP) of 65.6% and an average recall (AR) of 71.2% on the dataset. The proposed model achieves the highest levels in both mAP and AR when compared to the popular object detection models, such as Cascade R-CNN, Faster R-CNN, GFL, Grid R-CNN, Libra R-CNN, FCOS, VFNet, RetinaNet, SSD, YOLOv7, and YOLOv8. For small-target diseases like rust and frog eye leaf spot, the average precision surpasses 87% with an intersection over union (IoU) threshold of 0.5. These results confirm that ALSDet achieves stable performance against existing methods, demonstrating its potential as a practical tool for intelligent orchard disease management.
Why it matches plant phenotyping methodsリンゴ葉の病斑を画像から検出する深層学習モデルを開発・比較評価しており、植物の病害状態の取得・推定が研究の中心である。
abstractThis work proposes an effective detector for apple leaf small-target diseases called ALSDet.
Tolerance against winter freeze is the main focus of variety development in Louisiana, which represents the northernmost sugarcane-growing region worldwide. Antifreeze metabolites, xylem structure, and fiber content represent interrelated physicochemical properties contributing to freeze tolerance. This study first classified the cold tolerance of sugarcane cultivars using metabolites in juice as predictor variables. The best-fit model (XGBoost discriminant analysis) estimated the higher cold tolerance of the final on-station clone progeny to the stress tolerance-inducing wild germplasm line. Stalks of the tolerant sugarcane genotype contained higher fiber for mechanical support against cellular injury, compared to susceptible varieties. Fluorescence microscopy visualized phospholipids responsible for maintaining membrane fluidity during frost in lignin surrounding the vascular bundle. Thermal imaging is proposed for real-time monitoring of spatiotemporal temperature changes, as stalk injury is initiated by ice formation at sub-freeze temperatures during winter freeze. As additional datasets for independent prediction become available, developed methods could be used to explore the biomarkers for stress resistance in simpler multivariate discriminant analysis and the distribution of specific biomarkers in cellular components by microscopic imaging, and to trace stalk injury hot spots as a function of time and relationships with resistance markers.
Why it matches plant phenotyping methodsサトウキビの耐寒性という植物状態を、XGBoost判別モデルと画像・熱画像によって分類・評価する方法が研究の中心であり、単なる生物学的測定ではない。
titleMultivariate and imaging methods to classify cold tolerance of sugarcane ( Saccharum spp. hybrids) cultivars and breeding clones.
Introduction The precise detection of microscopic tea leaf diseases is a prerequisite for sustainable precision agriculture. While recent deep learning advancements often favor architectural complexity, this "complexity bias" frequently introduces computational redundancy-a "complexity tax"-that destabilizes gradient flow and fails to resolve critical resolution bottlenecks for micro-lesion identification. Methods We propose Opti-YOLOv11n, a minimalist optimization paradigm prioritizing physical input fidelity and stabilized gradient dynamics. Utilizing a dataset of six pathological categories, our framework employs high-resolution scaling (832×832) combined with a momentum-based SGD optimizer and a cosine annealing schedule to reconstruct essential spatial textures. Results Opti-YOLOv11n achieved a peak Precision of 98.87% and a Recall of 95.97%, while reducing the parameter count to 2.35 M-a 9.2% decrease relative to the baseline-and maintaining a real-time inference speed of 104.7 FPS on edge-simulated hardware. Discussion Statistical verification via 5-fold cross-validation confirms superior generalization stability. These results substantiate that strategic structural pruning and physical input scaling provide a more robust technical benchmark for autonomous plant protection than the adoption of excessive architectural depth.
Why it matches plant phenotyping methods茶葉病害の微小病斑を画像から検出する深層学習手法を開発し、精度・計算量・推論速度・交差検証で技術評価しているため、植物病害フェノタイピング手法が中心である。
abstractWe propose Opti-YOLOv11n, a minimalist optimization paradigm prioritizing physical input fidelity and stabilized gradient dynamics.
Leaf chlorophyll content (LCC) is a key indicator for assessing the photosynthetic capacity and nutritional status of winter wheat. Among traditional LCC estimation methods, empirical models lack a physical basis and have poor generalisability, while physical models are widely applicable but suffer from ill-posed inversion problems. Hybrid inversion methods, which integrate radiation transfer models such as PROSAIL with machine learning, offer both the interpretability of physical models and the efficiency of machine learning; however, they are still affected by the domain shift between simulated and measured data, which limits their generalisation performance. Transfer Component Analysis (TCA), a domain adaption method, can effectively alleviate this problem. In this study, hyperspectral and LCC data were collected in the field, and simulated data were generated using the PROSAIL model; a sensitivity analysis was conducted to identify LCC-sensitive bands. A genetic algorithm was applied to the measured data for band selection and, together with the results of the sensitivity analysis, yielded an optimal set of 30 characteristic bands for subsequent modelling. Three datasets were constructed: measured data only, a direct mixture of measured and simulated data, and a TCA-fused mixture of measured and simulated data. Four models-gradient boosting regression (GBR), random forest (RF), support vector regression (SVR) and deep neural network (DNN)-were developed for each dataset. The results show that: (1) the LCC-sensitive bands are concentrated in the 450-660 nm and 680-720 nm ranges; (2) the model built on the TCA-fused data (R² = 0.722, RMSE = 6.792) outperformed those built on the measured-only data (R² = 0.682, RMSE = 7.259) and the directly mixed data (R² = 0.616, RMSE = 7.976); (3) for the TCA-fused data, the four models differed considerably in accuracy, with SVR performing best (R² = 0.723, RMSE = 5.363), followed by RF (R² = 0.630, RMSE = 6.201) and GBR (R² = 0.575, RMSE = 6.650), whereas the DNN performed worst (R² = 0.388, RMSE = 7.947), probably owing to the limited sample size.
Why it matches plant phenotyping methods冬小麦の葉緑素含量という植物形質を、ハイパースペクトルデータとPROSAIL・機械学習・転移学習で推定する手法を開発・比較しており、形質取得とモデル性能評価が研究の中心である。
abstractHybrid inversion methods, which integrate radiation transfer models such as PROSAIL with machine learning, offer both the interpretability of physical models and the efficiency of machine learning
The precise identification of microspore and pollen at the optimal developmental stages to be induced towards embryogenesis (vacuolated microspores and young pollen) is essential for induction of in vitro androgenesis in plants. Such identification is not always easy, and it is especially difficult in recalcitrant species such as Vicia faba . The present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry. We measured flower bud and anther length and width, grouping them at different intervals, and performed a detailed microscopical and anatomical analysis of buds, anthers and microspores/pollen at different stages. Our results demonstrated that flower buds in V. faba exhibit complex and irregular morphologies, with considerable variation in both sepal length and shape. Furthermore, the determination of microspore and pollen developmental stages in this species is constrained by pronounced developmental asynchrony and strong genotype dependence. Although anther length measurements correlate closely with microspore and pollen developmental stages, their practical use can be challenging. Therefore, measuring flower bud length, while excluding sepals, remains the most practical criterion for routine applications. Combining this refined morphometric approach with microscopic validation appears to be the most effective strategy for improving the identification of flower buds containing microspores or pollen at developmental stages suitable for androgenesis induction in this recalcitrant legume species.
Why it matches plant phenotyping methods花蕾・葯の形態計測と顕微鏡検証を用いて、微小胞子・花粉の発達段階を推定する実用的な植物フェノタイピング手法を評価しており、方法開発・検証が中心である。
abstractThe present study evaluates the relationship between floral bud and anther morphometric parameters, and microspore/pollen developmental stages in V. faba using various methods including fluorescent staining, differential interference contrast microscopy, and morphometry.
TomatoLiDAR / point cloudLeafMorphology / geometry measurementSegmentationLeaf traits
Leaf parameters are crucial indicators reflecting the growing status of plants. Monitoring and analysis of leaf parameters significantly contributes to the improvement of crop yield and food quality. This study focused on three tomato plant varieties commonly grown in the Netherlands and proposed a fully automatic pipeline for leaf phenotyping. Three-dimensional (3D) point clouds of target plants were acquired with a specially designed imaging unit naming Maxi-Marvin. A semantic segmentation of plant organs was performed with PointNet++ model. To mitigate point cloud resolution decrement, the down-sampling operation in the baseline model was replaced with a distributed segmentation strategy. Leaf instances were further identified with Density-Based Spatial Clustering of Applications with Noise (DBSCAN), followed by a morphological phenotypic trait quantification based on 3D geometrical analysis. Target phenotypic traits including leaf length, leaf width, and leaf area. The evaluation results indicated that the distributed segmentation strategy achieved the best F 1 scores of 0.98 with block size set to 30,000. The Mean Average Errors (MAE) of leaf length, leaf width, and leaf area estimation were 2.09 cm, 1.78 cm and 8.98 cm 2 respectively. The estimation accuracies for leaf length, leaf width, and leaf area were 91.98%, 92.66%, and 89.67%, respectively.
Why it matches plant phenotyping methodsトマト葉の3D画像取得、器官セグメンテーション、葉インスタンス識別、形態形質推定を統合した自動フェノタイピング手法を開発・評価しており、方法が研究の中心です。
abstractproposed a fully automatic pipeline for leaf phenotyping
Reproduction assets foundThe paper's tomato point cloud dataset (with semantic and leaf instance annotations used for the phenotyping pipeline) is publicly available on Kaggle via a footnote. NPEC website is a facility page, and Open3D is a generic library, so neither qualifies.Dataset · public2. ^ The dataset used in this study is available at: https://www.kaggle.com/datasets/xinbolai/vtc-tomatoOpen asset ↗Kaggle · xinbolai/vtc-tomatolines:545-624Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
The use of glufosinate-resistant GM soybean has expanded, raising concerns about resistant weed development and unintended transgene flow. To support monitoring for timely management, we propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment. We evaluated the potential of spectral imaging, using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors, for early detection of glufosinate resistance in soybean. In the dose-response test, the key spectral indices including NDI, temperature difference, F v /F m , and NPQ distinguished between resistant and susceptible soybeans within 4 to 24 hours after treatment (HAT). IR thermal and CF imaging showed higher sensitivity in identifying resistance than RGB imaging by detecting spectral responses associated with physiological changes before visual symptoms appeared. Validation test with a single dose treatment of glufosinate reconfirmed that image analysis by both the naked eye and machine learning (ML) can discriminate between resistant and susceptible soybeans in a single day after glufosinate treatment. ML-based classification using IR thermal index achieved 100% accuracy as early as 6 HAT and the classification by the naked eye using IR thermal images showed 96.6% accuracy at 24 HAT. These results suggest that plant imaging enables early and non-destructive identification of herbicide-resistant individuals by detecting early spectral changes to herbicide treatment. These findings support its use as a potential alternative to conventional diagnostic methods for detecting individuals containing transgenes in herbicide-resistant GM soybean cultivation for future applications in herbicide-resistant weed monitoring.
Why it matches plant phenotyping methodsスペクトル画像（RGB、熱赤外、クロロフィル蛍光）と機械学習を用いて、薬剤処理後の植物の生理応答から耐性を早期識別する方法を開発・検証しており、植物表現型の取得が中心である。
abstractwe propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment.
Introduction This study evaluates the applicability of digital morphometric analysis to images of archaeological carbonized olive endocarps as a proof-of-concept initial approach for archaeobotanical investigations. Conventional morphometric analyses of olive endocarps largely rely on manual measurements, limiting reproducibility and quantitative comparison. Methods Ten archaeological endocarps were selected from previously published archaeological assemblages based on the integrity of their outlines, apex-base morphology and overall preservation quality. Quantitative descriptors describing endocarp size, symmetry, curvature and contour geometry were extracted using the OliveID software and compared with a modern morphometric reference database comprising Greek and international olive cultivars. Results Reliable contour extraction and quantitative descriptor computation were successfully achieved for all archaeological specimens despite carbonization. Preliminary comparison of representative morphometric descriptors showed that the archaeological specimens were positioned within the morphometric variation observed among the modern reference collection. Hierarchical clustering consistently associated the archaeological endocarps with the modern Throumbolia morphotype, while distinguishing them from elongated, globular and mucro-bearing cultivars. Discussion These findings demonstrate the feasibility of applying digital image-based morphometric analysis to sufficiently preserved archaeological carbonized olive endocarps and indicate a similar morphometric affinity between the analyzed archaeological material and the modern Throumbolia cultivar. This proof-of-concept study highlights the potential of digital morphometric approaches for quantitative archaeobotanical investigations of archaeological olive remains, while emphasizing the need for larger archaeological datasets and standardized image acquisition to further validate the observed morphometric similarity.
Why it matches plant phenotyping methodsOliveIDを用いて炭化オリーブ内果皮の輪郭からサイズ、対称性、曲率、形状記述子を抽出し、デジタル画像形態計測の適用可能性と再現性を評価している。植物器官の形質抽出法が研究の中心である。
abstractThis study evaluates the applicability of digital morphometric analysis to images of archaeological carbonized olive endocarps as a proof-of-concept initial approach for archaeobotanical investigations.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe quantitative measurements of the archaeological specimens are presented in Supplementary Table 2 , whereas the corresponding mean values and standard errors for the modern cultivars are provided in Supplementary Table 3 .Open asset ↗lines:311-320Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus .
Why it matches plant phenotyping methodsスパーステスト設計とゲノム予測を用いて、複数環境での植物形質予測と表現型測定コスト削減を評価しており、表現型取得・予測手法が研究の中心である。
abstractSparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations.
Reproduction assets foundThe paper's data availability statement points to a public figshare deposit (DOI 10.6084/m9.figshare.31796794) containing the datasets analyzed in this Miscanthus sparse-testing genomic prediction study, including the phenotypic and genotypic data used for the models.Dataset · publicThe datasets analyzed for this study can be found in the figshare repository at https://doi.org/10.6084/m9.figshare.31796794 .Open asset ↗figshare · 10.6084/m9.figshare.31796794lines:603-621Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Background Accurate and efficient detection of citrus leaf diseases is important for orchard monitoring, early intervention, and intelligent disease management. However, practical application in natural orchard environments remains challenging because of complex backgrounds, large variation in symptom scale, strong interclass similarity, and limited edge computing resources. Methods In this study, a lightweight object detection model, SKM-YOLOv11, was developed based on YOLOv11n for citrus leaf disease recognition in natural scenes. A self-built dataset was constructed from public images and field images, containing 6414 images across six classes. StarNet-S050 was introduced as the backbone, C3k2-Star was designed to enhance feature fusion across scales, and a lightweight shared detection head, MNS-Head, was constructed to reduce prediction redundancy. Results Compared with YOLOv11n, SKM-YOLOv11 increased Precision, Recall, and mAP @0.5 by 2.92, 1.47, and 0.95 percentage points, respectively. Meanwhile, FLOPs, parameter count, and model size were reduced by 31.7%, 32.7%, and 34.55%, respectively. Edge deployment on Jetson Orin NX Super achieved an inference speed of 72.45 frames per second. Conclusion The proposed model has strong potential for real-time citrus disease screening on resource-constrained devices and provides a feasible solution for edge-based intelligent disease management in natural orchards.
Why it matches plant phenotyping methods柑橘葉の病害状態を画像から認識する軽量物体検出モデルを開発し、データセット、精度比較、エッジ実装性能まで評価しており、植物表現型（病害状態）の取得・推定手法が中心である。
abstracta lightweight object detection model, SKM-YOLOv11, was developed based on YOLOv11n for citrus leaf disease recognition in natural scenes.
Plant stress recognition plays a vital role in precision agriculture by enabling the early detection of diseases, insect infestations, and nutrient deficiencies that adversely affect crop productivity. Although deep learning models have achieved promising results, existing CNN-, Transformer-, and hybrid architectures often struggle to capture complex spatial dependencies, distinguish visually similar stress symptoms, and handle class imbalance in multi-label classification. To address these challenges, this paper proposes Q-TriLSTM-Vision, a novel hybrid deep learning framework that integrates an EfficientNet-B0 visual encoder, a tri-stream long short-term memory (TriLSTM) network, and a lightweight quantum-inspired interference gate. Unlike quantum computing-based approaches, the proposed interference mechanism is implemented entirely using classical neural operations to enhance feature representation without requiring quantum hardware. The model further employs an entanglement-inspired attention fusion module, focal binary cross-entropy loss with label smoothing, weighted sampling, and per-class threshold calibration to improve discriminative learning and minority-class recognition. The proposed framework was evaluated on the OLID-I dataset and further validated on the PlantVillage and PlantDoc benchmark datasets. Experimental results demonstrate that Q-TriLSTM-Vision achieved Macro-F1 scores of 0.9127, 0.9624, and 0.8975 on OLID-I, PlantVillage, and PlantDoc, respectively, outperforming representative CNN-, Transformer-, and hybrid deep learning models while achieving lower Hamming loss and improved recall. Cross-validation, ablation studies, and statistical significance analysis further confirm the robustness and effectiveness of the proposed framework. Overall, Q-TriLSTM-Vision provides an accurate, computationally efficient, and reliable solution for intelligent plant stress recognition in precision agriculture.
Why it matches plant phenotyping methods植物ストレス症状を画像から認識する深層学習手法を開発し、複数の植物画像ベンチマークで検証しているため、表現型取得・抽出法が中心である。
Broccoli is a globally significant vegetable, but climate change and soil salinization increasingly threaten its productivity. Precise seedling phenotyping is essential for selecting salt-tolerant germplasm, yet traditional manual methods are labor-intensive and error-prone. This study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings at the germination and early developmental phase under salt stress. High-fidelity 3D point clouds were reconstructed from a precision three-view imaging system using Structure from Motion (SfM) algorithms. LBD-PointNet++ introduces three core optimizations: (1) a Large Kernel Attention (LKA) mechanism using 3D sparse decomposition to capture long-range global dependencies; (2) a Dual Uncertainty and Shape-Adaptive Sampling (DUSAS) mechanism to preserve high-frequency features of fragile stems and margins; and (3) a joint Boundary-Aware Nested Contrastive and Adaptive Varifocal Joint Loss (BNCV-Loss) to effectively isolate overlapping leaves. Experimental results demonstrate superior performance, achieving an overall mean Intersection over Union (mIoU) of 88.07% across all three categories (Leaf, Stem, and Pot) and a Mean F1-score of 93.48%. Compared to state-of-the-art Transformer architectures like PTv3, LBD-PointNet++ achieves higher accuracy with less than 6% of the parameter volume and over twofold faster inference speed. Furthermore, dynamic monitoring across NaCl gradients (0-250 mmol/L) revealed a potential non-linear threshold effect, identifying 100 mmol/L as a preliminary phenotypic threshold under these conditions. Beyond this threshold, growth inhibition intensified rapidly; At 250 mmol/L, plant height decreased by 54.43% and the 3D entity volume shrank to approximately one-fifth of the control group. In summary, LBD-PointNet++ provides a high-efficiency solution for phenotypic identification and digital breeding of salt-tolerant Brassicaceae crops.
Why it matches plant phenotyping methods3D点群分割ネットワークと三視点SfM撮像を開発し、ブロッコリー幼植物の表現型形質抽出を中心的に評価しているため。
abstractThis study develops LBD-PointNet++, an optimized 3D point cloud semantic segmentation model for automated phenotypic parameter extraction of broccoli seedlings
Introduction Timely and accurate detection of plant diseases is essential for ensuring global food security and supporting sustainable agriculture. Conventional diagnostic approaches, such as manual inspection and laboratory testing, are often time-consuming, labor-intensive, and impractical for large-scale or remote agricultural environments. Although deep learning models, particularly Convolutional Neural Networks (CNNs), have significantly improved automated plant disease classification, they often lack interpretability and struggle to generalize under diverse field conditions. Methods This study proposes AgriX-SENet, an explainable deep learning framework that integrates Squeeze-and-Excitation (SE) blocks with a DenseNet121 backbone to enhance disease classification performance. The SE blocks recalibrate channel-wise feature responses to emphasize disease-relevant information while suppressing background noise. To improve model transparency, Grad-CAM, SHAP, and LIME were incorporated to provide visual and feature-level explanations of the model’s predictions. The framework was trained and evaluated using the Plant Pathology 2020 dataset containing four classes: healthy, rust, scab, and multiple diseases. Results AgriX-SENet achieved a training accuracy of 97.47% and a validation accuracy of 95.07%, outperforming fourteen state-of-the-art deep learning models. The classification report demonstrated high precision and recall across most disease categories, although the scab class exhibited comparatively lower recall, indicating an opportunity for further improvement. The explainability analyses consistently showed that the model focused on pathologically relevant regions of leaf images, validating the reliability of its predictions. Discussion The proposed AgriX-SENet framework effectively combines high classification performance with model interpretability, addressing a key limitation of existing CNN-based plant disease detection systems. Its ability to provide accurate and explainable predictions makes it a promising solution for scalable agricultural diagnostics. Future work will focus on improving classification performance for challenging disease categories and optimizing the framework for deployment on mobile and edge computing devices to enable real-time field applications.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する説明可能な深層学習フレームワークを開発・評価しており、植物表現型取得・判定手法が中心である。
abstractThis study proposes AgriX-SENet, an explainable deep learning framework that integrates Squeeze-and-Excitation (SE) blocks with a DenseNet121 backbone to enhance disease classification performance.
Reproduction assets foundThe paper trains and evaluates AgriX-SENet on the public Plant Pathology 2020 (FGVC7) Kaggle image dataset, which is the paper-specific plant image input for its disease-classification measurements. No author analysis code, trained model checkpoints, or supplementary code/data deposit is mentioned; the data statement (Dataset · publicPlant Pathology 2020 - Fgvc7 . Available online at: https://www.kaggle.com/competitions/plant-pathology-2020-fgvc7/data .Open asset ↗Kaggle · plant-pathology-2020-fgvc7lines:895-974Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Wheat leaf biomass is a key indicator of crop growth, nitrogen status, and yield potential, and its accurate estimation is essential for precision agriculture. Unmanned aerial vehicle (UAV) remote sensing provides multi-stage phenological observations for non-destructive biomass monitoring. However, existing approaches often fail to capture the superimposed temporal patterns inherent to crop phenology, including short-term physiological fluctuations driven by management events and long-term seasonal growth trends, as well as the cumulative causal effects of early-stage conditions on final biomass accumulation. Methods This study proposed a dual-branch perception and hybrid attention integrated framework (DBAFN) for temporal estimation of wheat leaf biomass from UAV multi-temporal observations across key growth stages. Results and discussion Experimental results demonstrated that the DBAFN achieved the best performance, with the coefficient of determination (R²) of 0.87, root mean square error (RMSE) of 38.41 g/m², mean absolute error (MAE) of 27.77 g/m², and relative RMSE (RRMSE) of 17.29%. Overall, the proposed framework provided an effective solution for temporal biomass estimation and demonstrated strong generalization capability, as further validated by independent experiments across different ecological regions and wheat genotypes (R² = 0.816-0.820). Compared with conventional machine learning models, the DBAFN showed consistently higher accuracy and lower prediction error. Multi-source feature analysis indicated that the combination of reflectance, vegetation indices, and canopy height provides the most accurate estimation. Ablation experiments further confirmed the effectiveness of each module in improving model performance. The SHapley Additive exPlanations (SHAP) analysis revealed that the canopy height and key spectral features contribute most to biomass prediction, highlighting the importance of integrating structural and physiological information. This study demonstrates that integrating multi-scale temporal dynamics, hybrid attention mechanisms, and transformer-based dependency modeling significantly improves the reliability of UAV-based biomass estimation. It offers a practical, data-driven pathway for intelligent crop monitoring and precision nitrogen management.
Why it matches plant phenotyping methodsUAVマルチ時期リモートセンシングから小麦葉バイオマスという植物形質を推定する手法を提案し、独立地域・遺伝子型で検証しているため、フェノタイピング手法が中心である。
abstractThis study proposed a dual-branch perception and hybrid attention integrated framework (DBAFN) for temporal estimation of wheat leaf biomass from UAV multi-temporal observations across key growth stages.
Rice stem internal structure is a critical micro-phenotype influencing lodging resistance and yield; however, its analysis remains constrained by labor-intensive manual methods. Here, we present a publicly available dataset of 686 rice stem cross-sections, with 21,027 large vascular bundles (LVBs) and 19,342 small vascular bundles (SVBs) manually annotated. Five deep learning architectures were systematically evaluated, among which UNet-VGG16 achieved the best performance with a mean intersection over union (mIoU) of 87.4% (82.49% for LVBs and 74.41% for SVBs). An improved model, UNet-ECA-Bio, further raised mIoU to 89.32% and SVB IoU to 78.97% by integrating Efficient Channel Attention (ECA) and biologically informed class weighting using an image-level dataset. Leveraging these high-accuracy phenotypic predictions, genome-wide association studies (GWAS) indicated concordance between annotated and predicted traits, with SNP overlap rates of 96% (LVB count: 1,217/1,262), 43% (SVB count: 6/14), 98% (stem area: 122/124), and 100% (cavity area: 3/3) at −log10(p) ≥ 6. Meanwhile, compared with manual annotation (estimated 10–30 minutes per image), the proposed approach processed all 686 images within 10 minutes, representing a >600-fold increase in throughput. We further developed a user-friendly software tool, “Rice_Stem_Pre_V1.1.exe,” for automated phenotyping of 14 stem traits, providing a cost-effective platform for genetic studies of lodging resistance and yield improvement.
Why it matches plant phenotyping methodsイネ茎維管束の画像から複数の表現型形質を自動抽出する深層学習モデル、データセット、検証、ソフトウェアを中心的に開発しており、明確な植物フェノタイピング手法研究である。
abstractwe present a publicly available dataset of 686 rice stem cross-sections, with 21,027 large vascular bundles (LVBs) and 19,342 small vascular bundles (SVBs) manually annotated.
MilletNeRF / 3D Gaussian SplattingWhole plant / canopy / plot / fieldGrowth / time-series analysisVisualization / data managementGrowth / development / phenology
Finger millet ( Eleusine coracana (L.) Gaertn.) is a small seeded cereal known for its health benefits and its ability to grow in water-limited and saline environments. Despite its health and adaptation advantages, there is no standardized BBCH scale developed for finger millet, precluding standardization of a defined growth and development scale. The initial step in any breeding program and related research, is the precise, consistent and standardized description of crop growth stages. Therefore, we present a standardized phenological scale to describe and compare different stages of growth and development. In this study, we used four-finger millet accessions from the U.S.D.A. National Plant Germplasm System (NPGS) collection. Using a standardized BBCH chart as the baseline, we describe nine main stages of development to provide a simplified and user-friendly phenological growth staging scale for finger millet. A novel feature of this study was the use of Neural Radiance Fields (NeRF) to generate three-dimensional (3-D) renderings that align growth stages with 3-D imaging, paving the way for future phenological staging studies to incorporate 3-D visualization as a tool for standardization, researcher training, and reduction of observer bias across environments.
Why it matches plant phenotyping methods指のキビの生育段階を標準化する手法を開発し、NeRFによる3D画像化を生育ステージ判定・標準化に組み込んでいるため、植物フェノタイピング手法が中心である。
titlePhenological growth stages of Finger millet (Eleusine coracana (L.)) using 3-D phenotyping with relevance to breeding applications
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Accurate assessment of plant nitrate status is critical for growth and productivity, yet early and non-destructive quantification remains challenging. Although Raman spectroscopy has been used to detect nitrate deficiency in plants, quantitative estimation of nitrate concentration from Raman spectra has not been demonstrated. Here, we evaluated whether Raman spectroscopy can be used to quantitatively predict leaf nitrate concentrations during early nitrogen stress. Two-week-old Pak Choi (C3) and Amaranthus (C4) plants were subjected to nitrate deprivation for 1–3 days, and Raman spectra were collected and correlated with nitrate concentrations determined by biochemical assays. A strong linear relationship was observed between nitrate concentration and the intensity ratio of the nitrate-associated Raman peak at 1046 cm - ¹ to the neighboring 1067 cm - ¹ peak. This relationship was consistent among plants of the same species and across different levels of nitrogen deficiency. Linear regression models achieved root-mean-square errors of 101 µg g - ¹ fresh weight (FW) in Pak Choi (~7% of nitrate under sufficient nitrogen) and 32 µg g - ¹ FW in Amaranthus (~19%), closely matching biochemical measurements and revealing species-specific nitrate dynamics. These findings demonstrate that Raman spectroscopy enables rapid, non-destructive, and quantitatively reliable estimation of leaf nitrate levels during early nitrogen stress, providing a promising platform for precision nutrient management and real-time plant phenotyping.
Why it matches plant phenotyping methodsラマン分光法による葉の硝酸濃度の非破壊・定量推定手法を開発・検証しており、植物フェノタイプ取得が研究の中心である。
abstractquantitative estimation of nitrate concentration from Raman spectra has not been demonstrated
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Climate extremes increasingly threaten agricultural production, yet many artificial intelligence systems in agriculture remain local, reactive and narrowly trained for one crop, region or sensing modality. Methods We present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions. AgriFM was pretrained using self-supervised objectives on 2.4 million field-season sequences and evaluated on a curated benchmark spanning maize, wheat, soybean, rice and sorghum across five agroclimatic regions. Results In held-out geography and time-split evaluations, AgriFM improved early stress detection and yield-failure prediction over statistical, crop-model and deep-learning baselines. The largest gains occurred during compound drought and heat events, for which AgriFM produced alerts 18 to 24 days earlier than the satellite-only baseline while maintaining improved calibration. Phenology-conditioned fusion improved transfer across planting calendars, and uncertainty calibration reduced false alerts at fixed recall. Discussion Because the study is based on retrospective datasets, these findings establish cross-region retrospective performance rather than prospective field efficacy. The results support further field-based evaluation of multimodal foundation models for climate-resilient crop monitoring.
Why it matches plant phenotyping methods作物ストレス状態と収量失敗リスクを衛星・レーダー・熱画像等から推定する基盤モデルを開発し、複数作物・地域のベンチマークで評価しており、植物状態の取得・推定手法が中心である。
abstractWe present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions.
Precision agriculture increasingly relies on detailed structural information, such as canopy height and canopy volume, to enhance crop health monitoring and operational safety. However, existing methods based on costly LiDAR or RGB-D sensors are often impractical for large-scale deployment in dynamic and unstructured horticultural environments. Furthermore, conventional 2D segmentation and SLAM-based pipelines typically generate sparse, geometrically inconsistent semantic maps which are insufficient for actionable structural analysis in agricultural applications. To overcome these limitations, we propose a monocular 3D structural mapping framework tailored for horticultural plants via semantic scene completion. At inference, the proposed model takes a single RGB image as input and predicts voxel-wise geometry and semantics, from which task-oriented structural maps, including canopy height, canopy volume, and obstacle-aware traversability layers, are derived. Specifically, we first introduce a Depth-Aware Decoder Module that explicitly recovers depth in the spatial domain and fuses 2D-to-3D features, thereby mitigating depth ambiguity and reducing reliance on accurate pose. Second, an NCS-Guided Geometry Encoder is designed to inject normalized depth into voxel positional embeddings, enabling self-attention to perform global relational modeling within a depth-aware geometric coordinate system. In addition, a Global Encoder is utilized to refine local structural details, while an occupancy head produces the final 3D semantic completion outputs. We construct a horticultural 3D semantic scene dataset using an RGB-D sensor, which serves as a benchmark for evaluating our method, while the deployed model remains RGB-only. Extensive quantitative and qualitative experiments are conducted on both the Semantic-KITTI dataset and our dataset. On our dataset, the method achieves 82.31% occupancy IoU, 84.26% mIoU, and 86.25% precision. Beyond voxel-level evaluation, manual field measurements further show canopy height MAE values of 0.019-0.026 m and canopy volume proxy relative errors of 8.4%-11.4%. These results demonstrate the effectiveness of our approach in real-world agricultural scenarios, providing actionable structural insights for crop monitoring and autonomous robotic operations.
Why it matches plant phenotyping methods単眼RGB画像から植物の樹冠高・樹冠体積などの構造形質を推定する3Dフェノタイピング手法を開発し、データセット構築と実測検証も行っているため、方法が研究の中心である。
abstractwe propose a monocular 3D structural mapping framework tailored for horticultural plants via semantic scene completion.
Introduction Safeguarding the yield and quality of field crops against abiotic stresses is critical for large-scale agricultural production and precision agronomy. This study addresses the challenges of detecting concealed fruit stress and overcoming label scarcity in unmanned aerial vehicle (UAV) multispectral monitoring, using blossom-end rot (BER) in processing tomatoes as a case study. Methods We developed a leaf-fruit synergistic Roll and Color Disease Index (RCDI), integrating fruit incidence with visible canopy phenotypic features to characterize the combined canopy-fruit stress status associated with BER. A three-level screening strategy was used to identify the optimal spectral feature set. A Multi-model Collaborative Cyclic Self-Training (MCC-ST) framework was subsequently developed to address the limited availability of severity-labeled samples. Results The combination of GRVI, NDVI, and SAVI was identified as the optimal spectral feature set, achieving stable within-dataset binary classification performance of approximately 97% in repeated cross-validation. Under 30 random-seed repeated stratified three-fold cross-validations, MCC-ST + DT and MCC-ST + RF achieved RCDI-based BER severity-grading accuracies of 85.00% ± 0.31% and 85.07% ± 0.42%, respectively. Compared with the corresponding original DT and RF models, MCC-ST improved repeated-validation accuracy by 16.19-4.09 percentage points. Discussion The RCDI helps bridge the observational gap between canopy signals and concealed fruit stress, while MCC-ST alleviates the bottleneck associated with label scarcity. The proposed approach provides a promising framework for crop abiotic-stress monitoring under limited-label conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からトマトの果実ストレス状態・BER重症度を推定する指標と、ラベル不足に対応する機械学習フレームワークを開発・検証しており、植物表現型取得・推定手法が研究の中心である。
abstractThis study addresses the challenges of detecting concealed fruit stress and overcoming label scarcity in unmanned aerial vehicle (UAV) multispectral monitoring, using blossom-end rot (BER) in processing tomatoes as a case study.
Introduction Accurate and non-destructive estimation of wheat biomass is essential for crop growth monitoring, yield prediction, and precision agriculture. Unmanned aerial vehicle (UAV)-based remote sensing, integrating both spectral and structural information, has shown great potential for biomass estimation. However, the mechanisms by which different types of variables contribute to biomass prediction remain poorly understood, especially when using machine learning models. Methods In this study, we fused spectral reflectance, vegetation indices, and canopy height data derived from a UAV multispectral camera to estimate wheat biomass across four growth stages (jointing, booting, heading, and filling). Four machine learning algorithms-XGBoost, Random Forest Regressor (RFR), Support Vector Regressor (SVR), and LASSO-were employed and compared. Results and discussion The results showed that XGBoost achieved the highest accuracy (R 2 = 0.919, RMSE = 102.43 g/m², MAE = 77.43 g/m², RRMSE = 19.71%). Furthermore, SHAP (SHapley Additive exPlanations) analysis revealed that canopy height (CH) was the most important variable, followed by spectral indices such as R842 and GNDVI. The univariate and global contribution analyses demonstrated that structural and spectral variables played complementary roles in biomass estimation. This study provides a mechanistic understanding of variable contributions and offers a robust framework for UAV-based wheat biomass estimation.
Why it matches plant phenotyping methodsUAVスペクトル・キャノピー高データから小麦バイオマスを推定し、複数機械学習法を比較・評価する方法論的研究であり、植物形質取得が中心である。
abstractwe fused spectral reflectance, vegetation indices, and canopy height data derived from a UAV multispectral camera to estimate wheat biomass across four growth stages
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Drought is recognized as the primary abiotic stress limiting global crop productivity and poses a significant threat to food security. Consequently, the genetic improvement of drought tolerance has become a priority for modern plant breeding. Developing resilient cultivars requires a fundamental understanding of the physiological, biochemical, and molecular mechanisms that plants employ to counteract water deficits. This review provides a comprehensive analysis of drought-induced effects across various developmental stages in legumes, detailing the signaling networks that facilitate stress perception and response. Furthermore, we evaluate the experimental parameters and methodologies frequently used to assess drought tolerance, weighing their respective advantages and limitations. Finally, we analyze the revolutionary role that high-throughput phenotyping could play in stress assessment and precision breeding.
Why it matches plant phenotyping methodsマメ科植物の乾燥耐性評価に用いる実験パラメータ・方法論をレビューし、高スループット表現型解析の役割も論じるため、植物フェノタイピング方法のレビューが中心である。
abstractFurthermore, we evaluate the experimental parameters and methodologies frequently used to assess drought tolerance, weighing their respective advantages and limitations.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Fall dormancy (FD) and forage yield (FY) are two key traits in alfalfa ( Medicago sativa L.) breeding programs. However, genetic progress has remained limited over the past decades, largely due to the complexity of alfalfa breeding and the reliance on labor-intensive phenotyping methods. High-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation. The objectives of this study were to i) estimate FD using UAV-derived canopy height and RGB vegetation indices and ii) evaluate the predictive performance of machine learning (ML) models for FY estimation. A total of 210 alfalfa populations with diverse genetic backgrounds were evaluated over two growing seasons (2023 to 2025) across seven harvests under Mediterranean conditions in central Chile. FY was measured manually, while FD was estimated using both manual and UAV-based approaches. A total of 19 RGB-derived indices (VIs) including plant height (PH) were extracted and used as predictor variables. Five complex predictive ML models were evaluated: PLS, PCR, SVM, ANN, and MLR. The results showed that UAV-derived FD was significantly correlated with FD obtained through conventional methods ( R 2 = 0.88). The automated UAV-based FD phenotyping framework demonstrated slightly higher precision ( R 2 = 0.92) and broad-sense heritability ( H 2 = 0.69) compared to manual measurements ( R 2 = 0.87–0.89; H 2 = 0.64), providing a more reliable selection tool for breeders. Among the tested ML models, SVM and ANN achieved the highest accuracy ( R 2 ≈ 0.73) for FY prediction. These findings demonstrate that integrating low-cost RGB imagery with complex modeling offers a promising avenue that could assist in refining future selection strategies for this genetically complex species.
Why it matches plant phenotyping methodsUAV画像からアルファルファの休眠性と収量関連形質を推定する高スループット表現型解析手法を開発・検証し、手動測定との比較と機械学習モデル評価を行っているため、方法が研究の中心である。
abstractHigh-throughput phenotyping (HTP) using unmanned aerial vehicles (UAVs) represents a promising alternative for rapid and non-destructive crop evaluation.
Accurate and reliable diagnosis of grape leaf diseases is essential for sustainable viticulture, enabling timely intervention, reducing economic losses, and supporting precision crop management. Although deep learning (DL) models have demonstrated remarkable classification performance, their reliability under real-world field conditions remains insufficiently explored. In particular, confidence estimates often fail to reflect true predictive correctness when models are exposed to distributional shifts, limiting their practical applicability. To address this challenge, this study proposes a confidence- and uncertainty-aware DL framework for grape leaf disease diagnosis that extends evaluation beyond conventional accuracy-based metrics. Two publicly available grape leaf datasets were employed for stratified five-fold cross-validation in binary and multiclass classification tasks, while a third independently collected dataset was reserved exclusively for leakage-free external validation. EfficientNet-B0 and MobileNetV3-Large were evaluated under four inference configurations: raw inference, temperature scaling (TempScaling), Monte Carlo dropout, and an ensemble strategy combining uncertainty estimation with calibration. External validation demonstrated that both architectures maintained discriminative capability under domain shift. Under ensemble inference, EfficientNet-B0 achieved an accuracy of 73.8%, a macro-F1 score of 73.3%, a Matthews correlation coefficient (MCC) of 0.468, and a receiver operating characteristic area under the curve (ROC-AUC) of 0.804, while MobileNetV3-Large achieved 71.8% accuracy, 71.2% macro-F1, an MCC of 0.429, and a ROC-AUC of 0.787. Despite these promising results, raw predictions exhibited substantial overconfidence, with Expected Calibration Error (ECE) values of 0.287 and 0.312 for EfficientNet-B0 and MobileNetV3-Large, respectively. TempScaling markedly improved calibration quality, reducing ECE to 0.038 and 0.042 without affecting classification performance. Ensemble inference further enhanced the balance between predictive discrimination and reliability. The results demonstrate that strong classification performance alone is insufficient for trustworthy deployment in agricultural environments. Confidence calibration and uncertainty quantification provide complementary information for identifying overconfident predictions and improving decision reliability under field variability. The proposed framework offers a reliability-oriented approach for developing trustworthy artificial intelligence systems for grape leaf disease diagnosis in precision agriculture.
Why it matches plant phenotyping methodsブドウ葉の病徴を画像から診断する深層学習手法を開発し、信頼度校正・不確実性推定と外部検証を中心に評価しているため、植物フェノタイピング手法として適格。
abstractthis study proposes a confidence- and uncertainty-aware DL framework for grape leaf disease diagnosis
Florida and California produce 98% of U.S. strawberries, with Florida growers' profitability depending on high yields early in the season (November-January), when prices are the highest in the U.S. market. This study aims to model the cumulative Marketable Yield and Delta Yield curves (difference in cumulative Marketable Yield between consecutive harvest time points) of strawberry genotypes to facilitate selection for greater early season productivity. The dataset comprised thirteen seasons (2013-14 to 2025-26) of advanced selection trial data. Marketable Yield trajectories were modeled using Legendre polynomial smoothing, with optimal degree selection balancing flexibility and noise reduction. The resulting coefficients served as surrogate phenotypes for genomic prediction. Forward prediction cross-validation was implemented for five seasons (2021-22, 2022-23, 2023-24, 2024-25, and 2025-26), with each season predicted using the information from all preceding seasons. For cumulative yield, across all five seasons, reconstructed curves from the Legendre models presented a clear temporal trend, with predictive ability increasing from low early-season values to peaks around Trait Dates (weeks) 7-8. In the 2021-22 and 2022-23 seasons, Legendre models showed higher predictive ability than single time-point predictions but were comparable to single-time point predictions for the other seasons. Legendre polynomial models utilizing Delta Yield achieved moderate predictive ability across five validation seasons, with consistent advantages over single time point models particularly in earlier seasons, indicating that genetic control extends beyond total yield to the trajectory of yield accumulation. Overall, Legendre modeling effectively captured the temporal dynamics of yield development while describing the trajectory with only a few parameters.
Why it matches plant phenotyping methodsイチゴの収量軌跡をLegendre多項式でモデル化し、少数の係数を代理表現型としてゲノム予測に利用・検証しており、収量表現型の計算的抽出が研究の中心である。
abstractMarketable Yield trajectories were modeled using Legendre polynomial smoothing, with optimal degree selection balancing flexibility and noise reduction.
Detecting cotton leaf diseases in open-field environments is challenging due to cluttered backgrounds, scale variation, and irregular lesion morphology. Conventional detectors rely on isotropic receptive fields and coupled box-regression losses, which limit their ability to localize elongated lesions with poorly defined boundaries. We present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages. In the backbone, an Anisotropic Morphological Contrast Aggregation module (AMCA) enhances direction-aware representation and lesion-background contrast via re-parameterizable strip convolutions and high-frequency residual extraction. A Dynamic Semantic Boundary Transfer mechanism (DSBT) then captures boundary priors from shallow layers before they are lost to downsampling and injects them into the neck. A Morphological-Spectral Synergistic Feature Pyramid Network (MFS-FPN) preserves these cues during multi-scale fusion through spatial-domain operations compatible with edge hardware. Finally, an Anisotropic Boundary-Decoupled IoU loss (ABD-IoU) independently penalizes each of the four box boundaries and sustains optimization signals in high-IoU regimes via a logarithmic modulation factor. On the self-constructed Complex Cotton Leaf Disease dataset (CCLD; 6,856 images, 6 classes), the method achieves 78.50% mAP@50 and 65.00% mAP@50:95, improving the YOLOv11n baseline by 4.80% and 2.70% with only 2.73 M parameters at 202 FPS. Cross-domain evaluations on PlantDoc and RWD confirm consistent improvements. The framework runs in real time on NVIDIA Jetson edge platforms with INT8 quantization.
Why it matches plant phenotyping methods綿花葉の病斑・病害状態を画像から検出する手法を開発し、複数データセットとベースラインで性能検証しているため、植物表現型取得が中心である。
abstractWe present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages.
Reproduction assets foundThe authors explicitly state that their source code, trained models, and implementation details are publicly available, and the data availability statement points to the same repository, which hosts the self-constructed CCLD cotton leaf disease dataset (6,856 images, 6 classes) used for the paper's phenotyping/disease-Code · publicFurthermore, to facilitate future research, our source code, trained models, and implementation details have been made publicly available at https://github.com/DynaVLA/ABAD-CLD .Open asset ↗DynaVLA/ABAD-CLDlines:331-343Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/DynaVLA/ABAD-CLD .Open asset ↗DynaVLA/ABAD-CLDlines:1278-1317Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Early-stage detection and classification of lettuce heat responses are essential for non-destructive phenotyping, yet conventional assessment mainly relies on visible symptoms and manual observation. Methods This study constructed a lettuce hyperspectral dataset comprising heat-sensitive and heat-tolerant varieties under control and high-temperature treatments, and proposed the Dynamic Selective Peak Transformer (DSPformer). DSPformer integrates edge-enhanced feature extraction, dynamic multi-scale spatial-spectral representation, Peak-k selective attention, and a confusion-aware dynamic focal loss to enhance discriminative features while reducing spectral redundancy, class imbalance, and inter-class confusion. Results Under the patch-level evaluation protocol, DSPformer achieved 96.22% accuracy, 95.55% recall, 96.35% precision, and 95.95% F1-score, outperforming the compared CNN- and Transformer-based models. Day-wise evaluation showed that DSPformer reached 82.61% accuracy on Day 1 and 96.55% on Day 3, before visible heat-stress symptoms appeared on Day 6. Under a plant-level partition protocol, DSPformer maintained robust performance with 93.76 +/- 0.49% accuracy. Additional evaluation on the Indian Pines benchmark further demonstrated the applicability of DSPformer to general hyperspectral image classification. Discussion These findings suggest that hyperspectral imaging can capture heat-stress-sensitive information beyond visual phenotypes, and that DSPformer provides a promising framework for early, non-destructive lettuce heat-response screening and hyperspectral phenotyping-assisted breeding.
Why it matches plant phenotyping methodsレタスの熱ストレス応答を非破壊的に早期分類するため、ハイパースペクトル画像データセットと新規Transformer手法を開発・評価しており、植物表現型取得・抽出が中心である。
abstractMethods This study constructed a lettuce hyperspectral dataset comprising heat-sensitive and heat-tolerant varieties under control and high-temperature treatments, and proposed the Dynamic Selective Peak Transformer (DSPformer).
Accurate identification of crop varieties is essential for plant breeding programs and the protection of Plant Breeders' Rights (PBR), yet traditional morphological assessment methods remain subjective and time-consuming, particularly for species with complex morphological diversity such as Rubus crataegifolius . This study demonstrates that geometric morphometric techniques provide an objective, quantitative complementary approach for distinguishing Korean raspberry varieties, addressing the limitations of subjective visual assessment while remaining compatible with molecular marker analysis. We employed three complementary morphometric approaches: landmark-based analysis (19 anatomical points capturing vein junctions and leaf margins), Elliptic Fourier Descriptors (EFD) for outline contours, and a hybrid landmark-EFD dataset. Using these approaches, we analyzed primocane and floricane leaves from 10 accessions of R. crataegifolius comprising 8 varieties and 2 landraces and performed principal component analysis (PCA) and linear discriminant analysis (LDA) with leave-one-out cross-validation. As a result, among the three morphometric approaches applied to primocane and floricane leaves, landmark-based analysis of primocane leaves achieved the highest classification accuracy (87.2%), with an overall average accuracy of 72.0% (range: 51.1-87.2%) across all six analytical combinations. LDA visualization suggested the presence of four major morphological groups, and primocane leaves exhibited higher discriminatory power than floricane leaves, which may reflect greater morphological uniformity under normal growing conditions. Landmark analysis effectively detected subtle differences in leaf venation and leaflet architecture that are difficult to distinguish visually, highlighting the capacity of morphometrics for objective and multidimensional morphological analysis. These findings suggest that morphometric analysis provides a practical and cost-effective preliminary screening tool, complementary to molecular approaches, for supporting Distinctness, Uniformity, and Stability (DUS) examination in raspberry variety evaluation. This approach shows strong potential for offering a scalable solution for variety registration and protection and supporting sustainable horticultural development.
Why it matches plant phenotyping methods葉の形態を幾何学的モルフォメトリクスで定量化し、品種識別とDUS評価に応用した研究で、表現型の取得・解析手法が中心である。
abstractThis study demonstrates that geometric morphometric techniques provide an objective, quantitative complementary approach for distinguishing Korean raspberry varieties
Accurate yield prediction for major grain and oilseed crops, including soybean, corn, wheat, and rice, is essential for food-security assessment and precision field management. This study presents a structured integrative review of UAV-based crop yield prediction and follows PRISMA-guided procedures for literature search, screening, and evidence synthesis. Seventy peer-reviewed studies published between 2018 and 2025 were synthesized within a "Data-Ground Truth-Model-Decision" framework. Beyond summarizing UAV platforms, sensor configurations, feature-engineering strategies, and model architectures, the review explicitly distinguishes among microplot, field, and regional prediction scales, and evaluates the characteristics and limitations of yield-label acquisition methods, including manual harvest, plot-combine harvest, and combine yield-monitor data. Existing evidence indicates that the reliability of UAV-based yield prediction depends not only on optimal image acquisition windows, multi-source feature fusion, and model architecture, but also on scale-consistent yield labels, spatially aware validation strategies, and clearly defined model outputs, such as plot-level scalar yield, field-scale yield maps, and regional yield estimates. Major bottlenecks include scale mismatch between UAV imagery and yield labels, error propagation during yield-map generation, limited cross-year and cross-region transferability, weak causal interpretability, and difficulties in deploying models under complex operational field conditions. Future research should emphasize scale-explicit benchmark datasets, quality-controlled ground-truth yield acquisition, UAV-satellite-ground data fusion, spatiotemporal deep learning, and edge-cloud collaborative systems that can translate prediction outputs into agronomic decisions. This review provides a practical pathway for developing robust, interpretable, and deployable UAV-based yield prediction systems for major grain and oilseed crops.
Why it matches plant phenotyping methodsUAV画像から作物の収量という植物形質を推定する手法を中心に、プラットフォーム、特徴量、モデル、検証尺度、グラウンドトゥルースを体系的にレビューしているため、方法レビューとして収載。
abstractThis study presents a structured integrative review of UAV-based crop yield prediction and follows PRISMA-guided procedures for literature search, screening, and evidence synthesis.
The genetic identity of coffee cultivars is fundamental to the specialty coffee sector, where premium prices are paid under the assumption that the purchased planting material corresponds to the declared variety. However, many producing countries lack the certification infrastructure necessary to guarantee this identity in their informal seed systems, exposing producers to undetected varietal non-conformity. In this study, we examine a case from a specialty coffee ( Coffea arabica L.) farm in southern Ecuador where seeds labeled as Sidra (USD 100/kg) and Gesha (USD 500/kg) were purchased without genetic or phytosanitary certification. Using a combination of SSR-based DNA fingerprinting and quantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics, we documented varietal identity and assessed the discriminant capacity of morphological traits across the four resulting morphotypes. Using eleven microsatellite markers for SSR fingerprinting, we found that two of the four morphotypes did not match their declared commercial identity. One plant sold as Sidra was identified as compatible with Batian, a composite variety of Kenyan origin that is genetically unrelated to Ethiopian landraces. The plants acquired as Gesha corresponded to a pure Ethiopian landrace that is genetically similar to, but not identical to, the Panamanian Geisha reference accession T.02722. Only two morphotypes were confirmed as Sidra. Furthermore, the placement of Sidra within the Core Ethiopia genetic group is consistent with prior population-level analyses and with its likely status as a selected Ethiopian landrace rather than a variety of hybrid origin. Morphological linear discriminant analysis achieved 82.4% overall classification accuracy under leave-one-out cross-validation (LOOCV), with internode length dominating the first discriminant function (LD1 = 66.6%). These results demonstrate that varietal nonconformity in the specialty coffee seed sector can extend to the inadvertent introduction of genetically unrelated material and underscore the urgent need for accessible seed certification.
Why it matches plant phenotyping methodsコーヒー品種識別のための形態形質測定と判別分析が研究の中心であり、形態形質の識別性能をLOOCVで検証しているため、植物フェノタイピング手法の適用・検証に該当する。
abstractquantitative morphological characterization, including plant architecture, leaf functional traits, and fruit characteristics
Reproduction assets foundThe paper's morphological/functional trait dataset (used for the phenotyping and LDA analysis) is explicitly stated to be publicly available on Figshare (10.6084/m9.figshare.32841344). No author analysis code repository is stated; other URLs in the text are generic libraries or cited prior work.Dataset · publicThe morphological and functional trait dataset generated and analyzed in this study is publicly available in the Figshare repository at 10.6084/m9.figshare.32841344 .Open asset ↗Figshare · 10.6084/m9.figshare.32841344lines:526-568Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction: Cucumber diseases severely affect yield and quality. Deep learning-based analysis of microscopic pathogen images enables high-throughput identification and counting of pathogens, thereby facilitating early disease detection. However, most existing pathogen-recognition methods focus mainly on qualitative identification and cannot quantitatively characterize pathogen morphology, which limits their ability to reveal the developmental characteristics and functional differentiation of different infection structures from the perspective of pathogen morphology-function adaptability. Methods: To address this issue, this study focused on cucumber powdery mildew and downy mildew and achieved precise extraction and characterization of pathogen morphological features based on microscopic image instance segmentation. First, an in situ stained microscopic image dataset of cucumber pathogens was constructed. Second, an instance segmentation model, SWS-YOLO11n, was developed for cucumber pathogen infection structures to accurately identify and segment different infection structures in microscopic images. Finally, morphological analysis methods were used to quantitatively extract and characterize pathogen infection-structure features. Results: values greater than 0.90. In addition, category-wise morphological distribution analysis showed that different infection-structure types exhibited clear differentiation in size, contour complexity, and elongation. Discussion: This study provides an effective tool for high-throughput phenotyping of cucumber pathogen infection structures. The proposed method offers methodological support for disease diagnosis, pathogen morphological phenotyping, and precision disease management in horticultural production.
Why it matches plant phenotyping methods顕微鏡画像のインスタンスセグメンテーションを開発し、キュウリ病原体の感染構造の形態形質を定量抽出する手法が中心である。
abstractan instance segmentation model, SWS-YOLO11n, was developed for cucumber pathogen infection structures to accurately identify and segment different infection structures in microscopic images.
Plant-driven lighting control has been proposed as a strategy to regulate supplemental light-emitting diode (LED) intensity according to real-time plant physiological status. This study developed a multiple linear regression (MLR) model to predict quantum yield of photosystem II (Φ PSII ) from environmental variables and evaluated its integration into a chlorophyll fluorescence-based biofeedback light control. The model incorporated light intensity, CO 2 concentration, air temperature, vapor pressure deficit, short-term light history, and diurnal effects. In a greenhouse validation experiment, supplemental lighting was regulated using either direct chlorophyll fluorometer measurements of Φ PSII (sensor-based control) or Φ PSII values predicted by the machine learning model (ML-based control), and compared with a constant photosynthetic photon flux density (PPFD) treatment. Both sensor- and ML-based control stabilized photochemical activity across the photoperiod relative to constant PPFD. Although plant growth did not differ among treatments, sensor-based ETR control achieved the highest energy use efficiency for LED lighting in this study. These findings demonstrate the feasibility of integrating predictive ML models into plant-based lighting control systems and indicate that sensor-based biofeedback control improved the energy-use efficiency of greenhouse supplemental lighting without compromising crop growth.
Why it matches plant phenotyping methods植物の光合成生理状態（ΦPSII）を予測・計測するモデルを開発し、蛍光センサーによるフィードバック照明制御へ統合して検証しており、植物フェノタイピング手法が中心です。
abstractThis study developed a multiple linear regression (MLR) model to predict quantum yield of photosystem II (Φ PSII ) from environmental variables and evaluated its integration into a chlorophyll fluorescence-based biofeedback light control.
Sweet potato virus disease (SPVD) is one of the most destructive diseases affecting sweet potato production worldwide, causing severe yield losses and posing a significant threat to food security. Vision-based intelligent diagnosis has emerged as a promising solution for large-scale SPVD monitoring due to its low cost and scalability. However, existing publicly available datasets for SPVD are extremely limited and typically focus on a single task, such as disease classification or lesion segmentation, under constrained imaging conditions. This lack of comprehensive, task-oriented datasets significantly restricts the development, evaluation, and fair comparison of advanced computer vision methods for SPVD analysis. In this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks. Rather than constructing a single homogeneous dataset, SPVD-Field is deliberately organized into two complementary task-oriented sub-datasets: SPVD-DET, designed for disease detection with bounding-box annotations, and SPVD-SEG, designed for fine-grained lesion segmentation with pixel-level masks. The two sub-datasets were independently collected using different acquisition protocols optimized for their respective tasks, while sharing a unified semantic definition of SPVD symptoms, crop growth stages, and field environments. SPVD-Field captures substantial real-world variability in imaging scale, viewpoint, illumination, background complexity, and symptom manifestation, reflecting the inherent challenges of fieldbased disease diagnosis. We provide detailed documentation of data acquisition, annotation strategies, and quality control procedures, along with baseline benchmark results for both detection and segmentation tasks to demonstrate the usability and difficulty of the dataset. By offering a structured dataset suite rather than a single-task collection, SPVD-Field aims to support diverse research directions, including detection, segmentation, multi-task learning, and disease severity analysis, and to facilitate reproducible and comparable research in SPVD-related plant phenotyping.
Why it matches plant phenotyping methodsサツマイモの病徴を対象とする画像データセットで、検出・病斑セグメンテーション、データ取得・アノテーション・品質管理、ベンチマークを中心的に提供しており、植物病害状態の画像フェノタイピング手法・データ基盤に該当する。
abstractIn this study, we present SPVD-Field, a task-oriented multi-task visual dataset suite composed of two independently collected sub-datasets optimized for different computer vision tasks.
Reproduction assets foundThe paper's core asset is the SPVD-Field dataset (SPVD-DET detection images with bounding-box annotations and SPVD-SEG segmentation images with pixel-level masks), explicitly deposited in a public repository via the data availability statement with a DOI link.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://dx.doi.org/10.21227/hq1q-jp43 .Open asset ↗10.21227/hq1q-jp43lines:664-703Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Maize is one of the most important food crops in the world, and foliar diseases can lead to significant yield losses if identification is not performed on time. Experts conducting manual inspections find it less effective and more subjective. Deep learning-based approaches utilizing Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) have been demonstrated as a viable approach to automate disease diagnosis. Thus, while CNNs fail to capture wider context due to their local feature focus and ViTs need larger datasets and tend to miss finer-grained details. To overcome these limitations, we present EDISP a hybrid framework that connects Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for local feature extraction, as well as global contextual learning. Methods The EDISP framework brings together the strengths of CNNs and ViTs to overcome their individual weaknesses. It is trained on a dataset that includes both controlled-environment and real-field maize leaf images, which helps it handle different environmental conditions. The data undergoes thorough preprocessing, including normalization, augmentation, and stratified splitting into training, validation, and test sets to support generalization. The CNN focuses on detailed local disease features, while the ViT captures broader contextual information across the maize leaf surfaces. Results The proposed EDISP model significantly outperforms standalone CNN and ViT Models in multiple performance metrics, achieving an overall classification accuracy of 99.40%, precision of 99.43%, recall of 99.38%, and an F1-score of 99.40%. Experimental results demonstrate that EDISP excels in identifying maize leaf diseases, including Common Rust, Gray Leaf Spot, Northern Leaf Blight, and Healthy leaves, with minimal false positives and negatives. External validation with an independent dataset further highlights the model's robustness and ability to generalize to real-world conditions. Discussion The EDISP hybrid architecture, integrating CNNs and ViTs, provides a stronger method for accurate, automated maize leaf disease detection. Its robust performance, consistent results on controlled and field datasets shows robustness in diverse environments. However, EDISP's effectiveness may be limited by image quality, lighting, or disease types not seen in training. These results highlight the promise of hybrid deep learning in precision agriculture and offer a scalable solution for disease detection, supporting farmers without expert diagnostic resources.
Why it matches plant phenotyping methodsトウモロコシ葉の病害状態を画像から検出・分類するCNN-ViT手法の開発と独立データセットによる検証が研究の中心であり、植物フェノタイピング手法に該当する。
abstractwe present EDISP a hybrid framework that connects Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for local feature extraction, as well as global contextual learning.
This paper presents a method to estimate apple tree flower cluster count using image analysis techniques. The main research question is how accurately flower clusters on apple trees can be counted using a camera-based approach and weakly-supervised learning, compared to traditional visual estimation. A camera is used to capture images of blooming apple trees from two sides. These images are processed by a weakly-supervised model based on a ResNet feature extractor and a feature pyramid network serving as the feature aggregator. The model is trained and validated using reference data obtained through manual flower cluster counts in the orchard and from estimations based on camera images. The model was trained using field-validated data and visually-estimated data, enabling a comparative evaluation. The proposed model surpassed conventional image segmentation methods in estimating both manually counted and visually estimated flower clusters. The proposed method achieves a relative error of 10.26% in estimating flower cluster quantities, demonstrating its effectiveness and improved accuracy over traditional approaches. Its reliance on field-validated reference data adds to its robustness and practical relevance.
Why it matches plant phenotyping methodsリンゴ樹の花房数という植物形質を、カメラ画像と弱教師あり学習で推定する手法の開発・検証が研究の中心であるため。
abstractThis paper presents a method to estimate apple tree flower cluster count using image analysis techniques.
Introduction Canopy water content (CWC) is an important indicator of crop water status **and** supports precision irrigation decision-making. Plot-level CWC estimation using UAV imagery often relies on canopy mean features, whereas the role of within-plot canopy-signal distributional information remains insufficiently examined. Methods In this study, spring maize at the Shiyanghe site was monitored using UAV-based multispectral and thermal infrared imagery. Mean, percentile, and dispersion features were extracted from effective canopy pixels within each plot. RFECV feature selection, 50 repeated random train-test splits, paired statistical tests, simulated spatial aggregation, and four regression models were used to evaluate the stage- and scale-dependent contribution of these features. Results and discussion Water stress affected both overall spectral-thermal responses and within-plot signal distributions. Before tasseling, percentile and dispersion features were frequently selected and provided complementary information, especially for tree-based models and finer aggregation scales. After tasseling, mean features generally showed more stable performance, although some distributional features still contained CWC-related information. The supplementary Xinxiang site-internal analysis suggested that, under weak water-gradient and small-sample conditions, distributional features may be frequently selected but may not consistently improve prediction accuracy. Overall, the contribution of distributional features was growth-stage-, scale-, and model-dependent.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像からトウモロコシ群落の水分含量を推定する特徴抽出・選択・回帰手法を中心に、反復分割や統計検定で技術的に評価しているため。
abstractPlot-level CWC estimation using UAV imagery often relies on canopy mean features, whereas the role of within-plot canopy-signal distributional information remains insufficiently examined.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction: Plant phenotyping requires accurate and repeatable three-dimensional structural information, but practical acquisition conditions in greenhouses, seedling rooms, and indoor pot experiments often include complex backgrounds, handheld motion blur, and thin leaf structures. These factors reduce the robustness of conventional three-dimensional reconstruction methods and limit their use in low-cost and automated phenotyping. Methods: To address this problem, this paper proposes F2DMAS, an automated three-dimensional plant phenotyping workflow using consumer-grade smartphone videos. The workflow first converts multiview RGB videos into image sequences and removes motion-blurred frames through frequency-domain quality filtering. A frequency-spatial plant segmentation module, termed FSAM3, is then introduced to separate plant structures from complex backgrounds without task-specific annotated training data. The segmented image sequences are further reconstructed using 2D Gaussian Splatting, followed by TSDF-based meshing, scale recovery, and virtual measurement for extracting plant height, canopy width, leaf length, and leaf width. Results: Experiments were conducted on 15 plant species under two acquisition scenarios. The proposed workflow achieved stable plant reconstruction under non-ideal background conditions, with PSNR, SSIM, and LPIPS values of 31.09, 0.9711, and 0.0365, respectively. Compared with the baseline reconstruction workflow, F2DMAS substantially reduced the processing time for mesh extraction while improving reconstruction quality. The extracted phenotypic traits showed strong agreement with manual measurements, with R² values ranging from 0.90 to 0.99, RMSE values ranging from 0.64 to 1.21 cm, and MAPE values ranging from 4.50% to 9.73%. Discussion: These results indicate that F2DMAS can provide an end-to-end workflow from smartphone video acquisition and plant segmentation to three-dimensional reconstruction and phenotypic trait extraction. The proposed method offers a practical and deployable solution for greenhouse seedling cultivation, potted plant experiments, and low-cost three-dimensional plant phenotyping.
Why it matches plant phenotyping methodsスマートフォン動画から植物の3D構造を再構成し、複数の形態形質を抽出・検証するワークフロー自体が中心的な方法論的貢献である。
abstractThe extracted phenotypic traits showed strong agreement with manual measurements, with R² values ranging from 0.90 to 0.99
Accurate lesion segmentation is essential for automated plant disease analysis in precision agriculture. Although the Segment Anything Model (SAM) exhibits strong generalization ability, its direct application to plant disease images in natural field environments remains challenging due to cluttered backgrounds, dense leaf veins, uneven illumination, and frequent occlusions. In particular, SAM mainly relies on global structural cues and is often insufficiently sensitive to subtle lesion textures and weak local details, which can result in missed small or early-stage lesions and inaccurate boundary delineation. To address these limitations, we enhance SAM with a disease-specific detail compensation module for plant disease lesion segmentation. A ResNet-50based branch is employed to extract fine-grained local texture features that are difficult for SAM to capture. These fine-grained features are fused with SAM encoder representations and then injected into the SAM decoder, enabling more accurate lesion prediction while preserving SAM’s strong global modeling capability. More importantly, we propose a reliability-guided variational fusion framework to further improve the interaction between heterogeneous features. Specifically, instead of conventional similarity or addition-based fusion, we introduce an uncertainty-aware variational fusion strategy that explicitly quantifies the confidence of each feature stream. An uncertainty encoder models feature distributions probabilistically, and a variational fusion module dynamically assigns higher weights to more reliable features while suppressing uncertain or interfering responses. In addition, Kullback-Leibler divergence regularization is introduced to stabilize cross-feature alignment and improve fusion robustness. Extensive experiments on PlantSeg, PlantDoc-Seg, and ATLDSD demonstrate that the proposed method outperforms state-of-theart approaches, achieving DSC scores of 81.05%, 91.12%, and 88.27%, respectively. The proposed method addresses SAM’s weakness in fine-grained disease feature extraction, accurately identifies early and small lesions, and delivers reliable segmentation for field plant disease automatic diagnosis.
Why it matches plant phenotyping methods植物病斑を対象とする画像セグメンテーション手法を開発し、複数データセットで性能検証しているため、病害状態のフェノタイピング手法が中心である。
abstractwe enhance SAM with a disease-specific detail compensation module for plant disease lesion segmentation.
Reproduction assets foundThe paper evaluates ReLeaf-SAM on three public plant disease segmentation datasets. One of them, PlantDoc-Seg, is explicitly a community-provided Kaggle dataset with a verbatim URL matching an allowed URL; it is a public plant image/mask dataset directly used for this paper's segmentation measurements. PlantSeg and ATLDataset · publicTherefore, we used a community-provided segmentation subset from Kaggle 1 , which we refer to as PlantDoc-Seg in this study. This subset is derived from PlantDoc and contains 588 diseased leaf images with corresponding binary masks, enabling supervised leaf disease segmentation.Open asset ↗Kagglelines:48-115Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 15 Sept 2026
Introduction Accurate and transferable rice yield prediction is essential for precision agriculture and food security, yet existing remote sensing-based models often suffer from limited generalization across regions, cultivars, and field scales. Methods This study developed an interpretable automated machine learning framework for rice yield prediction using UAV-based multispectral imagery collected at the maturity stage. A total of 143 rice samples, including 79 experimental plots and 64 production fields across 15 counties in Sichuan Province, China, were investigated. 20 vegetation indices and 36 gray-level co-occurrence matrix texture features were extracted from multispectral orthomosaics, and three feature selection strategies: Pearson correlation coefficient (PCC), Random Forest feature importance (RF-I), and AutoGluon feature importance(AutoGluon-I), were systematically compared. Four regression approaches, including CatBoost, ExtraTrees, Random Forest, and an AutoGluon stacked ensemble, were evaluated using R 2 , RMSE, and MAE. Results The results showed that the AutoGluon ensemble consistently outperformed individual machine learning models, improving testset R 2 from 0.403-0.670 to 0.528-0.736. The best performance was achieved by combining Pearson correlation-based feature selection with AutoGluon, yielding a training R 2 of 0.821 and a test R 2 of 0.736, with RMSE and MAE values of 0.749 and 0.568 t ha -1 , respectively. Shapley Additive Explanations (SHAP) analysis further revealed that texture features, particularly red-band contrast and angular second moment features, contributed substantially to yield prediction, indicating the importance of canopy structural heterogeneity at maturity. Discussion Overall, the proposed PCC-AutoGluon-SHAP framework provides a lightweight, accurate, and interpretable approach for UAV-based rice yield estimation across heterogeneous field conditions, offering practical potential for scalable precision agriculture and regional yield monitoring.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からイネの収量を推定する画像・特徴抽出・機械学習フレームワークを開発し、複数モデルと特徴選択法を比較検証しており、植物表現型取得・推定が研究の中心である。
abstractThis study developed an interpretable automated machine learning framework for rice yield prediction using UAV-based multispectral imagery collected at the maturity stage.
Introduction Wheat is one of the most widely planted staple crops worldwide and underpins global food security. Fungal diseases severely threaten wheat growth and trigger massive yield losses during cultivation. Traditional manual diagnosis is time-consuming and highly subjective, while existing deep learning models often struggle to achieve high accuracy and robustness in complex field environments. Accurate identification of these fungal diseases is therefore vital to secure grain production. Methods This paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification. First, the original basic blocks of ShuffleNetV2 are substituted with SS-Conv-SSM modules to strengthen the extraction of fine-grained lesion features amid visually analogous fungal disease samples; half convolution is embedded to cut down model computational overhead. Second, a Mixed Local Channel Attention (MLCA) unit is attached to the convolution branch of each SS-Conv-SSM module, which adaptively highlights discriminative disease features and filters irrelevant background noise. Standard training configurations and five-fold cross-validation are adopted for fair model evaluation. Results Comparative experiments reveal that the presented network reaches a classification accuracy of 91.35%, which surpasses the original ShuffleNetV2 baseline by 1.16 percentage points. Controlled ablation tests verify the independent performance gain of each core component: the SS-Conv-SSM module raises overall accuracy by 0.89%, and the MLCA mechanism brings an extra 0.27% accuracy increment. Discussion The proposed ShuffleNetV2_SSM_MLCA architecture strikes a favorable trade-off between model lightweight property and classification performance. It delivers a low-computation, high-precision recognition scheme for wheat fungal diseases and lays a solid technical foundation for real-time disease monitoring in intelligent agricultural scenarios.
Why it matches plant phenotyping methods小麦葉片の病斑特徴を画像から抽出し、植物の真菌病状態を分類する軽量深層学習手法を開発・検証しており、病害表現型の取得・推定が研究の中心である。
abstractThis paper constructs a lightweight convolutional neural network named ShuffleNetV2_SSM_MLCA for wheat fungal disease classification.
Reproduction assets foundThe paper's plant-image measurements are based entirely on publicly available wheat disease image datasets: a primary Kaggle dataset (Wheat Plant Diseases by Kushagra Agarwal) used for model development, and two additional public datasets (Alibaba Cloud Developer Community and CSDN Modelers) used for generalization andDataset · publicThe dataset is publicly available at https://www.kaggle.com/datasets/kushagra3204/wheat-plant-diseases and was accessed on September 5, 2025.Open asset ↗Kaggle · kushagra3204/wheat-plant-diseaseslines:322-374Dataset · publicThe second dataset was contributed by blogger DL data set and released on December 25, 2025 via the CSDN Modelers platform ( https://modelers.csdn.net/69a67f4c7bbde9200b9c3240.html )Open asset ↗CSDN Modelerslines:644-669Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction The identification and advancement of superior maize hybrids under the All India Coordinated Research Project (AICRP) on Maize rely on multi-environment evaluation integrating grain yield, maturity, and agronomic performance. Interpretation of large multi-environment datasets is often complex, time-consuming, and susceptible to subjectivity, highlighting the need for objective and reproducible decision-support tools. This study evaluated the effectiveness of REMATTOOL-R (Relative Maturity Adjustment Tool in R) in validating the existing hybrid advancement framework adopted under the AICRP on Maize. Methods Multi-environment trial data from the National Initial Varietal Trial (NIVT)-Late conducted during Kharif 2020-21 across five locations representing the Central West Zone (CWZ) of India were analysed. The dataset comprised 45 entries, including 40 experimental hybrids, four commercial checks, and one filler entry. REMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits. Least-square means generated from mixed-model analysis were used to identify superior hybrids based on a predefined grain yield superiority threshold (≥5%) over the standard check while maintaining comparable maturity and agronomic performance. Results REMATTOOL-R enabled rapid visualization and integrated assessment of multiple agronomic traits, allowing objective identification of superior hybrids. Five experimental hybrids-PM 21109L (Entry 30), R8050 (Entry 35), PM 21111L (Entry 32), BIO 978 (Entry 4), and DKC 9226 (Entry 9)-recorded ≥5% higher grain yield than the standard check Bio 9682 while maintaining statistically comparable days to 50% anthesis, grain moisture at harvest, and harvested plant stand. All five hybrids identified by REMATTOOL-R corresponded with the official AICRP decisions for advancement from NIVT to Advanced Varietal Trial-I (AVT-I), while three hybrids (R8050, PM 21111L, and DKC 9226) progressed further to AVT-II during subsequent testing cycles, confirming the reliability of the analytical framework. Discussion The findings demonstrate that REMATTOOL-R provides an efficient, transparent, and reproducible framework for the simultaneous evaluation of grain yield, maturity, and adaptation-related traits in maize multi-environment trials. By complementing the existing AICRP hybrid evaluation procedure, the tool facilitates objective advancement decisions and reduces subjectivity associated with manual interpretation of complex datasets. REMATTOOL-R therefore represents a valuable decision-support approach for coordinated maize breeding programmes and has considerable potential for application in large-scale hybrid evaluation systems.
Why it matches plant phenotyping methodsREMATTOOL-Rという解析ツールを開発・評価し、収量、成熟期、収穫時水分、植立本数を統合してハイブリッドの表現型・適応性を客観的に評価することが中心である。
abstractREMATTOOL-R integrated grain yield with days to 50% anthesis, grain moisture at harvest, and harvested plant stand to facilitate simultaneous evaluation of grain yield, maturity, and adaptation-related traits.
Non-heading Chinese cabbage is a cool-season crop, and high temperature has become a key factor limiting its quality and yield. Given that plant heat tolerance is a complex quantitative trait regulated by multiple genes, establishing a comprehensive evaluation system integrating multiple physiological and biochemical indicators is of great significance. In this study, 35 varieties of non-heading Chinese cabbage germplasm were used to investigate heat damage indices (HDI) and measure physiological and biochemical indicators under summer high-temperature stress, aiming to provide a basis for heat tolerance evaluation. Correlation analysis revealed significant correlations among the physiological and biochemical indicators, indicating information overlap. Principal component analysis (PCA) was subsequently employed to extract six independent composite indicators. A composite index of heat tolerance productivity, namely the Heat Tolerance Productivity Index (HTPI), was obtained through membership function analysis, and cluster analysis classified the tested germplasm into four heat tolerance levels. A regression equation for evaluating heat tolerance in non-heading Chinese cabbage was successfully established. Eleven key heat tolerance indicators were identified, and two highly heat-tolerant varieties, B21 and B32, with excellent comprehensive traits were selected. The comprehensive evaluation system established in this study not only provides an effective tool for high-throughput screening of heat-tolerant germplasm resources but also lays a solid foundation for subsequent genetic improvement and molecular breeding of heat-tolerant varieties. However, this study was conducted only at the seedling stage, and did not evaluate heat tolerance during the more sensitive reproductive stages (flowering and bolting).
Why it matches plant phenotyping methods複数の生理・生化学指標を統合し、PCA、メンバーシップ関数、回帰式による耐暑性評価システムを開発しており、植物表現型の抽出・スクリーニング手法が研究の中心である。
abstractestablishing a comprehensive evaluation system integrating multiple physiological and biochemical indicators is of great significance
Precise identification of chili flowers and buds is essential for enabling intelligent robotic pollination, continuous crop monitoring, and early yield prediction in protected horticulture. Reliable visual sensing remains challenging because chili targets are extremely small, densely distributed, and frequently occluded by foliage, while greenhouse environments introduce strong illumination variations and background reflections. These factors often lead to insufficient feature extraction and unstable detection accuracy in existing models, limiting their practical deployment in automated monitoring systems. To address these challenges, this study proposes GCP-YOLO, a lightweight yet high-performance detection framework built upon the YOLOv11n architecture. The model enhances small-target perception through three key improvements. First, a redesigned Generalized Feature Pyramid Network (GFPN) strengthens cross-scale feature interaction, improving the fusion of fine-grained texture cues and deep semantic information. Second, a C2CGA context-guided attention module is introduced to emphasize floral structural features while suppressing background noise caused by reflections and canopy clutter. Third, extended multi-scale detection heads (P2-P6) incorporate broader contextual information to reduce missed detections and false positives in dense planting scenarios. Experimental results on a custom chili flower dataset show that the proposed method achieves 92.8% precision, 83.7% recall, 90.8% mAP50, and 72.7% mAP50-95, improving upon the YOLOv11n baseline by 2.1, 1.3, 3.9, and 6.6 percentage points, respectively. Deployment on an NVIDIA Jetson AGX Orin edge platform demonstrates real-time inference at 97.9 FPS, confirming its suitability for on-device phenotyping. Overall, the proposed approach significantly improves detection robustness under complex greenhouse conditions, providing an effective visual sensing methodology for automated crop monitoring and data-driven yield estimation.
Why it matches plant phenotyping methodsチリの花・蕾という植物器官の画像検出手法を中心に、モデル改良、専用データセットでの性能評価、エッジ実装まで行っており、植物表現型取得の技術的貢献が明確である。
titleGCP-YOLO: high-precision detection of tiny chili flowers in complex greenhouse scenes.
Field / plotObject detectionStress / disease detectionDisease symptoms / severity
Introduction To address the challenges of low detection precision, severe background interference, and high model complexity in tiny crop disease lesion detection (defined as lesions occupying 8×8 to 32×32 pixels at 640×640 input resolution) under complex field environments, this study proposes a lightweight detection model named FDA-YOLO by integrating frequency-domain attention and improved YOLOv11. Methods The model employs EfficientFormerV2 as the backbone to extract multi-scale features with low computational cost, and introduces a frequency domain attention module to enhance high-frequency tiny disease lesion details and suppress background noise. Results Comprehensive experiments on the PlantDoc dataset demonstrate that the proposed model achieves 96.3% mAP@0.5, 96.8% precision, and 36.4 FPS with only 28.5M parameters, outperforming the selected baseline detectors under the adopted experimental setting. Discussion The model realizes an optimal balance between accuracy, efficiency, and lightweight performance, providing a reliable and practical solution for real-time tiny lesion detection inprecision agriculture and edge device deployment.
Why it matches plant phenotyping methods植物病斑という植物の病態を画像から検出するモデルを開発し、PlantDocデータセットで精度・速度・計算量を比較検証しているため、植物フェノタイピング手法が中心である。
abstractthis study proposes a lightweight detection model named FDA-YOLO
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Introduction Accurate and rapid diagnosis of plant leaf disease symptoms is critical for sustainable agricultural crop production, yet traditional methods often lack efficiency and robustness under field conditions. Methods Here, we propose a deep learning framework based on an improved Vision Transformer architecture that integrates a dynamic sparse attention mechanism, termed KBTNet, for targeted feature extraction in symptom-affected regions of leaf images. The model incorporates a learnable nonlinear enhancement module to capture subtle visual disease symptom variations such as lesions, discoloration patterns, and spot distributions, and a lightweight Transformer design to reduce computational cost. Results Evaluated on a multisource dataset containing soybean and tomato leaf images representing diverse disease symptom patterns, our approach achieved 93.19% classification accuracy, outperforming current state-of-the-art models. Additional evaluations on public plant disease datasets from multiple crops further demonstrate the model's ability to recognize disease symptom patterns across diverse crop species. Discussion The proposed framework achieves stable performance across diverse crop and disease symptom categories, maintains high efficiency under reduced parameter complexity, and exhibits strong potential for realtime field diagnostics on edge devices. This work provides a scalable and efficient tool for plant disease symptom detection and classification and supports the integration of visionbased intelligence into crop disease monitoring and management systems.
Why it matches plant phenotyping methods植物葉画像から病徴（病斑、変色、斑点分布）を抽出・分類する深層学習手法を開発しており、植物病害状態の表現型取得が研究の中心である。
abstractwe propose a deep learning framework based on an improved Vision Transformer architecture that integrates a dynamic sparse attention mechanism, termed KBTNet, for targeted feature extraction in symptom-affected regions of leaf images.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Assessing tools for rapid evaluation of new cultivars across large fields is essential for improving crop yields and ensuring future food security. Current phenotyping approaches remain labor-intensive and imprecise at the field scale, particularly for traits determining light interception and the light extinction coefficient ( K ). While robotic phenotyping and three-dimensional (3D) models have gained interest in estimating light interception in plant canopies, primarily at the single-plant scale or using single-plant-derived virtual canopies, applications at the field-scale canopy level remain limited. In this study, a semi-automated robotic phenotyping platform, PhenoLinc, was deployed to obtain canopy-level multispectral 3D data across 200 diverse wheat genotypes grown under field conditions over two years. 3D canopy architecture revealed substantial genotype-specific variation in inclination angle and K , which influenced radiation use efficiency (RUE), contrasting with the constant K commonly assumed in conventional approaches. This architectural variation was classified as two distinct architectural phenotypes, erectophiles (median angle 65°) and planophiles (59°) at pre-anthesis stages, which converged toward being homogenous by the anthesis stage. Erectophile phenotypes exhibited higher RUE (33.1%) than planophile phenotypes, leading to a higher yield. In yield prediction analyses, 3D-derived architectural traits provided comparable predictive performance to conventional measurements, however, architectural phenotype information reduced prediction error. Together, these findings highlight the value of field-scale robotic canopy phenotyping for characterizing genotype-specific canopy architectural traits and their relationship with yield.
Why it matches plant phenotyping methodsフィールド規模のロボット型3D・マルチスペクトル計測プラットフォームを用いて、コムギ群落の建築形質を取得・評価することが研究の中心である。
abstracta semi-automated robotic phenotyping platform, PhenoLinc, was deployed to obtain canopy-level multispectral 3D data across 200 diverse wheat genotypes grown under field conditions over two years.
Accuracy crop distribution mapping and reliable yield estimation are essential for overcoming fragmentation and decentralization in smallholder farming systems of the Loess Plateau gully region. Multi-source remote sensing data, ancillary datasets, and machine learning techniques were integrated to map maize distribution and estimate yield. First, Sentinel-2 temporal spectral features, vegetation indices, and topographic variables were integrated to identify the optimal maize mapping model by a comparing machine learning algorithms: Random Forest (RF), Extra Trees (ET), Gradient Boosting Decision Tree (GBDT), and Histogram-Based Gradient Boosting Decision Tree (HGBDT). Subsequently, Sentinel-2 optical data and ERA5-Land meteorological data were dynamically resampled and spatiotemporally fused. A maize yield estimation model was then developed by integrating these fused predictors with in-situ measured maize yield samples. Finally, SHapley Additive exPlanations (SHAP) analysis was applied to quantify the feature contribution to both crop mapping and yield estimation, improving model transparency and interpretability. The results indicate that RF model achieved superior performance for maize identification in heterogeneous agricultural landscapes, with an overall Accuracy of 0.825, Precision of 0.849, Recall of 0.933, and F1-Score of 0.889. In the multi-source fusion-based yield estimation task, the HGBDT model yielded the highest predictive accuracy, with an R² of 0.6, RMSE of 1.07 t/ha, relative RMSE (rRMSE) of 11.49%, and MAE of 0.86 t/ha. Here, a methodological advancement is presented toward accurate and interpretable crop mapping and yield estimation in the ecologically complex and topographically fragmented Loess Plateau.
Why it matches plant phenotyping methodsトウモロコシの収量という植物形質を、リモートセンシング・気象データ融合と機械学習で推定する手法を開発・評価しており、単なる農業実験の routine 測定ではない。
abstractA maize yield estimation model was then developed by integrating these fused predictors with in-situ measured maize yield samples.
In Controlled Environment Agriculture (CEA), traditional fixed set-point control is replaced by dynamic control strategies. These strategies enable joint optimization of resource use efficiency and biomass output, leverage electricity price fluctuations to reduce energy costs, and employ targeted environmental stressors to enhance crop quality and physiological resilience. Implementation of dynamic control strategies, however, builds upon real-time monitoring, robust data integration and management, and high-fidelity predictive modeling. These capabilities can be effectively provided through a Digital Twin (DT). This study introduces a novel open-source DT framework designed to support dynamic control strategies in CEA, addressing challenges in scalability, generalizability and interoperability. The framework is centered on the IoT platform ThingsBoard, providing unified, scalable data acquisition and management across heterogeneous sensor and actuator networks through vendor-agnostic integration and standardized interfaces. A significant contribution is its physics-based modeling backend, built on ordinary differential equation models developed in Modelica and exported as Functional Mock-up Units (FMUs). To ensure model accuracy across varying biological conditions, a parameter estimation pipeline is developed to calibrate and adapt these FMUs against experimental data. Building on this, a dedicated simulation backend is implemented to leverage the calibrated FMUs, providing the dynamic predictive capabilities necessary for proactive system control. Furthermore, the framework incorporates a Multirate Moving Horizon Estimation (MMHE) state estimator to estimate critical unmeasured variables, such as plant biomass. This estimator is specifically designed to handle multirate data, maintaining continuous estimates even when certain sensors provide frequent data while others are sparse or infrequent. Demonstrated through a simulation-based case study modeling lettuce growth in a vertical hydroponic farm, the DT framework's architectural feasibility and virtual modeling capabilities are verified. Using synthetic data generated from a known true parameter set, the calibrated growth model achieved a low cross-validation prediction error, with an RMSE of 0.221g and an NRMSE of 6.62% on an independent test set. The MMHE-based state estimator effectively maintained continuous biomass estimates despite sparse synthetic measurements and model mismatch. These findings underscore the framework's potential as a robust and extensible foundation for future physical DT implementations in CEA, enabling a 31 transition toward dynamic, data-driven, and energy-aware operations.
Why it matches plant phenotyping methods植物バイオマスという観測可能な植物形質を、デジタルツインの状態推定器と動的モデルで継続的に推定する方法を開発・検証しており、単なる栽培制御や routine measurement ではない。
abstracta dedicated simulation backend is implemented to leverage the calibrated FMUs, providing the dynamic predictive capabilities necessary for proactive system control.
Accurate yield estimation and crop load monitoring are essential for precision orchard management, supporting targeted fertilization, pruning, thinning, harvest planning, and marketing decisions. However, reliable in-situ monitoring remains challenging because commercial orchards are characterized by severe canopy occlusion, fruit overlap, heterogeneous tree architecture, variable illumination, and complex backgrounds. This review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions. First, yield-related indicators are summarized, including direct phenotypic traits such as fruit number, size, volume, and spatial distribution, as well as indirect structural and physiological proxies such as canopy volume, vegetation indices, flowering intensity, and spectral maturity attributes. Second, representative sensing devices and carrying platforms are reviewed, including red-green-blue (RGB) cameras, red-green-blue-depth (RGB-D) sensors, light detection and ranging (LiDAR), hyperspectral and multispectral systems, unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs). Third, the evolution of estimation methods is discussed, from traditional image processing and machine learning to object detection, instance segmentation, multi-object tracking, point-cloud analysis, remote-sensing regression, and multi-modal fusion. The review shows that no single sensor or algorithm can satisfy all orchard monitoring requirements. Ground-based vision and depth sensing are more suitable for fine-scale fruit counting and sizing, whereas UAV and spectral sensing provide advantages for regional yield mapping and quality-enhanced assessment. Future research should emphasize occlusion-aware perception, robust cross-environment generalization, lightweight edge deployment, standardized benchmarks, and integrated quantity-quality monitoring frameworks for actionable crop load management.
Why it matches plant phenotyping methods果実数・サイズ・体積・空間分布などの植物形質を対象に、センシング機器と画像解析・深層学習による収量推定法を体系的にレビューしており、フェノタイピング手法が中心である。
abstractThis review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions.
To address the challenge of simultaneously optimizing model accuracy and computational efficiency in the automated inspection of wheat grain appearance quality, this study proposes a lightweight deep learning model called MobileNet-WDD. Based on the MobileNetV4-small architecture, this model incorporates the SimAM attention mechanism to enhance feature discrimination capabilities and employs Ghost convolutions and the Mish activation function to optimize the network structure, thereby significantly reducing model complexity while maintaining high recognition accuracy. Using six typical categories of wheat grains-diseased spots, insect damage, mold, sprouting, damage, and intact grains-as the research subjects, experimental results show that compared to the baseline model, MobileNet-WDD achieves a 6.1% increase in accuracy, reaching 94.2%; a 30.3% reduction in the number of parameters; computational cost by 27.6%; and inference speed from 155 FPS to 183 FPS, representing a 1.18-fold acceleration. Quantitative analysis confirms that this model achieves high-precision recognition while offering significant advantages in terms of lightweight design and efficient computational performance, providing an efficient and feasible technical solution for real-time non-destructive inspection of wheat grains.
Why it matches plant phenotyping methods小麦粒の病斑・虫害・カビ・発芽などの可視状態を画像から分類する軽量深層学習手法を開発しており、表現型状態の取得・抽出が研究の中心である。
abstractthis study proposes a lightweight deep learning model called MobileNet-WDD
The convergence of multi-omics profiling, high-throughput phenotyping (HTP), and artificial intelligence (AI) has expanded our ability to characterize crop stress responses at unprecedented resolution (Danilevicz et al., 2025;Pacheco-Ruiz et al., 2025;Tsega and Mullualem, 2026). Researchers can now routinely identify candidate genes, construct gene regulatory networks, and train machine learning models to predict terminal phenotypes, such as yield under drought, biomass under salinity, and disease severity scores. Yet the practical impact remains limited: delivering a single improved crop variety to market still requires approximately one decade and more than 14 million euros, a timeline that has barely changed in 30 years despite the exponential growth in data generation (Pacheco-Ruiz et al., 2025). We argue that a fundamental cause of this translational gap is what we call the temporal poverty of current GP: models often predict static endpoints rather than the dynamic processes that determine them.Classical GP models, including multi-omics-informed variants, are overwhelmingly trained on traits measured at single time points, such as final yield or end-of-season stress scores. However, stress tolerance is inherently temporal, reflecting a cascade of physiological decisions whose sequence and timing ultimately determine field survival: when to close stomata, how rapidly to accumulate osmolytes, and whether to prioritize root extension or shoot preservation. A model that predicts terminal drought tolerance without distinguishing whether it arises from early water conservation, sustained photosynthesis, or post-stress recovery offers limited support for rational gene stacking or knowledge transfer across environments and crops (Danilevicz et al., 2025). Based on these observations, we hypothesize that making the dynamic trajectories of intermediate physiological traits the direct targets of GP, rather than treating them as auxiliary variables, will substantially improve prediction accuracy, mechanistic interpretability, and the rate of genetic gain for stress resilience in crop breeding.To bridge this temporal gap, we propose a dynamic explainable genomic prediction framework centered on intermediate physiological trajectories (Figure 1). The framework integrates dynamic phenotyping, environmental information, and mechanistic multi-omics anchors to support trajectory-aware prediction and climate-resilient breeding.Conventional genomic prediction focuses on static endpoint prediction, whereas the proposed framework targets dynamic intermediate phenotypes and integrates environmental and multi-omics information to enable trajectory-aware, interpretable prediction for climate-resilient crop breeding.This reframing is becoming increasingly feasible. The recent dynamicGP model combines GP with dynamic mode decomposition (DMD) to predict full temporal trajectories of multiple morphometric and colorimetric traits scored by HTP in maize and Arabidopsis thaliana (Hobby et al., 2025). DynamicGP was also shown to predict traits at time points beyond the training period. It was the only model tested with this capability and showed superior longitudinal accuracy in capturing developmental dynamics (Hobby et al., 2026). The model further revealed that traits with more temporally stable heritability can be predicted with higher accuracy, providing practical guidance on which dynamic intermediates to prioritize (Hobby et al., 2025).Mechanistic modeling offers a complementary line of evidence: computational models of grass inflorescence morphodynamics recently guided the discovery of the duo2 mutant allele in wheat, which accelerates developmental progression and improved yield by 7-11% under field conditions (Wang et al., 2026b). These two independent advances, one data-driven and one mechanism-driven, converge on the same insight: incorporating time as a central dimension in prediction can provide predictive power and mechanistic understanding that are inaccessible to static models, thereby connecting prediction with actionable breeding decisions.A realistic view of current data availability shapes this strategy. The current and near-future foundation for dynamic intermediate phenotype prediction is largely HTP.The marginal cost of repeated phenotyping with automated platforms, UAVs, and low-cost sensors has decreased substantially, and public time-series datasets covering wheat, sorghum (LeBauer et al., 2020), soybean, and several other crops are rapidly expanding. This supports a pragmatic, asymmetric integration strategy: HTP provides the dense temporal skeleton of dynamic trait trajectories, while multi-omics is deployed selectively at a small number of mechanistically critical time windows, such as the onset of stress signaling, the transition from alarm to acclimation, or the peak of a known physiological trade-off. These sparse but information-rich omics snapshots serve as "explainability anchors." When explainable AI approaches are used to link these molecular profiles to parameterized dynamic modes extracted from HTP, the resulting model can reveal which genes, transcripts, or metabolites modulate specific phases of the dynamic response and when. For instance, SHAP-based interpretation of Random Forest models applied to soybean multi-omics data revealed that the isoflavone derivative daidzin and specific drought-tolerant microbes are major contributors to phenotypic variation under drought stress, while SHAP-based interaction networks uncovered cross-omics links between metabolites and microbial taxa (Yoshioka et al., 2026). Extracting biologically meaningful signals from such comparisons requires specialized computational tools. The MODAS2 pipeline, for example, uses contrastive principal component analysis, a machine learning algorithm, to disentangle stress-responsive molecular QTLs from background genetic effects in multi-omics data, enabling the identification of salt-responsive genetic variants in maize (Liu et al., 2025). Such tools are essential for implementing the explainability-anchor strategy at the stress-transition windows targeted by our framework.Advocating for dynamic intermediate phenotypes as prediction targets does not mean abandoning terminal agronomic traits. The two approaches serve complementary roles at different stages of the breeding pipeline. In early-generation selection, when thousands of lines must be evaluated and resources for multi-environment yield trials are limited, dynamic intermediates, which are often measurable earlier, at lower cost per data point, and with higher heritability, can help enrich populations for stress-resilient candidates (Melsen et al., 2025). In later-stage trials, direct prediction of yield under target stress environments remains indispensable for final variety release decisions. In practice, the central question is not which target is superior in isolation, but whether their combined use can accelerate genetic gain for stress tolerance.Dynamic intermediate phenotypes can contribute to genetic gain through three main routes. First, many dynamic intermediates show higher heritability and earlier measurability than terminal yield under stress, enabling more accurate early-generation selection and shorter breeding cycles. Second, a dynamic physiological module, such as rapid osmotic adjustment within 48 hours of soil drying, can become a reusable building block once it has been genetically dissected and validated. Such modules could then be stacked, introgressed, or transferred across genetic backgrounds and crop species. Third, selecting on the shape of a response curve rather than a single terminal value can reduce environmental noise, because temporal patterns are often more genetically determined than absolute end-point values (Hobby et al., 2025). This framework is also inherently cross-crop: a conserved physiological module such as "stomatal response speed to soil drying" is unlikely to be restricted to a single species. Orthologous genes and conserved pathways can inform candidate selection in legumes, vegetables, and under-researched crops, extending advanced breeding methodologies beyond the major cereals (Kundu and Tanti, 2026;Wang et al., 2026b).The dynamic GP framework we advocate remains incomplete without explicit environmental inputs. At present, many AI-driven prediction models treat the environment as a categorical label or a set of static summary statistics. This approach can conflate genetic and environmental effects and limit performance forecasting across mega-environments or novel climatic scenarios. To support the development of climate-resilient varieties, models should instead incorporate environmental data as an explicit, dynamic data layer, including time series of temperature, soil moisture, and vapor pressure deficit, that co-determines the trajectory of intermediate phenotypes.Recent advances suggest that this is increasingly feasible. Enviromics and reaction-norm approaches that incorporate high-dimensional environmental covariates through penalized regression can now approach the accuracy of deep learning while retaining interpretability and an explicit description of genotype-by-environment interactions (Avagyan et al., 2025). In parallel, hybrid frameworks that couple crop growth models with whole-genome prediction can link genetic, environmental, and management inputs to dynamic physiological outputs (Laurent et al., 2025). Building on these developments, embedding environmental time series into the temporal kernels of dynamic models such as dynamicGP could, in principle, enable prospective, environment-aware forecasting of genotype-specific response curves. This would shift selection from retrospective mega-environment classification toward forward-looking prediction across single or multiple target mega-environments. However, robust extrapolation to untested environments and stress combinations remains a key open challenge. Careful envirotyping and cross-environment validation will therefore be essential before such models can guide variety deployment decisions in practice.The framework proposed here has important implications for future crop production.By shifting the breeding target from terminal yield to the temporal architecture of stress responses, breeders could develop selection strategies that are both physiologically informed and operationally efficient. Dynamic intermediate phenotypes, captured through increasingly affordable HTP platforms, can serve as early indicators of resilience, allowing breeders to discard susceptible lines long before harvest. This could shorten the breeding cycle, especially when combined with genomic selection and speed breeding, while also enabling the deliberate assembly of stress-resilience modules that are robust across environments. Empirical evidence already shows that integrating HTP-derived spectral data with genomic information can substantially improve cross-environment prediction accuracy (McBreen et al., 2025;Nannuru et al., 2025). As climate variability intensifies, the ability to design varieties with predictable temporal behavior under drought, heat, or salinity will become increasingly important for enhancing yield stability and food security.Challenges remain. Dynamic GP models have yet to be systematically validated across radically different environments, and their ability to predict trait dynamics under novel stress combinations remains unproven. Data from controlled HTP platforms must also be calibrated against field conditions. In addition, the integration of heterogeneous multi-omics datasets creates persistent bottlenecks that constrain practical application beyond proof-of-concept studies (Pacheco-Ruiz et al., 2025;Tsega and Mullualem, 2026). Multi-omics time series, even at the sparse sampling density we advocate, remain limited by high technology costs and scalability challenges that restrict their routine deployment in breeding programs (Syeda, 2025;Younas et al., 2025). However, pilot-scale time-series multi-omics studies demonstrate both feasibility and value. For example, transcriptomic and ionomic profiling of Sorghum bicolor across a 21-day micronutrient stress time course revealed iron-zinc regulatory crosstalk and conserved gene regulatory networks (Mishra et al., 2025), while high-resolution time-series transcriptomic and metabolomic profiling of salt-tolerant and salt-sensitive maize inbred lines identified the hub gene ZmGLN2 and constructed dynamic regulatory networks governing salt-responsive metabolite biosynthesis (Zhang et al., 2025). Furthermore, the MODAS2 pipeline, which uses contrastive principal component analysis to extract stress-responsive signals from multi-omics comparisons, illustrates that the computational methods needed to integrate heterogeneous omics layers are already under active development (Liu et al., 2025).Equally important is the ability of AI models to capture the non-linear genetic interactions that underpin complex stress responses. Linear mixed models, the backbone of classical GP, primarily model additive effects and therefore have limited capacity to capture epistasis, gene-by-environment interactions, and threshold-type responses (Wang et al., 2026a). In contrast, deep learning architectures can learn hierarchical, non-linear mappings from high-dimensional input spaces. When applied to dynamic intermediate phenotypes, these models could learn not only which genomic regions influence the temporal shape of a trait, but also how those regions interact with each other and with environmental triggers over time. For example, convolutional neural networks have outperformed classical methods such as LASSO and Bayes C in predicting integrative traits, with the combination of CNNs and crop model parameters further enhancing prediction accuracy (Larue et al., 2024). Fully harnessing non-linear interactions for dynamic trait prediction will likely require hybrid approaches that embed mechanistic constraints into flexible AI architectures, ensuring that predictions remain both powerful and biologically plausible.As outlined in the preceding section, integrating environmental time series into dynamic GP models remains a frontier, but the computational tools and conceptual frameworks are now within reach. The integration of environmental data with multiple omics layers for genotype-by-environment prediction has been identified as a major emerging frontier, although it is currently addressed in fewer than 20% of studies (Tsega and Mullualem, 2026). This gap underscores the urgency of the framework we advocate. These are not sequential prerequisites, but parallel investments that reinforce one another. We therefore call on the crop science community to: (i) prioritize the generation of time-series phenotypic data in multi-environment stress trials, leveraging increasingly affordable HTP platforms; (ii) adopt explainable AI as standard practice, not merely reporting prediction accuracy but also elucidating which features drive predictions, when, and through which physiological mechanisms (Danilevicz et al., 2025); and (iii) develop selection indices that explicitly reward favorable dynamic trajectories alongside terminal trait values.Multi-omics and AI should not merely describe how stress resistance appears at harvest; they must reveal how it unfolds over time and how it can be rationally assembled. Bridging this temporal gap will connect current data abundance with the practical goal of delivering climate-resilient crops to farmers' fields.In
Why it matches plant phenotyping methods動的HTP phenotypingと軌跡予測を中核とする概念・方法論的枠組みを提案し、既存モデル、環境データ統合、検証課題を体系的に論じているため。
abstractThe recent dynamicGP model combines GP with dynamic mode decomposition (DMD) to predict full temporal trajectories of multiple morphometric and colorimetric traits scored by HTP in maize and Arabidopsis thaliana
Drought poses a global threat to food security and demands intensified efforts from breeding programs. Yet the lack of efficient methods for selecting this trait increases the cost and time required to develop new cultivars. The goal of this work was to assess the feasibility of using spectral data from RGB or multispectral sensors for drought-tolerance classification across various machine-learning models under the most practical cross-validation scenarios typical in breeding programs. The genotypes were assessed during trials conducted under either optimal (irrigated) or drought-stress conditions across two years, and evaluated using up to 10 field traits to determine their drought-tolerance classification based on membership function values related to drought. RGB and multispectral vegetation indices collected during several flights throughout the crop cycle were used to train machine learning models. We found that drought trials offer the best training data. Specificity was the metric most affected by sensor type and the nature of the training data. The multispectral sensor outperformed the RGB sensor on most evaluation metrics in both years. AdaBoost and linear discriminant analysis models demonstrated the strongest consistency across all prediction scenarios. Together, they achieved an overall accuracy, specificity, and F1-Score of 0.71, 0.56, and 0.77, respectively. The most influential vegetation indices for model performance consistently included the NIR band. Spectral information, such as vegetation indices, is a useful tool for plant researchers to complement drought tolerance evaluations in the field. This data-driven approach facilitates automation, paving the way to speed genetic gains by including early assessments of drought tolerance in breeding pipeline, and improves resource utilization efficiency.
Why it matches plant phenotyping methodsUASのRGB・マルチスペクトルデータと機械学習を用いて干ばつ耐性を分類し、センサー比較や交差検証を行うことが研究の中心であるため、植物フェノタイピング手法として適格です。
abstractThe goal of this work was to assess the feasibility of using spectral data from RGB or multispectral sensors for drought-tolerance classification across various machine-learning models under the most practical cross-validation scenarios typical in breeding programs.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Crop diseases pose a serious threat to agricultural yield and global food security. Accurate detection using Unmanned Aerial Vehicle (UAV) remote sensing imagery is of great significance for precision agriculture. However, this task remains challenging due to complex field backgrounds, diverse spectral-spatial characteristics of diseased leaf regions, irregular lesion boundaries, and variable texture patterns. To address these issues, this paper proposes a Task-Driven Attention VM-UNet (TDAVM-UNet), a novel deep learning model for crop disease detection from UAV imagery. The model integrates two task-driven attention modules: (1) Disease-Aware Dynamic Attention (DADA), which enhances the representation of diseased regions through disease feature enhancement, multi-scale dynamic channel attention, and texture-guided spatial attention; and (2) Channel-Spatial Visual State Space (CSVSS), which enables efficient long-range dependency modeling and local-global feature fusion while maintaining linear computational complexity. A hybrid loss strategy combining binary cross-entropy (BCE) loss, Dice loss, and cross-entropy (CE) loss with optimized coefficients is employed to address class imbalance and boundary delineation challenges. Extensive experiments are conducted on a self-constructed UAV crop disease unified mix dataset, comprising soybean disease images from Maharashtra, India, and rust disease images from wheat, corn, and other crops in Yangling, China, totaling 6,680 raw collected images, which after deduplication yields 5,000 images for experimentation. The results demonstrate that TDAVM-UNet achieves 26.87M parameters and 31.45 GFLOPs for 256×256 inputs, maintaining O(N) linear complexity (80% lower than TransUNet’s 156.78 GFLOPs), with 82.22% mIoU. This work provides a high-accuracy, robust, and computationally efficient method for UAV-based crop disease detection, offering significant technical support for precision agriculture applications.
Why it matches plant phenotyping methodsUAV画像から作物病害の病変領域・病害状態を推定する深層学習モデルを開発し、データセット上で性能評価しており、植物表現型取得・抽出手法が研究の中心である。
abstractthis paper proposes a Task-Driven Attention VM-UNet (TDAVM-UNet), a novel deep learning model for crop disease detection from UAV imagery.
This study presents an intelligent greenhouse lighting control framework that integrates a CNN-ELM photosynthesis prediction model with MOEA/D-based multi-objective optimization to improve tomato production while reducing the carbon impact of supplemental LED lighting. The CNN-ELM model was trained using key environmental variables, including photosynthetic photon flux density (PPFD), red-to-blue light ratio, canopy temperature, CO 2 concentration, and relative humidity. Within the experimental conditions, the model achieved high predictive accuracy, with an R² of 0.976 and an RMSE of 0.712 µmol m -2 s -1 . Using these predictions, the MOEA/D algorithm generated Pareto-optimal lighting strategies, which were ranked through entropy-weighted TOPSIS and implemented via cloud-based control connected to a LoRa wireless sensor network and pulse-width-modulated LED drivers. The system was evaluated during a 110-day tomato cultivation trial and compared with single-parameter control and ambient-condition treatments. Results showed a 38.4% reduction in LED-related carbon emissions, a 22.6% increase in net photosynthetic rate, and a 31.7% improvement in harvestable yield relative to ambient conditions. Physiological analyses further indicated enhanced photosynthetic performance, radiation-use efficiency, and light utilization. Overall, the findings demonstrate that data-driven, closed-loop lighting management can simultaneously enhance productivity and reduce greenhouse gas emissions in controlled-environment agriculture when applied within the validated operational domain.
Why it matches plant phenotyping methods光合成という植物生理形質を予測するCNN-ELMモデルを中核に、センサーネットワークと閉ループ制御を統合・評価しており、単なる栽培試験ではなく形質推定手法の応用が主要内容である。
abstractThis study presents an intelligent greenhouse lighting control framework that integrates a CNN-ELM photosynthesis prediction model with MOEA/D-based multi-objective optimization
Introduction Stomata are vital gatekeepers of plants that regulate the fundamental trade-off between carbon gain and water loss. Precise, high-throughput identification of stomatal traits is therefore essential for assessing plant stress tolerance and water-use efficiency. However, conventional bounding box detection struggles to accurately localize densely distributed and arbitrarily oriented stomata. Methods This study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction. Based on a maize stomatal dataset expanded from 1,053 to 3,597 microscopic images, DFA-YOLO integrates three task-specific components: (1) a cross-dataset MGD distillation strategy that transfers structural priors from single-stomata images to dense multi-stomata scenes; (2) a fixed-threshold Focaler-CIoU localization-loss reweighting strategy (u stomata = 0.95, d stomata = 0.00) to emphasize hard positive samples during OBB regression; and (3) a C3k2_AssemFormer feature-aggregation module that combines convolutional local feature extraction with linear attention-based context aggregation. Results DFA-YOLO achieved 94.1% mAP50, 84.8% mAP75, 74.1% mAP50-95, and 90.0% recall, with higher recall, mAP50, mAP75, and mAP50-95 than the YOLOv11-OBB baseline under the same OBB evaluation protocol. When deployed on an automated platform, the system processed images at 44.9 FPS and supported stomatal localization, density estimation, and preliminary orientation-aware size description. Discussion Under the tested maize microscopic imaging workflow, DFA-YOLO enables rapid extraction of detection-oriented stomatal traits and provides a prototype tool for high-throughput maize stomatal phenotyping.
Why it matches plant phenotyping methodsトウモロコシ気孔の画像検出・配向局在化と形質抽出を目的とするYOLOベース手法を開発し、データセット、精度比較、自動化プラットフォームでの性能を評価しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Field / plotCountingObject detectionStress / disease detectionDisease symptoms / severity
This paper targets the practical needs of plant disease and pest object detection and counting in complex field environments and proposes a lightweight improved framework based on YOLOv10. A MEMBA-F multi-scale feature enhancement module is introduced on the Neck to strengthen representations of small targets and weak-texture lesions, and a CSCAF cross-scale context-aware fusion module is designed to adaptively align high-level semantics with low-level details via cross-scale attention and gated selection, suppress background interference, and improve localization stability. The proposed method is systematically compared with two-stage detectors, YOLO-series models, and Transformer-based detectors on three public datasets, and is further investigated through ablation studies, confusion matrix analysis, and Grad-CAM interpretability analysis. In addition, a density-binned counting evaluation is conducted to validate robustness from sparse to dense scenarios. Experimental results demonstrate that the proposed method achieves superior performance in Precision, Recall, mAP@50, and mAP@50-95, and significantly reduces counting errors in dense scenes under deployable inference cost, providing reliable support for precision plant protection monitoring and decision making.
Why it matches plant phenotyping methods植物病害の病変と害虫を画像から検出・計数するYOLOv10改良法を開発し、複数データセット、比較実験、アブレーション、密度別評価で性能検証しており、植物の病害状態の取得方法が中心である。
abstractThis paper targets the practical needs of plant disease and pest object detection and counting in complex field environments and proposes a lightweight improved framework based on YOLOv10.
Introduction eaf area index (LAI) and leaf nitrogen accumulation (LNA) are key indicators of wheat growth and nitrogen nutritional status. However, existing prediction methods predominantly rely on single-modal information and single-output models, limiting their ability to characterize the complex structural and physiological traits of crops. This study aimed to develop a multimodal learning framework for the simultaneous and accurate prediction of wheat LAI and LNA. Methods Spectral, image, and canopy structural features were extracted from wheat canopies across different cultivars, nitrogen treatments, and growth stages. A canopy height correction-based preprocessing method was developed to improve the extraction of structural features. A Dual-Output Bayesian Neural Network (DO-BNN) was then constructed to simultaneously predict LAI and LNA. In addition, an Extreme Sample Mining (ESM) strategy and a joint loss function were introduced to strengthen the learning of complementary information across modalities and the intrinsic correlation between the two target variables. Results The DO-BNN achieved its best predictive performance when all feature modalities were fused. The coefficients of determination (R²) for LAI and LNA were 0.89 and 0.77, respectively, while the corresponding relative root mean square errors (RRMSEs) were 0.15 and 0.35. Compared with single-modal and conventional single-output approaches, the proposed method provided more accurate and robust predictions of both wheat growth parameters. Discussion The results demonstrate that integrating spectral, image, and structural information can improve the characterization of wheat canopy traits. By jointly modeling LAI and LNA, the DO-BNN effectively exploited the physiological relationship between crop growth and nitrogen accumulation. The proposed framework provides a promising approach for the high-accuracy, collaborative monitoring of wheat growth and nitrogen nutritional status.
Why it matches plant phenotyping methods小麦キャノピーのスペクトル・画像・構造情報からLAIと葉窒素蓄積を推定するマルチモーダル手法を開発し、前処理、ニューラルネットワーク、性能比較まで中心的に扱っているため。
abstractThis study aimed to develop a multimodal learning framework for the simultaneous and accurate prediction of wheat LAI and LNA.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Precise plant disease segmentation in real-world agricultural environments presents challenges that general-purpose models often fail to address, primarily due to the anisotropic spread of lesions, blurred biological boundaries, and severe background dominance. To overcome these bottlenecks, this paper proposes PlantFormer, an end-to-end network that effectively integrates and adapts advanced architectural components to address these domain-specific issues. Specifically, PlantFormer employs an InteractSwin Backbone with a Cross-Level Fusion (CLF) module to preserve early pathological details. To model highly directional disease propagation, a GlobalAnisotropic Context Aggregation (GACA) neck utilizing strip pooling is introduced. Furthermore, a Semantic-Guided Fusion (SGF) decoder acts as a feature “boundary purifier” to suppress field noise, while a decoupled boundary-aware loss function explicitly shifts the optimization focus from healthy leaf regions to subtle necrotic transition zones. Comprehensive experiments demonstrate the effectiveness of our approach: PlantFormer achieves 41.78% mIoU on the complex PlantSeg dataset (unstructured field conditions) and 93.54% mIoU on the structured NLB dataset (vein-aligned lesions). It outperforms generalist models such as DeepLabV3+ and Segformer in key metrics like mIoU and mAcc. Despite these promising results, limitations remain, particularly regarding performance in scenarios with high-density, early-stage disease outbreaks, which will be the focus of future work.
Why it matches plant phenotyping methods植物病斑・壊死領域を直接セグメンテーションする画像解析手法の開発とベンチマークであり、植物病害状態の定量化が中心である。
abstractPrecise plant disease segmentation in real-world agricultural environments presents challenges that general-purpose models often fail to address
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Accurate characterization of plant 3D architecture and semantic parsing of key cotton organs represent an essential prerequisite for crop phenotyping, precision field management, and cultivar breeding and selection. However, the overall 3D structure of field-grown cotton plants is highly complex in field environments. The morphological traits and spatial distribution of various organs are modulated by multiple factors including genetics, environmental conditions, and cultivation management practices, resulting in pronounced phenotypic variation under field conditions. Thus, this study proposed an integrated technical framework for UAV-based 3D reconstruction and organ semantic segmentation tailored for field-grown cotton. High-fidelity 3D point clouds of the crop canopy are generated by coupling an field-adapted low-altitude unmanned aerial vehicle (UAV) acquisition strategy with neural radiance fields (NeRF). Despite these attractive characteristics, segmenting and analyzing such intricate point clouds can be quite challenging. To effectively parse these complex geometric structures, a novel deep learning architecture named FieldCotSeg-Net is introduced. This model integrates an Anisotropy-aware Local Attention (ALA) module and a Hierarchical Feature Refinement Gate (HFRG) module to capture fine-grained features for precise point cloud segmentation. Experimental results demonstrate that the proposed model achieves outstanding performance on both the Huaxing3Dcot and Crops3D cotton datasets. On the Huaxing3Dcot dataset, the model yields a mean intersection over union (mIoU) of 75.7%, representing a 4.5% improvement over the baseline model. On the Crops3D cotton dataset, after retraining the model on this dataset, it attains an mIoU of 74.5%, showing substantial adaptability and effectiveness in organ segmentation. This technical system provides a scalable and robust solution for field cotton phenotyping analysis in breeding research and commercial production.
Why it matches plant phenotyping methods綿花の圃場表現型解析を目的に、UAVによる3D再構成と器官セグメンテーション手法を開発・評価しており、植物形態の取得が研究の中心である。
abstractThis technical system provides a scalable and robust solution for field cotton phenotyping analysis in breeding research and commercial production.
StrawberryField / plotFlowerFruitObject detectionGrowth / development / phenology
To address the challenges of recognizing small strawberry targets and achieving accurate phenological perception in complex field environments, this paper proposes a novel end-to-end lightweight detection architecture named HCMS-Net. The backbone is a Residual Efficient Layer Aggregation Network (R-ELAN) enhanced with a Multi-Scale Convolutional Attention (MSCA) mechanism, which emphasizes subtle color and texture variations to differentiate key phenological phases. For feature fusion, hypergraph convolution (from HyperC2Net) and a Mixed Aggregation Network (MANet) are incorporated, modeling the clustered morphology of strawberries and strengthening the representation of sparse small fruits. The detection head incorporates a lightweight Conv2Former module to capture long-range dependencies and spatial contextual information across growth stages, thereby enhancing the model's capacity to represent continuous phenological changes. A Shape-Normalized Wasserstein Distance (Shape-NWD) loss is introduced to stabilize optimization against minor pixel deviations. Experimental results demonstrated that HCMS-Net achieved a mean average precision (mAP) of 94.9% and an F1-score of 90.0%. Specifically, the average precision (AP) values for the flowering, young fruit, green fruit, veraison, and mature fruit stages reached 99.3%, 88.3%, 90.9%, 97.0%, and 98.2%, respectively. Heatmaps confirmed HCMS-Net's precise attention focus across all five phenological stages, effectively suppressing irrelevant backgrounds. Compared to ten mainstream detectors, HCMS-Net surpassed alternatives such as RT-DETR and the YOLOv5n to v13n by 3.4-8.0 percentage points in mAP. It even surpassed YOLOv12s by 2.7 percentage points, while containing only 32.86% of its parameters. The model offers high accuracy and efficiency for phenological period detection, supporting selective harvesting and intelligent agricultural management.
Why it matches plant phenotyping methodsイチゴの生育フェノフェーズを画像から認識する新規検出モデルを開発し、複数手法との性能比較・検証を行っているため、植物フェノタイピング手法が中心である。
abstractthis paper proposes a novel end-to-end lightweight detection architecture named HCMS-Net
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-
Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。
Introduction Wheat stem rust (Puccinia graminis f. sp. tritici) remains a major threat to wheat production worldwide. Detecting the disease at the pre-symptomatic stage is important for earlier warning and more timely management. Methods We evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9). Seven representative deep learning models were compared across DPI stages. A weighted cross-entropy strategy was then applied to the three strongest models, and model interpretability was examined using input gradient analysis, SHAP attribution, and vegetation-index screening. Results The weighted optimization increased overall F1-scores by 10.0%-18.4%. At the pre-symptomatic stage, the best model achieved an F1-score of 0.94 at DPI 4 and 0.99 at DPI 5, enabling detection before visible symptom development at DPI 6-7. Across the interpretability analyses, the 480-550 nm blue-green region emerged as the main source of information for pre-symptomatic detection, whereas the 750-870 nm near-infrared region contributed more general information on disease presence. Discussion These results show that hyperspectral imaging paired with deep learning can support accurate pre-symptomatic detection of wheat stem rust under controlled experimental conditions and provide useful evidence for future field-scale studies of early disease warning.
Why it matches plant phenotyping methods小麦の病害状態をハイパースペクトル画像と深層学習で検出する方法が研究の中心であり、時系列評価・モデル比較・性能改善・解釈性分析を含むため、植物フェノタイピング手法として含める。
abstractWe evaluated hyperspectral imaging and deep learning for pre-symptomatic wheat stem rust detection using a time-series dataset collected at 4-9 days post inoculation (DPI 4-9).
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe data can be accessed at: https://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:787-847Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Plants release Volatile Organic Compounds (VOCs) in response to insect attacks. VOC facilitates communication with neighboring, undamaged plants. In response to VOC from insect damaged plants, neighboring undamaged plants upregulate their own defenses as if they were being attacked themselves. To date, Green Leaf Volatiles (GLVs) within VOC have been widely considered a primary mediator for plant communication. GLV is a six-carbon compound which all land plants emit immediately and in large quantities after wounding. We hypothesized that GLVs’ lack of specificity and abundance is unlikely to account for key aspects of plant communication like increased sensitivity between closely related plants. To test our hypothesis, we used an Arabidopsis accession which does not produce GLVs. We also developed a non-invasive imaging technique to visualize plant communication utilizing expressions of insect stress marker gene VSP1 . Our analysis confirmed that plant communication occurs even without GLVs. Cytosolic calcium ion concentration increased before this timing, and moved towards the tip of the leaf in undamaged plants. Additionally, when plants were damaged by insects, acetophenone and alkanes accumulated the experiment’s enclosed space. This suggests that plants communicate independently of GLV using alkanes and acetophenone, which are known to attract natural enemies of herbivore insects like parasitoid wasps.
Why it matches plant phenotyping methods植物間コミュニケーションとストレス状態を可視化する非侵襲的イメージング手法の開発・適用が研究の中心である。
abstractCytosolic calcium ion concentration increased before this timing, and moved towards the tip of the leaf in undamaged plants.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
MaizeGrowth chamberRootMorphology / geometry measurementSegmentationSkeletonization / topologyRoot system architecture
monitoring capabilities, and existing models have limited accuracy in root segmentation. To address these issues, we developed a crop root phenotyping system integrating crop cultivation and data collection. We also proposed a DB-UNet model for hydroponic maize root segmentation. DB-UNet builds a CNN-ViT dual-branch parallel structure during encoder downsampling level. The lightweight ViT branch uses sequential downsampling to achieve global topological dependency modeling while reducing computational costs. An attention fusion module dynamically calibrate dual-branch features weights, achieving complementary fusion of local root edge details and global context information. we constructed a mixed loss function combining Dice loss, Focal loss, and structural consistency KL loss to solve class imbalance, hard sample segmentation, and semantic divergence of dual-branch features. On our custom hydroponic maize root dataset, DB-UNet achieved an mIoU of 91.02%, an FG IoU of 82.78%, and a Centerline-Dice of 97.72%.Compared to classic UNet, mIoU, FG IoU, and Centerline-Dice increased by 0.92%, 1.84%, and 1.99%, respectively. Plant-level five-fold cross-validation further showed that DB-UNet maintained stable segmentation performance across different plant-level partitions. Based on DB-UNet segmentation results, we propose a custom skeleton-based algorithm for multi-trait root phenotyping, enabling the extraction of total root length and root branch points. Root area is calculated from binary mask pixel statistics. Compared to the traditional Zhang-Suen algorithm, the average relative error of root length measurement is reduced to 3.14%, which is 8.42 percentage points lower than the traditional method. Furthermore, we analyzed relationships between segmentation accuracy metrics and phenotypic relative errors. Higher segmentation quality generally led to lower phenotypic relative errors and more reliable trait measurements. In particular, Centerline-Dice was closely associated with root length estimation, whereas pixel-level segmentation consistency was more closely related to root area measurement. Pearson and Spearman correlation analyses showed a strong positive correlation between maize plant height and total root length, with coefficients of 0.8466 and 0.8634, respectively.
Why it matches plant phenotyping methods画像ベースの根セグメンテーションと骨格解析を開発・検証し、根長や分枝点などの形質を抽出するシステムが研究の中心であるため。
abstractwe developed a crop root phenotyping system integrating crop cultivation and data collection.
Phytophthora cinnamomi is considered as one of the world's worst plant pathogens, infecting about 5,000 plant species including those of agricultural and environmental significance. Disease management is largely dependent on chemical control, particularly synthetic fungicides such as phosphonic acid-based fungicides, e.g., phosphite/potassium phosphonate. While phosphonic-acid-based fungicides have been highly effective for more than 40 years, their prolonged use has led to the development of tolerance and decreased sensitivity in P. cinnamomi . Novel control agents that are effective but environmentally sustainable are therefore urgently needed. RNA-based biopesticides, which use exogenously applied double-stranded RNA (dsRNA) specific to the target pest or pathogen to avoid off-target effects on other organisms in the environment including beneficials, have emerged as a potential novel disease management strategy against P. cinnamomi . Due to the limited availability of bioassays to study the efficacy of this novel control agent against P. cinnamomi , we developed water-based lupin and pineapple bioassays using readily available plastic cups and glassware with mycelial plugs as inoculum. Infection rate was assessed 3 to 7 days post-inoculation (dpi) for lupin and 7 to 14 dpi for pineapple by measuring root lesion length and rating root rot. Potassium phosphonate (Agri Fos 600) and dsRNA were tested as example control agents, with dsRNA uptake tested via northern blotting. The bioassays were found suitable for P. cinnamomi pathogenicity assays, with one mycelial plug an effective inoculum; fungicide sensitivity testing, with doses as low as 0.45 g L -1 Agri Fos® 600 providing protection; and exogenous dsRNA studies targeting root pathogens, with dsRNA able to be taken up by germinating lupin seeds. Overall, the assays are soil-free and thus overcome dsRNA stability issues in the soil and enable the collection of intact clean roots for molecular analyses. Furthermore, the bioassays are non-destructive, allowing root lesion symptoms to be visually monitored and repeatedly measured across different timepoints.
Why it matches plant phenotyping methods植物病害の根病徴を測定する土壌フリー・バイオアッセイを開発し、その適用性を検証しており、表現型取得法が研究の中心です。
abstractwe developed water-based lupin and pineapple bioassays using readily available plastic cups and glassware with mycelial plugs as inoculum.
Maize leaf diseases in field environments often exhibit large variations in lesion scale, irregular morphology, blurred boundaries, and complex backgrounds. These factors pose challenges for existing detection models, particularly in detecting small lesions and achieving precise bounding-box localization. To address these issues, this study proposes LSL-YOLO11n, a maize leaf disease detection model based on the YOLO11n framework. The proposed model improves feature representation, localization quality modeling, and bounding-box regression to enhance disease detection performance under complex field conditions. Experiments were conducted on a dataset containing 15,119 images and 29,366 annotated instances across eight categories, including seven maize disease categories and healthy leaves. To evaluate the effectiveness of the proposed model, ablation experiments, comparative experiments with mainstream object detection models, and visual detection analyses were carried out. The ablation results show that the improved components contribute positively to the overall detection performance. LSL-YOLO11n achieves a Precision of 84.4%, Recall of 73.9%, and mean Average Precision (mAP) of 83.3%, which is 3.1 percentage points higher than that of the baseline YOLO11n model. Compared with YOLOv8n, YOLOv9t, YOLOv10n, and YOLOv12n, the proposed model improves mAP by 4.7, 3.3, 5.3, and 10.9 percentage points, respectively. The visual detection results further indicate that LSL-YOLO11n performs more stably in complex backgrounds and small-lesion scenarios. These findings provide technical support for rapid maize disease recognition and intelligent field monitoring.
Why it matches plant phenotyping methodsトウモロコシ葉の病斑・病害状態を画像から検出するYOLOベース手法を開発し、アブレーション比較や既存モデルとの性能評価を行っており、植物病害表現型の取得法が中心である。
abstractTo evaluate the effectiveness of the proposed model, ablation experiments, comparative experiments with mainstream object detection models, and visual detection analyses were carried out.
Plant leaf diseases are a major factor leading to crop yield reduction, seriously threatening food security and sustainable agricultural development. Timely and accurate detection is crucial for scientific prevention and control. Traditional manual detection is time-consuming, labor-intensive, and highly subjective. Existing deep learning detection models still have significant shortcomings in complex field scenarios, struggling to balance detection accuracy, real-time performance, and stability. They are easily affected by background interference, differences in lesion scale, and sample imbalance, and some models are not lightweight enough to meet the needs of real-time field detection. Therefore, this paper uses YOLOv13 as the baseline model and constructs a YOLOv13-LM model through multi-module collaborative optimization. The optimization directions cover the backbone, neck, detection head, and loss function, strengthening lesion feature extraction and multi-scale fusion, reducing task interference, and improving localization accuracy. Model validation was completed in a complex farmland environment. The results show that the model’s mAP@0.5 is improved by 5.4 percentage points to 87.9% compared to the original YOLOv13, the FPS is improved by 21.1% to 46 frames/second, and the number of parameters and computational cost are reduced by 27.3% and 25.7% respectively. The overall performance is better than the mainstream YOLO models of the same scale. However, the lightweight nature of the model is still not as good as that of the ultra-lightweight model, and its generalization and interpretability need to be improved. In the future, we will focus on ultra-lightweight design, expanding the generalization ability of multiple crops and diseases, and studying the interpretability of the model to further adapt to the actual needs of field applications.
Why it matches plant phenotyping methods植物葉の病斑を画像から検出するYOLOモデルを開発・改良し、複雑な圃場環境で精度とリアルタイム性能を検証しており、病害状態の表現型取得が中心である。
abstractTherefore, this paper uses YOLOv13 as the baseline model and constructs a YOLOv13-LM model through multi-module collaborative optimization.
Introduction Nutrient deficiencies in coffee plants significantly impact bean quality and yield, making timely detection crucial for successful cultivation. Current assessment methods rely on manual inspection, which is labor-intensive and time-consuming, posing challenges for large-scale field management. This approach often results in inconsistent evaluations and delayed interventions. Methods This study presents CoNutriNet, an automated deep learning architecture that integrates DenseNet121 with a novel Graph-Enhanced Attention Feature Network (GEAFNet) for classifying nutrient deficiencies in coffee leaves. DenseNet121 provides deep hierarchical and regional feature representation, while GEAFNet captures local, fine-grained spatial features through Inception, Ghost, and Efficient Channel Attention (ECA) modules. Furthermore, a Graph Convolutional Network (GCN) is included to model spatial dependencies and structural variations between leaf regions. Feature representations from both pathways are concatenated and refined using a Coordinate Attention (CA) module to enhance discriminative capability. Results Evaluation on the CoLeaf dataset demonstrates that CoNutriNet achieves an accuracy of 94.5%. The integration of lightweight attention mechanisms, dense connectivity, and graph-based modeling improves both performance and computational efficiency. Conclusion These results indicate that CoNutriNet achieves and efficient performance in nutrient deficiency detection in coffee crops, highlighting its potential for deployment in agricultural environments to support precision farming and optimize yield.
Why it matches plant phenotyping methodsコーヒー葉の栄養欠乏という植物状態を画像から分類する深層学習手法を開発し、データセットで性能評価しており、表現型取得・推定が研究の中心である。
abstractThis study presents CoNutriNet, an automated deep learning architecture that integrates DenseNet121 with a novel Graph-Enhanced Attention Feature Network (GEAFNet) for classifying nutrient deficiencies in coffee leaves.
Reproduction assets foundThe paper's phenotyping analysis (coffee nutrient deficiency classification) is performed on publicly available leaf image datasets. The data availability statement links a Mendeley Data repository containing the analyzed data, which is an allowed URL. No author analysis code or trained model checkpoints are explicitlyDataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/brfgw46wzb/1Open asset ↗brfgw46wzb/1lines:866-910Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Soil salinization has become a critical factor limiting global agricultural production. Characterizing the growth and developmental responses of okra to salt stress and developing efficient and accurate salt-stress phenotyping techniques can provide an important methodological reference for okra cultivation in saline lands and future multi-cultivar salt-stress phenotyping studies. Traditional manual measurement of plant phenotypic parameters suffers from low efficiency and insufficient detection accuracy, making it difficult to achieve rapid and non-destructive analysis of plant phenotypic traits under salt stress. Therefore, this study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model. Using dual-view feature fusion, we constructed a dedicated dataset. On the basis of PointNet++-MSG, the original MLP layers were replaced with C2F modules, and the SGE attention mechanism was integrated to enhance morphological feature extraction, thereby constructing a lightweight CSP-MSG Net architecture adapted to okra seedling point clouds for semantic segmentation of okra point clouds combined with DBSCAN clustering to complete instance segmentation, phenotypic parameters including plant height, stem diameter and canopy width were further calculated. This scheme enables high-throughput data acquisition, improves measurement accuracy, effectively reduces model parameters and computational overhead, and realizes lightweight operational performance. The results show that okra seedlings can still grow with increasing salt stress concentration, while the growth rates of the three measured traits are all inhibited, indicating that high-concentration salt stress impairs the growth activity of okra seedlings. To verify the calculation accuracy of the model, the phenotypic parameters predicted by the model were compared with manually measured values. The coefficients of determination for stem diameter, canopy width and plant height of okra seedlings reached 0.96, 0.99 and 0.99, respectively. These results strongly demonstrate the excellent reliability and effectiveness of the proposed method, providing methodological support for non-destructive and accurate phenotypic detection of okra seedlings under salt stress.
Why it matches plant phenotyping methodsオクラ幼苗の点群から草丈・茎径・樹冠幅を抽出する計算フェノタイピング手法を開発し、手動測定との比較で精度検証しているため、方法が研究の中心である。
abstractthis study proposes a computational phenotyping parameter extraction method based on the CSP-MSG Net model.
Common beanLeafClassificationStress / disease detectionDisease symptoms / severity
Introduction Accurate disease diagnosis is crucial for enhancing agricultural productivity and reducing postharvest losses, directly impacting food quality and safety. Traditional detection methods often rely on extensive feature modeling and perform poorly in complex field environments. Methods This study proposes a deep learning model called ZDAM, based on an improved ZFNet integrated with a dual attention mechanism. The classical ZFNet is first optimized to improve feature extraction efficiency. A combined channel and spatial attention mechanism is then incorporated to refine feature representation for disease identification in key crops. Finally, a residual module is added to boost accuracy. Results Evaluated on a dataset of 11,903 bean leaf images covering healthy leaves and four disease types, including leaf mould, rust, mosaic, and white spot, the model achieves an average recognition accuracy of 99.02%, outperforming MobileMamba, Vision Transformer, and Chest- OMD. Discussion This approach offers a scalable solution for automated disease monitoring, supporting postharvest quality preservation and sustainable crop production.
Why it matches plant phenotyping methods豆葉の病害状態を画像から推定する深層学習モデルを開発し、複数モデルとの性能比較も行っており、植物フェノタイピング手法が研究の中心である。
abstractThis study proposes a deep learning model called ZDAM, based on an improved ZFNet integrated with a dual attention mechanism.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publictomato leaf disease data from the open-source dataset New Plant Disease Dataset ( https://www.kaggle.com/vipoooool/new-plant-diseases-dataset ) were also utilized. Both datasets include healthy samples and four disease categories: rust disease, mosaic disease, leaf mold disease, and white spot disease. ( https://pan.baidu.com/s/197Lyn2TGdIjLCE2gylsiHA?pwd=krpw )Open asset ↗pan.baidu.comlines:405-484Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Plant height during the early growth stage of rice is a key indicator reflecting canopy establishment rate, tillering potential, and overall growth vigor, all of which critically determine final yield formation. Conventional manual measurements fail to capture high-frequency, continuous, and non-destructive monitoring of plant height dynamics, limiting the understanding of early growth vigor and its genetic mechanisms. In this study, a UAV-based LiDAR system was employed to acquire canopy point clouds of 211 rice accessions across ten time points within 40 days after transplanting. High-resolution canopy height models (CHMs) were generated, and continuous plant height trajectories H(t) were reconstructed using piecewise cubic Hermite interpolation (PCHIP). The first derivative V(t) quantified growth rate dynamics and identified the timing of maximum growth (T max ), enabling precise differentiation of early growth patterns among geno-types. Genome-wide association analysis (GWAS) using a mixed linear model (MLM, Q+K) detected 604 significant SNPs, among which 33 were stably expressed across environments. Five candidate genes were identified within ±200 kb windows, mainly encoding proteins related to cell elongation, hormone signaling, and photosynthetic metabolism. The results highlight that LiDAR-based dynamic monitoring of plant height, coupled with genomic association analysis, provides a robust framework for quantifying rice early growth vigor and elucidating its molecular basis, offering valuable guidance for breeding high-vigor “early-establishing” rice cultivars.
Why it matches plant phenotyping methodsUAV-LiDARによるイネ草丈の時系列取得・CHM生成・成長率推定が研究の中心であり、GWASはその測定形質の応用分析。
abstractConventional manual measurements fail to capture high-frequency, continuous, and non-destructive monitoring of plant height dynamics
Introduction Rubber tree powdery mildew is a major foliar disease that threatens the yield and quality of natural rubber. Its lesions are typically small, irregular, and embedded in complex backgrounds, making accurate automated detection difficult. Methods To address this challenge, we propose RubberFormer, an end-to-end detection framework based on a refined Transformer architecture for detecting small powdery mildew lesions in complex scenarios. RubberFormer adopts MobileNetV4 as a lightweight backbone, introduces the Hierarchical Attention with Local-global Optimization (HALO) module for multiscale local-global feature fusion, incorporates the Unified Cross-Attention Network (UCAN) to enhance multidimensional feature interaction, and applies Normalized Wasserstein Distance (NWD) Loss to improve small-object localization. Results Extensive experiments were conducted on PM-Dataset-Plus, which contains 9,765 images, and PD-40, a large-scale plant disease dataset containing 80,369 images across 40 disease categories and 8 crops. RubberFormer achieved superior detection accuracy and generalization performance compared with existing methods, while maintaining computational efficiency suitable for practical agricultural monitoring. Discussion These results demonstrate that RubberFormer is effective for detecting small and irregular rubber tree powdery mildew lesions under complex conditions. The framework has practical value for rubber tree disease monitoring and provides a transferable design strategy for agricultural vision tasks involving small objects and complex backgrounds.
Why it matches plant phenotyping methodsゴム樹の病斑という植物の病害状態を画像から検出するTransformer手法を開発し、複数データセットで性能検証しており、植物表現型取得が中心である。
abstractwe propose RubberFormer, an end-to-end detection framework based on a refined Transformer architecture for detecting small powdery mildew lesions in complex scenarios.
Reproduction assets foundThe paper's authors publicly release both plant disease image datasets used in this study: PM-Dataset-Plus (9,765 rubber tree powdery mildew images) and PD-40 (80,369 images, 40 categories, 8 crops), each with an explicit availability statement and GitHub URL matching the allowed URLs. No analysis code or trained modelDataset · publicPM-Dataset-Plus is available at https://github.com/wfcyliyuheng-dev/PM-Dataset-PlusOpen asset ↗wfcyliyuheng-dev/PM-Dataset-Pluslines:1199-1255Dataset · publicPD-40 is available at https://github.com/wfcyliyuheng-dev/PD40-DatasetOpen asset ↗wfcyliyuheng-dev/PD40-Datasetlines:1199-1255Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Cucumber is a core cultivated facility vegetable in China. Drought stress at the seedling stage severely inhibits its growth and development. The regulatory mechanism and optimal application concentration of SiO 2 nanoparticles in alleviating drought stress in cucumber seedlings remain unclear. Moreover, traditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress. To address these issues, this study focused on phenotypic detection under drought stress and analysis of the regulatory effects of SiO 2 nanoparticles. An improved compact and low-redundancy segmentation model, DDCANet, was proposed based on PointNet++-SSG. Combined with 3D point cloud technology and the Euclidean clustering algorithm, it enables automatic extraction of phenotypic parameters from cucumber seedlings treated with SiO 2 nanoparticles under drought stress. In this study, Trailing cucumber seedlings were used as experimental materials. 3D point cloud data of cucumber seedlings were collected under treatments with different concentrations of SiO 2 nanoparticles and PEG-simulated drought stress. A dataset containing 70 valid samples was constructed and labeled into two categories: Stem and Leaf. The core optimizations of the DDCANet model are as follows: Firstly, an Adaptive Density-Aware Feature Enhancement (ADFE) module is embedded to accurately capture point cloud density heterogeneity induced by SiO 2 ;Secondly, a Channel Attention and Normalization-enhanced SA Layer (CANL) is designed to strengthen the coupling of local and global drought-related phenotypic features. Thirdly, a Drought-Aware Hybrid Loss (DHL) function is constructed to alleviate the class imbalance of seedling stem and leaf point clouds under drought stress. Results show that the DDCANet model achieves a mean Intersection over Union (mIoU) of 89.01 ± 0.32% and a Stem IoU of 83.6 ± 0.45%, representing improvements of 6.55% and 9.6% respectively compared with the baseline PointNet++-SSG model, and a 30.5% improvement in stem segmentation accuracy compared with the classic PointNet model. It thus enables high-throughput, non-destructive detection of drought phenotypes in cucumber seedlings under SiO 2 nanoparticle treatment. Ablation experiments verified the positive contributions of the ADFE, CANL, and DHL modules. Furthermore, instance segmentation and phenotype extraction were completed using the Euclidean clustering algorithm to analyze the drought-alleviating effects of SiO 2 nanoparticles under PEG-simulated drought stress. Results indicate that a low concentration of 20 mg/L exhibits a weak alleviating effect, medium concentrations of 40-60 mg/L show bidirectional regulatory characteristics, and a high concentration of 100 mg/L causes negative physiological effects. The optimal application concentration is 80 mg/L, which comprehensively improves key phenotypes such as seedling height and volume under drought stress and exerts a positive regulatory effect on seedling growth under drought conditions. The DDCANet model constructed in this study provides an efficient technical tool for the accurate phenotypic detection of crop seedlings treated with SiO 2 nanoparticles under drought stress. It clarifies the optimal application concentration of SiO 2 nanoparticles, offers a precise concentration threshold and theoretical support for the scientific application of SiO 2 nanoparticles in drought-stressed cultivation of protected cucumber, and establishes a novel methodological reference for the research on phenotypic regulation of crops under drought stress via nano-agricultural technology.
Why it matches plant phenotyping methods3D点群分割モデルを開発・検証し、キュウリ幼苗の茎葉分離と形質抽出を自動化することが研究の中心であるため、植物フェノタイピング手法論文として採用。
abstracttraditional manual measurement and classic point cloud segmentation models struggle to achieve high-throughput accurate detection of cucumber seedling phenotypes under drought stress.
Introduction Tomato brown rugose fruit virus (ToBRFV) represents a growing threat to global tomato production, causing severe losses in crop yield and fruit quality. Although the standard RT-qPCR assay is highly accurate, its reliance on laboratory processing, specialized equipment, and trained personnel limits its applicability for rapid on-site diagnostics. To address this limitation, this study evaluated a biosensing method that does not require labels and combines surface-enhanced Raman scattering (SERS) with machine learning to distinguish tomato leaves infected with ToBRFV from healthy leaves. Methods Following health status confirmation via RT-qPCR, leaf extracts were directly deposited onto silver nanorod arrays for SERS spectral acquisition. Three classification models were evaluated: PCA-LDA, PLS-DA, and SVM. Results The results showed that all models were able to discriminate infected samples from healthy samples in the present dataset. Notably, the SVM model exhibited the best performance, achieving an accuracy of 91.67%, a sensitivity of 100.00%, a specificity of 81.48%, and an area under the ROC curve (AUC) of 0.993. Discussion This result suggests that SERS spectra may contain biochemical information associated with ToBRFV infection and that such information can be used for sample classification using machine learning models. In its present form, this approach is intended as a rapid, low-cost, field-deployable preliminary screening tool - not a replacement for RT-qPCR or other confirmatory molecular assays. The reported accuracy was obtained on mechanically inoculated plants of a single cultivar under controlled greenhouse conditions and should therefore be interpreted as a proof-of-concept upper bound; field-scale validation is the focus of ongoing work.
Why it matches plant phenotyping methodsSERSと機械学習を用いて感染トマト葉と健全葉を識別する植物病害状態の取得・分類法が研究の中心であり、複数モデルの性能評価も行っている。
abstractthis study evaluated a biosensing method that does not require labels and combines surface-enhanced Raman scattering (SERS) with machine learning to distinguish tomato leaves infected with ToBRFV from healthy leaves.
Clubroot disease, caused by Plasmodiophora brassicae , is one of the major constraints in rapeseed production. Breeding disease-resistant cultivars is the best way to control this devastating disease. However, breeding reliable resistant germplasm and genes is limited. Inactivation of susceptible genes has been shown to be a new and effective strategy for developing resistant crops. Therefore, we aimed to screen key candidate susceptible genes in this study. Firstly, we established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection. At 14 days post-inoculation (dpi), the earliest time point with a clear record of scorable root swelling, remarkable variations in the speed of gall formation were observed among 85 genotypes. Secondly, genome-wide association studies (GWAS) were performed to identify genes involved in gall development. Three and two consecutive significant peaks were detected at 14 and 21 dpi, respectively. Thirdly, comparative transcriptomic analysis was conducted between 2AF195 and 2AF058 at 7 and 14 dpi; these two materials exhibit contrasting speeds of gall development. Gene clustering analysis revealed two opposite expression patterns at 14 dpi. One pattern comprised 1,383 genes downregulated in 2AF195 but upregulated in 2AF058, which were significantly enriched in 10 KEGG pathways, including Environmental Information Processing and Plant-pathogen interaction, and involved core repressors JAZ8/10 in the jasmonic acid (JA) signaling pathway, as well as nucleotide-binding site (NBS) protein-encoding genes. The opposite pattern consisted of 79 genes upregulated in 2AF195 but downregulated in 2AF058, which were enriched in an additional 10 KEGG pathways, predominantly related to Carbohydrate Metabolism and the Ubiquitin System. These genes were functionally annotated mainly as pectin methylesterases, xyloglucan endotransglucosylase/hydrolases (XTHs), and lignin biosynthesis-related enzymes. These findings demonstrated that distinct regulatory networks exist in different susceptible rapeseed genotypes. Finally, through the combined analysis of haplotype and transcriptome data, we co-localized and identified the candidate gene BnaC08g46100D , a nodulin-related gene belonging to the MtN21 transporter family. These results provide a theoretical basis for developing novel disease-resistant materials by editing the key susceptibility genes involved in root gall formation. The candidate genes identified in this study are the most promising targets for this purpose.
Why it matches plant phenotyping methods根こぶ形成を高スループットに可視化・判定する方法の確立が明示され、感染植物の病徴を測定する手法として研究の主要な技術要素になっている。
abstractwe established a stable, high-throughput visualization method for identifying gall formation at the early stage of P.brassicae infection.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 2
Disease incidence data of 85 rapeseed accessions at various time points following inoculation with the Xinmin strain.Open asset ↗lines:502-594Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction: Root anatomical traits and spatial architecture play a critical role in crop water acquisition and utilization, directly impacting drought tolerance. However, comprehensive studies examining the synergistic effects of deep root configuration and cortical tissue organization under drought stress during the seedling stage remain scarce. Additionally, the underlying physiological mechanisms are not yet well understood. Methods: In this study, we utilized a high-throughput, paper-based phenotyping platform to simulate drought stress using 10% PEG. An efficient, multi-trait evaluation framework was employed to classify the 28 tested genotypes into five drought tolerance categories. Results: This approach enabled the identification of drought-tolerant cultivars "Ruichun 1," "Ningchun 11," and "Ningchun 57," as well as the drought-sensitive cultivar "Dingxi 48." Root traits, including maximum depth, convex hull area, and plant height, demonstrated strong explanatory power and could serve as valuable phenotypic indicators for seedling stage screening. Our findings suggest that drought adaptation in spring wheat involves a strategic coupling in which specific cortical configurations facilitate the development of deep root architecture. While previous studies have often focused on individual parameters, we show that drought-tolerant genotypes optimize root growth in deeper segments of the growth medium by adjusting cortical tissue proportions, potentially minimizing metabolic costs. Discussion: This integrated perspective offers a detailed physiological framework for understanding drought resilience and moves toward a mechanism-based interpretation of resource reallocation. However, it is important to note that these results were obtained using a paper-based phenotyping platform under PEG-induced osmotic stress, reflecting the genotypic potential at the seedling stage rather than actual field drought tolerance. In conclusion, combining the paper-based high-throughput phenotyping platform with a multi-trait evaluation framework allows for the accurate classification of drought tolerance types and the efficient identification of representative spring wheat cultivars. The findings emphasize the importance of deep root configuration and optimized cortical allocation as fundamental components of the root structural basis for drought adaptation in spring wheat. These results provide clear phenotypic targets for early-stage screening, which should be further validated at later developmental stages and under field conditions before being applied in breeding programs.
Why it matches plant phenotyping methods紙ベースのハイスループット表現型解析プラットフォームと多形質評価フレームワークが、根形態を用いた耐乾性分類の中心的手法として明示されているため。
abstractwe utilized a high-throughput, paper-based phenotyping platform
Litchi is an important economic fruit in southern China, and its precision management relies on the rapid and accurate estimation of the Soil and Plant Analyzer Development (SPAD) values in leaves. Addressing the limitations of existing SPAD detection methods, such as limited rapid coverage, inadequate modeling of dynamic environmental interference, and shallow fusion of multi-source data, this study constructed an Internet of Things (IoT) system to collect real-time environmental data from a litchi orchard, combined with unmanned aerial vehicle (UAV) multispectral imagery to obtain canopy vegetation index and texture features. A Long Short-Term Memory (LSTM) network model integrated with a feature level attention mechanism (MLSTM) was proposed to fuse IoT time-series data, vegetation index, and high dimensional texture features for dynamic SPAD value prediction. The results indicate that multi-source feature fusion significantly improves SPAD estimation accuracy. The MLSTM model achieved optimal performance under the all-features situation, with a coefficient of determination (R²) of 0.897 and a root mean square error (RMSE) of 2.638, outperforming other comparative models. The attention mechanism effectively enhanced the model's focus on key features, improving feature utilization efficiency and model interpretability. The multi-source data fusion method and MLSTM model proposed in this study enable high precision, dynamic estimation of SPAD values in litchi leaves, providing reliable data support for precision fertilization, stress diagnosis, and yield prediction in litchi orchards, as well as theoretical support for promoting the practical application of this technology in smart agriculture.
Why it matches plant phenotyping methodsIoT・UAVマルチスペクトル画像から葉のSPAD値を推定するデータ融合システムとMLSTMモデルを開発・評価しており、植物形質取得手法が研究の中心です。
abstractthis study constructed an Internet of Things (IoT) system to collect real-time environmental data from a litchi orchard, combined with unmanned aerial vehicle (UAV) multispectral imagery to obtain canopy vegetation index and texture features.
Reproduction assets foundThe paper's data availability statement points to a public Zenodo repository containing the study's multi-source SPAD/IoT/multispectral dataset.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/records/18308090 .Open asset ↗zenodo · 18308090lines:427-441Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Wheat spike detection is essential for yield estimation in precision agriculture, yet it remains challenging due to the small size of targets, dense distribution, and complex field environments. In this study, we propose LiteMS-YOLO, a lightweight object detection framework based on YOLO26n. The model integrates a Feature Complementary Mapping (FCM) module to enhance spatial-semantic feature interaction and a Multi-Kernel Perception (MKP) unit to improve multi-scale feature representation. In addition, targeted redundancy reduction strategies are introduced to significantly lower model complexity. Experiments are conducted on a combined dataset comprising the public Global Wheat Head Detection (GWHD) dataset and 100 field images collected by the Tangshan Academy of Agricultural Sciences, with a total of 6,378 high-resolution images and over 44,000 annotated wheat spikes. LiteMS-YOLO achieves a mAP50 of 92.28% and a mAP50–95 of 52.56%, while using only 0.627 million parameters. Compared with YOLO26n and YOLOv8n, the proposed method reduces parameters by approximately 75% and 79%, respectively, while maintaining competitive accuracy. These results demonstrate that LiteMS-YOLO strikes an excellent balance between detection accuracy and efficiency, making it well-suited for real-time deployment in resource-constrained agricultural scenarios.
Why it matches plant phenotyping methods小麦穂の検出による収量推定を目的に、画像ベースの検出モデルを開発し、複数データセットで性能検証している。植物器官の検出・計数に基づく表現型取得が研究の中心である。
abstractWheat spike detection is essential for yield estimation in precision agriculture
Field / plotFlowerObject detectionPose / keypoint estimation
Agricultural engineering informatics is playing an increasingly important role in enabling intelligent perception, decision-making, and automated operations in modern horticultural production systems. Within this context, accurate visual perception of reproductive structures is essential for agricultural informatization tasks such as flowering-stage monitoring, precision pollination, and information-driven fruit-set management in chili cultivation. However, reliable detection and pose-aware recognition of chili flowers remain challenging because of small target size, dense distribution, foliage occlusion, and illumination variability in natural or semi-controlled environments. To address these challenges, this study proposes a lightweight and robust edge vision framework, termed CFPR-YOLO, for chili flower detection and pose-aware perception under complex agricultural conditions. Built upon an improved YOLOv11n architecture, the proposed framework incorporates EfficientFormerV2 to strengthen global-context feature extraction, a C3k2_EMA module to enhance localization of small and occluded targets, and Poly-Scale Convolution (PSConv) to preserve structural details while reducing computational redundancy. In addition, a lightweight attention mechanism is introduced to improve feature discrimination in cluttered backgrounds. Experimental results on both self-constructed and generalization datasets show that the proposed method achieves a precision of 92.6%, a recall of 86.8%, and an mAP50 of 92.1% with only 7.26 M parameters. The framework also demonstrates strong robustness and generalization across different chili varieties. When deployed on an edge computing platform (NVIDIA Jetson AGX Orin), the model achieves real-time inference at 39.5 FPS. Furthermore, validation experiments under controlled indoor conditions show that the proposed framework can effectively support simulated pollination tasks, achieving a success rate of 90.0% for upwardfacing flowers. These results indicate that CFPR-YOLO provides an effective visual perception solution for agricultural engineering informatics-oriented pollination systems and offers practical potential for precision pollination and intelligent fruit-set management in horticultural production.
Why it matches plant phenotyping methodsチリ花の検出・姿勢推定という植物器官の画像計測手法を開発し、データセット、汎化性能、エッジ実装、実環境に近い条件での検証まで行っており、単なる受粉実験の補助計測ではない。
abstractthis study proposes a lightweight and robust edge vision framework, termed CFPR-YOLO, for chili flower detection and pose-aware perception under complex agricultural conditions.
Cold hardiness is a critical trait for grapevine survival and productivity in cold climates. This study examined the relationships among cane morphological characteristics, shoot color parameters, and cold hardiness in two grapevine cultivars ('Prairie Star' and 'Frontenac') across four dormant-season sampling times (ST 1-ST 4) and three internode diameter classes (small, normal, and large). Morphological traits, including internode length, shoot diameter, and cross-sectional area, did not show a consistent temporal trend across sampling periods, suggesting that the observed variation was primarily associated with sampling time and cane class rather than progressive structural change during dormancy. In contrast, colorimetric traits showed a clear seasonal pattern, with shoots becoming darker and redder from ST 1 to ST 4, consistent with advancing lignification and cane maturation. Cold hardiness, assessed using low-temperature exotherms of bud, phloem, and xylem tissues, increased substantially from early to mid-dormancy, with xylem tissues reaching the greatest freezing tolerance by ST 3-ST 4. 'Prairie Star' showed slightly greater xylem cold hardiness than 'Frontenac', while bud survival remained consistently high across all treatments. Strong associations between shoot color and LTE values indicate that color traits, particularly at the fifth internode, may serve as reliable non-destructive indicators of cold hardiness status. Sampling time was the primary source of multivariate variation, with cultivar and internode class contributing secondary effects. These findings demonstrate that observable cane traits, especially shoot color, reflect the progression of seasonal cold acclimation and may support the evaluation and selection of cold-hardy grapevine germplasm.
Why it matches plant phenotyping methods枝の色・形態を用いてブドウの耐寒性を非破壊推定する指標として評価しており、単なる生物学的測定ではなく表現型取得法の妥当性評価が中心です。
abstractStrong associations between shoot color and LTE values indicate that color traits, particularly at the fifth internode, may serve as reliable non-destructive indicators of cold hardiness status.
The tomato is one of the most important economic crops worldwide; frequent occurrences of foliar diseases can severely affect its quality and yield, resulting in substantial economic losses. However, state-of-the-art methods still struggle with multi-target, multi-scale disease detection in complex scenarios, lacking accuracy and speed for tomato leaf diagnosis. A novel improved YOLO v8s model is proposed in this study to achieve high-precision and fast identification of multi-target and multi-scale tomato leaf diseases. First, a multi-target, multi-scale image dataset encompassing seven typical tomato diseases was developed to effectively enhance the model's robustness under complex practical scenario by integrating multiple public datasets and employing diverse data augmentation techniques. Second, a transfer learning strategy was employed to transfer high-quality features from a pretrained model to the disease detection task, thereby improving convergence speed and generalization ability. Finally, the CBAM (Convolutional Block Attention Module) channel-spatial attention mechanism was introduced into the YOLO v8s network, enabling the model to adaptively focus on critical regions and significantly enhance feature extraction and target localization performance. Experimental results demonstrate that the improved YOLOv8s-CBAM model achieves superior performance in complex scenarios, with a precision of 96.9%, recall of 97.3%, F1 score of 97.0%, and mAP@0.5 of 99.1%, representing improvements of 2.5%, 2.0%, 2.2%, and 1.8%, respectively, over the original YOLO v8s model. Moreover, the model size was reduced to 24.8 MB, a decrease of 11.7 MB compared to the original, achieving an effective balance between accuracy and lightweight design. These results indicate that the proposed method exhibits enhanced feature extraction and localization stability in multi-target, multi-scale disease identification tasks, providing an effective technical solution for automated detection in complex agricultural disease scenarios.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出・識別するYOLOv8s-CBAM手法を開発し、データセット構築と性能比較検証を行っており、植物病害状態の画像ベース表現型計測が中心である。
abstractA novel improved YOLO v8s model is proposed in this study to achieve high-precision and fast identification of multi-target and multi-scale tomato leaf diseases.
Field / plotClassificationStress / disease detectionDisease symptoms / severity
Introduction Existing convolutional neural networks and Transformers cannot effectively capture fine-grained local lesion features and long-range contextual dependencies simultaneously in field-collected plant images. To address this research limitation, we aim to design an effective lightweight model suitable for plant disease identification in complex field scenarios. Methods This work proposes an improved Vision Mamba network for plant disease classification based on the challenging PlantDoc dataset. Three dedicated modules are embedded into the framework, including the Multi-Scale Feature Fusion Module (MFFM), Adaptive Channel Attention Mechanism (ACAM) and Lightweight Residual Connection (LRC). The MFFM fuses multi-scale texture, shape and semantic lesion features extracted from shallow, medium and deep network layers. The ACAM adaptively highlights disease-related feature channels and suppresses irrelevant background interference. The LRC structure is adopted to relieve the gradient vanishing problem existing in deep selective state space model (SSM) networks. Results Experimental results on the filtered PlantDoc dataset show that the presented model obtains an overall accuracy of 92.67%, macro precision of 91.83%, macro recall of 91.56% and macro F1-score of 91.70% on independent test samples, which outperforms the original Vision Mamba baseline by 5.33% in accuracy. Five-fold stratified cross-validation achieves stable accuracy at 92.41 ± 0.24%, and paired t-tests prove that the performance improvement is statistically significant with p Discussion Error analysis and confusion matrix visualization reveal that the main classification errors are derived from high similarity among different plant disease categories. This study fully verifies the application potential of state space models in agricultural computer vision tasks. The proposed method can serve as an efficient technical scheme for intelligent identification of crop diseases and is well applicable to edge device deployment in precision agriculture practice.
Why it matches plant phenotyping methods植物画像から病害状態を推定する分類モデルを開発し、PlantDocデータセットで性能検証しており、病害表現型の取得・抽出手法が中心である。
abstractThis work proposes an improved Vision Mamba network for plant disease classification based on the challenging PlantDoc dataset.
Accurate identification of tomato lateral shoots is essential for automated pruning and plant monitoring in greenhouse production. However, complex illumination, leaf occlusion, and morphological variability often reduce detection reliability in optical vision systems. This study proposes an optical vision-based framework that integrates deep learning perception with large language model assisted pruning decision support. A tomato lateral Shoot image dataset was constructed using RGB imaging in greenhouse environments. A lightweight YOLOv8n instance segmentation model with the Convolutional Block Attention Module (CBAM) was developed to enhance feature representation. Data augmentation strategies were applied to simulate illumination variations and improve model robustness. Model interpretability was analyzed using Principal Component Analysis (PCA) and Gradient weighted Class Activation Mapping (Grad CAM). Experimental results show that the proposed YOLOv8n-seg+CBAM model achieves a mAP 0.5 of 98.1% with only 3.28M parameters and an average inference time of 8.0 ms per image. Monte Carlo Dropout was further introduced to estimate the spatial uncertainty of cutting points. These structured perception features were provided to a large language model (LLM), enabling context aware pruning decision assistance. The proposed framework integrates vision-based shoot detection, uncertainty estimation, and LLM-assisted reasoning into a unified pipeline, enabling more reliable pruning decisions and improving safety and robustness compared with vision-only approaches in greenhouse environments.
Why it matches plant phenotyping methodsトマト側枝をRGB画像から検出・セグメンテーションし、不確実性推定まで行う画像ベースの植物形態計測手法を開発しており、方法論が中心である。
abstractThis study proposes an optical vision-based framework that integrates deep learning perception with large language model assisted pruning decision support.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Drought is the major abiotic stress limiting soybean growth and yield, yet accurately identifying genotypes that sustain yield under rainfed conditions remains a major bottleneck in soybean breeding. Canopy wilting scores are widely used as a proxy for evaluating plant responses to drought stress. However, most assessments rely on leaf-level visual observations that are inherently subjective and typically based on single time-point scores, providing only a snapshot of stress expression and failing to capture their relationship with yield retention under rainfed conditions. To address these limitations, this study used Unmanned Aerial Vehicle (UAV)-based high-throughput phenotyping at a single growth stage (R4/R5) as a more quantitative and objective alternative to visual scoring, with closer relevance to yield performance under drought conditions. From 2023 to 2025, a total of 85 soybean genotypes developed by soybean breeding programs in Arkansas, Missouri, Kansas, and North Carolina, along with commercial checks, were evaluated under irrigated and rainfed conditions in Stuttgart, Arkansas. Visual canopy wilting scores were recorded at R4/R5, along with vegetation indices captured using UAV-based multispectral imagery. UAV-derived indices showed significant correlations with yield ( r = 0.22 to 0.45, p<0.05) under rainfed conditions. In contrast, visual canopy wilting scores displayed weak and inconsistent associations with yield ( r = -0.28 to 0.35, p<0.05), suggesting limited ability to capture yield retention under rainfed conditions. Unsupervised k -means clustering ( n = 2) of UAV-derived vegetation indices separated genotypes into two distinct canopy response groups that were consistent across 2023 to 2025 rainfed seasons. Significant differences were observed among clusters for several vegetation indices (ARI, CIG, CIRE, GSAVI, GNDVI, GOSAVI, OSAVI, NDVI), indicating contrasting canopy stress responses. Under rainfed conditions, these UAV-defined clusters also differed for grain yield (2023: 1,925.6 vs 1,703.1 kg/ha; 2024: 1,849.9 vs 1,229.2 kg/ha; 2025: 2,056.7 vs 1,773.8 kg/ha), whereas visual wilting scores failed to distinguish yield-retaining genotypes. Overall, UAV-based high-throughput phenotyping offers a robust and yield-relevant alternative to visual wilting scores, supporting the development of drought-tolerant soybean germplasm and cultivars.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像による植生指数抽出を、目視評価と比較・検証し、干ばつ応答および収量保持に関連する表現型測定法として中心的に評価している。
abstractthis study used Unmanned Aerial Vehicle (UAV)-based high-throughput phenotyping at a single growth stage (R4/R5) as a more quantitative and objective alternative to visual scoring
To overcome the limitations of single remote-sensing features in estimating maize canopy leaf area index (LAI), this study developed a UAV-based estimation approach by integrating multispectral vegetation indices (VIs) with digital surface model (DSM) features and stacking ensemble learning. Field experiments were conducted in Dehong, Yunnan Province, China, during 2023-2024, and UAV multispectral images and DSM products were acquired for maize grown under three planting-density treatments. Five vegetation indices and three DSM-derived texture/structural features were retained according to their correlation with measured LAI, statistical significance, and complementary spectral or structural information. The VI-based random forest (VI-RF) model achieved an R 2 of 0.835 and an NRMSE of 9.5%, whereas the DSM-based model showed lower performance (R 2 = 0.641; NRMSE = 14.2%). Under the same random-forest modeling framework, fusing VIs with DSM features improved the overall model performance to R 2 = 0.892 and NRMSE = 7.6%, indicating that DSM-derived structural information mainly enhanced the feature representation of maize LAI. Using the same VI-DSM feature set, the stacking model with support vector machine (SVM) as the meta-learner further improved the overall performance to R 2 = 0.930 and NRMSE = 6.3%. The additional gain from stacking was moderate but consistent, whereas feature fusion contributed the dominant improvement. The combined VI-DSM-Stacking workflow improved prediction stability across planting densities, especially under low- and high-density canopy conditions where soil background interference and spectral saturation were more evident. These results demonstrate that integrating spectral and DSM-derived structural information with stacking ensemble learning can improve the accuracy and robustness of UAV-based maize LAI estimation.
Why it matches plant phenotyping methodsUAV画像・DSM・アンサンブル学習を統合し、トウモロコシのLAI推定法を開発・比較検証しており、表現型取得・推定手法が研究の中心である。
abstractthis study developed a UAV-based estimation approach by integrating multispectral vegetation indices (VIs) with digital surface model (DSM) features and stacking ensemble learning
Accurate and generalizable plot-scale maize yield prediction is critical for precision agriculture and food security. While UAV-based multispectral remote sensing provides rich phenotyping data, existing yield prediction models often struggle with insufficient mining of complex spatio-temporal dynamics, ineffective separation of spatial details from background noise, and inadequate focus on yield-sensitive features throughout the crop growth cycle. To address these limitations, this study proposes WaveST-Yield, a novel hybrid deep learning framework tailored for multi-temporal multispectral data. The proposed model integrates three core modules: a Spatio-Temporal Phenology Encoder (SPE) based on ConvLSTM to capture the temporal dynamic patterns and spatio-temporal correlations across the entire growth period; a Multiscale Frequency-Spatial Refiner (MFSR) utilizing Haar Wavelet Downsampling (HWD) to preserve image details and decouple noise without early loss of key physiological features; and an Adaptive Yield-Sensitive Re-calibrator (AYSR) leveraging a 3D-CBAM attention mechanism to enhance the extraction of critical yield-related traits while suppressing background interference. The model was rigorously evaluated on two independent maize experimental fields using 5-fold cross-validation and cross-plot external validation. Results demonstrate that WaveST-Yield consistently outperforms traditional machine learning algorithms and single-structure deep learning models, achieving the highest prediction accuracy (Overall R² of 0.883 and 0.775 in Field 1 and Field 2, respectively) with superior error control. Extensive ablation and multi-model comparison experiments confirm that the synergistic integration of spatio-temporal encoding, frequency-domain refinement, and 3D attention mechanisms significantly improves model robustness and cross-regional generalization ability. This study provides a highly accurate, robust, and generalizable methodological framework for high-throughput crop yield monitoring.
Why it matches plant phenotyping methodsUAVマルチスペクトル時系列からトウモロコシ収量を推定する深層学習フレームワークの開発と、独立圃場・交差検証による技術評価が研究の中心である。
abstractthis study proposes WaveST-Yield, a novel hybrid deep learning framework tailored for multi-temporal multispectral data.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Plant disease detection under complex field conditions remains a critical challenge for precision agriculture due to varying illumination, scale variations, subtle lesion patterns, and inter-class visual ambiguity. This study proposes MAFusionNet, a disease-aware hybrid vision framework integrating Mamba and Transformer architectures, with components explicitly designed for plant disease-specific challenges. The MAFusion Mixer operates parallel CS-Mamba and self-attention branches to simultaneously capture sequential lesion boundary evolution and global diseasecontext spatial relationships. The CS-Mamba branch employs the SS2D-LS Block with twodimensional selective scanning and Local-Selective enhancement for linear-complexity longrange modeling while preserving 2D lesion morphology. The PConv operator uses asymmetric directional kernels forming cross-shaped receptive fields to capture anisotropic disease patterns such as vein-aligned blights and directional rust streaks. We constructed PD40, a large-scale dataset with 80,369 expert-verified annotated images across 40 disease categories spanning eight major crops, with inter-annotator agreement Cohen’s κ = 0.874. Extensive experiments demonstrate that MAFusionNet achieves 94.7% mAP 50 and 81.8% mAP 50:95 on PD40, surpassing 25 state-of-the-art baselines including recent hybrid Mamba-Transformer detectors (CropMamba, HybridMamba, Mamba-DETR), with comprehensive ablation studies validating each component’s non-redundant contribution. Edge deployment analysis on NVIDIA Jetson hardware demonstrates practical feasibility: the compressed MAFusionNet-T-Lite variant (8.7M parameters) achieves 89.3% mAP 50 at 18.4 FPS on Jetson Nano with 8.3W power consumption. The dataset and code are available at PD40-Dataset GitHub Repository.
Why it matches plant phenotyping methods植物病害の症状を画像から検出・分類する視覚モデルを開発し、注釈付き大規模データセットで検証しているため、植物状態の画像ベース表現型計測が中心である。
abstractWe constructed PD40, a large-scale dataset with 80,369 expert-verified annotated images across 40 disease categories spanning eight major crops
Timely harvesting of fresh tomatoes is urgently needed. To address this issue, this study proposes DDC-YOLOv11n, a model suitable for real-time detection of tomato ripeness in complex greenhouse environments. A Zero-DCE adaptive enhancement module is first deployed at the input stage to restore and enhance the true color and texture details of the images. An improved Deep Residual Shrinkage Network (DRSN) is then added to YOLOv11n to perform adaptive soft-threshold filtering on feature maps, reducing the interference of image noise on the detection targets. Finally, the CBAM spatial attention is enhanced through dilated convolution and channel grouping to form the LKCBAM module, which expands the equivalent receptive field while controlling the increase in parameters, thereby improving tomato detection accuracy in occluded and dense scenes. Experimental results show that the DDC-YOLOv11n model achieves the best recognition performance: compared with the original YOLOv11n, its mAP@0.5, precision, recall, and F1 score are increased by 16.8%, 24.6%, 8.3%, and 18.1%, respectively. These findings facilitate real-time tomato ripeness detection in complex greenhouse environments and provide perceptual information for subsequent management tasks such as harvesting.
Why it matches plant phenotyping methodsトマト果実の成熟度という植物器官の状態を画像から推定するモデルを開発・評価しており、フェノタイピング手法が研究の中心である。
titleA real-time ripeness detection model for tomatoes in complex greenhouse environments.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Bread wheat ( Triticum aestivum L.) is a major staple crop, and timely, in-season prediction of grain yield (GY) and grain quality traits, grain protein content (GP), and grain test weight (TW), is critical for informed management and field-based high-throughput phenotyping (HTP). Unmanned Aerial Vehicle (UAV) remote sensing, coupled with artificial intelligence and deep learning (DL), offers a practical pathway for rapid, plot-scale trait estimation. Here, we investigate the value of multitemporal, multispectral UAV imagery for predicting winter wheat GY, GP, and TW, and we systematically compare two modeling paradigms: (1) handcrafted feature-based workflows that use plot-aggregated spectral and texture descriptors derived from UAV imagery, and (2) image-based, end-to-end workflows that learn directly from plot-level reflectance image chips. During the 2022 growing season, multispectral UAV data were collected repeatedly over seven experimental wheat sites in South Dakota, USA. For handcrafted feature-based modeling, we evaluated Support Vector Regression (SVR) and Random Forest Regression (RFR), along with DL models including a feedforward Deep Neural Network (DNN) and a one-dimensional Convolutional Neural Network (1D-CNN). For end-to-end image-based modeling, we implemented 2D-CNN, 3D-CNN, and a hybrid 2D-CNN–LSTM architecture to leverage both spatial information and multi-date dependencies. Our results show that: 1) the image-based modeling workflow yielded comparable to slightly better performance than the handcrafted feature-based modeling workflow across wheat GY, GP, and TW predictions; 2) 3D-CNN outperformed all other methods with R 2 of 0.65, 0.61 and 0.69 for GY, GP and TW estimations, respectively; 3) multitemporal UAV data outperformed the data collected from a single growth stage; and UAV data from wheat Feekes 10 (booting) stage yielded slightly better estimation results compared to the data collected from other growing stages, with R 2 of 0.62, 0.55, and 0.62 for GY, GP, and TW estimations, respectively. The results indicate that DL applied to high-resolution multitemporal and multispectral UAV imagery holds strong promise for predicting winter wheat yield and grain quality during the growing season, while also informing HTP efforts and site-specific management.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から収量・品質形質を推定するワークフローを開発・比較・評価しており、植物表現型取得が研究の中心である。
abstractwe systematically compare two modeling paradigms: (1) handcrafted feature-based workflows that use plot-aggregated spectral and texture descriptors derived from UAV imagery, and (2) image-based, end-to-end workflows that learn directly from plot-level reflectance image chips.
Rice false smut is an important fungal infection in the rice panicle stage, which occurs only in the panicle. Rice yield and quality will be seriously threatened after the occurrence of panicle disease. Early identification of disease is very important for precise prevention and control. However, in the actual field environments, complex light changes, the dense distribution of small disease spots, panicle overlapping shading, and other factors often result in the semantic attenuation of key discriminant information in the stage of visual feature extraction, which has brought great challenges to the early detection and prevention of the disease. To resolve the above problems, this study introduces a rice false smut detection model derived from an improved YOLOv11 framework, named Rice-Smut, to bolster the resilience and stability of the network regarding the identification of rice false smut disease under complex field backgrounds. Firstly, in order to enhance the feature capture capabilities for multi-scale and densely distributed lesions, the C3SC backbone feature extraction network combining the SCConv block is integrated. This architecture can significantly suppress the spatial and channel redundancy and augment the precise characterization of the texture features of the lesion. Then, the C2PSA-SE attention module is introduced to effectively filter the background interference and improve the precise positioning of dense small targets. Finally, to address the irregular structure of rice false smut lesions, the GIoU loss function serves as a substitute for the conventional CIoU, which enhances the network's proficiency in locating the irregular shape lesions. Experimental outcomes revealed that the Rice-Smut model yielded a precision of 79.3% and mAP@50 of 75.3%, which represented a 7.6 and 4.5 percentage point improvement over the baseline model YOLOv11. The model requires 2.41M parameters, with a model size of 4.9MB, which results in low computational complexity. The preliminary validation on mobile platforms shows that the method is viable for the potential to be applied to the real-time field detection and disease monitoring of rice false smut, and can provide support for disease control decision-making and field management.
Why it matches plant phenotyping methodsイネの病徴（病斑）を画像から検出・位置推定するYOLOベース手法を開発し、複雑な圃場条件で性能検証しているため、植物病害状態のフェノタイピング手法が中心である。
abstractthis study introduces a rice false smut detection model derived from an improved YOLOv11 framework, named Rice-Smut, to bolster the resilience and stability of the network regarding the identification of rice false smut disease under complex field backgrounds.
Introduction Nitrogen utilization efficiency (NUtE) directly reflects the efficiency of nitrogen remobilization to grains, serving as a key indicator of yield formation and environmental performance. However, conventional methods for assessing NUtE rely on destructive sampling and laboratory analysis, which are labor-intensive and time-consuming, whereas most existing remote sensing studies estimate NUtE by directly regressing spectral features against the final efficiency value without decomposing it into its underlying physiological components. Methods This study developed a remote-sensing-based indicator of rice NUtE based on chlorophyll-related vegetation indices at key rice growth stages, termed the Nitrogen Utilization Efficiency-Vegetation Index (NUtE-VI). NUtE showed a close and near-linear relationship with the ratio of panicle nitrogen accumulation from heading to dough stage (ΔPNA dough-heading , sink indicator) to leaf nitrogen accumulation at booting stage (LNA booting , source indicator). Therefore, with multi-site field experiments across different rice cultivars and nitrogen treatments, this study employed unmanned aerial vehicle imaging to accurately estimate rice leaf and panicle nitrogen accumulations, enabling rapid, large-scale evaluation of rice NUtE. Results This proposed index showed a strong correlation with measured NUtE (R 2 = 0.72, rRMSE = 10.84%) and effectively captured the distinct patterns of NUtE across different nitrogen treatments and cultivars. Discussion Our developed indicator is generalizable across diverse conditions for high-throughput selection of nitrogen-efficient cultivars and precision nitrogen management in sustainable agriculture.
Why it matches plant phenotyping methodsUAV画像とスペクトル指標からイネの窒素利用効率を推定する手法を開発し、多地点・品種・施肥条件で精度評価しており、植物形質の取得・推定法が中心である。
abstractThis study developed a remote-sensing-based indicator of rice NUtE based on chlorophyll-related vegetation indices at key rice growth stages, termed the Nitrogen Utilization Efficiency-Vegetation Index (NUtE-VI).
Understanding below-ground biomass dynamics is essential for improving crop performance in water-limited regions. Yet field-scale root monitoring remains constrained by destructive and labor-intensive sampling. This study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits. Across 405 samples collected during the 2024 growing season, eight algorithms were evaluated, among which Random Forest and XGBoost achieved the highest predictive accuracy (R² = 0.763 for millet, 0.688 for maize, and 0.659 for sorghum). SHAP analysis revealed that leaf area was the dominant predictor across all crops, with 2-3 times greater influence than other traits, while leaf water content and chlorophyll-related parameters exhibited species-specific effects associated with drought adaptation. Under the conditions tested, these results suggest that UAV-based multispectral phenotyping, combined with interpretable machine learning, can enable non-destructive estimation of root biomass at the field scale. Within the limits of this single-site, single-season study, the approach demonstrates potential for large-scale root phenotyping and for supporting crop improvement in semi-arid regions. We quantify a 15-25% reduction in R² relative to above-ground trait prediction, which we term the 'cost of indirect inference'-highlighting the inherent challenge of estimating below-ground biomass from canopy-level data. These findings offer insights for precision agriculture, subject to broader validation.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と説明可能な機械学習を用いて根 biomass という植物形質を非破壊推定する手法が研究の中心であり、実証・比較評価も行っている。
abstractThis study presents explainable machine learning models to estimate root biomass of maize, millet, and sorghum using UAV multispectral imagery and key canopy phenotypic traits.
Introduction Cashew farms are highly vulnerable to the attack of pests and plant diseases, which leads to massive losses in production and economic value. To diagnose the cashew disease, recent studies have explored deep learning methods; however, the existing models often have poor representative features and low generaliz-ability to different situations. Methods As part of these solutions, we suggest a hybrid late-fusion architecture (combining EfficientNetV2-M and MobileNetV3-S) to extract the features and train them with XGBoost and CatBoost to give the classification, using the BOHB optimisation to make the hyper-parameter choices. By combin-ing MobileNetV3-S and EfficientNetV2-M, we can benefit from fine-grained visual information and computer efficiency. Result and discussion The proposed model was experimented on the CCMT dataset comprising Anthracnose, Gummosis, Leaf Miner, Red Rust, and healthy leaf samples, achieving classification accuracies of 90% and 93%, with reduced computation times of 0.20 seconds for the XGBoost classifier and 0.07 seconds for the CatBoost classifier. As our findings show, boosting-based classifiers and efficient backbone networks can be combined to identify cashew disease effectively and computationally less complex.
Why it matches plant phenotyping methodsカシュー葉の病害状態を画像から分類する深層学習モデルを提案・評価しており、植物病害フェノタイピング手法が中心である。
abstractwe suggest a hybrid late-fusion architecture (combining EfficientNetV2-M and MobileNetV3-S) to extract the features and train them with XGBoost and CatBoost to give the classification
Introduction Confidence calibration, selective prediction, out-of-distribution scoring, and deep ensembles are mature techniques in machine learning, yet their efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked. Methods Models trained on PlantVillage were evaluated on PlantDoc leaf-level crop images under a parent-image-aware split protocol, and a suite of standard mitigation techniques was applied to characterize the reliability gap. Analyses included temperature scaling and selective prediction for a fine-tuned ResNet-50, quantitative image-level shift analysis, Grad-CAM visualization, simple target-aware adaptation baselines, frozen-feature backbone comparisons, and ensemble baselines. Results In the primary case study, a fine-tuned ResNet-50 suffered a 67.7-percentage-point accuracy collapse upon cross-domain transfer, while mean predicted confidence remained at 79.76%. Post-hoc temperature scaling reduced calibrated ECE to 0.3645 but left selective risk at 80% coverage at 64.30%. Quantitative image-level shift analysis confirmed large-effect-size differences in saturation ( d = 3.90), border edge density ( d = 3.33), and foreground-occupancy proxy ( d = 2.48) between the two domains, while Grad-CAM visualizations showed that the model shifts attention from lesion-centered regions in PlantVillage to background-dominated areas in PlantDoc. Simple target-aware mitigations, including adaptive batch normalization and feature moment matching, improved accuracy from 0.321 to 0.343 and 0.366, respectively, whereas DANN-style adversarial adaptation degraded performance to 0.252. A frozen-feature backbone comparison across five backbones showed that, within the energy-scoring frozen-backbone comparison, DINOv2-S/14 achieved the highest unknown-detection AUROC (0.764) and the lowest selective risk at 80% coverage (0.520), with paired Wilcoxon tests confirming statistically significant accuracy and macro-F1 differences across backbones. Two ensemble baselines were evaluated: a warm-start end-to-end ResNet-50 ensemble reduced calibrated ECE to 0.063 but achieved only 0.666 AUROC, while a lightweight DINOv2 linear-probe ensemble achieved 0.779 AUROC after calibration but under limited epistemic diversity. Discussion Neither ensemble established deployment-grade reliability: the best selective risk at 80% coverage across all configurations remained above 0.51. The principal contribution is a reproducible, deployment-oriented reliability characterization showing that standard post-hoc and lightweight adaptation techniques reduce but do not eliminate the severe reliability gap under controlled-to-field transfer in agricultural computer vision.
Why it matches plant phenotyping methods植物病害画像分類の信頼性・ドメインシフト・校正・選択的予測を体系的にベンチマークしており、病害状態を画像から推定する方法の技術評価が中心である。
abstracttheir efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) was therefore constructed by normalizing all labels to a canonical Crop_Disease format and retaining only those categories for which an unambiguous semantic match existed in both datasets.Open asset ↗lines:335-337Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
The rapid and accurate quantification of plant phosphorus (P) content is essential for the real-time assessment of crop P status and improvement of P fertilizer use efficiency. However, non-destructive and rapid approaches for P monitoring are limited. In this study, the feasibility of monitoring plant phosphorus content (PPC) in winter wheat was evaluated through multi-source feature fusion of unmanned aerial vehicle (UAV) imagery based on a long-term field experiment with five P treatments. Multiple spectral features, including color indices (CIs), fractional vegetation cover (FVC), vegetation indices (VIs), texture features (TFs) and texture indices (TIs), were extracted from UAV RGB and multispectral images. Sensitive spectral features were systematically screened using Pearson correlation analysis, random forest (RF) importance ranking, and the Relief algorithm. Selected features were then fed into three machine learning models, RF, support vector machine (SVM), and k-nearest neighbor (KNN) to predict PPC. The results showed that GRI, VARI, MGRVI, TGI, NDRE, and CIred edge were highly correlated with PPC at the maturity stage (r = 0.96). Both TFs and TIs demonstrated stronger correlations with PPC at the 750 and 840 nm bands, with most TIs outperforming TFs, confirming the feasibility of spectral-based PPC estimation. Based on the selected input variables including DTI (450-Ent, 750-Mea), 840-Mea, and RVI, the SVM model achieved the best performance (R 2 c=0.94, RMSEc=0.29, RPDc=4.03; R 2 v=0.92, RMSEv=0.36, RPDv=3.48). These results highlight the potential of combining VIs, TFs, and TIs features for training machine learning models for PPC prediction, while the organ-level physiological explanations warrantee further investigations under controlled P gradients. This study provides data-driven insights for UAV-based monitoring of plant P nutritional status under local experimental conditions.
Why it matches plant phenotyping methodsUAV画像から特徴量を抽出し、機械学習で冬コムギの植物リン含量という生理形質を推定する手法の開発・評価が中心であり、方法論的検証も実施している。
abstractMultiple spectral features, including color indices (CIs), fractional vegetation cover (FVC), vegetation indices (VIs), texture features (TFs) and texture indices (TIs), were extracted from UAV RGB and multispectral images.
Introduction Accurate monitoring of canopy nitrogen content is essential for sustainable nitrogen management, yield improvement, and environmental protection in industrial maize production. However, the high dimensionality of hyperspectral data and the limited accuracy and interpretability of existing models hinder practical applications. Methods This study was conducted in Heilongjiang Province, China, using the maize cultivar Jinboshi. Genetic Algorithm (GA), Successive Projections Algorithm (SPA), and their hybrid strategy were compared for spectral band optimization. Sensitive vegetation indices were selected using multiple evaluation criteria, and a 0-2 order fractional-order derivative (FOD) method was applied to construct optimal two-dimensional (2D) and three-dimensional (3D) spectral indices. A stacked ensemble learning model was developed using XGBoost, GBDT, and Ridge as base learners and Bayesian Ridge as the meta-learner. Interpretability techniques were applied to analyze feature contributions. Results The GA-SPA hybrid strategy effectively improved key spectral band selection. The 3D spectral index based on FOD achieved superior performance compared to vegetation indices and 2D indices (R 2 p = 0.801, RMSEP = 0.481). The optimized multi-source feature set combined with the stacked ensemble model yielded the best performance (R 2 p = 0.826, RMSEP = 0.450). Features from the red-edge and near-infrared regions, along with the 3D index, were the primary contributors to model predictions, consistent with plant nitrogen physiology. Discussion The proposed framework, integrating feature optimization, advanced modeling, and interpretability analysis, provides an effective tool for precise nitrogen management in industrial maize and supports improved production efficiency with reduced environmental impact.
Why it matches plant phenotyping methodsトウモロコシ群落の窒素含量という植物形質を、ハイパースペクトル特徴量最適化とアンサンブル学習で推定する手法が研究の中心であり、性能評価と解釈性分析も行っている。
abstractAccurate monitoring of canopy nitrogen content is essential
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Climate change is increasing the frequency of compound drought and heat events, threatening forest stability worldwide. While genomics has helped identify resilient genotypes, our ability to characterize adaptive traits - phenotyping - has not kept pace. This creates a bottleneck: we can sequence trees faster than we can understand how they physically respond to stress. Moving away from single-sensor monitoring, the field is now embracing multi-sensor data fusion, in which thermal imaging, Solar-Induced Fluorescence (SIF), hyperspectral remote sensing, and LiDAR are combined on platforms ranging from Unmanned Aerial Vehicles (UAVs) to ground-based robotic systems. These integrated approaches are proving effective for detecting physiological stress - such as changes in stomatal conductance - before visible damage appears. Deep learning models, meanwhile, are beginning to outperform traditional vegetation indices for specific tasks such as tree-crown segmentation and stress classification, although their performance remains constrained by overfitting, limited transferability, and domain shift across forest types in analyzing complex forest canopies. A major limitation remains, however: most high-throughput phenotyping (HTP) focuses on the canopy, largely ignoring the root system and the soil-plant-atmosphere continuum (SPAC), which are critical for drought resilience. In this review, we argue that developing climate-resilient forests requires looking below the canopy. We propose a constraint-based framework that couples aerial sensor data with eco-hydrological approaches and process-based modeling to narrow the range of plausible root functional strategies-rather than to directly identify root phenotypes, while critically evaluating the assumptions and validation challenges inherent in this approach. Future research should focus on standardized protocols, open benchmark datasets, and Explainable AI (XAI) to strengthen the link between above-ground signals and below-ground traits.
Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、マルチセンサー融合、深層学習、検証課題、標準化・ベンチマークをレビューしているため。
abstractIn this review, we argue that developing climate-resilient forests requires looking below the canopy.
Introduction Under small-sample conditions, hyperspectral leaf chlorophyll estimation is affected by high-dimensional collinearity, measurement noise, and cross-source acquisition discrepancies. Existing studies often treat training-distribution expansion and model-error complementarity separately. This study proposed a physically constrained composite spectral augmentation-weighted ensemble framework for reproducible small-sample chlorophyll estimation. Methods Using 1,113 valid spectrum-label pairs from the leaf subset of the GreenHySpectra dataset in the 400-1000 nm range, spectra and chlorophyll reference values were matched by sample identifiers and divided into training and validation sets. Low-magnitude Gaussian noise and smooth wavelength warping were applied only to the training set. XGBoost, partial least squares regression, and ridge regression were optimized with Optuna using a CMA-ES sampler, and ensemble weights were calibrated by Bayesian optimization. An independent external set of 90 tomato leaf samples was used to evaluate transferability. Results Composite augmentation improved model stability and reduced validation error relative to the non-augmented baseline. The weighted ensemble model achieved the best internal performance, with R² = 0.6392 and RMSE = 8.8883. On the external samples, the model achieved R² = 0.498 and RMSE = 9.801. Discussion The proposed workflow integrates physically plausible augmentation, heterogeneous learner complementarity, and independent external validation. The external results indicate partial cross-source transferability while highlighting distributional and measurement-chain discrepancies that still limit absolute generalization.
Why it matches plant phenotyping methods葉のクロロフィル量という植物形質をハイパースペクトルから推定する手法を開発し、外部データで転移性を検証しており、表現型取得・推定が研究の中心である。
titleHyperspectral estimation of leaf chlorophyll under small-sample conditions via spectral augmentation and weighted ensemble learning.
Reproduction assets foundThe paper's phenotyping analysis is built on the public GreenHySpectra hyperspectral dataset (leaf subset, 1,113 spectrum–chlorophyll pairs), which is a paper-specific, publicly available input with an authors' cited URL matching the allowed list. No author analysis code, trained models, or public deposit of the 90-solDataset · publicAvatarr05 ( 2023 ). GreenHySpectra/GreenHyperSpectra dataset (Hugging Face Datasets) [WWW document] . Available online at: https://huggingface.co/datasets/Avatarr05/GreenHySpectra (Accessed May 15, 2026).Open asset ↗Hugging Face Datasets · Avatarr05/GreenHySpectralines:749-785Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Colorimetric analysis of food using the CIELab/Ch colour space (i.e., from digital images of samples) is an accessible, non-destructive method for carotenoid and anthocyanin content prediction. Literature presents very well-fit, but rudimentary, models for pigment estimation (e.g., single/multiple linear regressions). However, standardised methods that statistically account for the high multicollinearity between CIELab/Ch colour parameters, varying light conditions and colour calibration, and samples with high genotypic variability are lacking. Methods An image analysis optimisation was developed for the prediction of carotenoid and anthocyanin content of 16 carrot genotypes of different colours. Samples were photographed under six light conditions with a digital camera and image colour was calibrated before analysis with the CIELab/Ch colour space. Total pigment contents and individual carotenoid contents were analysed chemically via spectrophotometry and high-performance liquid chromatography, respectively. Partial least squares (PLS) regressions were used to assess the colour-pigment relationships to correct for high multicollinearity amongst the independent variables (CIELab/Ch colour parameters). Results/discussion The PLS models achieved satisfactory accuracy for the prediction of total carotenoid content ( ca. R 2 = 0.77) and total anthocyanin content ( ca. R 2 = 0.81) under all light conditions. The two models are suggested as robust approaches to total pigment prediction with multi-dimensional colour spaces, varying light conditions, and for a sample group of high genotypic variability. The carrot samples proved to have very high genetic diversity within each cultivar, resulting in unsatisfactory models for prediction of individual carotenoids ( ca. R 2 = 0.45) under the default light condition. However, all the results can be used to expand databases (towards artificial intelligence) and aid breeding programmes in search for higher concentrations of these interesting antioxidants for human health.
Why it matches plant phenotyping methodsニンジン試料の画像色解析を最適化し、化学分析値を用いてカロテノイド・アントシアニン含量を予測する手法を開発・検証しており、植物形質取得が研究の中心である。
abstractThe PLS models achieved satisfactory accuracy for the prediction of total carotenoid content ( ca. R 2 = 0.77) and total anthocyanin content ( ca. R 2 = 0.81) under all light conditions.
Reproduction assets foundThe authors deposited the paper's data and protocols in public repositories (DOI links in the Data availability statement). The anthocyanin quantification protocol is explicitly linked (10.34894/BTPTSV), and the other two DOIs (10.34894/P37WCL, 10.34894/OUURRH) are stated to hold the paper's data. No separate author's'Dataset · publicData and protocols are available in the following links: https://doi.org/10.34894/P37WCL , https://doi.org/10.34894/OUURRH , https://doi.org/10.34894/BTPTSV .Open asset ↗10.34894/P37WCLlines:641-686Dataset · publicData and protocols are available in the following links: https://doi.org/10.34894/P37WCL , https://doi.org/10.34894/OUURRH , https://doi.org/10.34894/BTPTSV .Open asset ↗10.34894/OUURRHlines:641-686Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Spontaneous haploid genome doubling (SHGD) is a valuable trait in maize breeding, enabling the development of doubled haploid (DH) lines without chemical chromosome doubling. However, SHGD is a rare phenotype, expressed in only a small fraction of maize germplasm, making its identification resource-intensive. This study evaluated the efficiency of a two-stage field screening approach designed to identify maize genotypes with high haploid male fertility (HMF), a key indicator of SHGD potential. Simulation analyses showed that evaluating 50 haploid plants per genotype, combined with a 25% HMF threshold, provides an optimal balance between detection accuracy and resource efficiency. Across three growing seasons, HMF exhibited a highly skewed distribution, with most genotypes showing low HMF and a small subset exceeding 30% HMF. Field evaluations conducted in 2022, 2023, and 2024 consistently identified high-performing genotypes, including A427, N525, N516, and NK778, which maintained stable HMF expression across years. A genome-wide association analysis identified genomic regions associated with HMF. Our two-stage screening approach identified both SHGD donor lines and genomic loci and candidate genes for HMF.
Why it matches plant phenotyping methods希少な植物表現型SHGDを検出する二段階フィールドスクリーニング法を開発・評価し、サンプル数と閾値の最適化および複数年検証を行っているため、表現型取得法が中心的である。
abstractThis study evaluated the efficiency of a two-stage field screening approach designed to identify maize genotypes with high haploid male fertility (HMF), a key indicator of SHGD potential.
The accuracy of on-board yield monitoring for combine harvesters is limited by the difficulty of acquiring high-fidelity mass flow signals and achieving reliable cumulative integration under complex vibration and varying operating conditions. To address this challenge, this study developed a weighing-based on-board yield monitoring system (DW-ORMS), in which a flow-collecting weighing-type grain mass flow sensor (C-GMFS) served as the core sensing unit. The C-GMFS enables stable weighing observation in confined installation spaces through mechanical decoupling and a single force-transmission path. At the system level, a state-aware gating strategy and trusted update mechanism were introduced to improve the reliability of output and cumulative mass estimation during unsteady operating phases. Based on field-measured disturbance characteristics, a parameterized disturbance model was established and used to identify the valid operating window and fix the gating parameter range. Simulation results showed that the proposed method achieved band-limited interference suppression of A band ≥17.73 dB under strong disturbances while maintaining trend fidelity of PRR trend ≥41.73% and low processing delay. Field harvesting experiments were conducted at working speeds of 2–8 km·h − ¹. The cumulative strip mass showed excellent agreement with manual weighing references, with R ²=0.973 and RMSE = 0.84 kg, and the strip-level mass closure error remained within CE ≤ 5% (N = 35). No monotonic drift in error was observed across working-speed groups. These results demonstrate that the DW-ORMS provides a deployable and traceable solution for high-confidence on-board yield monitoring of combine harvesters under unsteady field conditions.
Why it matches plant phenotyping methods収量という植物由来の形質を対象に、動的秤量センサーと信号処理による圃場収量モニタリング手法を開発し、シミュレーションおよび実収穫データで検証しているため、方法が中心的である。
abstractthis study developed a weighing-based on-board yield monitoring system (DW-ORMS), in which a flow-collecting weighing-type grain mass flow sensor (C-GMFS) served as the core sensing unit.
Introduction Traditional tea disease detection methods suffer from low efficiency and strong subjectivity, while existing deep learning approaches often demonstrate inadequate detection accuracy and poor real-time performance in complex and variable environments. Methods Here, we present an intelligent tea disease detection method based on an improved Real-Time Detection Transformer, named DSA-DET. We propose a backbone network based on dynamic attention spatial pyramid modeling to achieve more accurate collaborative modeling of local features and global context. We design an encoder combining polarized linear attention with parallel spatial enhancement networks and multi-scale adaptive enhancement to improve feature extraction capabilities. We further develop an upsampling module employing efficient spatial-channel upsampling and shift mixing mechanisms to enhance the quality of reconstructed features. Results Experimental results show that the improved model achieves a precision of 94.73%, a recall of 89.65%, and an mAP 50 of 93.68%. Compared to the baseline model RT-DETR-R18, its precision is improved by 3.56%, recall by 2.96%, and mAP 50 by 3.02%; meanwhile, the model maintains a lightweight parameter scale of 15.4M and a real-time detection speed of 71.5 FPS. Discussion The improvement scheme in this study successfully enhances detection accuracy while maintaining a good balance between model complexity and inference speed, providing a practical and reliable technical solution for the intelligent diagnosis of tea diseases.
Why it matches plant phenotyping methods茶葉の病害状態を画像等から検出する深層学習手法を開発し、精度・再現速度を比較検証しており、植物病害表現型の取得手法が中心である。
abstractHere, we present an intelligent tea disease detection method based on an improved Real-Time Detection Transformer, named DSA-DET.
Introduction: Wheat kernel hardness, vitreousness, and creaseness are key determinants of milling performance, yet they reflect different physical scales of grain structure and are not necessarily coupled. Methods: We developed a digital phenotyping framework based on hyperspectral imaging and spectral unmixing to quantify these traits at both kernel and cultivar levels in a diverse panel of common wheat. Pixel-level spectral unmixing resolved glassy, intermediate, and mealy endosperm components within individual kernels, enabling vitreousness to be expressed as a continuous spatial index. Results: The hyperspectral-derived vitreousness index showed moderate associations with kernel protein content and the protein-to-starch ratio, consistent with variation in endosperm packing density, but weak relationships with kernel hardness and crease geometry. Kernel hardness, primarily determined by puroindoline genotype, showed limited association with bulk protein and starch composition. Crease geometry, quantified using composite indices from RGB images, captured macroscopic grain features largely independent of both hardness and vitreousness. Discussion: These results demonstrate that hardness, vitreousness, and creaseness represent complementary but largely independent dimensions of grain quality, corresponding to molecular-scale adhesion, mesoscale packing, and macroscopic geometry, respectively. The proposed framework provides a scalable, non-destructive approach for resolving intra-kernel heterogeneity, enabling improved digital phenotyping for wheat breeding and quality assessment.
Why it matches plant phenotyping methodsハイパースペクトル画像とスペクトルアンミキシングを用いて小麦粒の硝子質を定量するデジタル表現型解析フレームワークを開発しており、形質取得手法が中心的である。
abstractWe developed a digital phenotyping framework based on hyperspectral imaging and spectral unmixing to quantify these traits at both kernel and cultivar levels in a diverse panel of common wheat.
Reproduction assets foundThe paper's data availability statement deposits full hyperspectral image cubes and RGB image datasets on Figshare, and the supplementary material includes Python analysis scripts (Supplementary Code S1–S2) and processed feature tables (Supplementary Table S3) directly reproducing the paper's phenotyping measurements.Dataset · publicfull hyperspectral image cubes and associated RGB imagedatasets are available via Research Datas 1 – 3 at Figshare: https://doi.org/10.6084/m9.figshare.31259530Open asset ↗Figshare · 10.6084/m9.figshare.31259530lines:151-201Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
To achieve high-precision and high-efficiency estimation of the Leaf Area Index (LAI) of jujube trees using drone remote sensing, and to overcome the limitations of traditional vegetation index methods, such as saturation in the later stages of crop growth, sensitivity to background noise, and difficulty in capturing temporal dynamics, this study proposes a parallel hybrid deep learning framework using CNN-GRU. The model adaptively extracts spatial-spectral local features from drone RGB images through a convolutional neural network (CNN) branch, while a gated recurrent unit (GRU) branch learns the sequential evolution of LAI during key phenological periods. Finally, a meta-learner integrates spatial-temporal information for decision-making. To verify the model's effectiveness and prediction performance, the study systematically collected multi-temporal ground-measured LAI data and synchronized drone remote sensing images during critical growth stages of jujube trees in two independent years: 2024 (Bachu, Xinjiang) and 2025 (Alaer, Xinjiang). A series of spectral and texture indices were extracted as model inputs. The experimental results show that the proposed CNN-GRU model exhibits excellent learning and fitting capabilities on the training set, with an R 2 value of 0.839. On the test set, after optimization with data augmentation strategies, the model's prediction accuracy is significantly improved, with prediction accuracy reaching its best level, with an R 2 of 0.83 and an RMSE of 0.150. All error metrics outperform mainstream comparative models such as Transformer, KNN, MLP, and CNN. This study demonstrates that the hybrid deep learning architecture, combining spatial feature extraction and time-series modeling, is an effective approach for accurate and robust remote sensing inversion of crop LAI in complex agricultural scenarios, providing a reliable technical tool for the digital management of smart orchards and precise agricultural decision-making.
Why it matches plant phenotyping methodsUAV RGB画像からナツメ樹のLAIを推定するCNN-GRU手法を開発し、複数年データと比較モデルで性能検証しており、植物形質取得が中心である。
abstractthis study proposes a parallel hybrid deep learning framework using CNN-GRU
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 5 Sept 2026
Introduction With the continuous advancement of smart agriculture, multi-modal remote sensing based on unmanned aerial vehicles (UAVs) offers new technical approaches for monitoring and managing crop moisture in fields. However, significant challenges remain in developing high-precision field-scale crop Plant Moisture Content (PMC) prediction models and translating them into actionable irrigation strategies. Methods This study focuses on winter wheat, employing field experiments with PMC and water use efficiency (WUE) as indicators of crop water status. Vegetation indices (VIs) derived from UAV data were used to construct a leaf area index (LAI) inversion model. Crop Height was extracted from oblique photogrammetry point cloud data. By combining the Penman-Monteith equation with dual crop coefficients, an improved evapotranspiration (ET) model was developed, utilizing multispectral data from UAVs, thermal infrared data, point cloud-derived plant height, and LAI inversion results. Further utilizing VIs, temperature indices (TIs), and machine learning algorithms (Random Forest Regression (RFR), Back Propagation Neural Network (BPNN), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), we established PMC prediction models for winter wheat at different growth stages. These models, integrated with WUE, form the basis for an irrigation scheduling optimization framework at the field scale. Results Results indicate that VIs, the difference between canopy temperature and air temperature (ΔT), Crop Water Stress Index (CWSI), and ET exhibit varying correlations with PMC during three critical growth stages of winter wheat, with ET showing the highest correlation during the jointing and heading stages (absolute correlation coefficient |r| ≥ 0.639). Compared to PMC prediction models constructed with different combinations of VIs, ET, VIs+ET, and VIs+TIs, the model employing the RFR algorithm with multimodal inputs (VIS+TIs+ET) demonstrated the best performance. The model’s predictive accuracy gradually improved across all growth stages, peaking during the grain-filling stage, with the coefficient of determination(R 2 ) of 0.900 and a normalized root mean square error (nRMSE) of 2.688%. Optimal WUE varied across growth stages under different irrigation treatments. The highest values were achieved at the jointing stage under treatment W3 (PMC = 81.8%), and at the heading and grain-filling stages under treatment W1 (PMC = 76.8% and 64.0%, respectively). Discussion The study suggests that stage-specific irrigation scheduling based on PMC thresholds can improve overall water use efficiency. This study shows that integrating multi-modal UAV data with machine learning and an improved ET model enables high-precision PMC monitoring, supporting data-driven irrigation scheduling in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチモーダルデータと機械学習により、作物水分状態（PMC）、LAI、草高、蒸発散量を推定する手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractCrop Height was extracted from oblique photogrammetry point cloud data.
To address the significant morphological variability of cotton Verticillium wilt lesions and the complex background interference present in field environments, existing detection models often struggle to achieve an effective balance between detection accuracy and model complexity. In this study, a precise and lightweight detection model, YOLO-SCOD, is proposed based on the YOLOv12n framework to enhance lesion recognition performance. During the feature extraction stage, YOLO-SCOD adopts the StarNet architecture as the backbone network. Its efficient feature mapping mechanism enhances the ability to interact with multi-scale features, thereby optimizing the model's capacity to represent multi-scale lesion information. Meanwhile, a channel aggregation block is integrated into the C3k module of the neck network. Through adaptive channel reallocation and enhancement of key lesion features, the perception capability of the C3k2 and A2C2f modules for discriminative lesion features is improved. In the detection head, depthwise convolution is replaced with omni-dimensional dynamic convolution, which dynamically and adaptively adjusts convolutional weights through multi-dimensional attention collaboration, further improving the model's localization accuracy and recognition capability. Experimental results demonstrate that the YOLO-SCOD model achieves improved performance improvements in the task of cotton Verticillium wilt detection. Compared with YOLOv12n, its precision and recall increase to 0.960 and 0.911, respectively, while mAP50-95 improves by 6.436%. In addition, the number of model parameters, FLOPs, and model size are reduced by 13.728%, 20.635%, and 12.727%, respectively, and inference speed increased by 4.167%. While maintaining high detection accuracy, YOLO-SCOD exhibits favorable lightweight characteristics, providing a viable solution for efficient automatic identification and intelligent detection of cotton Verticillium wilt.
Why it matches plant phenotyping methods綿花の萎凋病病斑という植物の病害状態を画像検出するモデルを開発し、精度・計算量・推論速度を比較検証しており、病害表現型の取得手法が中心的です。
abstracta precise and lightweight detection model, YOLO-SCOD, is proposed based on the YOLOv12n framework to enhance lesion recognition performance.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Efficient phenotyping monitoring of cauliflower is crucial for its breeding and production. However, traditional manual measurement methods are time-consuming and labor-intensive, and existing deep learning (DL) methods mostly focus on the seedling stage, lacking systematic research covering the entire growth period. In this study, RGB images of cauliflower from seedling to harvest were collected. Through systematic screening and evaluation of instance segmentation models, accurate segmentation of plants and curds was achieved, and plant canopy width, leaf area, and curd traits were automatically extracted to track their dynamic changes. Evaluation results showed that YOLO12s-seg was the optimal model. It can achieved a segmentation mask mAP 50 of 99.4% for plants and curds in sparsely planted images and showed an advantage in identifying partially occluded early curds beneath inner leaves. Traits such as plant canopy width and curd diameter automatically extracted from segmentation results were highly consistent with manual measurements (R 2 > 0.90). Furthermore, the Richards model and Sine model were used to accurately fit the growth dynamics of leaf area and curd area, respectively. Based on growth kinetics, curds were classified into three types: mature and compact type, peak-burst type, and steady-increase type. Cluster analysis of 47 germplasms based on high-throughput phenotyping data revealed four groups and their growth characteristics: comprehensively coordinated type, mid-maturity compact type, large high-yield type, and curd-dominant type. Integrating the above functions, a platform for cauliflower growth monitoring and phenotypic analysis was developed. It provided full-process support from automatic image processing to growth dynamic analysis. This work provides an effective automated solution for high-throughput phenotyping analysis and growth dynamic monitoring of cauliflower, and offers a referable analytical framework for crop growth pattern research and intelligent breeding decision-making.
Why it matches plant phenotyping methods植物のインスタンスセグメンテーションから葉面積・草冠幅・花蕾形質を自動抽出し、手動測定との技術検証と成長動態解析、統合プラットフォーム開発を行っており、フェノタイピング手法が研究の中心である。
abstractThrough systematic screening and evaluation of instance segmentation models, accurate segmentation of plants and curds was achieved, and plant canopy width, leaf area, and curd traits were automatically extracted to track their dynamic changes.
Introduction Plant diseases pose a major challenge to sustainable agriculture, particularly in regions that heavily depend on farming. Early and accurate identification of plant diseases is crucial for ensuring food production and minimizing crop losses. The rapid advancement of deep learning, particularly in convolutional neural networks (CNNs), has significantly enhanced plant disease classification performance. However, many models often struggle to generalize effectively in real-world scenarios due to challenges such as low-intensity visuals, low contrast between the background and foreground of the suspected sample, noise, and chrominance variation. Methods To address the challenges mentioned above, we introduce ConvGem-NeXt, an end-to-end deep learning architecture specifically designed for fine-grained plant disease classification, built on the ConvNeXt baseline model featuring enhanced generalization capabilities. More precisely, our method incorporates a learnable Generalized Mean pooling layer and ReLU activation in the ConvNeXt model to enhance spatial feature representation, and a custom classifier head that integrates batch normalization, ReLU activation, and dropout to mitigate overfitting and improve classification accuracy. Results We tested the presented model on two large-scale and diverse databases, PlantVillage and the PlantDoc. The model achieved 99.65% accuracy on the PlantVillage dataset and 94.69% accuracy on the real-world PlantDoc dataset, demonstrating the efficacy of our method for reliably classifying plant diseases. Discussion This work contributes to the rapidly growing field of agricultural automation by providing a reliable framework for timely disease diagnosis and supporting the enhancement of crop productivity.
Why it matches plant phenotyping methods植物病害を画像から分類する深層学習アーキテクチャを開発し、複数データセットで性能検証しており、植物の病害状態の取得・推定が研究の中心である。
abstractwe introduce ConvGem-NeXt, an end-to-end deep learning architecture specifically designed for fine-grained plant disease classification
Reproduction assets foundThe paper's plant disease classification experiments were performed on two public image datasets, PlantVillage and PlantDoc, both explicitly linked in the data availability statement. No author code or model checkpoints are released.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗kaggle.com/datasets/abdallahalidev/plantvillage-datasetlines:1466-1516Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Background Accurate identification of visible disease symptoms is essential for the sustainable management of tea ( Camellia sinensis ) cultivation. However, balancing high diagnostic accuracy with the computational efficiency required for deployment on agricultural edge devices remains a significant challenge. Methods We propose GL-MobFormer, a lightweight hybrid deep learning framework. This architecture integrates the local feature extraction capabilities of MobileNetV3 with the global contextual modeling of a Transformer Encoder. To improve model robustness in unstructured field environments, we applied the CutMix data augmentation strategy. The framework was evaluated on a dataset comprising 5,278 tea leaf images across seven phytosanitary categories. Results Empirical evaluations demonstrate that GL-MobFormer achieved a classification accuracy of 95.13% and a Matthews Correlation Coefficient (MCC) of 0.9417. Crucially, this performance was maintained with a low computational footprint of merely 0.33 G FLOPs(Floating Point Operations). Importantly, an occlusion-based sensitivity protocol was implemented to provide quantitative grounding for model interpretability. Results revealed that systematically masking only the top 5% of critical activation regions led to an average reduction of 70.61% in classification confidence, empirically confirming that the model's diagnostic logic is faithfully anchored on pathologically relevant lesion features rather than background noise. Conclusion GL-MobFormer achieves an optimal trade-off between diagnostic precision and computational overhead. It provides a practical and highly efficient solution for on-site, real-time phytosanitary monitoring in precision agriculture.
Why it matches plant phenotyping methods茶葉の病斑画像から病害状態を推定する軽量CNN・Transformer手法を開発し、精度・計算量・解釈性を評価しており、植物フェノタイピング手法が中心です。
abstractWe propose GL-MobFormer, a lightweight hybrid deep learning framework.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Introduction: Recent technological advances in high resolution image capture and analysis have led to increased adoption of high-throughput digital phenotyping in plant science research. High-throughput digital phenotyping provides a nondestructive method to quantify changes in plant growth and health in response to environmental factors or developmental cues. Moreover, it allows researchers to conduct large experiments in a time- and cost-efficient manner. The TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information. Leaf reflectance is presented as five vegetation indices (e.g., normalized difference vegetation index). Methods: This project evaluated nitrogen (N), phosphorus (P), and potassium (K) deficiency in greenhouse grown ornamental and vegetable plants using the TraitFinder. Plant species included celosia, coleus, marigold, petunia, and tomato. Plants were fertilized with a complete Hoagland's solution (control), and three modified solutions: Hoagland's solution without nitrogen (-N), phosphorus (-P), or potassium (-K). Each plant species was evaluated separately with eight replicate plants per treatment, organized as a randomized complete block design. Results: Treatment with -N, -P, and -K solutions resulted in reduced vegetative growth and decreased concentration of the corresponding macronutrient in leaf tissue for all species evaluated. We observed that the presence of flowers would negatively affect calculations of the vegetation indices due to their distinct spectral properties; therefore, flowers must be excluded to accurately quantify plant health parameters. In general, we observed a common trend where GLI (green leaf index) and NDVI (normalized difference vegetation index) decreased, and NPCI (normalized pigment chlorophyll index) and PSRI (plant senescence reflectance index) increased in response to macronutrient deficiency. The measure of GLI, NDVI, NPCI, and PSRI were different from the control plants, but these observations were dependent on the nutrient deficiency and species tested. Discussion: Our results underscore the importance of accounting for species-specific spectral signatures when assessing plant responses to nutrient deficiencies. This project also provides reference values for interpreting vegetation indices, offering valuable guidance for scientists implementing digital phenotyping in their experimental protocols. Digital phenotyping can significantly improve experimental throughput and provide quantitative insights into plant health.
Why it matches plant phenotyping methodsTraitFinderによる形態・スペクトル形質の取得と、花の除外や種特異的スペクトルへの対応を含むデジタルフェノタイピングの実質的な適用・評価が中心である。
abstractThe TraitFinder is a digital phenotyping system developed by Phenospex (Heerlen, Netherlands) that measures plant morphological (e.g., digital biomass) and spectral (leaf light reflectance) information.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Eggplant (Solanum melongena L.) is a widely cultivated vegetable crop worldwide, occupying an important position in the agricultural industries of Asia, the Middle East, and Southern Europe. Its significance extends beyond agricultural economics to diverse dimensions such as dietary nutrition, rendering it of considerable research and application value. Traditional crop phenotyping methods suffer from low efficiency, substantial manual errors, and a tendency to damage tender seedlings, while existing three-dimensional phenotyping techniques face challenges including strong background interference and large data volumes. These dual constraints limit the accuracy and application feasibility of seedling phenotyping. To address these issues, this study proposes a non-destructive phenotyping method for eggplant seedlings, with the improvement of the PointNet++ architecture as its core and point cloud background purification as a key preprocessing step, aiming to enhance eggplant breeding efficiency and seedling screening accuracy. The raw point clouds first undergo background purification to actively remove seedling tray points, thereby improving point cloud purity and reducing data size. Concurrently, based on the PointNet++ model, we develop an improved point cloud segmentation model, EggplantPointNet++, by introducing multi-scale residual blocks, integrating channel attention mechanisms, incorporating a global context module, and refining the feature propagation layer. In conjunction with the DBSCAN clustering algorithm, this approach achieves semantic and instance segmentation of eggplant seedling point clouds, with certain improvements in segmentation accuracy and model efficiency under small-scale and occluded scenarios. To validate the technical effectiveness, multiple comparative experiments and ablation studies were conducted. The results demonstrate that EggplantPointNet++ outperforms the original model, background purification preprocessing provides positive gains, and each improved module contributes positively. The final model achieves improvements in core metrics including Recall and F1-score. Based on the segmented point cloud data, this study calculates core phenotypic parameters including plant height, stem diameter, cotyledon angle, and cotyledon area. Using the technical system established in this study, we completed the time-series measurement of three-dimensional morphological changes in eggplant seedlings during the cotyledon stage, providing quantitative references for seedling growth assessment and superior plant selection.
Why it matches plant phenotyping methodsナス幼苗の3D点群から形質を抽出する非破壊フェノタイピング手法を開発し、比較実験・アブレーションで技術性能を検証しているため、方法が中心的である。
abstractthis study proposes a non-destructive phenotyping method for eggplant seedlings
Accurate plant disease segmentation is often constrained by the availability of large, finely annotated datasets, particularly for rare diseases. This work presents a synthetic data generation pipeline that combines 3D leaf modelling with diffusion-based disease synthesis to address this limitation. Procedurally-generated leaf geometries are built in the 3D modelling package Blender to provide exact ground-truth masks, after which style-transfer is applied using Stable Diffusion, fine-tuned with Low-Rank Adaptation (LoRA) and guided by ControlNet conditioning to both preserve leaf structure and enforce correct lesion placement. The approach is evaluated on apple leaf diseases using a deliberately restricted subset of the PlantVillage dataset, simulating a controlled low-data-resource environment. Downstream task effectiveness is measured through leaf disease segmentation. The results show that combining data from the pipeline with limited real data leads to consistent improvements in segmentation performance.
Why it matches plant phenotyping methods植物病斑の画像セグメンテーション性能向上を目的に、3D葉モデルと拡散モデルによる合成データ生成パイプラインを開発・評価しており、植物病害状態の画像ベース推定が中心である。
abstractThis work presents a synthetic data generation pipeline that combines 3D leaf modelling with diffusion-based disease synthesis to address this limitation.
Reproduction assets foundThe authors publicly deposited the paper's annotated PlantVillage subset (75 images with segmentation masks) plus 300 synthetic images with ground-truth masks on Zenodo, directly reproducing this paper's phenotyping/segmentation data.Dataset · publicThis annotated subset of PlantVillage is available at https://doi.org/10.5281/zenodo.18659728 . The repository contains the 75 images from the restricted dataset with the corresponding segmentation masks along with 100 synthetic images per disease generated using Blender and Stable Diffusion, each with corresponding ground truth masks.Open asset ↗zenodo · 10.5281/zenodo.18659728lines:314-325Code / dataset availability confirmedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Latest imaging technologies play a vital role in the extraction of plant phenotypic traits in high ranges. Most existing analytical methods treat these traits as independent features, overlooking the complex interaction patterns that focus on plant responses to environmental stress. Proposed Perceptual Graph Kernel (PGK) framework model address the limitation in terms of image-derived phenotypic traits plat information graph structured interaction networks leverages perceptual similarity learning to capture higher-order phenotypic patterns. In the PGK framework, traits extracted from RGB (Red, Green, Blue) and multispectral imagery are encoded as nodes, and biologically meaningful relationships amongst trait pairs are represented as weighted edges. Extracted trait values are continuously transformed into perceptual states to enhance biological interpretability, and a graph kernel is employed to measure similarity between trait graphs. Experiments performed in an agricultural field with a precision agriculture dataset for plant stress phenotyping demonstrated that the proposed PGK achieved 93.8% classification accuracy, improving performance by 5.3 percentage points over the CNN baseline. The outcome results clearly highlight the effectiveness of the perceptual graph model for plant phenotyping and provide a robust, interpretable computational framework for sustainable crop monitoring and decision-support in precision agriculture.
Why it matches plant phenotyping methods画像由来の植物形質を抽出・関係グラフ化し、ストレス表現型分類を行う計算手法が研究の中心であるため。
abstractProposed Perceptual Graph Kernel (PGK) framework model address the limitation in terms of image-derived phenotypic traits
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository (marathonengineer/Agriproject) containing the datasets used in this plant stress phenotyping study. The other allowed URL (PlantCV) is a generic phenotyping library, not a paper-specific asset.Dataset · publicThe datasets used in this study are available in publicly accessible online repositories. The repository can be accessed at: https://github.com/marathonengineer/Agriproject.Open asset ↗marathonengineer/Agriprojecthtml-lines:589-657Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Chili pepper (Capsicum spp.) is a high-value industrial horticultural crop widely utilized in food processing, pharmaceuticals, and natural pigment production. Accurate monitoring of flowering is critical for yield formation, pollination management, and early-stage production forecasting in industrial chili production systems. However, in greenhouse environments, chili flowers typically exhibit small object scale and are affected by issues such as lighting variations and occlusion, which pose significant challenges for reliable visual detection. These factors often result in missed detections and unstable performance in practical phenological monitoring tasks. To address these challenges, this study proposes CF-DETR, a robust transformer-based framework for small-scale chili flower detection. Built upon the RT-DETR architecture, the proposed method introduces an efficiency-optimized FasterNet backbone to enhance fine-grained feature extraction for small targets while maintaining computational efficiency. In addition, a dynamic upsampling mechanism is incorporated to preserve structural details during feature reconstruction, and a Bidirectional Multi-scale Attention Feature Pyramid Network (BiMAFPN) is designed to strengthen cross-scale feature interaction under complex greenhouse backgrounds and occlusion conditions. Experiments conducted on a self-constructed greenhouse dataset demonstrate that CF-DETR achieves a Precision of 94.1%, mAP50 of 83.5%, and mAP50-95 of 64.5%, outperforming the baseline RT-DETR-r18 model. Furthermore, deployment on an NVIDIA Jetson AGX Orin platform achieves real-time inference at 30.65 FPS, validating its practical applicability in edge-enabled agricultural systems. The proposed framework provides a reliable visual sensing solution for small-scale phenology monitoring, enabling intelligent pollination management, early yield prediction, and data-driven decision-making in industrial chili production. This work contributes to the advancement of precision horticulture and the digital transformation of industrial crop production systems.
Why it matches plant phenotyping methodsチリ花の検出を目的とする画像ベースの手法を開発し、データセット上で性能評価とエッジ実装検証を行っており、植物の開花状態を取得する方法が中心である。
abstractthis study proposes CF-DETR, a robust transformer-based framework for small-scale chili flower detection.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Global changes in agricultural and environmental systems will necessitate new crop research methodologies in the future years to ensure more effective use of natural resources and food security. The progress in next-generation sequencing has led to the emergence of multi-omics techniques as successful crop improvement strategies. Multi-omics studies using high-throughput techniques have been critical in understanding growth, senescence, yield, and biotic and abiotic stress responses in an array of crops. When multi-omics provide a high-resolution map of the molecular frameworks governing stress responses, advanced deep phenotyping systems can utilize advanced sensors to quantify dynamic physiological and morphological traits non-destructively. The systematic integration of these multi-layered datasets through association mapping and machine learning frameworks allows for the identification of superior alleles and regulatory hubs. Currently, the non-invasive imaging methods have effectively incorporated computer vision, machine learning, and deep learning components of AI. The use of machine learning and deep learning have progressively increased the effectiveness of data gathering and analysis. The supervised, unsupervised, and deep learning architectures have become effective tools for overcoming the genotype-to-phenotype gap, enabling more accurate predictions of yield and stress tolerance. Despite challenges related to data dimensionality, high infrastructure costs, and the need for standardized protocols, the convergence of these fields offers a robust architecture for predictive breeding. By linking microscopic molecular shifts to macroscopic field performance, integrated strategies accelerate the discovery of adaptive traits and the delivery of high-yielding, climate-smart cultivars. This review examines the revolutionary potential of combining deep phenotyping and multi-omics data for developing a thorough, high-throughput crop improvement strategy that can revolutionize crop breeding.
Why it matches plant phenotyping methods深層フェノタイピングと非破壊センサー・画像解析を中心に、作物形態・生理形質の高スループット計測とマルチオミクス統合を論じる方法論的レビューである。
abstractThis review examines the revolutionary potential of combining deep phenotyping and multi-omics data for developing a thorough, high-throughput crop improvement strategy that can revolutionize crop breeding.
AppleField / plotFruitObject detectionGrowth / development / phenology
Introduction Deep learning-based apple maturity detection supports precise management in smart agriculture. However, deployment on resource-constrained edge devices requires minimizing network weights while ensuring accuracy, a challenge compounded by inclement weather and dense fruit clustering in orchard environments. Methods To address these challenges, we propose HRLN-YOLO, a lightweight high-efficiency apple maturity detection model that minimizes network weights while ensuring accuracy. Specifically, we design a lightweight backbone HGBackbone to enhance feature extraction and accelerate inference, construct an enhanced neck module RCF_Neck to improve multi-scale feature fusion under occlusion, develop a lightweight detection head LADH-Head to alleviate task conflicts with minimal computational cost, and introduce NWD-Loss to improve localization stability for small-scale targets. Results Experiments on the Orchard Apple Maturity Dataset demonstrate that HRLN-YOLO improves mAP@0.5 by 1.7% over the YOLO11n baseline while reducing parameters by 37.3% and computational complexity by 34.9%. Discussion The core contribution of this study lies in minimizing network weights while ensuring detection accuracy, providing a practical solution for edge deployment in smart orchard automated harvesting.
Why it matches plant phenotyping methodsリンゴ果実の成熟状態を推定する軽量画像認識モデルの開発と性能評価が中心であり、単なる収穫対象の位置検出ではなく、植物器官の状態を測定する方法である。
abstractwe propose HRLN-YOLO, a lightweight high-efficiency apple maturity detection model that minimizes network weights while ensuring accuracy.
Introduction Addressing the core bottleneck in traditional crop models-the disconnect between morphology and physiological function at the organ scale and their limited dynamic response to environmental changes-this study aimed to construct a multi-source data fusion maize growth model for simultaneous organ-scale simulation. Methods We developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture. By integrating environmental time-series data, RGB images, and 3D point clouds, we created a multimodal fusion model based on a gated attention network. This approach adaptively weights multi-source features and pioneers a bidirectional morphology-physiology feedback loop based on physiological development time (PDT) and NURBS surfaces. The WOFOST moisture response function was also improved. Results The model significantly enhanced the simulation accuracy of organ-scale growth, reducing the root mean square error (RMSE) for plant height by 74.6% through a morphology-physiology dynamic weighting mechanism. More fundamentally, it resolved the disconnect between morphological and physiological processes. The improved plant height prediction validates the model's effectiveness at the organ scale. Discussion The pioneering "physiology-morphology" parallel simulation architecture provides an interpretable theoretical model and robust quantitative tools for designing high-photosynthetic-efficiency plant architecture and enabling precision water-fertilizer management.
Why it matches plant phenotyping methodsRGB画像・3D点群・環境データを統合し、器官スケールの形態と生長をシミュレーションする手法を開発しており、植物形質（草丈など）の推定が中心的な技術貢献である。
abstractWe developed a closed-loop Environment-Driven-Functional Response-Morphological Feedback (EDFM) architecture.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Accurate seed-yield prediction is essential for optimizing nitrogen (N) management in buffalograss seed production. However, current UAV-based approaches often rely directly on vegetation indices (VIs), which provide limited physiological insight and not transfer well across growing seasons. To address this limitation, we developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC), representing crop structural and physiological status, respectively. Field experiments were conducted from 2022 to 2024 under seven N application rates. Using data from 2022 and 2023, we calibrated a quadratic PH-CNC model and then evaluated its predictive performance with an independent 2024 dataset. We also compared this framework with a conventional direct VI-based model. The trait-based model explained 89% of the variation in seed yield during calibration and showed better cross-year predictive performance than the VI-based model (R 2 = 0.70, NRMSE = 17% versus R 2 = 0.52, NRMSE = 22%). In addition, the model captured the decline in seed yield under excessive N input, indicating that it reflected biologically meaningful crop responses. These results demonstrated that combining structural and physiological traits can provide a more robust and interpretable alternative to conventional VI-based methods for UAV-based yield prediction. This framework has practical potential for improving precise and sustainable N management in buffalograss seed production.
Why it matches plant phenotyping methodsUAV由来の草高と群落窒素濃度を統合した形質ベース予測法を開発し、独立年データで検証・従来法と比較しており、表現型取得と解析手法が中心である。
abstractwe developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC)
Background Recently, plant disease detection and classification have become major concerns in agriculture. Early detection of plant diseases supports farmers to take precautionary actions to prevent the spread of infections across different parts of the plant. However, detecting and classifying plant leaf diseases remain challenging tasks due to the overlapping characteristics of different diseases. Methods To mitigate these limitations, this research developed a Multi-FusNet–convolutional neural network (Multi-FusNet–CNN) with an improved Huber loss function to classify multiple classes of plant leaf diseases. Here, a multipath residual network (Multi-RG) with cross-filtering fusion is integrated, and the pixel shuffling fusion method is developed for fusing low-level to up-sampled features. An improved Huber loss function is incorporated into the Multi-FusNet–CNN to effectively handle outliers and enhance the model’s generalization capability during training. Results The developed Multi-FusNet–CNN with improved Huber loss function achieved 99.95% accuracy, 99.13% F1-score, 99.87% recall, 99.27% precision, and 99.93% specificity, thereby outperforming existing conventional techniques. Conclusion The proposed Multi-FusNet–CNN model improved the generalization capability of the method during the training process on plant leaf disease detection and classification.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類するCNN手法の開発と性能評価が研究の中心であり、植物の病害状態を推定するフェノタイピング手法に該当する。
abstractthis research developed a Multi-FusNet–convolutional neural network (Multi-FusNet–CNN) with an improved Huber loss function to classify multiple classes of plant leaf diseases.
Reproduction assets foundThe paper uses two public plant leaf image datasets directly in its analysis: the Plant Village dataset (Kaggle) as the primary training/classification dataset and the RoCoLe dataset (datasetninja) for coffee leaf disease samples. Both have explicit public URLs in the references. No author code, models, or checkpoints,Dataset · public26
Plant village dataset . Available online at: https://www.kaggle.com/datasets/emmarex/plantdisease (Accessed February 03, 2026 ).Open asset ↗Kaggle · emmarex/plantdiseaselines:1010-1131Dataset · public31
RoCoLe dataset . Available online at: https://datasetninja.com/rocoleing (Accessed February 03, 2026 ).Open asset ↗datasetninja · rocoleinglines:1010-1131Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Field / plotClassificationStress / disease detectionDisease symptoms / severity
Plant disease diagnosis in field settings is challenged by subtle symptomology, high inter-class visual similarity, and class imbalance, making automated detection particularly difficult. While deep learning models achieve high accuracy, traditional architectures impose prohibitive computational costs that hinder deployment on resource-constrained hardware. This paper proposes a novel Reduced Order Modelling (ROM) framework integrating a YOLOv8m backbone for spatially sensitive feature extraction, PCA-based compression to isolate the most discriminative features, and classical classification. A treatment-based label engineering approach was applied to consolidate the PlantWildV2 dataset from 115 to 11 agronomically relevant classes. Experimental results showed that a highly compressed feature space acts as a natural regularizer, with accuracy peaking at 100 principal components and declining beyond that threshold. The tuned SVC classifier achieved a test accuracy of 87.52% and a macro F1-score of 0.882, outperforming all other classifiers evaluated. The proposed ROM framework surpassed EfficientNet-B0 in accuracy (87.52% vs. 82.50%) while reducing training time from 5.8 hours on GPU to 30.8 seconds on CPU, a 670-fold efficiency gain, demonstrating the viability of Reduced Order Modelling for plant disease detection on low-resource hardware.
Why it matches plant phenotyping methods植物画像から病害状態を推定する分類手法の開発が研究の中心であり、圧縮特徴抽出・分類器・計算効率を評価しているため、植物フェノタイピング手法として採用。
abstractThis paper proposes a novel Reduced Order Modelling (ROM) framework integrating a YOLOv8m backbone for spatially sensitive feature extraction, PCA-based compression to isolate the most discriminative features, and classical classification.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction: Tiller production is a critical determinant of turfgrass canopy density and plant performance, yet manual tiller counting is too labor-intensive for large breeding programs. Methods: To address this limitation, we evaluated 770 plants from an interspecific bentgrass hybrid population and developed three automated approaches for tiller quantification: a classical edge-based segmentation pipeline and two deep-learning models, Faster R-CNN and YOLOv8. Using a large annotated image dataset, we assessed each method's accuracy, robustness under occlusion, and computational efficiency. Results: Although two-stage detectors are often expected to provide superior precision for complex plant structures, the one-stage YOLOv8 model achieved the highest accuracy (R² = 0.97) and processed images substantially faster than Faster R-CNN, while both the edge-based method and Faster R-CNN showed reduced performance in dense canopies. Discussion: These findings demonstrate that recall-oriented one-stage detection can outperform more complex two-stage models for phenotyping tasks involving fine, highly occluded structures. The resulting workflow provides a reliable, high-throughput solution for generating biologically meaningful tiller counts and offers a transferable framework for integrating image-derived phenotypes into genetic analyses and breeding pipelines across grass species.
Why it matches plant phenotyping methodsイネ科植物の分げつ数を画像から自動抽出する複数手法を開発・比較検証しており、植物表現型取得法が研究の中心である。
abstractdeveloped three automated approaches for tiller quantification: a classical edge-based segmentation pipeline and two deep-learning models, Faster R-CNN and YOLOv8.
To address the low detection accuracy caused by leaf occlusion, the loss of disease targets, and complex backgrounds in citrus leaf disease detection, this study proposes a leaf disease detection method termed DBG-DETR (a real-time detection transformer with DMGF, BDFF, and GSDT). Firstly, a DMGF-ResNet18 (dynamic multi-scale gating fusion block) is designed as the disease feature extraction module. By leveraging multiscale parallel depthwise separable convolutions, this module adaptively extracts and fuses rich disease-related features. Secondly, a GSDT (gated sparse dynamic transformer) is introduced to focus on deep features. Through a dynamic gating mechanism and Top-K sparse attention, GSDT reduces model parameters while enabling the network to concentrate on disease regions. Finally, a BDFF (bi-directional dense feature fusion module) is proposed to facilitate effective interaction between shallow and deep features, achieving efficient disease feature fusion. Experimental results on a real or chard dataset demonstrate that, compared with the baseline model, DBG-DETR improves P, mAP mmAP, R and F1 by 3.31%, 3.40%, 4.11%, 3.89% and 3.59%, respectively, while reducing the number of parameters by 3.78 MB. These results indicate that the proposed method significantly enhances disease detection performance in complex background environments and provides reliable technical support for intelligent citrus orchard management.
Why it matches plant phenotyping methods柑橘葉の病害領域を画像から検出する手法を提案・実験評価しており、植物病害状態の取得・推定が研究の中心である。
abstractthis study proposes a leaf disease detection method termed DBG-DETR
Common beanLeafClassificationStress / disease detectionDisease symptoms / severity
Context Plant diseases are a serious danger to the world's food security since they drastically lower crop output. Traditional manual plant leaf inspection is time-consuming, labor-intensive, and frequently subjective. Recent developments in deep learning provide effective and scalable methods for image-based analysis-based automated plant disease identification. Techniques Three deep learning architectures-a proprietary Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT)-are used in this study to examine automated bean leaf disease identification. The Augmented iBean dataset, which has three classes-angular leaf spot, bean rust, and healthy leaves-was used to train and assess the models. Every model was trained using the same preprocessing and training settings to provide fair benchmarking. Receiver Operating Characteristic (ROC) curves, accuracy, precision, and confusion matrices were used to assess the model's performance. Outcomes ResNet18 fared better than CNN and Vision Transformer models, according to a comparative analysis. ResNet18 maintained a high level of computing efficiency while achieving 99% accuracy and 99.01% precision. Its better categorisation capacity across all disease categories was validated using confusion matrix and ROC analysis. In conclusion The study shows that ResNet18 offers the optimal trade-off between accuracy and efficiency and creates a standard benchmarking framework for bean leaf disease identification. The results demonstrate its applicability for real-time deployment in precision agricultural systems for better crop management and early disease identification.
Why it matches plant phenotyping methods豆葉の病徴を画像から認識する深層学習手法を比較・ベンチマークしており、植物病害状態の取得手法が研究の中心である。
abstractThree deep learning architectures-a proprietary Convolutional Neural Network (CNN), ResNet18, and Vision Transformer (ViT)-are used in this study to examine automated bean leaf disease identification.
Reproduction assets foundThe paper's data availability statement points to the Augmented iBean dataset on IEEE DataPort, the public bean leaf image dataset used for all phenotyping/classification experiments in this study. No author analysis code or trained model checkpoints are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://ieee-dataport.org/documents/bean-leaf-disease-augmented-ibean-dataset.Open asset ↗ieee-dataport · bean-leaf-disease-augmented-ibean-datasethtml-lines:446-496Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 5 Sept 2026
To address the low efficiency of manual inspection for corn sowing quality, which is labor-intensive and time-consuming, this study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision. A high-clearance mobile platform equipped with a ZED 2i stereo camera and an industrial computer was developed to acquire RGB images and depth information of corn seedlings in the field in real time. Using the YOLOv11-Pose model, plant keypoints were detected and localized; combined with camera calibration and 3D reconstruction techniques, inter-plant distances were computed automatically. A sowing-quality evaluation framework was then established to enable automated analysis of the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation. Experimental results indicate that the system operates effectively under three preset plant spacings (15 cm, 20 cm, and 25 cm), achieving a keypoint-detection mAP@0.5 of 0.990 and an mAP@0.5:0.95 of 0.989. In sowing-quality evaluation, the qualified indices produced by the system were 77.83%, 80.36%, and 82.46%, respectively, and the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation showed trends consistent with manual measurements. The proposed method enables efficient, nondestructive detection of seedling-stage plant spacing and sowing quality, providing reliable technical support for precision sowing and field management.
Why it matches plant phenotyping methods3Dマシンビジョン、姿勢検出、校正、3D再構成を組み合わせ、トウモロコシ個体間距離を自動推定・検証する手法が研究の中心である。
abstractthis study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Static networks often exhibit limited generalization on few-shot data, particularly given the scarce samples and unstructured background noise inherent to precision agriculture. To address these limitations, an adaptive recognition network for few-shot plant diseases and pests based on homeostatic neuromodulation and meta-plasticity (HNeuroNet) is proposed. Methods This framework incorporates dynamic plasticity inspired by biological systems to mitigate the data dependency paradox. First, a Neuro Modulatory Generator (NMG) is constructed utilizing a hypernetwork architecture. Simulating neurotransmitter gating mechanisms, affine transformation parameters are dynamically generated for feature channels based on support set samples. Consequently, instantaneous weight reconstruction is enabled without expensive gradient fine-tuning, thereby overcoming structural rigidity and catastrophic forgetting during rapid adaptation. Second, a Homeostatic Suppression Mechanism (HSM) integrating visual perception is introduced. Leveraging Bienenstock-Cooper-Munro (BCM) theory, an adaptive activation function is employed to regulate neuron thresholds based on historical feature map statistics. High-frequency noise from complex environments is suppressed, significantly enhancing feature extraction and target saliency in low signal-to-noise ratios. Finally, an end-to-end Dynamic Meta-Plasticity (DMP) strategy is implemented. By coupling parameter generation and threshold regulation within a bi-level optimization framework, biological homeostatic adaptation is simulated to adjust perception strategies. Context-dependent feature interaction patterns are established to secure robust discriminative boundaries under extreme few-shot conditions. Results Experimental results demonstrate that HNeuroNet significantly outperforms state-of-the-art methods on IP102, PlantDoc, and Mini-ImageNet. Notably, 5-way 1-shot accuracy on the PlantDoc dataset surpasses the second-best baseline by 4.33%. Furthermore, a 1-shot accuracy of 71.36% is achieved on the cross-domain Mini-ImageNet task. Discussion These results confirm the potential of bio-inspired computing in addressing data scarcity.
Why it matches plant phenotyping methods植物画像から病害・害虫状態を認識する適応型深層学習手法を開発し、PlantDoc等で性能検証しており、病害状態の推定と手法自体が研究の中心である。
abstractan adaptive recognition network for few-shot plant diseases and pests based on homeostatic neuromodulation and meta-plasticity (HNeuroNet) is proposed.
Introduction Early detection of plant diseases is essential for maintaining crop health and ensuring sustainable agricultural productivity. Guava fruit and leaf diseases, if not identified at an early stage, can lead to significant yield losses. Recent advances in deep learning offer promising solutions; however, challenges remain in achieving both high accuracy and model interpretability for practical agricultural deployment. Methods This study proposes an explainable deep learning-based framework for the classification of guava fruit and leaf diseases. A real-world dataset consisting of 527 annotated images across five classes-Disease Free, Phytophthora, Red Rust, Scab, and Styler and Root Rot-was utilized. Six hybrid model architectures were developed by integrating transfer learning backbones (VGG16, MobileNetV2, InceptionV3, and ResNet50) with custom convolutional neural network (CNN) classifiers. Model performance was evaluated using accuracy, precision, recall, F1-score, and class-wise metrics. To enhance transparency, Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to visualize disease-relevant regions. Results Among all evaluated models, the proposed VGG16 + MobileNetV2 hybrid architecture achieved the best performance, attaining an accuracy of 96%, an F1-score of 0.96, and strong generalization across all disease classes. Comparative analyses using confusion matrices, ROC-AUC curves, precision-recall curves, and radar plots confirmed the superior and consistent performance of the proposed model over other hybrid configurations. Discussion The results demonstrate that combining deep feature extractors with lightweight architectures enhances both classification accuracy and computational efficiency. The integration of Grad-CAM provides meaningful visual explanations, increasing trust and interpretability in AI-assisted disease diagnosis. This framework shows strong potential for deployment in real-time smart farming systems and mobile-based diagnostic applications, particularly in resource-constrained agricultural environments.
Why it matches plant phenotyping methodsグアバの葉・果実画像から病害状態を推定する深層学習手法が研究の中心であり、複数モデルの比較評価とGrad-CAMによる説明可能性検証も行っているため、植物フェノタイピング方法論として採用する。
abstractThis study proposes an explainable deep learning-based framework for the classification of guava fruit and leaf diseases.
Reproduction assets foundThe paper's plant image dataset (527 annotated guava fruit/leaf disease images) is a public Kaggle deposit explicitly cited by the authors with a URL, making it a paper-specific, publicly actionable asset. No author analysis code or trained model checkpoints are stated as publicly available; the data availability only指Dataset · publicKaggle ). Available online at: https://www.kaggle.com/datasets/noamaanabdulazeem/guava-dataset (Accessed January 10, 2024 ).Open asset ↗Kagglelines:550-617Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Maize ( Zea mays L. ) production is severely affected by diseases and insect pests, leading to significant yield losses when timely diagnosis and management interventions are not implemented. Although automated image-based diagnostic systems have shown promising results, most existing studies address diseases or pests independently, rely on controlled datasets, and offer limited robustness under real field conditions. To address these limitations, this study proposes a unified deep learning-based framework for integrated identification of maize diseases and insect pests under natural field environments by combining object detection and image classification within a mobile-assisted diagnostic system. Four economically important diseases and insect pests were investigated: Maydis Leaf Blight (MLB), Turcicum Leaf Blight (TLB), Common Rust, and Fall Armyworm (FAW). MLB and TLB were addressed using YOLO-based object detection architectures, while Common Rust and FAW were treated as image-level classification tasks using lightweight deep learning models optimised for mobile inference. A self-collected dataset comprising 10,343 images across four classes was acquired under real field conditions to capture variability in background complexity, illumination, phenological stages, and symptom expression. Experimental results on an independent test set comprising original images demonstrate that MobileViT achieved the highest classification accuracy (99%) for image-level disease and pest recognition, whereas YOLOv11n outperformed other detection models, achieving the best performance for MLB and TLB lesion detection with mAP@0.5 of 0.875. Grad-CAM-based visual explanation analysis confirmed that the classification models focused on disease lesions and pest-infested regions, supporting interpretability. The framework was successfully deployed via a mobile application, enabling image acquisition, automated validation, diagnosis, and the generation of management recommendations. The results highlight the accuracy, robustness, and operational feasibility of the proposed system for in-field diagnosis of maize diseases and insect pests, supporting early detection and sustainable crop protection.
Why it matches plant phenotyping methodsトウモロコシ葉の病斑と害虫被害領域を圃場画像から検出・分類する画像解析手法と、モバイル診断プラットフォームを開発・評価しており、植物の病害状態の取得が中心的です。
abstractthis study proposes a unified deep learning-based framework for integrated identification of maize diseases and insect pests under natural field environments by combining object detection and image classification within a mobile-assisted diagnostic system.
Efficiently estimating the protein nitrogen content of rice leaves (LPN) is crucial for monitoring the nutritional health of rice and guiding precision fertilization based on requirements. Unmanned aerial vehicle (UAV)-acquired hyperspectral imagery is a key tool for estimating rice nitrogen content. Previous studies have demonstrated the potential of machine learning models for this task. However, these models typically require substantial data for supervised training to ensure high performance and generalizability. Acquiring a large sample size is challenging due to weather conditions, high collection costs, and other factors. Moreover, machine learning models have low interpretability. Enhancing it is vital for understanding the model's decision-making. To address these issues, we utilized the Wasserstein-generative adversarial network (WGAN) algorithm to expand the sample dataset. This method employs statistical regression (multiple linear regression (MLR) and partial least squares regression (PLSR)) and machine learning (support vector machines (SVM) and K-nearest neighbor (KNN)) algorithms to establish an estimation model for the LPN. The Shapley Additive exPlanations (SHAP) method was used to analyze the contributions of the input features to LPN estimation. An experiment was conducted at the National Agricultural Science and Technology Park, Guangzhou, Baiyun District, Guangdong, China. The model based on the KNN provided the optimum estimation performance, and the model accuracy was improved by adding the augmented dataset, resulting in a 10.39% improvement in the R 2 value. The SHAP values revealed that B 775.6 , double-peak canopy nitrogen index (DCNI), and MERIS terrestrial chlorophyll index (MTCI) were the core variables for LPN estimation. These findings provide significant references for precision fertilization and improving nitrogen use efficiency in rice cultivation.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像からイネ葉の窒素・タンパク質含量を推定する計測・解析手法が研究の中心であり、データ拡張、複数モデル比較、説明可能性解析を含むため対象とする。
abstractTo address these issues, we utilized the Wasserstein-generative adversarial network (WGAN) algorithm to expand the sample dataset.
Effective high-throughput phenotyping is crucial for modern plant breeding, yet the optimal image acquisition parameters for UAV-based systems in forage crops remain poorly defined. We optimized UAV-based phenotyping methods for a Megathyrsus maximus biparental population, examining how ground sampling distance (GSD), environment, and harvest date affect the accuracy of RGB-derived digital traits in predicting yield and canopy height. Machine learning algorithms and mixed model analyses were applied to evaluate predictive power and heritability. Pixel count and Haralick's entropy showed strong correlations with conventional yield measurements, particularly in Environment 2, while most vegetative indices were poor predictors. Integrating machine learning substantially enhanced predictive power for green and dry matter yield (r > 0.80). For canopy height, machine learning models achieved correlations of 0.71 with ground truth measurements despite weak pairwise correlations. Mixed model analysis revealed high broad-sense heritability (0.7 < H 2 < 0.87) for yield traits, pixel count, and entropy, while vegetative indices and canopy height showed greater environmental susceptibility. Moderate GSD resolutions (0.5–1.0 cm) consistently outperformed both very high (0.27 cm) and very low (1.5 cm) resolutions. Coincidence index analysis demonstrated 80% correspondence between top genotypes ranked by pixel count and conventionally measured dry matter yield. This study provides an optimized framework for UAV-based phenotyping in M. maximus , demonstrating that combining advanced digital traits with machine learning accurately predicts key agronomic traits and significantly enhances genotype selection efficiency in forage breeding programs.
Why it matches plant phenotyping methodsUAV画像取得条件、RGBデジタル形質、機械学習による収量・草高推定を最適化・検証する研究であり、植物表現型取得法が中心的です。
abstractWe optimized UAV-based phenotyping methods for a Megathyrsus maximus biparental population, examining how ground sampling distance (GSD), environment, and harvest date affect the accuracy of RGB-derived digital traits in predicting yield and canopy height.
Reproduction assets foundThe paper's data availability statement points to a public Mendeley Data repository containing the study's UAV-derived digital phenotyping and conventional trait datasets. No author analysis code repository is explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://data.mendeley.com/datasets/jrrb76x82h/1 .Open asset ↗jrrb76x82h/1lines:435-487Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Field / plotRootGrowth / development / phenologyRoot system architecture
Understanding root growth and phenology is essential for improving the productivity, resilience, and sustainability of pasture-based systems. However, roots remain one of the most difficult components of plant systems to measure and monitor, particularly in managed, high-turnover pastures, such as those in New Zealand (NZ) dairy systems. As a result, root processes are often underrepresented in both experimental studies and pasture system models. This perspective paper identifies critical, but underdeveloped areas in root research, with particular focus on root phenology. Current studies are limited by insufficient temporal resolution, a lack of species- and cultivar-specific trait data in mixed swards, and weak integration of root dynamics into breeding programmes and farm system models. These constraints limit our ability to link root processes to pasture persistence, nutrient cycling, and climate resilience. To address this gap, we propose that root phenology should be treated as a dynamic functional trait that links plant responses to environmental and management drivers with ecosystem-level outcomes. This framing provides a conceptual foundation for integrating root dynamics into pasture research and modelling, particularly in systems subject to frequent defoliation and environmental variability. We further highlight opportunities arising from rapid advances in sensing technologies, automation, and data analytics, which enable continuous, high-resolution root monitoring systems at multiple scales. However, realising this potential requires integration of complementary measurement approaches and alignment with system-level research questions. In this context, NZ provides a unique platform for developing scalable, pasture-based root monitoring framework that integrates science, management and policy. We argue for a coordinated effort that bridges fundamental root biology with applied pasture management, supported by long-term datasets, methodological integration, and engagement with end users. Embedding root traits and phenological dynamics into the next generation of pasture models and decision-support tools will be critical for improving system performance and environmental outcomes. This perspective aims to stimulate a shift towards more integrated, temporally explicit approaches for studying root systems in pasture environments, with relevance to grazing system beyond NZ and across temperate regions.
Why it matches plant phenotyping methods根の成長・フェノロジーという植物形質の測定課題と、センシング・自動化・データ解析を統合した高頻度モニタリング手法を中心に論じる方法論的パースペクティブである。
abstractroots remain one of the most difficult components of plant systems to measure and monitor
Introduction Herbicide dose-response assays are routinely implemented to compare herbicide resistance among weed biotypes, which requires plant biomass to estimate the dose that reduces growth by 50% relative to untreated plants (GR 50 ). The Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters, including Digital Biomass (DB), which offers the opportunity for researchers to eliminate the time and labor associated with manual biomass collection. However, DB is the product of 3D Leaf Area and Plant Height (PH) Mean, making it a measurement of plant volume and an indirect indicator of biomass. While DB is highly correlated with true biomass, digitally collected plant volume data has not been implemented for dose-response assays or assessed for accuracy relative to true biomass data. Additionally, inaccurate PH measurements could impact the accuracy of DB measurements. Methods This study sought to assess the accuracy and utility of DB and the 19 remaining parameters in dose-response assays by comparing dose-response curves and GR 50 estimates generated from digital data and fresh biomass (FB) data. Accuracy of PH measurements were also assessed by comparing digital and manual measurements with the paired t-test. Pre-emergence dose-response assays using fomesafen and atrazine were implemented with common lambsquarters ( Chenopodium album L.). At 21 days after treatment, manual measurements of FB and PH were collected following digital data collection. Results Consistently strong correlations ( r = 0.97, P < 0.05) were observed between digitally collected data and their equivalent manual measurements. Comparisons of the dose-response curves indicated that only 3D Leaf Area, DB, Convex Hull Area, Projected Leaf Area, and Voxel Volume Total generated highly similar curves and GR 50 estimates relative to FB data, indicating that any one or all of these parameters could be utilized instead of FB. Small differences (approximately 1.06 to 1.77 mm) between manual and digital PH measurements were identified with the paired t-test, but since DB consistently produced similar dose-response curves and GR 50 estimates relative to FB, these differences did not impact the accuracy of DB measurements. Discussion Without requiring manual biomass collection, turnaround time for dose-response and other phenotyping assays decreases and allows faster sharing of research. Furthermore, herbicide-resistant plants can be preserved for phenotyping at later growth stages, tissue collection, and to produce progeny for future experiments.
Why it matches plant phenotyping methodsデジタル表現型システムで植物体積・草丈などを取得し、手作業の生体重測定との精度比較および除草剤用量反応曲線への有用性を検証しており、表現型取得法が中心です。
abstractThe Phenospex TraitFinder is a high-throughput, non-destructive, digital phenotyping system that collects data from 7 spectral parameters and 13 morphological parameters
Reproduction assets foundThe paper's digital phenotyping dose-response datasets are publicly deposited: the data availability statement names Ag Data Commons DOI 10.15482/USDA.ADC/29815082 and a figshare link, both paper-specific. No author analysis code repository is explicitly stated.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: 10.15482/USDA.ADC/29815082 or https://figshare.com/s/64d1bbac59a95c4721f1 .Open asset ↗figshare · 10.15482/USDA.ADC/29815082lines:548-573Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction The integration of hyperspectral technology with machine learning and deep learning algorithms offers an effective method for accurately and non-destructively estimating the percentage of nitrogen in apple tree leaves, as well as for rapid nutrient diagnosis. Methods This study was conducted in apple orchards in Qixia City, Shandong Province, where hyperspectral data were collected from Red Fuji apple trees during the new-shoot-stop-growing stage (NSS) and the autumn-shoot-stop-growing stage (ASS). Following hyperspectral preprocessing and characteristic wavelength selection, regression models-including random forest (RF), support vector machine (SVM), convolutional neural network (CNN), and particle swarm optimization convolutional neural network (PSO-CNN)-were developed and compared. Results The results showed that CNN significantly outperformed RF and SVM, and that PSO-CNN further improved prediction performance. The PSO-CNN model achieved an R² of 0.886 on the training set and an R² of 0.774, an RMSE (%) of 0.095, and an RPD of 2.086 on the test set. Validation using samples from different phenological stages demonstrated acceptable prediction accuracy ( R 2 = 0.625, RMSE = 0.173, RPD = 1.529), with uniformly distributed errors and no systematic bias, indicating good generalization capability and stability. SHAP analysis revealed that the PSO-CNN model primarily relied on the near-infrared and short-wave infrared bands, where reflectance was negatively correlated with nitrogen content. Discussion These spectral regions are closely associated with leaf biochemical structure, suggesting that the model predicts nitrogen content by capturing spectral information related to leaf biochemical characteristics. Overall, the PSO-CNN model improves nitrogen prediction performance by expanding the hyperparameter search space while preserving the CNN architecture, enabling rapid and accurate nutrient diagnosis in apple leaves.
Why it matches plant phenotyping methodsリンゴ葉の窒素含量という植物形質を、ハイパースペクトル計測とPSO-CNNで推定する手法を開発し、複数モデル比較と異なる生育段階での検証を行っており、表現型取得・推定が研究の中心である。
abstracthyperspectral technology with machine learning and deep learning algorithms offers an effective method for accurately and non-destructively estimating the percentage of nitrogen in apple tree leaves
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
SoybeanGrowth chamberRootMorphology / geometry measurementObject detectionRoot system architecture
). Accurate quantification of nodule traits is essential for understanding host-microbe interactions and genetic determinants of nodulation. However, traditional manual or semi-quantitative approaches are labor-intensive, subjective, and unsuitable for large-scale studies. Here, we present a high-throughput phenotyping pipeline based on the YOLO deep learning architecture for the automated detection and extraction of soybean root traits. The pipeline quantifies nodule count, dimensions, and spatial distribution, enabling measurement of 24 distinct nodulation-related traits. Using root images from 21-day-old hydroponically grown soybean plants, the model achieved a precision of 0.94, a recall of 0.95, and an F1 score of 0.94 for nodule detection, maintaining accuracy across count ranges. It processes 50 root images in 37 seconds on a single GPU (45 GB memory), representing a ~227-fold improvement in efficiency compared to manual scoring (~2 h 20 min). As proof of concept, we applied this pipeline in a genome-wide association study (GWAS) using the FarmCPU approach and identified 50 significant SNPs associated with multiple nodulation traits, including novel ones. Several candidate genes linked to these loci suggest potential new regulators of nodulation. This YOLO-based phenotyping framework provides a robust, scalable, and reproducible tool for trait discovery and genetic analysis, advancing research in legume genomics and crop improvement. To promote the adoption of this user-friendly nodulation phenotyping pipeline and to support its further development, we have made all essential resources publicly available at: https://github.com/Salk-Harnessing-Plants-Initiative/soybean-nodule-detection.
Why it matches plant phenotyping methodsYOLOを用いた根粒形質の自動取得パイプラインを開発し、検出精度・処理速度を検証した方法中心の研究である。
abstractHere, we present a high-throughput phenotyping pipeline based on the YOLO deep learning architecture for the automated detection and extraction of soybean root traits.
Accurate and non-destructive acquisition of leaf chlorophyll content (LCC) in cotton plant canopies is of significant importance for real-time monitoring of cotton growth and implementing precise water and nitrogen management in cotton fields. This study utilized UAV-based RGB imagery combined with real-time kinematic (RTK) technology to efficiently and accurately retrieve LCC under different nitrogen application levels in a cotton field, employing 6 machine learning algorithms: Least Absolute Shrinkage and Selection Operator regression (LASSO), Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Ridge Regression (Ridge), and Support Vector Regression (SVR). Among these, the SVR model demonstrated the best overall performance, with coefficient of determination (R²), root mean square error (RMSE), relative root mean square error (rRMSE), and mean absolute percentage error (MAPE) values of 0.82, 0.14 mg/g, 8.91%, and 6.99% for the training set, and 0.75, 0.14 mg/g, 8.90%, and 7.83% for the testing set, respectively. Furthermore, the SVR model was applied to retrieve LCC pixel-by-pixel from UAV imagery, and pseudo-color rendering techniques were used to generate spatial distribution maps of LCC in the cotton canopy, visually presenting the spatial variability characteristics of LCC within the field. The results indicate that the cotton canopy LCC estimation method based on UAV RGB imagery combined with RTK technology achieves comparable accuracy to the more expensive multispectral and hyperspectral techniques, without a significant reduction in precision. This approach provides an efficient, low-cost, and reliable method for detecting canopy LCC in small-scale cotton fields.
Why it matches plant phenotyping methodsUAV RGB画像とRTK、機械学習を組み合わせ、ワタ群落の葉緑素含量を推定・検証する手法が研究の中心であるため、植物フェノタイピング手法として採用する。
abstractThis study utilized UAV-based RGB imagery combined with real-time kinematic (RTK) technology to efficiently and accurately retrieve LCC under different nitrogen application levels in a cotton field, employing 6 machine learning algorithms
Introduction Rapid population growth and climate change have intensified the need for sustainable agricultural productivity. Plant leaf diseases significantly impact the crop yield, quality, and food safety, necessitating accurate and automated detection methods. Methods This study proposes a deep learning (DL)-based framework for automated detection and classification of tomato and soybean leaf diseases. The proposed framework is trained and evaluated over a large-scale datasets comprising 16,012 tomato leaf images and 6,410 soybean leaf images. Multiple convolutional neural network (CNN) models, including DenseNet121, MobileNetV2, and InceptionV3, are employed for classification. Object detection is performed using YOLOv12. To enhance interpretability, Gradient-Weighted Class Activation Mapping (Grad-CAM) is integrated. Furthermore, a novel Hybrid Attention-Based Stacking Ensemble Model is developed using ResNet152V2, VGG19, and EfficientNetB0, combined with Convolution Block Attention Module (CBAM) and spatial attention mechanisms. Results The CNN models achieved classification accuracies of 97% for DenseNet121, 98% for MobileNetV2, and 99.94% for InceptionV3. YOLOv12 attained a mean average precision (mAP) of 99.5%. The proposed hybrid ensemble model achieved an accuracy of 99.18%, demonstrating improved feature learning through combined channel and spatial attention. Grad-CAM visualizations confirmed that the model effectively identifies the disease-relevant regions. Discussion The results indicate that the proposed framework has attained a high accuracy, robustness, and interpretability for plant disease detection. The integration of attention mechanisms and explainable AI enhances model reliability and transparency. This framework shows a strong potential for the real-time agricultural monitoring, although further validation across diverse crops and real-world field conditions is required.
Why it matches plant phenotyping methods植物葉の病害状態を画像から自動検出・分類する深層学習フレームワークの開発であり、病害表現型の取得・推定が研究の中心。
abstractThis study proposes a deep learning (DL)-based framework for automated detection and classification of tomato and soybean leaf diseases.
Reproduction assets foundThe paper's leaf-disease classification/detection experiments are built on two public Kaggle image datasets cited by the authors as the study's data sources: a soybean leaf dataset (Patil 2024) and a tomato leaf disease dataset (Rex 2019). No authors' analysis code, trained model checkpoints, or paper-specific phenotypDataset · publicPatil A. ( 2024 ). Soyabean-Latest Dataset (
Kaggle ). Available online at: https://www.kaggle.com/datasets/adityapatil1205/soyabean-latestOpen asset ↗Kagglelines:1523-1646Dataset · publicRex E. ( 2019 ). Plant Disease Dataset (Tomato Leaf Diseases) (
Kaggle ). Available online at: https://www.kaggle.com/datasets/emmarex/plantdisease197Open asset ↗Kagglelines:1523-1646Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
FruitClassificationGrowth / development / phenology
Introduction Cashew apple is a nutrient-rich fruit containing abundant minerals, vitamins, and energy. However, its fleshy texture and delicate skin significantly limit its storage life and market value. Accurate maturity grading is therefore essential for improving post-harvest management and transportation efficiency. Methods This study proposes a lightweight vision transformer (ViT) student model trained using multi-granular knowledge distillation (KD) from a stronger data-efficient image transformer (DeiT)-Base teacher. The distillation framework integrates response-based soft-label supervision, attention transfer, and token-level feature regression to enhance representation learning under limited data conditions. Auxiliary lightweight architectures, including MobileNet, ConvNeXt, and EdgeNeXt, were trained independently to provide complementary predictions, and a weighted fusion strategy was employed for ensemble evaluation. Results The proposed ensemble ViT-KD with EdgeNeXt achieved 90% accuracy under the evaluated test split. To ensure statistical reliability and address potential partition bias, a stratified fivefold cross-validation was conducted on the dataset, yielding a mean accuracy of 86.89% ± 2.89% with consistent F1 scores and recall. The relatively low variance across the folds indicates stable internal generalization. Comparative experiments with conventional convolutional neural network (CNN) baselines and lightweight CNN baselines such as MobileViT-S and ShuffleNetV2 were performed, with the proposed ensemble framework achieving improved accuracy while maintaining computational efficiency. Computational analysis indicates that the stand-alone distilled ViT maintains a real-time inference capability of 8.79 ms per image, which supports suitability for edge-oriented agricultural applications. Discussion These results highlight the effectiveness of knowledge-distilled lightweight transformers for data-efficient maturity grading of cashew apples.
Why it matches plant phenotyping methodsカシューナッツ果実の成熟度という植物器官の状態を画像から推定する手法を開発し、交差検証・比較実験・推論速度評価まで行っており、フェノタイピング手法が中心である。
abstractThis study proposes a lightweight vision transformer (ViT) student model trained using multi-granular knowledge distillation (KD) from a stronger data-efficient image transformer (DeiT)-Base teacher.
Reproduction assets foundThe paper's cashew apple maturity grading experiments use a public image dataset from IEEE Dataport (Sawant, 2025), explicitly linked in the data availability statement. No author code or models are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://ieee-dataport.org/documents/goa-cashew-apple-maturity-grading .Open asset ↗goa-cashew-apple-maturity-gradinglines:837-851Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Introduction Accurate recognition of plant pests and diseases under field conditions remains challenging due to complex symptom morphology, environmental variability, and limited annotated data. While deep learning has been widely adopted for image-based diagnosis, existing studies are often model-centric and evaluated under heterogeneous experimental settings, making it difficult to derive paradigm-level insights into data efficiency, robustness, and practical deployment suitability. Methods In this research, we systematically compare three main types of vision models: Convolutional Neural Networks (CNNs), Vision Transformers, and mixed State Space Model-based structures (MambaVision). These models are applied to classify images of pests and diseases across multiple crops. Using a unified and reproducible experimental framework, we benchmark representative models across multiple training regimes, diverse crop species, and symptom types reflecting realistic agricultural scenarios. Results Results reveal clear paradigm-level differences. CNN-based models perform competitively on diseases dominated by localized lesion textures but show limited robustness for symptoms requiring global spatial interpretation. Transformer-based models benefit from global dependency modeling yet exhibit increased instability under small-sample conditions. In contrast, hybrid MambaVision-based models consistently demonstrate superior data efficiency and robustness, retaining approximately 60–80% accuracy under extreme data scarcity (1% training samples) and achieving stable, high F1-scores across symptom types that require joint modeling of fine-grained textures and long-range spatial distribution. Furthermore, performance–efficiency analysis shows that hybrid MambaVision-based models achieve a more favorable accuracy–computational cost trade-off than CNN-based and Transformer-based models, supporting deployment under practical resource constraints. Discussion Overall, this study provides pathology-oriented and deployment-aware insights into how architectural inductive bias interacts with symptom morphology and data availability, highlighting hybrid MambaVision-based models as a robust and effective solution for real-world plant pest and disease recognition.
Why it matches plant phenotyping methods植物病害の症状画像を対象に、複数の深層学習アーキテクチャを統一条件で比較・ベンチマークしており、植物の病害状態を推定する方法の評価が研究の中心である。
abstractUsing a unified and reproducible experimental framework, we benchmark representative models across multiple training regimes, diverse crop species, and symptom types reflecting realistic agricultural scenarios.
Automated quantification of plant-level development from multi-plant greenhouse scenes requires separating individual plants from shared scene-level reconstructions and quantifying organ-level development, a challenge that single-plant acquisition workflows do not directly address. This study presents an end-to-end phenotyping pipeline built on 3D Gaussian Splatting (3DGS) and a post-reconstruction extraction framework, LCR-GS, designed to isolate plant instances from full greenhouse scenes without scene-specific model retraining. LCR-GS integrates zero-shot 2D cues with multi-view lifting, geometric clustering, and chromatic refinement to convert large scene-level reconstructions (~2M Gaussians) into compact per-plant subsets (~16K Gaussians). Experiments on greenhouse-grown muskmelon at the early vegetative stage demonstrate high plant-extraction precision (0.933) and strong organ-level instance segmentation (mean AP50 = 0.924). Plant height and leaf count are validated against manual measurements (height R² = 0.98, RMSE = 1.88 cm; leaf count R² = 0.86), whereas additional morphological traits, including leaf area, leaf area index, mean internode length, and stem node count, are reported as pipeline-derived descriptors for within-cohort comparison. By decoupling semantic inference from reconstruction, the pipeline reduces scene-scale data by over 99% and provides a practical route to derive compact per-plant 3D representations from multi-plant greenhouse imagery for downstream organ-level analysis.
Why it matches plant phenotyping methods3DGS画像から個体・器官を抽出し、植物形質を定量化するフェノタイピング手法の開発と検証が中心である。
abstractThis study presents an end-to-end phenotyping pipeline built on 3D Gaussian Splatting (3DGS) and a post-reconstruction extraction framework, LCR-GS, designed to isolate plant instances from full greenhouse scenes without scene-specific model retraining.
Reproduction assets foundThe paper's Data Availability Statement points to a public GitHub repository containing the muskmelon 3DGS phenotyping dataset (scenes, Gaussian-level plant/background annotations, and point-level organ labels) used in this study.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/bblabNTU/3dgs-muskmelon-phenotyping-dataset.Open asset ↗bblabNTU/3dgs-muskmelon-phenotyping-datasethtml-lines:485-547Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
TomatoStem / branchMorphology / geometry measurementSegmentationGrowth / development / phenology
Introduction The industrial cultivation of tomato seedlings requires a high degree ofuniformity and consistency in grading. However, traditional grading methods based on phenotypictraits such as leaf area and canopy width are susceptible to environmental conditions, therebylimiting the accuracy and efficiency of grading. Since cumulative internode length is relativelystable and closely correlated with seedling vigor, this study aims to develop an accurate methodfor measuring and grading the cumulative internode length of tomato seedlings. Methods A tomato seedling cumulative internode length detection method, termedDSH_YOLO (Deformable Convolution-SIoU-Haar Wavelet Downsampling YOLO), wasproposed based on the YOLOv8-seg framework. First, deformable convolution was introducedinto the backbone to enhance feature extraction for curved stems and occluded regions. Second,the original loss function was replaced with SIoU to improve the alignment between predictedregions and the actual stem structure. Third, a Haar wavelet downsampling module was embeddedinto the backbone to preserve high-frequency detail information and reduce information loss underocclusion conditions. An intelligent grading system was further developed to verify the practicalapplicability of the proposed method. Results Experimental results showed that DSH_YOLO achieved a Precision of 96.1%, aRecall of 94.3%, and an mAP@0.5 of 92.1%. Compared with the baseline YOLOv8 model, theproposed method substantially improved segmentation performance for cumulative internoderegions in tomato seedlings. In prototype validation, the intelligent grading system achieved anaverage grading success rate of 87.50%, with an average cumulative internode length error of 8.0mm. Discussion The results indicate that DSH_YOLO and the grading system can meet therequirements for large-scale grading and detection of tomato seedlings, demonstrating highdetection accuracy and success rates. This approach can provide insights for grading other types ofseedlings during their growth stages and offer support for seedling production and thedevelopment of intelligent agricultural equipment.
Why it matches plant phenotyping methodsトマト苗の累積節間長という植物形態形質を画像分割・検出し、実用的な等級判定まで検証する手法開発が研究の中心であるため。
abstractA tomato seedling cumulative internode length detection method, termedDSH_YOLO (Deformable Convolution-SIoU-Haar Wavelet Downsampling YOLO), wasproposed based on the YOLOv8-seg framework.
Self-incompatibility (SI) significantly reduces crop yield, often far below its genetic potential. Developing self-compatible varieties is the most effective strategy for overcoming SI in crops. Most macadamia ( Macadamia SPP.) species exhibit SI or partial self-incompatibility (PSI), so the efficient identification of self-compatible germplasms has emerged as a crucial topic. To characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility. That is, the degree of SI was based on the final self-incompatibility index (F_SI), which was calculated based on the open-pollination final nut set per raceme (OP_FNS) and self-pollination final nut set per raceme (SP_FNS) values (strong SI: F_SI ≥ 0.7, medium SI: 0.4 ≤ F_SI 35%). Through comprehensive analysis of the field-bagging and fluorescence-microscopy observations, thirteen varieties with strong SI (816, 778, 842, Special, 812, D, 820, 246, 772, A16, A4, 951, and 695), six varieties with moderate SI (851, 828, 508, 936, O.C, and D4), and four varieties with weak SI (915, HY, 836, and 814) were identified. Our study provides a theoretical foundation and technical support for advancing germplasm resource innovation, and the genetic improvement and breeding of self-compatible macadamia varieties.
Why it matches plant phenotyping methods自家不和合性という植物状態を対象に、圃場袋掛けと蛍光顕微鏡観察を用いた標準化分類体系を確立しており、表現型の取得・評価法が研究の中心である。
abstractTo characterize self-incompatibility phenotypes in macadamia germplasm resources, we conducted a four-year field study using the field-bagging method and established a standardized classification system of self-incompatibility.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Introduction Reliable identification of plant diseases from leaf images is essential for effective crop monitoring and the prevention of yield deterioration. With the growing adoption of deep learning in agricultural applications, convolutional neural network–based classifiers have demonstrated notable success in visual plant disease recognition. Methods In this study, we propose PlantPathNet , a purpose-built deep learning architecture for plant disease classification. Input images are transformed from the RGB to the HSV color space to enhance the representation of disease-related visual features. A novel Cross-layer Feature Integration Module (CFIM) is introduced to effectively aggregate discriminative features across multiple network depths. Additionally, an efficient channel attention mechanism based on ECANet is incorporated to emphasize disease-relevant representations. The model is optimized using a composite loss function combining modified softmax loss and center loss to address class imbalance and improve feature separability. Results Extensive experiments conducted on the PlantVillage dataset demonstrate that PlantPathNet outperforms several state-of-the-art models, including ResNet-50, Inception-V3, DenseNet121, VGG16, and Vision Transformer-based approaches. The proposed model achieves an overall accuracy of 99.57%, precision of 99.52%, recall of 99.54%, F1-score of 99.53%, and an AUROC of 99.84%. Discussion The results indicate that the integration of HSV-based preprocessing, CFIM, and channel attention significantly enhances classification performance. The proposed framework provides a robust and efficient solution for automated plant disease diagnosis and has strong potential for real-world agricultural applications.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する深層学習手法を新規開発し、既存モデルとの比較検証も行っているため、植物フェノタイピング手法が中心である。
abstractwe propose PlantPathNet , a purpose-built deep learning architecture for plant disease classification.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 13 Sept 2026
Global agricultural production is currently limited by ongoing climate change. Approximately 90% of crop species and numerous wild plants are dependent on pollinators for reproduction. The global threat to pollinators posed by climate change has grown considerably, as higher temperatures, shifting rainfall patterns, and more frequent extreme weather events disrupt the fragile relationships between plants and their pollinators. The decline in pollinators is also linked to shifts in land use, the widespread adoption of monocropping, and heavy reliance on agrochemicals. Therefore, the protection of pollinators and the preservation of agrobiodiversity are essential to uphold global food systems. Here, we synthesize the adverse impact of climate change on plant-pollinator interactions; throughput assay for phenotyping floral traits; assessing variability and molecular basis of floral display (flower size, shape, color, attractants etc.) and reward (nectar volume and composition, pollen, and fragrance in case of ornamental plants) traits; crop domestication and inbreeding, ploidy and mating systems differences impacting plant-pollinator interactions; volatiles and metabolites mediating plant-pollinator relationships; trade-offs involving reproductive and pollinator traits; and finally, progress in developing pollinator-friendly crop cultivars through conventional plant breeding and biotechnological interventions. Pollinator-assisted phenotyping and selection platform (DARkWIN) combined with other high-throughput phenotyping assays, has the potential to simultaneously quantify multiple interactions impacting pollinators’ visitation and foraging behaviors, and generate data on other parameters like stress tolerance, yield, and nutrition in the target populations. Assessing and exploiting functional diversity for plant-pollinator interactions, combined with the use of functionally characterized genes and associated markers for floral display ( AT2G31010 , AT4G17080 , CmGEG , CmCYC2c , CmJAZ1-like-CmBPE2 , Cyc2CL-1 , Cyc2CL-2 ) and reward ( SWEET9 , BrCWINV4A , EOBI , EOBII ) traits, can be deployed in breeding programs to develop pollinator-friendly crop cultivars. Numerous candidate genes, reported herein, must be functionally validated before being deployed in crop breeding programs.
Why it matches plant phenotyping methods植物―送粉者相互作用と花形質を対象とするレビューであり、花形質のスループット表現型解析やDARkWINによる送粉者支援型フェノタイピング基盤を主要な方法論として扱っている。
abstractthroughput assay for phenotyping floral traits
Accurate and timely identification of tomato leaf diseases is critical for precision agriculture. Although convolutional neural networks (CNNs) perform well in extracting local visual features, they often lack the ability to model global contextual relationships, limiting robustness in real-world field conditions. To overcome this challenge, we propose a hybrid architecture that jointly learns local and global representations. We present AG-Vision, a dual-module framework that integrates an EfficientNet-B4 CNN backbone (DeepFolia) for fine-grained local feature extraction with a Transformer encoder (VisiLeaf) to capture long-range global dependencies through self-attention. The architecture incorporates positional encoding and optimized attention heads to enhance spatial awareness. AG-Vision was evaluated on the controlled PlantVillage dataset and the real-world PlantDoc dataset. Ablation studies assessed the contribution of individual components, and Grad-CAM visualizations were used to analyze model interpretability. AG-Vision achieved state-of-the-art performance on both datasets, obtaining 99.97% accuracy and an F1-score of 99.53% on PlantVillage, and 96.97% accuracy with an F1-score of 94.47% on PlantDoc. Despite its high accuracy, the model maintained real-time efficiency with an average inference time of approximately 25 ms per image. Ablation experiments confirmed the importance of combining CNN and Transformer modules, positional encoding, and optimized attention mechanisms. Grad-CAM results demonstrated that the model consistently focuses on disease-relevant regions. The findings confirm that fusing local and global feature learning significantly enhances classification accuracy and robustness under diverse conditions. AG-Vision offers an efficient and scalable solution suitable for edge deployment in precision agriculture.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定するCNN・Transformer統合手法を開発し、複数データセットとアブレーションで検証しており、植物フェノタイピング手法が中心である。
abstractWe present AG-Vision, a dual-module framework that integrates an EfficientNet-B4 CNN backbone (DeepFolia) for fine-grained local feature extraction with a Transformer encoder (VisiLeaf) to capture long-range global dependencies through self-attention.
Introduction To accurately segment point clouds and quickly calculate leaf length and stem diameter, thereby enabling phenotypic analysis and variety selection of greenhouse tomato plants, this paper proposes a voxel grid downsampling (VGDS)-PointNet++-based model for point cloud segmentation and trait calculation. Methods The point clouds of the tomato canopy were acquired using a depth camera. After labeling, point cloud augmentation was performed, and the tomato point cloud dataset (TPCD) containing 1,552 sets of data was rebuilt. Voxel grid downsampling was applied to replace the original sampling strategy of PointNet++. Models of PointNet, PointNet++, VGDS-PointNet++, and Point Transformer were trained with the TPCD and compared on segmentation quality with accuracy and mean Intersection over Union (mIoU). After segmentation, skeletal morphology was fitted for non-occluded leaves by applying a series of surface fitting techniques. The leaf lengths and stem diameters were automatically calculated and compared with the manually measured values. Results The validation results showed that the average runtime of voxel grid downsampling was 0.132 s, which was lower than under the same number of sampled points. Compared to the other three models, the proposed model had higher accuracy and mIoU, reaching up to 96.80% and 88.95%, respectively. The proposal's accuracy and mIoU increased by 3.9% and 4.45% over PointNet++, respectively. The determination coefficient R 2 between the automatic calculation and manual measurement values of leaf length and stem diameter was 0.93 and 0.87, respectively. Discussion This can help extract phenotypic traits of tomatoes using depth cameras.
Why it matches plant phenotyping methods深度カメラ点群のセグメンテーションと葉長・茎径の自動推定手法を開発し、手測定および複数モデルと比較検証しており、表現型取得が研究の中心である。
abstractthis paper proposes a voxel grid downsampling (VGDS)-PointNet++-based model for point cloud segmentation and trait calculation
Double-cropping grape systems offer enhanced land productivity but face significant challenges from climate variability, particularly rain stress and pest outbreaks during critical phenological stages. Accurate phenological prediction is essential to synchronize management practices with crop development and improve ecological resilience. This study presents a novel deep learning framework that integrates MobileNet with an augmented version of Dream Optimizer [Augmented Dream Optimizer (ADO)] to model grape phenology using satellite-derived time series of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and rainfall from the CropHarvest dataset. The model transforms temporal data into pseudo-images for efficient spatiotemporal feature extraction, achieving 93% classification accuracy and a 6.1-day mean absolute error in stage prediction. ADO enhances convergence and generalization by optimizing key hyperparameters through a hybrid metaheuristic search. The system further identifies high-risk periods for rain damage and pest infestation, enabling proactive interventions. The model statistically significantly outperforms machine learning and deep learning baselines (p< 0.01) across three different agroecological zones [Mediterranean (California, USA, and southern Europe), subtropical (South Australia), and temperate (central Europe)] through spatially stratified fivefold cross-validation on 3,000 held-out test samples of the CropHarvest dataset. This work demonstrates the potential of optimized lightweight neural networks in sustainable viticulture, providing a scalable tool for precision management in climate-resilient double-cropping systems.
Why it matches plant phenotyping methods衛星時系列からブドウの生育ステージを推定する深層学習手法を開発し、交差検証とベースライン比較で性能を検証しており、植物フェノタイピング手法が中心である。
abstractThis study presents a novel deep learning framework that integrates MobileNet with an augmented version of Dream Optimizer [Augmented Dream Optimizer (ADO)] to model grape phenology using satellite-derived time series of Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and rainfall from the CropHarvest dataset.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction High-throughput field phenotyping (HTFP) holds great potential for elucidating the relationship between genomes and phenotypes. However, obtaining high-quality three-dimensional point cloud data of field populations and achieving single-plant phenotypic analysis remain challenging. Methods This study develops an integrated framework for field crop reconstruction based on 3D Gaussian splatting, incorporating a geometry-aware dynamic constraint algorithm to achieve instance segmentation and extract key phenotypic traits of individual plants. Using 3D Gaussian splatting technology, field-scale cotton population modeling is accomplished, generating dense 3D point clouds for regions of interest. Furthermore, the concept of a crop localization domain is proposed, establishing a longitudinal mapping that associates plant positional coordinates with long-term phenotypic attributes. Finally, through a dynamic spatial constraint mechanism, the accuracy and computational efficiency of instance segmentation for crop population point clouds are significantly improved, enabling rapid extraction of individual plant traits such as cotyledon node height, plant height, and leaf area. Results The results demonstrate that PhenotypeAI successfully reconstructed nine cotton populations with PSNR exceeding 30.0 dB. It successfully extracted regions of interest from 403 cotton plants, achieving an average F-score of 91.32% for instance segmentation and an average accuracy of 91.35%. The extracted traits—cotyledon node height, plant height, and leaf area—exhibited strong correlations with manual measurements, with coefficients of determination ( R 2 ) of 0.90, 0.91, and 0.91, respectively. Discussion The proposed method provides a low-cost solution for high-throughput field phenotypic analysis of field cotton and improves the efficiency of cotton breeding.
Why it matches plant phenotyping methods3D再構成と動的空間制約による個体セグメンテーションおよび形質抽出が研究の中心で、綿花の草丈・葉面積などを検証しているため。
abstractThis study develops an integrated framework for field crop reconstruction based on 3D Gaussian splatting, incorporating a geometry-aware dynamic constraint algorithm to achieve instance segmentation and extract key phenotypic traits of individual plants.
Purpose/significance Sugarcane is a vital global crop, critical for sugar and energy production. The accurate and timely identification of its leaf diseases is paramount for sustaining the health and stability of the sugarcane industry. While deep learning models offer promising solutions, their deployment on mobile or edge devices is often hindered by substantial model size and high computational demands. Conversely, existing lightweight models frequently compromise on feature extraction capabilities and recognition accuracy. To bridge this gap, this study develops an architecturally improved lightweight model designed to achieve both high accuracy and computational efficiency. Methods We propose the ReMA-MobileViT model, which significantly enhances feature representation by incorporating a newly designed Residual Multi-head Attention (ReMA) module. This module ingeniously leverages a multi-head attention mechanism to capture richer contextual information from diverse subspaces, while its residual connection structure effectively mitigates network degradation and facilitates robust gradient flow. The proposed model underwent rigorous training and evaluation on a comprehensive Mendeley Data repository for classification tasks. Results Experimental evaluations demonstrate that the ReMA-MobileViT model achieves an outstanding classification accuracy of 99.02% on the sugarcane leaf disease dataset, substantially surpassing existing state-of-the-art methods. An ablation study confirms the module's efficacy, showing that the ReMA-MobileViT model, integrated with the ReMA module, improved accuracy, recall, and F1-Score by 1.58, 1.76, and 1.58 percentage points, respectively, over the baseline MobileViT. Comparative analyses further illustrate ReMA-MobileViT's superior overall performance; it exceeds classic lightweight MobileNetV2 by 15.77 percentage points and the mainstream Vision Transformer by 2.96 percentage points in accuracy. Critically, ReMA-MobileViT achieves this with significantly fewer model parameters and reduced computational complexity compared to Vision Transformer, establishing a superior balance between accuracy and efficiency. Conclusion The proposed ReMA-MobileViT model offers an effective and lightweight solution for improving sugarcane leaf disease recognition accuracy, particularly in challenging complex backgrounds. Its ability to balance high accuracy with computational efficiency presents a promising technical avenue and a deployable solution for high-precision crop disease diagnosis systems on resource-constrained mobile or edge platforms.
Why it matches plant phenotyping methodsサトウキビ葉の病害状態を画像から認識する軽量深層学習モデルを開発し、精度・計算量・アブレーションを評価しており、植物表現型取得・判定手法が中心である。
abstractWe propose the ReMA-MobileViT model, which significantly enhances feature representation by incorporating a newly designed Residual Multi-head Attention (ReMA) module.
Reproduction assets foundThe paper's sugarcane leaf disease image dataset (2022 Sugarcane Leaf Disease Dataset, Thite et al.) is publicly available on Mendeley Data and directly constitutes the image inputs used for the paper's disease recognition experiments. No author analysis code or trained model checkpoints are reported.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/9424skmnrk/1 .Open asset ↗9424skmnrklines:757-778Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Accurate plant disease identification is of great importance for ensuring agricultural productivity and food security. However, complex illumination variations, leaf occlusion, and diverse disease spot scales throughout plant growth stages significantly increase the difficulty of real-time detection, leading to limited accuracy and robustness in existing approaches. Methods To address these challenges, we propose an improved RT-DETRv2-based plant disease detection model, termed AF-RT-DETR. A Bidirectional Cross Gate (BCG) module is introduced in the feature extraction stage to reduce channel redundancy and enhance discriminative feature representation through multi-level feature interactions. The original RepVGG structure is replaced with a Dynamic Channel Shift (DCS) module, effectively enlarging the receptive field and strengthening contextual feature fusion without additional computational overhead. Additionally, an improved Scale-aware Multi-level Loss (SML) emphasizes low-quality feature maps to improve detector robustness. Results The model achieves mAP50 and mAP50:95 of 93.6% and 67.2% on the Plant-Disease dataset, surpassing the baseline by 5.1% and 4.5%. Furthermore, the model was evaluated on multiple crops and growth stages under diverse field conditions, demonstrating robust performance and adaptability. Discussion These results indicate that AF-RT-DETR effectively enables real-time plant disease detection in complex field environments.
Why it matches plant phenotyping methods植物の病徴を画像から検出するモデルの開発と、複数作物・生育段階・圃場条件での性能評価が中心であり、植物病害状態の表現型計測手法に該当する。
Reproduction assets foundThe paper evaluates AF-RT-DETR on three public Roboflow plant-disease image datasets, each cited with an explicit public URL. No author analysis code or trained model release is mentioned. The Ultralytics YOLOv8 repository is a generic third-party library, not a paper-specific asset.Dataset · publicRoboflow Detecting rice crop diseases object detection dataset . Available online at: https://universe.roboflow.com/crop-diseases-l2qhk/detecting-rice-crop-diseases/dataset/21Open asset ↗lines:816-932Dataset · publicRoboflow Disease detection object detection dataset . Available online at: https://universe.roboflow.com/projects-h0apg/disease-detection-0slunOpen asset ↗lines:816-932Dataset · publicRoboflow Plant Disease v2 512×512 . Available online at: https://universe.roboflow.com/sangeeth-mathew-john-nl43i/plant-disease-czcfe/dataset/2Open asset ↗lines:933-1045Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Introduction In protected horticultural production, early disease identification and precise intervention are critical for safeguarding crop yield and quality while reducing chemical pesticide inputs. However, early-stage greenhouse diseases often exhibit extremely subtle visual symptoms, and their occurrence and progression are highly dependent on environmental condition variations, making stable and reliable early warning difficult to achieve using conventional methods based on single visual information or simple multimodal fusion. Methods To address this challenge, a visual-environment joint early disease perception framework for greenhouse horticultural crops is proposed. Through an environment-guided visual attention mechanism and a spatial-temporal joint modeling strategy, environmental variables such as temperature, humidity, vapor pressure deficit, and CO 2 concentration are transformed from passive features into active priors, thereby guiding visual feature learning and enhancing sensitivity to weak disease signals. The proposed method is systematically validated on a real-world greenhouse multimodal temporal dataset. Results Experimental results demonstrate that the proposed approach achieves an accuracy of 91.3%, a recall of 88.9%, and an F1-score of 89.8% in overall disease detection tasks, significantly outperforming multiple baseline models based on convolutional neural networks (CNNs), Transformers, and existing multimodal fusion strategies. In early-stage disease detection scenarios, early precision and early recall reach 88.5% and 86.1%, respectively, with the lead time extended to 2.7 days, indicating a clear advantage in early warning capability. Ablation studies further verify the critical roles of environment-guided attention, spatial-temporal joint modeling, and the joint loss function in improving early detection performance and stability. Discussion This study provides a practically valuable technical pathway for early intelligent warning and precise regulation of greenhouse crop diseases. By integrating environmental dynamics with visual perception, the proposed framework improves the sensitivity and robustness of early disease detection in complex greenhouse conditions, showing strong potential for practical deployment in protected horticulture.
Why it matches plant phenotyping methods環境情報と画像・時系列情報を統合して、植物病徴を早期検出する深層学習フレームワークの開発と実データでの系統的検証が中心であり、植物の病害状態を直接推定するため。
abstracta visual-environment joint early disease perception framework for greenhouse horticultural crops is proposed.
This study presents an unmanned aerial vehicle (UAV)-based approach for estimating shoot biomass and characterizing growth patterns in single-staked white Guinea yams ( Dioscorea rotundata ). Multi-angle aerial images from nadir and oblique views were used to extract vegetation- and height-related indices that served as predictors in machine learning models. Support vector regression using combined-view imagery provided the highest prediction accuracy (R² = 0.79) and remained robust across growth stages, years, fertilizer treatments, and genotypes. Notably, the combined-view configuration outperformed single-view imaging, demonstrating the advantage of capturing complementary canopy-structure information in complex staked-vine canopies. Time-series biomass estimates enabled the fitting of genotype-specific Richards growth curves using Bayesian inference. Significant genotypic variations were observed in parameters associated with maximum biomass and early growth rate, whereas phenology-related parameters showed comparatively minimal differences. These parameter differences may reflect variation in canopy architecture and growth allocation among genotypes. Overall, this integrated workflow provides a scalable tool for nondestructive monitoring of yam growth dynamics and for summarizing biomass trajectories with interpretable parameters, supporting breeding efforts aimed at improving yam productivity and yield stability across diverse cultivation conditions.
Why it matches plant phenotyping methodsUAV画像からヤムのシュートバイオマスと生育を推定する画像解析・機械学習ワークフローが研究の中心であり、複数視点画像の比較検証と時系列形質推定を行っている。
abstractThis study presents an unmanned aerial vehicle (UAV)-based approach for estimating shoot biomass and characterizing growth patterns
Introduction Intercropping regimes enhance the efficiency of land use and ecological sustainability but present serious problems to automated disease analysis since the overlapping canopy and the similarity of symptoms in crop species are visually indistinguishable. Methods This work presents an explainable artificial intelligence (XAI)-based hyperspectral analysis on leaf disease in intercropping systems. The framework combines the spectral-spatial feature generators that utilize transformers including vision transformer (ViT), Swin transformer, pyramid vision transformer (PVT), and detection transformer (DETR) to identify nuanced biochemical and structural changes in crop combinations for maize-soybean and pea-cucumber. In order to reduce spectral redundancy and high dimensionality, an enhanced greedy political optimization (EGPO) algorithm is used as a wrapper-based feature selection strategy. A capsule spatial shift neural network (CSSNet) is used to predict the classification of diseases. Explainable AI methods, such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) feature attribution analysis and gradient-weighted class activation mapping (Grad-CAM) visualization of disease-relevant regions, provide model transparency. The DETR + EGPO + CSSNet framework is tested on the conventional feature selection methods. Results and discussion The results or findings on publicly available hyperspectral datasets on intercropping show an average recall of 99.998% with high region consistency (Dice score: 99.997%) of activation maps and expert-marked disease regions. These findings affirm that the proposed framework is highly accurate, stable, and interpretable to identify subtle and overlapping disease in leaves in a complex system of intercropping.
Why it matches plant phenotyping methods植物葉の病害領域・病状をハイパースペクトル画像から推定する解析手法の開発と評価が中心であり、植物病害表現型の取得・抽出に直接関係する。
abstractThis work presents an explainable artificial intelligence (XAI)-based hyperspectral analysis on leaf disease in intercropping systems.
Introduction Accurate identification of rice seedling age is essential for guiding precise field management and optimizing agronomic practices. However, traditional identification methods mainly rely on manual experience or simple visual cues and often lack robustness under complex field conditions such as illumination variation, background interference, and subtle morphological differences between adjacent growth stages. Therefore, developing a reliable and automated method for fine-grained recognition of rice seedling stages is of great importance. Methods To address this problem, this study proposes two deep learning models for automatic recognition of 13 rice seedling stages. The first model, Lresnet50, enhances visual feature representation by improving the baseline Resnet50 with a Row-Prior Strip Attention (RPS) mechanism, a Feature Pyramid Network (FPN) for multi-scale feature extraction, and Dynamic Channel Pruning (DCP) to reduce redundant channels and improve computational efficiency. Based on this model, a multimodal framework named M-Lresnet50 is further developed by integrating image features with temporal environmental data through a Long Short-Term Memory (LSTM) network, enabling cross-modal feature fusion and improving recognition of continuous seedling growth stages. Results Experimental results demonstrate that the proposed models achieve high accuracy in recognizing 13 rice seedling stages. The Lresnet50 model achieves an average classification accuracy of 97.70%, outperforming several existing convolutional neural network architectures and showing strong performance in transitional growth stages where morphological differences are subtle. By integrating visual features with temporal environmental information, the multimodal M-Lresnet50 further improves the accuracy to 98.33%. The model contains 27.656 million parameters with a computational complexity of 13.965 GFLOPs, indicating a good balance between recognition accuracy and computational cost. Discussion The results confirm the effectiveness of the proposed improvements and multimodal fusion strategy. The Row-Prior Strip Attention (RPS) enhances the model's ability to focus on row-structured crop regions, while the Feature Pyramid Network (FPN) improves multi-scale feature representation. In addition, Dynamic Channel Pruning (DCP) reduces redundant channels and improves computational efficiency. The integration of temporal environmental information through the multimodal framework further enhances the robustness and consistency of seedling stage recognition. Overall, the proposed approach provides a practical solution for intelligent monitoring of rice seedling growth in greenhouse environments.
Why it matches plant phenotyping methods画像と環境時系列データからイネ幼苗の生育段階を自動認識する深層学習手法を開発し、複数モデルとの精度比較も行っているため、植物状態の取得・推定が中心である。
abstractthis study proposes two deep learning models for automatic recognition of 13 rice seedling stages.
Introduction Manual detection of peanut leaf diseases is plagued by a significant time lag, which frequently enables diseases to develop from isolated, sporadic outbreaks into large-scale epidemics. This delay ultimately leads to substantial regional yield losses in peanut production. Consequently, the precise detection capability of intelligent monitoring equipment is essential for mitigating the risk of large-scale peanut disease outbreaks. Detection algorithms serve as the core technology underpinning intelligent detection devices, highlighting the need for optimized, high-performance algorithms to address this challenge. Methods This study takes the YOLOv12 algorithm as the baseline model and proposes an improved model named YOLO-SDA. To enhance the model's performance while reducing its computational burden, three key modules-StarNet, DySample, and A2C2f_SCSA-are integrated into the original YOLOv12 framework. The integration of these modules is designed to optimize feature extraction, sampling efficiency, and feature fusion, thereby improving the model's detection accuracy and reducing its resource consumption. Results Experimental results demonstrate that the proposed YOLO-SDA model outperforms the baseline YOLOv12 model in both performance and efficiency. Specifically, compared with YOLOv12, the YOLO-SDA model achieves a 44% reduction in parameters, a 38.5% decrease in GFLOPs (giga floating-point operations per second), and a 43.6% reduction in model size. Simultaneously, the model's detection precision and mAP@0.5-0.95 (mean average precision at intersection over union thresholds from 0.5 to 0.95) are improved by 2.0% and 2.5%, respectively. Discussion The superior performance of the YOLO-SDA model confirms the effectiveness of integrating StarNet, DySample, and A2C2f_SCSA modules into the YOLOv12 framework. The significant reduction in parameters, GFLOPs, and model size addresses the practical challenge of deploying intelligent detection algorithms on resource-constrained equipment, making it more suitable for on-site peanut leaf disease monitoring. The concurrent improvement in detection precision and mAP@0.5-0.95 ensures that the model can accurately identify peanut leaf diseases even in complex field environments, providing a reliable technical support for preventing large-scale disease outbreaks and safeguarding peanut yield.
Why it matches plant phenotyping methods落花生葉の病害状態を画像から推定するYOLOベースの検出モデルを開発・評価しており、植物フェノタイピング手法が研究の中心である。
abstractThis study takes the YOLOv12 algorithm as the baseline model and proposes an improved model named YOLO-SDA.
CoffeeField / plotFruitObject detectionGrowth / development / phenology
Introduction Coffee cherry ripeness assessment is critical for harvesting efficiency and product quality, yet traditional manual inspection methods suffer from subjectivity and low efficiency. Methods To address the challenges of detecting small, occluded, and densely distributed coffee cherries in complex field environments, this study proposes an Occlusion and Density Aware Network (ODANet). Built upon the YOLOv8 framework, ODANet integrates three innovative modules: (1) Condition-Guided Windowed Attention (CGWA), which incorporates occlusion and density maps as auxiliary guidance signals for efficient feature enhancement; (2) Attention-guided Space-Preserving Convolution (ASPC), which employs space-to-depth transformation with cascaded attention to preserve spatial information during downsampling; and (3) Dual-Adaptive Dynamic Upsampling (DADU), which achieves content-adaptive feature reconstruction through dual-branch offset prediction with learnable fusion weights. Results Comprehensive evaluation on a publicly available dataset demonstrates that ODANet achieves state-of-the-art performance among 17 diverse detection architectures, attaining 76.7% mAP@0.5 with a 6.3 percentage point improvement over baseline YOLOv8, while maintaining computational efficiency (8.1 GFLOPs, 30.4M parameters) suitable for real-time deployment. Ablation studies validate the contributions of each module: ASPC improves performance by 2.2%, DADU by 0.6%, and CGWA by 3.5%. Discussion The model demonstrates robust performance across varying lighting conditions, occlusion levels, and growth stages, making it particularly suitable for practical agricultural deployment. This research provides an efficient solution for small object detection in precision agriculture.
Why it matches plant phenotyping methodsコーヒーチェリーの成熟度という植物器官の状態を画像から推定する検出手法を開発し、複数モデル比較・アブレーションで技術的に検証しているため、植物フェノタイピング手法が中心である。
abstractthis study proposes an Occlusion and Density Aware Network (ODANet)
Reproduction assets foundThe paper analyzes a publicly available coffee cherry dataset hosted on Kaggle, explicitly linked in the data availability statement. This is the paper-specific image dataset used for its coffee cherry ripeness detection experiments. No author code or model checkpoints are stated as available.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/harisyunanda/dataset-coffee-cherry/data .Open asset ↗Kaggle · harisyunanda/dataset-coffee-cherrylines:682-758Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Hyperspectral remote sensing provides a rapid and non-destructive approach for monitoring plant nutrient status; however, its application for magnesium (Mg) estimation in flue-cured tobacco remains limited. In this study, two cultivars, Yunyan 87 and Zhongyan 100, were grown in a hydroponic system with five Mg concentration gradients (0, 0.2, 1, 5, and 25 mmol L -1 ). Hyperspectral reflectance data of fresh leaves were collected at different growth stages. Three preprocessing methods, including first derivative (FD), standard normal variate (SNV), and multiplicative scatter correction (MSC), were applied, and partial least squares regression (PLSR) was used to identify the optimal preprocessing strategy. Characteristic wavelengths were selected using competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and genetic algorithm (GA), and were further combined with extreme learning machine (ELM), support vector regression (SVR), and radial basis function (RBF) neural network models to estimate Mg content. The results showed that spectral preprocessing significantly improved the relationship between hyperspectral data and Mg content, with optimal methods varying across cultivars and growth stages. Selected wavelengths were mainly located in the near-infrared region. The developed models achieved high prediction accuracy, particularly during the middle and late growth stages, where the coefficients of determination (R 2 ) of all test sets exceeded 0.90. In addition, Yunyan 87 exhibited higher prediction accuracy than Zhongyan 100. These findings demonstrate that hyperspectral technology combined with feature wavelength selection and machine learning enables accurate and non-destructive estimation of Mg content in flue-cured tobacco leaves, providing a reliable tool for Mg nutrition diagnosis and precision management. However, further validation under diverse field conditions is required to enhance model robustness.
Why it matches plant phenotyping methodsタバコ葉のMg含量という植物生理状態を、ハイパースペクトル計測と波長選択・機械学習で非破壊推定する方法が研究の中心であり、モデル精度評価も行っている。
abstractThe developed models achieved high prediction accuracy, particularly during the middle and late growth stages, where the coefficients of determination (R 2 ) of all test sets exceeded 0.90.
Precise detection of crop leaf damage is essential for real-time plant health monitoring and yield estimation. However, conventional deep learning models often exhibit poor generalization when deployed across varying species and complex, unstructured field environments. To address these limitations, we propose a new modeling paradigm that shifts from traditional task-specific training to foundation model adaptation. Specifically, we introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation. By incorporating a Spatial Prior Module (SPM) and a Projection Module, our approach effectively bridges the gap between general-purpose pre-training and domain-specific requirements. Experimental results on coffee and black gram datasets demonstrate that this paradigm consistently outperforms standard networks, including Unet, Unet++, and SwinUnet. On the coffee leaf dataset, the proposed model achieves an Intersection over Union (IoU) of 78.31% and a Pixel Accuracy of 88.00%, surpassing the baseline Unet by over 10.5% in IoU. Remarkably, the architecture reduces inference time by approximately 93.6% (from 63.41s to 4.07s), proving that high-parameter foundation models can be adapted for extreme computational efficiency in agricultural scenarios. To further validate scalability, we conduct additional experiments on a larger dataset, AMG HS . The proposed paradigm achieves the best overall detection performance while maintaining superior computational efficiency, confirming its robustness under increased data scale. Interpretability analysis reveals that the foundation model backbone effectively captures high-level semantic features of lesions, providing a clear explanation for its superior performance and cross-domain reliability. This research establishes a scalable, high-performance paradigm for intelligent crop protection, demonstrating that coupling customized encoders with foundation models is a superior strategy for cross-domain agricultural tasks.
Why it matches plant phenotyping methods植物葉の病変を画像からセグメンテーションし、葉の損傷状態を定量化する手法の開発・検証が研究の中心であるため。
abstractwe introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation.
Reproduction assets foundThe paper analyzes two publicly available plant image datasets hosted on Mendeley Data: a coffee leaf rust/leaf miner dataset and a black gram leaf disease dataset, both explicitly linked in the Data Availability Statement. No author analysis code or trained model checkpoints are disclosed.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/vfxf4trtcg/5; https://data.mendeley.com/datasets/45djgf3p96/1.Open asset ↗html-lines:473-493Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://data.mendeley.com/datasets/vfxf4trtcg/5; https://data.mendeley.com/datasets/45djgf3p96/1.Open asset ↗html-lines:473-493Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
To address the challenge of balancing model lightweight and detection accuracy in maize leaf disease detection, as well as the limitations of edge device deployment resources, we propose an enhanced target detection model, YOLOv11n-DualPC-Lite.Firstly, the C2fDualPConv module was designed, integrating PartialConv to replace some C3k2 modules in the backbone and neck networks. This approach enhances feature representation while reducing the number of parameters. Secondly, the Slim-Neck architecture is introduced in the neck network. To improve accuracy without increasing the number of parameters, the VoVGSCSPC_SimAm module enables the new Slim-Neck structure to reduce parameters while strengthening feature representation. Finally, an EfficientHead detection head is introduced that uses an inverted bottleneck MBConv module to improve performance. This significantly reduces computational load while efficiently extracting features. This study constructed a maize leaf disease dataset integrating a publicly available Kaggle dataset and a field-collected dataset from Anhui Science and Technology University's experimental plots. The dataset includes four categories: Blight, Common_Rust, Gray_Leaf_Spot, and Health. Through techniques such as rotation and gamma correction, the dataset was expanded from 3,876 to 5,165 images for model training and performance validation. Test results show this improved model performs better than other popular lightweight models overall, with a mAP50 score of 90.9%. Meanwhile, the model has only 2.13 million parameters; its computational complexity is reduced to 4.55 G, and the model size is 4.41 MB. Compared with the original YOLOv11n, its mAP50 is 1.9% higher, while the number of parameters is down by 17.8%, computational complexity is cut by 29.3%, and file size is reduced by 15.7%. When run on a Raspberry Pi 5, the model's detection speed reaches 2.3 FPS, an increase of 27.8%. This model achieves a good balance between detection accuracy and lightweight performance for maize leaf diseases, providing an efficient and practical method for real-time crop disease monitoring.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴を画像から検出・分類する軽量モデルを開発し、データセット構築、性能比較、エッジデバイス検証まで行っており、植物病害状態の画像ベース表現型取得が中心である。
abstractwe propose an enhanced target detection model, YOLOv11n-DualPC-Lite
Reproduction assets foundThe paper's maize leaf disease detection study uses a public Kaggle maize leaf disease image dataset (Dataset 1) combined with a field-collected dataset. The Kaggle dataset is a public, paper-specific image asset directly used for the model's training and validation. No author analysis code, trained model checkpoints,或Dataset · publicre, the model was successfully run on a Raspberry Pi 5 edge device, realizing stable, real-time detection and providing a workable technical method for field disease monitoring.
2
Materials and methods
2.1
Dataset introduction
The dataset constructed in this study comprises two datasets: Dataset 1 from the Kaggle data website ( https://www.kaggle.com/datasets/hendriyunuswijaya/maize-leaf-disease ) and Dataset 2 collected from the experimental field at Anhui Science and Technology University in Chuzhou City, Anhui Province. Dataset 1 contains a total of 4,188 images, including 1,162 images in the Health category. All images depict only specific regions of healthy maize leaves without complex Open asset ↗Kaggle · hendriyunuswijaya/maize-leaf-diseaselines:46-63Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Populus pruinosa is the key foundation tree species in desert riparian forests in arid areas of northwestern China. Timely and accurate monitoring of the physiological status of P. pruinosa is crucial for restoring the damaged ecosystem. Anthocyanins are one of the important physiological indicators that reflect the environmental adaptability of P. pruinosa under stress. Existing studies have extensively applied hyperspectral technology for the quantitative prediction of crop leaf pigments. However, research on hyperspectral prediction of anthocyanin concentration in woody halophytes is still lacking, particularly in the integration of spectral preprocessing, species-specific vegetation index construction, and machine learning modeling. Methods In this study, the hyperspectral technology was used to estimate the anthocyanin concentration of P. pruinosa leaves collected in five months (June - October) under five groundwater depth conditions (0-2, 2-4, 4-6, 6-8, and 8-10 m). Based on first-order (FD) and second-order (SD) derivative processing, competitive adaptive reweighted sampling (CARS), https://xueshu.baidu.com/usercenter/paper/show?paperid=bea4d6371f19161f21aac22941cc4408&site=xueshu_se shuffled frog leaping algorithm (SFLA), and recursive feature elimination with cross-validation (RFECV) were used to extract spectral features of P. pruinosa leaves to construct the anthocyanin reflectance index, composite index, difference vegetation index, and normalized anthocyanin reflectance index. After that, the top 10 sets of data with high correlation with anthocyanin concentration were selected from each vegetation index to form a total data set (40 sets in total) for modeling. Twelve models were constructed using support vector machine (SVM) and one-dimensional convolutional neural network (1D-CNN) methods. Results The FD and SD derivative transformations of the spectral reflectance significantly enhanced the correlation with anthocyanin concentration. The feature extraction methods SFLA and RFECV were superior in extracting the bands highly related to anthocyanin concentration, and the vegetation indices constructed based on these two methods had a high correlation with anthocyanin concentration in the red and near-infrared regions. The optimal prediction model was FD-SFLA-SVM (R 2 = 0.852, RMSE = 86.851 mg m -2 , RPD = 2.596). Discussion Unlike existing vegetation index-based studies, the research develops a systematic approach to construct vegetation indices and models for estimating the anthocyanin concentration in the woody halophyte P. pruinose in deserts. The research will provide technical support for non-destructive monitoring of the physiological status of P. pruinosa , and also contribute to the restoration of desert riparian ecosystems.
Why it matches plant phenotyping methods葉のハイパースペクトルからアントシアニン濃度という植物生理形質を推定する手法を開発・比較しており、形質取得とモデル化が研究の中心である。
abstractthe hyperspectral technology was used to estimate the anthocyanin concentration of P. pruinosa leaves
Introduction Intelligent rice disease prevention and control are crucial components in the development of smart agriculture. In recent years, with the rapid advancement of computer vision technologies, a variety of deep learning-based methods for rice disease identification have been proposed, and some models have already surpassed the diagnostic performance of agricultural technicians. However, the existing models generally suffer from high computational complexity and limited generalization capabilities, rendering them difficult to deploy on edge devices for real-time and accurate disease recognition under offline field conditions. Methods To promote engineering applications of related technologies, this study investigated leaf disease identification methods for mountain-grown rice oriented toward edge intelligence. Based on a self-constructed image dataset of mountain rice leaf diseases and following the design principles of lightweight convolutional neural networks, a novel lightweight mountain rice disease recognition model architecture suitable for edge intelligent devices was constructed. Furthermore, a mountain rice leaf disease recognition application was developed for smartphones on the Android platform. Results Field validation experiments demonstrated that the application achieves an average accuracy of 92.41% across multiple disease categories and an average inference speed of approximately 22.47 frames per second on various smartphone models, indicating high real-time performance and recognition accuracy. Discussion The research outcomes will provide a reliable theoretical foundation and technical support for the intelligent prevention and control of mountain rice diseases.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から認識する軽量CNN、データセット、スマートフォン実装を開発し、精度と推論速度を検証しており、植物病害表現型の取得・抽出が中心である。
abstracta novel lightweight mountain rice disease recognition model architecture suitable for edge intelligent devices was constructed
Purpose The study aims to develop a method for detecting and discriminating grapevine yellows (GY) diseases, including Flavescence dorée (FD), Bois noir (BN), and Palatinate grapevine yellows (PGY), using hyperspectral imaging (HSI). Specifically, it seeks to address the challenge of symptom misclassification among visually similar diseases, such as distinguishing between GY diseases and other biotic and abiotic stresses. Methods Hyperspectral images of detached leaves from field with GY diseases, Grapevine Leafroll-associated Virus (V), leafhopper (LH) infestation, iron deficiency (Fe), or magnesium deficiency (Mg) were acquired using a mobile platform in the laboratory or in the field. The images were taken in the spectral range of 400-1,000 nm. Classification models were trained on the mean spectra of the leaves to distinguish between different classes. Results The model achieved F1-scores ranging from 54.2% to 96.8% for white cultivars and exceeded 95% for all six classes for black cultivars. When limited to GY classes and nonsymptomatic leaves, the F1-scores were 89.8%, 78.7%, 63.4%, and 53.4% for nonsymptomatic, FD, PGY, and BN, respectively. Conclusion The study demonstrates the feasibilityof using HSI to detect and discriminate different biotic and abiotic stresses on grapevine leaves, including distinguishing between symptom-similar GY diseases. The developed mobile platform and classification models show promise for largescale monitoring and diagnosis of GY diseases, potentially improving disease management and reducing the risk of symptom misclassification.
Why it matches plant phenotyping methodsブドウ葉のハイパースペクトル画像から病害・ストレス状態を識別する手法と移動型プラットフォーム、分類モデルを開発・評価しており、植物表現型取得が中心である。
abstractThe study aims to develop a method for detecting and discriminating grapevine yellows (GY) diseases, including Flavescence dorée (FD), Bois noir (BN), and Palatinate grapevine yellows (PGY), using hyperspectral imaging (HSI).
Accurate delineation of crop growth stages under real-world field conditions remains a long-standing challenge in computational phenotyping, particularly for wheat whose developmental phases are characterized by subtle, continuous morphological transitions and environmental noise. In this study, we propose AMFR-Net, an Adaptive Multi-Scale Feature Refinement Network tailored for fine-grained wheat stage identification using ground-level RGB imagery. Unlike conventional architectures that struggle with ambiguous inter-stage boundaries and rigid receptive structures, AMFR-Net leverages a ResNet-101 backbone augmented by a novel Adaptive Multi-Scale Attention Fusion (AMSAF) module-comprising cross-scale interaction blocks and confidence-weighted feature aggregation-to hierarchically recalibrate spatial-semantic representations. This design enables the network to adaptively amplify phenologically salient cues while suppressing irrelevant context, ensuring robust generalization under constrained annotation and deployment conditions. Evaluated on the expert-labeled CGIAR benchmark, AMFR-Net achieves state-of-the-art performance across all major metrics (Top-1 Accuracy: 89.10%; Macro-F1: 89.10%; AUC: 97.88%) and demonstrates superior discriminability in phenologically adjacent stages compared to lightweight and deep CNN baselines. Ablation studies validate the synergistic effect of multi-level attention and scale-aware refinement. The proposed framework offers a scalable, interpretable, and field-deployable solution for in-situ phenology monitoring, and sets a foundation for future integration of multimodal sensing, weak supervision, and cross-seasonal adaptation.
Why it matches plant phenotyping methods小麦の生育ステージを地上RGB画像から推定する新規深層学習手法を開発し、ベンチマーク、比較、アブレーションで検証しており、植物フェノタイピング手法が研究の中心です。
abstractwe propose AMFR-Net, an Adaptive Multi-Scale Feature Refinement Network tailored for fine-grained wheat stage identification using ground-level RGB imagery.
Reproduction assets foundThe paper's phenotyping analysis is built on the public CGIAR Wheat Growth Stage Challenge dataset (ground-level RGB wheat images with growth-stage labels), which the authors explicitly state is publicly available on Zindi with a direct link. No author analysis code, trained model checkpoints, or supplementary code/deeDataset · publicPublicly available datasets were analyzed in this study. This data can be found here: Dataset name: CGIAR Wheat Growth Stage Challenge Primary repository: Zindi (official competition page) Direct link: https://zindi.africa/competitions/cgiar-wheat-growth-stage-challengeAccession/Open asset ↗Zindilines:808-824Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Advancements in agricultural technologies have increasingly emphasized technical innovations aimed at improving the predictability and reliability of agricultural outputs. These aspects encompass developments in agricultural machinery, automation technologies, biotechnology, and controlled environment farming systems. This article focuses on Remote Sensing (RS)-based approaches applied to agricultural yield estimation for both crops and plants. RS technologies offer enhanced precision and scalability, making them particularly effective for large-scale agricultural monitoring and analysis. A systematic classification of RS-based methodologies employed for crop yield estimation is presented in this study. These methodologies are categorized into: (i) Sensor-Based approaches, (ii) Platform-Based approaches, (iii) Analytical and Modeling-based methods, and (iv) Machine Learning (ML)-driven models. Based on findings reported across multiple studies, it is observed that Deep Learning (DL)-based architectures consistently achieve superior performance across key evaluation metrics, including accuracy, precision, recall, and F1-score. This performance advantage stems from their capacity to learn hierarchical representations, capture complex non-linear relationships, scale efficiently with large datasets, and reduce reliance on manual feature engineering. Following this classification, our article presents a comprehensive discussion of the limitations associated with these methodologies. These challenges are organized into four major categories: (i) Environmental, (ii) Algorithmic, (iii) Hardware and Operational, and (iv) Wireless Sensor Networks (WSNs) related limitations. The adopted classification framework helps readers identify and address the key challenges associated with effective yield estimation in crops and plants. Moreover, the article concludes by outlining several future research directions intended to support and guide both early-career and experienced researchers in this domain.
Why it matches plant phenotyping methods作物収量という植物形質のリモートセンシング推定法を体系的に分類・比較し、環境・アルゴリズム・ハードウェア上の限界を論じる方法論レビューであり、フェノタイピング手法が中心である。
titleA review of remote sensing-based crop yield estimation: machine learning techniques and environmental, algorithmic, and hardware limitations
Doubled haploid (DH) technology can fast-track crop breeding. Haploid induction yields haploids with only one set of genomes, which are usually sterile. Haploid fertility (HF) is the ability of haploid plants to set seed, and it is a critical bottleneck in DH pipelines. Genetic mechanisms to restore HF hold immense potential in DH crop breeding, yet its phenotyping remains manual, destructive, and inconsistent. While recent advances in imaging and machine learning have improved throughput for general plant traits, no curated image dataset exists for Arabidopsis thaliana that explicitly represents HF. Here, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification. AutoSiQ includes high-resolution scanned inflorescences annotated with a seven-class ontology encompassing green siliques, green fertile siliques, mature siliques, fertile siliques, cracked fertile siliques, cracked siliques, and flowers. This multi-class annotation scheme preserves biologically meaningful information beyond binary fertile/non-fertile distinctions, enabling reliable fertility estimation and future phenotyping applications. We release baseline object detection models (YOLOv5), trained using the AutoSiQ dataset, and evaluate their performance across confidence thresholds. Model predictions strongly correlate with manual counts, achieving R² up to 0.94 for total silique number estimation. We further demonstrate AutoSiQ's utility for automated haploid fertility rate (HFR) estimation and genotype discrimination between two contrasting genotypes (WT and bmf2 mutant). A longitudinal analysis identifies ~60 days after sowing (DAS) as the optimal harvest time for maximizing mature silique counts by balancing between the number of immature buds and silique shattering. By releasing both the dataset and baseline code, AutoSiQ provides a reproducible and extensible foundation for high-throughput fertility phenotyping in haploid Arabidopsis .
Why it matches plant phenotyping methodsハプロイド稔性を画像から定量するデータセットと深層学習パイプラインを開発・評価しており、植物フェノタイピング手法が中心である。
abstractHere, we present AutoSiQ, a dataset and baseline deep learning pipeline for automated HF quantification.
Reproduction assets foundThe paper's AutoSiQ dataset (annotated scanned Arabidopsis inflorescence images with seven-class silique ontology and manual fertility counts) is publicly deposited on Zenodo per the data availability statement. The YOLOv5 GitHub repository is a generic third-party library, not an authors' code asset.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://zenodo.org/records/17905566 .Open asset ↗zenodo · 17905566lines:367-402Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Traditional methods for identifying salt tolerance levels in soybean varieties are often cumbersome, time-consuming, and labor-intensive. These challenges are further exacerbated by the limited utility of chlorophyll fluorescence imaging phenotype data, which are insufficiently diverse and difficult to analyze. Additionally, the corresponding parameter text data have not been fully explored and utilized. In this study, salt stress experiments were conducted on 178 soybean varieties, and a multimodal dataset comprising chlorophyll fluorescence images and corresponding textual data was constructed using a chlorophyll fluorescence imaging instrument. A novel gated mechanism network for learnable image-text interaction (Mm-VitnNet) is proposed, which enables global cross-modal interaction between image and text data. The model introduces a gated mechanism to dynamically regulate the fusion intensity of cross-modal information and incorporates two learnable tokens that focus on feature learning for each individual modality. This approach effectively mitigates interference between modalities while preserving modality-specific features, thereby enhancing model performance. The proposed model demonstrates an accuracy rate of 98.97%, significantly outperforming typical models: it improves by 1.09 and 2.33 percentage points compared to CNN-based models such as EfficientNetV2-s (97.88%) and MobileNetV2 (96.64%), respectively, and by 3.21 and 2.60 percentage points compared to Transformer-based Swin Transformer_tiny (95.76%) and hybrid models like MobileViT_S (96.37%), respectively. The model has 10.22M parameters and a computational cost (FLOPs) of 1.84G, which is significantly lower than models like VGG and ResNet50, and only slightly higher than some lightweight CNNs, achieving an effective balance between accuracy and efficiency. The improved model demonstrates notable performance in identifying samples with varying salt tolerance levels, even under limited computational resources, ensuring reliable classification performance. Moreover, this multimodal non-destructive identification method based on chlorophyll fluorescence technology offers an efficient and feasible approach for assessing the salt tolerance levels of soybeans, while also advancing agricultural phenotyping towards greater precision and intelligence.
Why it matches plant phenotyping methodsダイズの塩耐性という植物状態をクロロフィル蛍光画像から推定するマルチモーダル画像解析手法を開発・評価しており、表現型取得と分類モデルが研究の中心である。
abstracta multimodal dataset comprising chlorophyll fluorescence images and corresponding textual data was constructed using a chlorophyll fluorescence imaging instrument.
Introduction Accurate and stable diagnosis of cotton nitrogen status across growth stages is essential for precision fertilization in drip-irrigated systems. However, the instability of conventional nitrogen-related indicators across different phenological stages often reduces diagnostic performance and limits their broader application. Methods A field experiment was conducted in Xinjiang, China, under four irrigation levels (60%, 80%, 100%, and 120% ET c ) and four nitrogen application rates (0, 245, 300, and 350 kg N ha -1 ). UAV multispectral imagery was acquired at the squaring, flowering, boll-setting, and boll-opening stages. Based on ground-measured leaf area index (LAI) and upper-canopy leaf nitrogen weight (LNWupper), an Integrated Nitrogen Diagnosis Index (INDI) was developed. Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) models were used to evaluate the inversion performance of INDI. In addition, the nitrogen nutrition index (NNI), derived from the critical nitrogen dilution curve, was used to validate the diagnostic stability of INDI. Results Multispectral vegetation indices were strongly correlated with LAI, LNWupper, and INDI, with red-edge- and near-infrared-based indices showing the highest sensitivity. Among the three models, XGBoost achieved the best inversion accuracy for INDI (R 2 = 0.85, RMSE = 0.61). INDI was significantly correlated with NNI across growth stages, with R 2 values of 0.58, 0.77, 0.81, and 0.70 at the squaring, flowering, boll-setting, and boll-opening stages, respectively, and the highest accuracy observed at the boll-setting stage. Moreover, the spatial distribution maps of INDI effectively distinguished nitrogen differences under different water-nitrogen treatments and were consistent with NNI-based classifications. Discussion INDI accurately captured nitrogen dynamics throughout cotton growth, and the INDI-XGBoost framework provided a robust approach for high-precision spatial nitrogen diagnosis. These results support precision fertilization management in drip-irrigated cotton fields in Xinjiang.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からワタの窒素状態を推定する指標とXGBoost反転手法を開発・検証しており、植物生理状態の取得・推定が研究の中心である。
abstractBased on ground-measured leaf area index (LAI) and upper-canopy leaf nitrogen weight (LNWupper), an Integrated Nitrogen Diagnosis Index (INDI) was developed.
SoybeanRootSeed / grainMorphology / geometry measurementObject detectionGrowth / development / phenologyRoot system architectureStress response / tolerance
Introduction Nanoparticle-induced treatments can promote seed germination and improve germination potential under environmental stresses such as drought and salinity. This study aimed to investigate the effects of Zinc oxide nanoparticles (ZnONPs) on soybean seed germination and to develop a precise evaluation method. Methods We developed a full-time sequence crop growth vitality monitoring system. Using germination rate and root length as primary evaluation indicators, we conducted full-time sequence germination vitality monitoring experiments on soybean seeds treated with ZnONPs. A dataset was constructed from images documenting embryonic root growth. The developed detection model was used to evaluate image detection accuracy during germination. Germination index and embryonic root length were also calculated. Further tests were performed on seeds exposed to 600 mg/L ZnONPs dispersion, followed by treatment with different concentrations of NaCl and PEG6000 solutions. Results At a concentration of 600 mg/L ZnONPs dispersion, soybean seeds showed the highest germination rate (an increase of 28%) and the longest radicle length (an increase of 42%). Compared with deionized water, the 600 mg/L ZnONPs dispersion accelerated initial germination time, increased germination rate, and enhanced radicle length under low-concentration stress. Discussion The results indicate that, at certain concentrations, ZnONPs dispersion positively influences soybean seed germination under varying salinity and drought conditions. We examined morphological and physiological changes in ZnONPs-treated seeds under stress, establishing a preliminary foundation for evaluating crop and variety vitality. These findings provide new insights that may contribute to improving soybean germination under simulated stress conditions, serving as a preliminary theoretical reference for potential applications in arid and saline environments.
Why it matches plant phenotyping methods発芽中の画像から発芽率・幼根長を抽出する連続モニタリングシステムと検出モデルを開発し、精度評価とデータセット構築を行っており、表現型取得手法が中心である。
Introduction The optimal stomatal regulation theory provides an eco-evolutionary framework for interpreting the trade-off between CO 2 uptake and water loss. This theory postulates that the marginal water cost of carbon gain ( λ=∂E/∂A ) remains approximately constant over short timescales, thereby offering a mechanistic basis for predicting stomatal behavior and gas exchange. Methods In this study, leaf-level meteorological variables and gas exchange parameters of orchard citrus were measured throughout the entire phenological period during 2021-2022. We developed a family of optimal stomatal conductance-based models (OSCMs), comprising six forms: Rubisco-limited forms (OSCvc and OSCvcd), RuBP-regeneration-limited forms (OSCvj and OSCvjd), and combined forms that dynamically select the prevailing biochemical limitation (OSC and OSCd). Results The key parameter λ was estimated daily and averaged over the entire phenological period. Using daily λ inputs, the three models produced stomatal conductance ( g s ) with accuracies ranked as OSCvjd (R 2 = 0.73) > OSCd (0.63) > OSCvcd (0.40). When a long-term constant λ was applied, model performance declined with accuracies ranked as OSCvj (0.66) > OSC (0.52) > OSCvc (0.38). Discussion The OSC model also produced intercellular CO 2 concentration ( c i ) and photosynthesis ( A ) reasonably well (R 2 = 0.78 and 0.48, respectively). Under moderate meteorological conditions (air temperature 30-40 °C and vapor pressure deficit 1-2 kPa), the OSC model showed its best performance with a mean absolute relative error of 35.2% for g s estimation. Overall, the OSCMs provided a mechanistic approach to simulate citrus leaf gas exchange requiring minimal species-specific traits and routine meteorological inputs. This modeling strategy supports rapid assessment of plant physiological status and estimation of foliar carbon-water fluxes in orchard management under subtropical climates.
Why it matches plant phenotyping methods柑橘葉のガス交換・気孔コンダクタンスを推定するモデル群を開発し、実測値との精度比較で検証しており、植物生理形質の取得・推定法が研究の中心である。
abstractWe developed a family of optimal stomatal conductance-based models (OSCMs), comprising six forms
Intelligent recognition and rapid grading of tea buds are crucial for advancing tea-picking machinery; however, complex plantation backgrounds and inconsistent bud growth have limited traditional algorithms to merely identifying picking points, neglecting bud pose and grade, which restricts harvesting efficiency. To address these challenges, we propose YOLO-PC, a deep neural network designed for simultaneous tea bud pose estimation and classification, which incorporates a dynamic snake convolution (DSConv) module for enhanced shape feature extraction, an ELASPP-CSPC attention mechanism for improved spatial pooling, and EIoU loss to accelerate regression and boost localization accuracy. Experimental results demonstrate that the model achieves detection accuracies of 91.5% for one-bud-one-leaf and 93.2% for one-bud-two-leaf scenarios, with an average keypoint detection accuracy (Pose_mAP) of 89.7% and a Normalized Mean Error (NME) of 0.047; furthermore, compared to YOLOv7-pose, it increases mean average precision by 7.26% and pose accuracy by 9.65% while reducing parameters by 14.99 M. Ablation studies confirm the superior performance of the proposed model in tea bud detection, indicating its potential to provide robust practical support for adaptive and intelligent tea harvesting systems.
Why it matches plant phenotyping methods茶芽の姿勢・等級という植物器官の状態を画像から推定する深層学習手法を開発し、精度比較・アブレーション評価まで行っており、単なる収穫対象の位置検出を超えたフェノタイピング手法が中心である。
abstractwe propose YOLO-PC, a deep neural network designed for simultaneous tea bud pose estimation and classification
As the key structure connecting the vegetative and reproductive organs of soybean plants, the main stem plays a crucial role, and its morphological parameters serve as core phenotypic indicators for evaluating plant growth, lodging resistance, and yield potential. At the mature stage, the main stem exhibits high similarity to pods in color and texture, along with complex curvature and severe occlusion by pods and leaves, making accurate and continuous extraction challenging for conventional segmentation methods. To address this, this study proposes RAM-UNet, a high-precision semantic segmentation model based on an improved U-Net architecture. The model adopts ResNet50 as the backbone and replaces standard convolutions with deformable convolutions to capture curved stem morphology and improve feature extraction for low-contrast edges. In the encoder, the Convolutional Block Attention Module (CBAM) is combined with an improved atrous spatial pyramid pooling (ASPP) module (C-ASPP) with four dilation rates, enhancing multi-scale feature representation compared to the original three-rate design. A multi-scale attention aggregation (MSAA) module in the decoder improves continuity and integrity of stem boundaries. During training, a composite loss function combining Dice loss and cross-entropy loss is employed to mitigate foreground pixel sparsity. Experimental results on a self-constructed dataset show that RAM-UNet achieves a mean Intersection over Union (mIoU) of 90.58%, with Recall and Precision reaching 94.99% and 94.58%, respectively. Compared with U-Net, DeepLabv3+, PSPNet, and SegNet, RAM-UNet improves mIoU by 6.41%, 10.51%, 22.41%, and 17.37%, respectively. Automatically measured stem lengths show high agreement with manual measurements (R² = 0.9746), validating practical applicability. RAM-UNet also generalizes well on the public PASCAL VOC 2012 dataset, achieving an mIoU of 73.14%. The results indicate that the proposed model enables high-precision and continuous segmentation of main stems in mature soybean plants, providing an effective technical solution for automated and non-destructive measurement of crop phenotypic parameters.
Why it matches plant phenotyping methods大豆主茎のセグメンテーションと長さ測定を目的とする画像解析手法を開発し、手動測定との一致性で検証しており、植物表現型取得が中心である。
abstractthis study proposes RAM-UNet, a high-precision semantic segmentation model based on an improved U-Net architecture.
Agriculture currently faces the dual pressures of ensuring global food security and adapting to rapid climate change. To cope with these challenges, researchers have introduced several modern mechanization technologies, including advanced farm machinery, autonomous navigation systems, artificial intelligence, sensing technologies, and communication tools, to enhance productivity and sustainability (Syed et al., 2025a). These technologies enable data-driven decision-making by allowing continuous, large-scale acquisition and analysis of crop and environmental information. Consequently, accurately predicting crop yields and monitoring plant health in real time have become critical prerequisites for precision agricultural management (Syed et al., 2025). Traditional measurement methods-often labor-intensive, destructive, and spatially limited-are increasingly unable to meet the demands of modern large-scale farming. In this context, the integration of Remote Together, these ten contributions illustrate the maturation of agricultural remote sensing, moving towards models that are not only more accurate but also lighter, more interpretable, and more resilient to environmental noise. By combining satellite and UAV data with advanced computational models, these innovative approaches are paving the way for a more resilient and productive global food system. Future research will increasingly focus on improving the precision of crop yield estimation models through multi-dimensional analyses. As agricultural environments grow more complex, integrating AI-powered models with multi-sensor fusion technologies will be essential. Innovations such as lightweight neural networks and multimodal cross-attention frameworks will enable the detection of small, occluded, and densely packed targets with greater accuracy, thereby refining crop-specific metrics such as photosynthetically active radiation (FPAR) and nitrogen content. This, in turn, will enhance crop health monitoring and yield predictions.Additionally, UAV-based remote sensing, combined with multitier feature selection, will improve nitrogen content analysis in crops such as cotton, while image dehazing models and light-use efficiency frameworks will bolster biomass estimation.Emerging technologies such as the Ta-YOLO framework will further optimize small fruit detection in dense canopies, advancing overall crop detection accuracy.A key challenge lies in adapting these models to handle real-world complexities, such as variable environmental conditions. Future work will focus on improving the robustness of these models through dynamic coding networks and performance optimization, ensuring they can operate in heterogeneous agricultural environments.Interdisciplinary collaboration between agriculture, AI, and remote sensing experts will accelerate the development and deployment of these approaches, paving the way for more efficient crop yield estimation systems that are critical for ensuring food security and sustainable agricultural practices.
Why it matches plant phenotyping methods作物収量・健康・バイオマス・窒素含量などの植物形質を、衛星・UAVリモートセンシングと計算モデルで推定する手法群を中心に扱う編集レビューであり、方法論的役割が明確。
titleInnovative approaches in remote sensing for precise crop yield estimation: advancements, applications, and future directions
A critical challenge in agricultural automation is the precise detection of mango leaf diseases that compromise crop quality and yield. To address the limitation of existing heavy models in resource-constrained agricultural environments, this study proposes MangoLeafNet-XAI, a novel lightweight deep learning architecture. The model synergistically integrates Efficient Channel Attention (ECA) modules with a DenseNet-121 backbone to adaptively refine features and capture subtle pathological patterns with high precision. The proposed framework was rigorously evaluated using a 5-fold cross-validation and soft-voting ensemble strategy across three public datasets (MLDID, Mango Leaf Disease, and Harumanis). These datasets encompass diverse environmental conditions and distinct disease classes, including Anthracnose, Bacterial Canker, Die Back, Gall Midge, Powdery Mildew, Sooty Mould, and Cutting Weevil. MangoLeafNet-XAI achieved state-of-the-art accuracies of 98.83% on MLDID, 98.09% on the Mango Leaf Disease Dataset, and 98.76% on the Harumanis dataset. A primary contribution of this work is the optimal balance between performance and computational efficiency, utilizing only 6.9 million parameters, making it highly suitable for deployment on edge devices. Moreover, the interpretability of AI methods, such as Grad-CAM and LIME, that are used to explain the rationale behind predictions to offer pathological explanations, also validate the focus on clinically important aspects of the model. The results discuss the key limitations of existing methods, such as computational complexity, inability to interpret the findings, and dataset-dependent overfitting, and demonstrate a high level of resilience and generalizability on diverse datasets. MangoLeafNet-XAI will be a new benchmark of reliable, deployable, as well as accurate disease diagnosis systems, in smart agriculture.
Why it matches plant phenotyping methodsマンゴー葉画像から病害状態を推定する軽量・解釈可能な深層学習手法を開発し、複数データセットで交差検証・性能評価しており、植物フェノタイピング手法が中心である。
abstractthis study proposes MangoLeafNet-XAI, a novel lightweight deep learning architecture.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Hydrogen peroxide (H 2 O 2 ) functions as a key signaling molecule in plants responding to stress. Although numerous detection methods have been developed, simple and non-destructive techniques for the semi-quantitative monitoring of H 2 O 2 in plant tissues remain scarce. Methods In this study, we developed a "turn-on" fluorescent probe specifically designed to detect endogenous H 2 O 2 in plant tissues, and conducted spectroscopic and in vivo toxicity tests. Furthermore, under experimentally controlled stress conditions, we utilized this probe to detect H 2 O 2 levels in four distinct plant types exposed to salt, waterlogging, cadmium, and drought stresses. Additionally, H 2 O 2 was detected in a grafting model under non-experimentally controlled stress conditions. Results The results showed that the probe demonstrated excellent selectivity, a strong linear correlation (R2 = 0.9849), and a low detection limit of 0.6450 μmol/L. Importantly, it exhibits good biocompatibility with plant tissues and effectively minimizes detection errors caused by transient H 2 O 2 fluctuations induced by environmental changes. Consequently, it provides more accurate and stress-reflective H 2 O 2 measurements. Under experimentally controlled stress conditions, the changes in relative fluorescence intensity conformed to the typical response patterns observed when plants experience graded levels of stress. Notably, even under complex grafting conditions without imposed stress gradients, applying the probe to bottle gourd (Lagenaria siceraria) rootstocks with different graft compatibility produced fluorescence dynamics consistent with the typical H 2 O 2 responses of compatible and incompatible rootstocks, and the distribution of relative fluorescence intensity within the population underscored the importance of prescreening plants for biological studies. Pearson correlation and Bland-Altman analyses confirmed good agreement between our method and the commercial assay kit. Discussion These results demonstrate that the LWS probe enables H 2 O 2 detection and, in combination with the IVIS in vivo imaging system, can screen individual plants differing in stress responses more effectively than other sensors. This non-destructive approach preserves the structural integrity of plant samples, enabling follow-up physiological, biochemical, and genomic analyses on the same specimens. This method provides a reliable prescreening platform for investigating plant stress responses at the biological level.
Why it matches plant phenotyping methods植物組織中のH2O2を非破壊的・半定量的に検出する蛍光プローブとIVIS画像化を開発し、性能検証およびストレス応答個体のスクリーニングに応用しており、植物状態の取得手法が中心である。
abstractwe developed a "turn-on" fluorescent probe specifically designed to detect endogenous H 2 O 2 in plant tissues
Field / plotLiDAR / point cloudWhole plant / canopy / plot / fieldYield / biomass estimationBiomass / plant weight
Introduction Accurate estimation of individual tree aboveground biomass (AGB) is essential for tree species selection, carbon accounting, and precision forestry. Unmanned aerial vehicle (UAV) LiDAR provides rapid access to detailed tree structural information, offering a promising tool for high-frequency biomass assessment. Methods In this study, a nonlinear mixed-effects (NLME) model integrating UAV LiDAR and field measurements was developed to quantify the influence of genetic heterogeneity and environmental factors on AGB estimation of Catalpa bungei . Data from 2,941 trees across 79 genotypes were collected in Henan Province, including LiDAR-derived tree height ( L H ), LiDAR-derived crown diameter ( L CD ), and AGB. By incorporating genotype as a random effect and planting density as a dummy variable, the NLME model significantly outperformed traditional dummy-variable models. Results Genotype effects explained significant AGB variation, achieving high accuracy (R²=0.7916, RMSE = 3.7095) and reducing TRE by 23.29% compared to the basic power function model. Leave-one-genotype-out cross-validation confirmed robustness. Calibration with the four largest trees yielded the best performance (TRE = 13.09%), while a simplified scheme using only two trees per genotype maintained high accuracy (TRE = 13.24%), markedly reducing field effort. Discussion These results highlight the superiority of NLME AGB models over linear approaches and demonstrate that accounting for genotype effects is critical for reliable biomass estimation. The proposed framework provides an efficient and cost-effective solution for biomass monitoring, tree breeding, carbon sink assessment, and precision forestry.
Why it matches plant phenotyping methodsUAV LiDARと非線形混合効果モデルを統合し、個体樹木の地上部バイオマスという植物形質を推定する方法を開発・検証しており、フェノタイピング手法が中心である。
abstracta nonlinear mixed-effects (NLME) model integrating UAV LiDAR and field measurements was developed to quantify the influence of genetic heterogeneity and environmental factors on AGB estimation of Catalpa bungei
Rice early tillering characteristics are key indicators for high-yield breeding, with tiller number and tillering rate as core parameters. High-throughput, temporal, and precise monitoring of tiller numbers via drone digital imagery provides quantitative support for tillering trait screening in breeding, serving as an important auxiliary tool for smart breeding. However, during the early tillering stage, complex backgrounds (e.g., water bodies, soil) and small, dense breeding plots pose challenges to high-throughput rice plant extraction and accurate tiller number estimation. To address this, this study proposes a rice tiller number estimation method based on an improved Swin-UNet model and multi-feature fusion. A PSO-optimized XGBoost model was constructed for tiller number estimation by integrating selected features. Experimental results show that the improved Swin-UNet model achieved a segmentation accuracy of 92.5% (7.2% higher than U-Net), and the PSO-XGBoost model, using 12 features (10 morphological and 2 color), yielded R²=0.85 and RMSE = 0.35. Application verification on 576 untrained breeding plots generated tiller number thematic maps, providing data support for germplasm tillering trait identification and advancing smart breeding.
Why it matches plant phenotyping methodsドローン画像からイネの分げつ数を抽出・推定する画像解析手法を開発し、セグメンテーション精度と推定性能を検証しているため、植物表現型計測が研究の中心である。
abstractthis study proposes a rice tiller number estimation method based on an improved Swin-UNet model and multi-feature fusion.
In agricultural automation, precise cotton segmentation is a key step for tasks such as intelligent harvesting and yield estimation. However, in complex field environments, factors such as background interference and irregular target shapes severely affect segmentation accuracy. Existing deep learning methods offer certain advantages but still generally suffer from limitations including insufficient accuracy, over-segmentation, and misidentification. To address these challenges, this study proposes a novel dual-branch cotton segmentation network, Cotton-aware Mamba-enhanced UNet (CMNet), which optimizes the ParaTransCNN architecture by incorporating the 2D Selective Scan (SS2D) module to replace the original Transformer branch, effectively balancing the extraction of local details and global semantic information while reducing computational burden. To enhance the model's perception of irregularly shaped cotton, a Deformable Convolutional Networks v1 (DCNv1) module is integrated into the Vision Mamba (VMamba) branch, further improving the delineation of target boundaries. Additionally, an Atrous Spatial Pyramid Pooling (ASPP) module is introduced at the end of the Convolutional Neural Network (CNN) branch to strengthen multi-scale feature representation. To optimize the fusion of channel and spatial information, the Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanism replaces the original module, enhancing feature modeling capability. Experimental results on an in-field cotton image dataset demonstrate that CMNet outperforms existing mainstream methods, achieving Dice, mIoU, and Accuracy of 91.06%, 84.18%, and 98.10%, respectively, while reducing parameter count and computational complexity, thus exhibiting excellent performance. Furthermore, generalization experiments on multiple other plant datasets also achieved outstanding results, validating the model's adaptability and potential for broader applications in multi-crop segmentation tasks, providing valuable insights for smart agriculture segmentation research. The source code and dataset of this work are publicly available at https://github.com/halidanmu/CMNet.git.
Why it matches plant phenotyping methods綿花画像から植物領域を抽出する新規セグメンテーション手法を中心に開発・検証しており、植物表現型の画像取得・抽出ワークフローに該当する。
abstractthis study proposes a novel dual-branch cotton segmentation network, Cotton-aware Mamba-enhanced UNet (CMNet)
Reproduction assets foundThe authors explicitly state that the source code and dataset for CMNet are publicly available on GitHub. The paper also uses several public Roboflow plant image datasets in its generalization experiments, cited with public URLs in the references.Code · publicThe source code and dataset of this work are publicly available at https://github.com/halidanmu/CMNet.git.Open asset ↗halidanmu/CMNethtml-lines:106-109Dataset · publicELTE (2023). Assignment 2 dataset. Available online at: https://universe.roboflow.com/elte-msgqy/assignment_2-mjhau (Accessed November 5, 2025).Open asset ↗html-lines:754-834Dataset · publicLaola (2024). Defect banana dataset. Available online at: https://universe.roboflow.com/laola/defect-banana-qf4f6 (Accessed November 5, 2025).Open asset ↗html-lines:754-834Dataset · publicLuffy24312 (2023). Cnn dataset. Available online at: https://universe.roboflow.com/luffy24312/cnn-myqtl.Open asset ↗html-lines:835-919Dataset · publicVyuha T. (2025). Rose dataset. Available online at: https://universe.roboflow.com/tech-vyuha/rose-kfpuf (Accessed November 4, 2025).Open asset ↗html-lines:835-919Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 15 Sept 2026
TeaField / plotNeRF / 3D Gaussian SplattingLiDAR / point cloudLeafWhole plant / canopy / plot / fieldCountingObject detection2D/3D reconstructionSegmentation
Accurate perception of tea buds is a fundamental prerequisite for intelligent and precise tea harvesting planning. However, in real tea plantation environments, reliable harvesting-oriented perception at the planning level remains highly challenging due to the small size of tea buds, severe occlusion, complex background clutter, and the lack of accurate three-dimensional spatial information. To address these challenges, we propose TeaNeRF, an integrated three-dimensional visual perception pipeline designed for harvesting-oriented tea bud analysis. Instead of treating detection, segmentation, and spatial analysis as independent tasks, TeaNeRF integrates sequential two-dimensional recognition, monocular depth estimation, and neural radiance field reconstruction into a coherent perception pipeline, allowing accurate spatial understanding of tea buds in complex natural scenes. It should be noted that the proposed integration is conducted at the perception-output level, where multiple modular components are connected through fixed interfaces, rather than through joint optimization or an end-to-end trainable formulation. The proposed framework combines an enhanced YOLO-based detector, prompt-guided segmentation, and monocular depth priors to guide NeRF-based three-dimensional reconstruction. By incorporating depth supervision and semantic-aware neural fields, TeaNeRF generates dense and geometrically consistent point clouds with reliable semantic separation. Quantitative evaluations show consistent improvements in reconstruction fidelity, as reflected by increased PSNR and reduced LPIPS across multiple tea tree scenes. Based on the reconstructed semantic point cloud, a three-dimensional clustering and geometric fitting strategy is further developed to enable tea bud counting and harvesting-oriented candidate point estimation at the perception level. Experiments conducted on a real-world dataset of 4,700 tea plantation images demonstrate that TeaNeRF improves detection accuracy (mAP@50 = 91.7%), segmentation quality (IoU = 0.640), and overall three-dimensional perception performance. Case-level counting results on representative tea trees indicate that the proposed 3D semantic point cloud-based approach can provide feasible tea bud counting behavior and consistent spatial guidance cues for downstream harvesting planning. By providing structured three-dimensional spatial information, including tea bud locations, counts, and harvesting-oriented candidate points, TeaNeRF offers practical perception-level outputs for downstream planning in automated tea harvesting systems.
Why it matches plant phenotyping methods茶芽の検出・セグメンテーション・3D再構成を統合し、茶芽の計数と3D位置推定を行う知覚パイプラインが研究の中心であり、単なる収穫対象の局在化を超えた器官形質の抽出を含む。
abstractwe propose TeaNeRF, an integrated three-dimensional visual perception pipeline designed for harvesting-oriented tea bud analysis.
Robotic pollination represents a pivotal component of smart agriculture, with foundational architectures for target recognition, path planning, and motion control having been progressively established. However, developing an efficient and robust pollination system that integrates perception, decision-making, and execution within real-world scenarios remains confronted with complex challenges. This study systematically reviews recent advancements in the field and distills the core technical issues of greenhouse robotic pollination into three primary domains: target detection and pose estimation, end-effector design, and pollination strategies combined with motion control. Focusing on the visual perception of flowers, actuator architecture, and operational tactics, this review synthesizes existing academic findings to evaluate the state-of-the-art in flower detection and pose estimation, characterize diverse end-effector designs, and analyze the evolutionary trajectory of motion control techniques. Specifically, the analysis encompasses the impact of detection algorithms on recognition accuracy and robustness, the structural classification and performance attributes of pollination mechanisms, and the optimization of control strategies. Furthermore, the study categorizes global research backgrounds, technical methodologies, and paradigmatic system cases, offering a critical evaluation of experiences in constructing automated pollination systems. Despite these advances, current robotic pollination technologies for peppers (chili) face significant bottlenecks characterized by immature methods for precise flower detection and pose estimation, the need for optimized specialized end-effector designs, and insufficient robustness in decision-making systems under dynamic environmental conditions. To address these issues, future development should prioritize constructing diverse, large-scale flower image and pose datasets while developing detection algorithms adaptable to complex environments to achieve high-precision identification. Additionally, implementing this system requires a hierarchical architecture where perception drives adaptive actuation. Deep learning models must localize flower targets and assess maturity in real-time, feeding coordinates to path planners that generate collision-free trajectories through foliage. These trajectories are executed via multimodal motion control, synchronizing the rigid manipulator with soft end-effectors. By embedding tactile feedback into the machine learning loop, the system creates a unified sensorimotor framework. This enables dynamic force modulation based on physical resistance, ensuring precise, non-destructive pollination tailored to chili plants.
Why it matches plant phenotyping methods温室コショウの花の検出・姿勢推定など、植物器官の観測・形質抽出を中核とするロボット受粉技術のレビューであり、方法論的貢献が中心。
abstractThis study systematically reviews recent advancements in the field and distills the core technical issues of greenhouse robotic pollination into three primary domains: target detection and pose estimation, end-effector design, and pollination strategies combined with motion control.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Plant leaf diseases pose a significant threat to plant growth and productivity, necessitating accurate and timely identification. While high-performance deep learning models exist, their complexity often hinders deployment in real-world, resource-constrained agricultural settings. To address the need for efficient and accurate plant disease identification, we developed a novel lightweight approach named the Lightweight Multi-Path Pruning Method (LMP-PM). LMP-PM offers flexible lightweight optimization, configurable via pruning parameters and path expansion ratios, enabling users to balance significant reductions in model parameters and FLOPs against potential inference time increases, thereby tailoring model size, performance, and real-time needs to specific application scenarios. Specifically, we first constructed an original, high-performance, and complex model (OMNet) incorporating various structures and a three-branch parallel module (TBP block). We then applied LMP-PM to OMNet to perform lightweight processing, resulting in several lightweight models. Through extensive experimentation, we identified the optimal model that balances performance and complexity, which we named LMNet (Lightweight Multi-Path Network). LMNet demonstrates remarkable efficiency, utilizing only 5.69% of the parameters and 3.80% of the FLOPs of OMNet. Despite this substantial reduction in complexity, LMNet achieved superior accuracy: 99.23% on the Plant Village dataset, representing an improvement of 0.58% over OMNet, and 87.27% on the AI 2018 Challenger dataset, surpassing OMNet by 1.91%. These results highlight that LMP-PM successfully creates highly efficient models like LMNet, which not only drastically reduce computational resources but also improve classification accuracy. This flexibility and enhanced performance make LMNet particularly suitable for real-time plant disease identification in resource-constrained environments, offering a practical and effective solution for agricultural applications.
Why it matches plant phenotyping methods葉画像から植物病害を認識する軽量モデルを開発し、複数データセットで精度と計算量を評価しており、植物の病害状態の取得・推定が研究の中心である。
abstractwe developed a novel lightweight approach named the Lightweight Multi-Path Pruning Method (LMP-PM).
Field / plotSeed / grainPhysiological trait estimation
Introduction Seed longevity is a key determinant of population persistence, spread, and outbreak potential in annual invasive plant species. Understanding longevity of invasive seed bank is crucial for determining colonization timing and assessing invasion potential, thereby supporting sustainable weed management strategies. While soil seed bank fluctuations have become a focus in invasion biology area, efficient and accurate methods for evaluating seed bank longevity in annual invasive plants remain scarce so far. In this study, we focus on a representative annual globally malignant invasive plant Solanum rostratum , investigating seed longevity by accelerated aging test (60°C and 85% relative humidity) across multiple regions and collection years. Methods We used a three-parameter Weibull distribution model to characterize seed aging and applied it to assess S. rostratum seed bank longevity in both grassland and abandoned farmland habitats. Results The results showed that S. rostratum seeds lost viability rapidly within 3 d under accelerated aging condition. Seeds from different regions in the same year exhibited similar aging patterns, while interannual variation led to significantly divergent aging curves. Based on polynomial regression of viability data and germination tests, the upper limit of seed longevity under natural field conditions was estimated to be approximately 8-9.79 years. Discussion This study demonstrates that combining accelerated aging assays with the three-parameter Weibull distribution provides an effective approach for assessing seed longevity and soil seed bank persistence. The method offers a practical, efficient, and reproducible framework for estimating seed bank persistence in annual invasive plants. Our findings highlight the critical role of persistent seed banks in facilitating the invasion success of S. rostratum , thereby offering a robust analytical basis for evaluating invasion risks. Moreover, the modeling framework developed here can be extended to other annual plant species for seed viability assessment, providing valuable theoretical support for the development of ecologically sustainable weed management strategies.
Why it matches plant phenotyping methods加速老化試験とWeibullモデルを組み合わせ、種子寿命・種子銀行持続性を推定する再利用可能な測定・解析手法が研究の中心であるため。
abstractThis study demonstrates that combining accelerated aging assays with the three-parameter Weibull distribution provides an effective approach for assessing seed longevity and soil seed bank persistence.
In wheat breeding, the number of spike grains is a key indicator for evaluating wheat yield, and timely and accurate detection of wheat spike grain is of great practical significance for yield estimation. However, in actual field production, the counting of spike grain still relies on manual counting after threshing, which poses problems such as complex measurement processes, time-consuming and laborious. At present, achieving automated and intelligent detection of wheat spike grain still faces significant challenge. Therefore, the focus of this study is to use the most advanced computer vision technology for fast and automatic detection of wheat spike grain. During the wheat filling stage, a total of 936 wheat spike grain images were collected, and these images were expanded through data augmentation to ultimately obtain 3700 wheat spike grain images. According to the partition ratio of the small scale dataset, 80% of the 3700 images are used for training, 10% for validation, and the remaining 10% for testing. This study selected six state-of-the-art deep learning models: YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x, and Faster R-CNN. In all wheat spike grain test, YOLOv8n showed high precision, recall, mAP50, and mAP50-95, with values of 96.8%, 96.8%, 98.9%, and 58.4%, respectively. The precision of other models was 96.7% for YOLOv8m, 96.5% for YOLOv8s, 96.3% for YOLOv8l, 96.2% for YOLOv8x, and 95.7% for Faster R-CNN. YOLOv8n not only has a lower number of parameters, FLOPs, inference time, model size, and GPU memory usage, as well as higher detection precision in wheat spike grain counting tasks, fully meet the spike grain counting requirements of wheat breeding. The multi-scale feature fusion and lightweight computing of YOLOv8n help improve model performance, and its performance is better compared to other deep learning models. This study designed and implemented a WeChat mini program for wheat spike grain counting, so as to achieve automatic detection and counting of wheat spike grains, which provided valuable reference for grain detection, counting, and yield estimation of other crops.
Why it matches plant phenotyping methods小麦穂粒数という植物形態・収量関連形質を、画像と深層学習で自動取得・計数する手法が研究の中心であり、複数モデルの性能比較とアプリ実装も行っているため。
abstractthe focus of this study is to use the most advanced computer vision technology for fast and automatic detection of wheat spike grain
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Heat stress damage leads to yield penalties in many wheat-growing areas. Climate change models predict warmer scenarios and more frequent heat shocks. Consequently, wheat breeders need to develop more productive varieties for warm conditions, and therefore, the identification of heat-tolerance traits is needed. Albedo is an integrative trait of the optical properties of the canopy defined as the ratio of reflected light to total light received. High albedos in warm conditions may help reduce damaging radiation. Despite its potential relevance for heat avoidance, albedo has been little explored in wheat breeding. In this work, a selection of 30 wheat ( Triticum aestivum L.) genotypes of diverse origin were sown at two sowing dates in Australia (NSW) in 2018 and 2019. A high-throughput phenotyping method based on spectroradiometer measurements [Analytical Spectral Devices (ASD)] to measure canopy albedo was developed to explore its relationship with temperature and other heat tolerance-related traits. ASD albedo was validated via continuous albedometer measurements on a subset of genotypes. Data were captured at flowering (one of the most critical periods for heat-related damage). Genotypic differences for albedo were found in most environments. However, genotypic effects were most noticeable at noon in optimally sown materials (H 2 0.71–0.86). Albedo was directly related to canopy architecture and light interception (r = 0.74) and varied depending on genotype and genotype by environment interaction. Air temperature in the canopy profile and canopy temperature (CT) were also monitored continuously in a subset of genotypes to explore the relationship between albedo and canopy micrometeorology. Canopies with higher albedos had larger air temperature differences across the canopy profile at the flowering stage (r = 0.48). However, canopy temperature was not related to albedo, even though it was strongly correlated (r = 0.99) with air temperature around the spike. Overall, these results indicate that canopy architecture is the primary influence on albedo under warm conditions. Although higher albedo was not associated with lower canopy temperature, its influence on canopy micrometeorology suggests that albedo may contribute to heat avoidance and could therefore be considered an additive trait for phenotyping and breeding for environments under high temperatures.
Why it matches plant phenotyping methods小麦群落アルベドを分光放射計で測定するハイスループット表現型計測法を開発し、連続アルベドメーターで検証しているため、方法が研究の中心である。
abstractA high-throughput phenotyping method based on spectroradiometer measurements [Analytical Spectral Devices (ASD)] to measure canopy albedo was developed
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
Introduction: Accurate counting and spatial localization of soybean seeds-particularly Seeds Per Plant (SPP)-are critical for yield estimation and cultivar evaluation. In field environments, however, complex backgrounds, pod occlusion, and uneven grain filling make high-precision counting challenging, and traditional methods often struggle to balance accuracy and robustness. Methods: To address these challenges, this study proposes SoyCountNet, a deep learning framework for automatic soybean seed counting and localization at the single-plant level under field conditions. The model is built on a self-constructed field-based phenotyping platform and optimized using the lightweight Point-to-Point Network (P2PNet). For feature extraction, a VGG19_BN backbone and a Super Token Sampling Vision Transformer (SViT) module are employed to enhance local feature representation and global contextual understanding. During feature fusion, the Efficient Channel Attention (ECA) mechanism strengthens seed-related features while suppressing interference from leaves, stems, and soil. Furthermore, an improved loss function that combines point-distance constraints with overlap penalties enhances both counting precision and spatial consistency. Results: Experimental results demonstrate that SoyCountNet outperforms existing approaches on the field soybean dataset. It achieves a mean absolute error (MAE) of 4.61, a root mean square error (RMSE) of 6.03, and a coefficient of determination (R²) of 0.94. The model demonstrates consistent performance across the tested soybean cultivars, providing reliable SPP estimates within the evaluated dataset. Discussion: These findings indicate that SoyCountNet offers a reliable and scalable solution for precise soybean seed counting and localization in complex field environments. Its lightweight architecture allows deployment on intelligent agricultural platforms, supporting high-throughput phenotyping, yield prediction, and precision breeding, while providing a foundation for the future development of intelligent and sustainable agricultural technologies.
Why it matches plant phenotyping methods単一個体の種子数（SPP）を画像から自動計数・位置推定する手法を開発し、圃場データで性能評価しているため、植物表現型取得が中心である。
abstractthis study proposes SoyCountNet, a deep learning framework for automatic soybean seed counting and localization at the single-plant level under field conditions
Sweetpotato ( Ipomoea batatas (L.) Lam.) is a crucial crop for global food security. However, its sustainable production is hindered by low nutrient use efficiency. Reliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking. To bridge this gap, we established a novel two-phase evaluation system integrating hydroponic seedling screening with multi-nutrient field validation. We conducted principal component and regression analyses of 35 germplasms lines under controlled deficiencies of nitrogen (N), phosphorus (P), and potassium (K). Five conserved seedling traits were identified, including leaf number per plant, shoot fresh weight, root fresh weight, shoot dry weight, and net photosynthetic rate (Pn). These traits consistently correlated with tolerance to N, P, or K deficiency, thereby supporting their utility as reliable early indicators of nutrient stress. Field validation further confirmed that storage root fresh and dry weight, nutrient content, accumulation, and use efficiency varied significantly among nutrient treatments and genotypes, serving as key indicators of field performance. This integrated approach successfully identified elite germplasm with specific nutrient use efficiency: XN1985-7 as a low-N-tolerant and N-efficient utilization genotype, XN17104-132 as low-K-tolerant and K-efficient utilization, XN2141-3 as low-P-tolerant and P-efficient utilization, and notably XN2153-5, which exhibited concurrent tolerance to low N, P, and K with broad-spectrum efficiency. Our integrated two-phase framework provides a scalable model for screening nutrient-efficient germplasm in root crops, thereby contributing to sustainable breeding programs.
Why it matches plant phenotyping methods栄養効率遺伝資源を評価するための二段階スクリーニング系を構築し、複数の形態・生理形質を初期指標として検証しているため、植物フェノタイピング手法が中心的です。
abstractReliable screening protocols that accurately identify nutrient-efficient germplasm of this crop across developmental stages are still lacking.
Avocado ( Persea americana Mill.), with a global production estimated at 10.4 million tons in 2023, suffers annual losses of 20-30% due to diseases such as anthracnose ( Colletotrichum gloeosporioides ) and scab ( Sphaceloma perseae ), resulting in substantial economic impacts for major producing countries (Mexico, Peru, and Colombia). This study introduces an advanced system that integrates a binary sequential voting architecture (VotingBS) with a fully functional web application, for the automated identification of two high-incidence diseases: anthracnose and scab, both of which critically affect fruit quality and yield. The proposed VotingBS architecture implements a hierarchical two-stage classification strategy. In the first stage, a five-model deep learning ensemble differentiates between healthy and diseased fruits. In the second stage, another ensemble determines which of the two diseases is present. For this purpose, a collection of 674 labeled fruit images was used for training and validation. Experimental results demonstrate outstanding model performance, achieving key metrics such as 98.92% precision, 98.89% recall, and 99.03% accuracy, significantly outperforming traditional approaches. Moreover, the solution was deployed through a web app featuring dedicated modules for crop management, phytosanitary analysis, and disease diagnosis. This architecture enhances the system's practical utility and facilitates its adoption by farmers, field technicians, and agricultural monitoring agencies. Overall, this work demonstrates how combining hybrid deep learning models with accessible digital platforms can revolutionize plant disease diagnostics, fostering a more efficient, automated, and resilient precision agriculture.
Why it matches plant phenotyping methodsアボカド果実の画像から健全・罹病状態および病害種を推定する深層学習分類システムとWebアプリを開発しており、植物病害表現型の取得・抽出が研究の中心である。
abstractThis study introduces an advanced system that integrates a binary sequential voting architecture (VotingBS) with a fully functional web application, for the automated identification of two high-incidence diseases: anthracnose and scab
Maize productivity is increasingly constrained by water deficit stress (WDS), particularly under erratic rainfall conditions. Efficient early-stage phenotyping coupled with field validation is critical for breeding WDS-tolerant genotypes. In this study, we developed a two-tier screening strategy integrating hydroponics-based root trait evaluation at pre-reproductive stage with subsequent field validation of maize inbreds under managed WDS at CIMMYT, Hyderabad. A set of 50 diverse maize inbreds were evaluated for root architectural traits and plant growth stages including grain yield components. Hydroponic screening applied PEG6000-induced osmotic stress to assess root length, tips, forks, segments and diameter, whereas field trials imposed pre-reproductive WDS through cumulative growing degree day-based irrigation withdrawal. Significant genotypic variation and genotype × trait interactions were observed across both environments, reflecting trait and environment-specific responses. Key root traits, including root tips, total length, forks and segments, showed strong positive correlations (r ≥ 0.70) with yield components and Normalized difference vegetation index (NDVI), underscoring their importance in WDS resilience. Multivariate analysis further confirmed the alignment of root vigor with kernel traits and canopy health as critical determinants of yield stability. Among the evaluated lines, introgressed ILM23 and ILM24 emerged as the principal donor lines, while PML1249, PML1275, and PML1285 were identified as promising donor sources, all exhibiting robust root systems, stable anthesis-silking interval (ASI) and superior stress tolerance indices. Spearman's rank correlation (ρ = 0.988) between hydroponics and field rankings highlighted the predictive reliability of controlled root phenotyping for field performance under WDS. This integrated hydroponics-to-field approach provides a rapid, efficient and cost-effective framework for the early identification of WDS-tolerant or high water-use-efficiency (WUE) maize hybrids, facilitating the accelerated breeding of resilient cultivars.
Why it matches plant phenotyping methods水耕栽培による根系形態フェノタイピングを開発し、圃場条件で予測信頼性を検証する二段階スクリーニング手法が研究の中心である。
abstractwe developed a two-tier screening strategy integrating hydroponics-based root trait evaluation at pre-reproductive stage with subsequent field validation
Bacterial blight (BB) poses a significant threat to rice production, necessitating efficient screening of resistant wild rice germplasm to facilitate breeding. Traditional methods are labor-intensive and subjective, while existing UAV-based approaches suffer from high costs or incomplete solutions. This study introduces XooNet, a novel UAV-based method for automated BB resistance screening in wild rice, which classifies wild rice into several levels based on BB resistance. To facilitate this method, a high-precision and lightweight oriented bounding box (OBB) detection algorithm for BB in wild rice has been developed. Experimental results show that the screening method achieved an accuracy of 97.5%. After applying the LAMP pruning strategy to balance performance and efficiency, the detection model achieved an accuracy of 93.1% with a significantly reduced parameter size of 1.4M and a computational complexity of 3.5 GFLOPs. This approach will facilitate the high-throughput screening of extensive wild rice germplasm for BB resistance, thereby expediting the discovery of valuable wild rice genetic resources.
Why it matches plant phenotyping methods野生イネの細菌性葉枯病抵抗性をUAV画像から自動推定・分類する手法を開発し、検出精度と計算効率を検証しており、植物表現型取得が中心的です。
abstractThis study introduces XooNet, a novel UAV-based method for automated BB resistance screening in wild rice
Sorghum is a globally important crop. Under the breeding goals of high yield and stress resistance, the precise selection of elite germplasm is crucial. Phenotypic parameters such as plant height and leaf area at the seedling stage are core indicators for evaluating growth vitality. However, traditional manual measurement is inefficient and error-prone, making it difficult to meet the needs of high-throughput research. To address this, this study proposes an improved model (PTV2-Fr) based on Point Transformer V2 (PTV2), which combines 3D point cloud technology to realize the automatic extraction of sorghum seedling phenotypic parameters and explores the regulatory effects of different gibberellin (GA 3 ) concentrations. In this study, videos of sorghum seedlings were collected using the relevant system of Nanjing Agricultural University, and reconstructed into.ply format 3D point cloud files via the open-source software Colmap. The core optimizations of the PTV2-Fr model are as follows: Firstly, it proposes a Multi-Radius Dual-Coordinate Attention (MRDCA) mechanism to address the problems of leaf overlap and uneven point cloud density, thereby enhancing feature discrimination ability; Secondly, it introduces a Point-Graph Invariant Feature Refinement (PG-InvFR) module to improve the sensitivity of the segmentation head to local geometric details; Thirdly, it constructs a composite loss function (EL Loss) combining class-weighted cross-entropy loss and Lovász loss to alleviate class imbalance and boost segmentation accuracy. We selected 50 valid datasets from 112 video groups, annotated into three categories: Stem, Leaf, and Pot. The results show that PTV2-Fr outperforms PTV2 by 2.5% in accuracy, with significant improvements in Recall and mean F1-score (mF1). Ablation experiments confirm the positive effects of MRDCA, PG-InvFR, and EL Loss. Furthermore, PTV2-Fr demonstrates good robustness in analyzing GA concentrations, revealing that 50-100 mg/L GA concentrations promote seedling growth, while concentrations exceeding 200 mg/L inhibit growth. The PTV2-Fr model provides an efficient solution for the automatic determination of sorghum seedling phenotypes, and the revealed GA 3 regulatory mechanism can offer theoretical references for high-quality seedling cultivation and hormone management.
Why it matches plant phenotyping methods3D点群分割ネットワークを開発・検証し、ソルガム幼苗の草丈や葉面積などの表現型形質を自動抽出する方法が研究の中心である。ジベレリン処理の解析は付加的な応用であり、方法論的貢献が明確。
abstractthis study proposes an improved model (PTV2-Fr) based on Point Transformer V2 (PTV2), which combines 3D point cloud technology to realize the automatic extraction of sorghum seedling phenotypic parameters
Single-boll weight (SBW) is difficult to estimate after defoliant application because canopy spectra include numerous mixed pixels from lint, soil, and senescent leaves, leading to strong background interference. Here we propose a UAV multispectral workflow that combines object-based boll extraction, spectral feature selection, and machine-learning regression to improve SBW mapping. Data were collected from a two-year drip-irrigated cotton experiment in Xinjiang, China involving four varieties evaluated under five planting densities treatments. Boll extraction was treated as a supervised object-based classification problem, and maximum likelihood, mahalanobis distance, and parallelepiped classifiers were compared. Fifteen vegetation indices were computed from the extracted boll pixels; informative features were identified using Pearson correlation and SHapley Additive exPlanations importance ranking. SBW was then estimated with ridge regression, random forest regression, and neural network regression using an independent validation dataset. Maximum likelihood consistently achieved overall accuracy above 97% with Kappa values above 0.93, outperforming the other classifiers. Indices derived from the red, red-edge, and near-infrared bands, particularly those designed to reduce soil background effects, showed the strongest relationships with SBW and ranked highest in SHAP. The best-performing model, which integrated maximum likelihood-based boll extraction with neural network regression, achieved a coefficient of determination of 0.80 and a root mean square error of 0.31 g on the validation set. Relative errors remained below 15% across different years, varieties, and planting densities. This workflow reduces background interference and enables transferable SBW spatial estimation for breeding evaluation and density and harvest management.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から綿花の単一ボール重量を抽出・推定する方法を開発し、分類器と回帰モデルを比較検証しており、植物形質取得が研究の中心である。
abstractHere we propose a UAV multispectral workflow that combines object-based boll extraction, spectral feature selection, and machine-learning regression to improve SBW mapping.
Apple leaf disease detection is crucial for maintaining crop health and ensuring food security, yet current detection methods face significant challenges in balancing accuracy with computational efficiency. Existing lightweight detection models struggle with spatial detail preservation and multi-scale feature representation when processing complex disease symptoms with subtle visual characteristics. This study presents SRC-YOLOv8n, a lightweight framework that integrates spatial detail preservation and multi-scale feature enhancement for fine-grained apple leaf disease detection. The framework incorporates four key innovations: the Spatial Detail Attention C2f (SDA-C2f) module that preserves critical spatial information through Space-to-Depth Convolution and SpatialGroupEnhance mechanisms, the Reparameterized Generalized Feature Pyramid Network (RepGFPN) that optimizes multi-scale feature fusion through training-inference decoupling, the Cross-Level Local Attention Head (CLLAHead) that enables effective cross-scale feature interaction, and the Inner-IoU loss function that improves bounding box regression accuracy. Comprehensive evaluation on the Plant-Pathology-2021-FGVC8 and AppleLeaf9 datasets demonstrates that SRC-YOLOv8n achieves superior performance with 94.1% precision, 92.3% recall, 96.1% mAP50, and 93.2% F1 score while reducing parameters by 16.6%, computational cost by 19.8%, and model size by 17.7% compared to baseline YOLOv8n. The framework provides an effective solution for real-world agricultural monitoring applications requiring both high accuracy and computational efficiency.
Why it matches plant phenotyping methodsリンゴ葉の病徴を画像から検出するための軽量画像解析フレームワークを開発し、複数データセットで性能評価しているため、植物病害状態の表現型取得手法が中心である。
abstractThis study presents SRC-YOLOv8n, a lightweight framework that integrates spatial detail preservation and multi-scale feature enhancement for fine-grained apple leaf disease detection.
Optimizing biomass partitioning is essential for achieving sustainable yield improvement in wheat, particularly under increasing environmental stress. Traits such as spike partitioning index (SPI), harvest index (HI), and fruiting efficiency (FE) are central to understanding how assimilates are allocated between vegetative and reproductive organs. However, their complex physiology and the difficulty of manual phenotyping have limited their routine use in breeding programs. This study assessed the potential of unmanned aerial vehicle (UAV)-based hyperspectral reflectance data to predict biomass partitioning traits and related yield components in wheat. Three trials of facultative soft wheat lines (2022-2024) and an independent validation set of advanced breeding lines were used to develop genomic prediction (GP), phenomic prediction (PP), and integrated multi-omic models combining genomic, phenomic, and environmental covariates (ECs). Kernel-based best linear unbiased prediction (BLUP), and machine-learning based, random forest regression and partial least squares regression were implemented to estimate predictive ability (PA). Phenomics-driven models markedly outperformed GP across most traits, achieving PA up to 0.61 for SPI, 0.56 for FE, 0.71 for grains/m 2 (GN), and 0.66 for grain yield (GY). Hyperspectral data provided higher accuracy than vegetation indices, and multi-omic integration slightly improved prediction (PA up to 0.73 for GN). These results demonstrate that UAV-based hyperspectral phenotyping can effectively capture canopy-level physiological signals associated with biomass partitioning, offering a scalable and data-driven approach for in-season selections. This can help wheat breeding programs to optimize biomass partitioning in modern wheat cultivars for long-term yield resilience and genetic gain.
Why it matches plant phenotyping methodsUAVハイパースペクトルデータによる作物形質推定と予測モデルの開発・検証が研究の中心であり、単なる生物学的実験の測定ではない。
abstractThis study assessed the potential of unmanned aerial vehicle (UAV)-based hyperspectral reflectance data to predict biomass partitioning traits and related yield components in wheat.
Introduction Sorghum ( Sorghum bicolor (L.) Moench) is a vital cereal crop for food, feed, and biofuel production. Accurate estimation of grain biochemical composition, crude protein (CP), lysine from grain (LysG) and protein (LysP), starch (SC), amylose from grain (AMLG) and starch (AMLS), and crude fat (CF), is crucial for improving breeding and management strategies. Our aim is not pre-harvest forecasting but reducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately. Methods We used machine learning (ML) models to predict grain quality traits in commercial sorghum hybrids under different management practices, including precision nitrogen application, cover cropping, and no-till methods. Multi-year field trials (2023-2024) in Saint Charles, Missouri, integrated agronomic, physiological, UAV-based, and environmental data for model training and validation. Results Phenotypic analysis showed that grain composition traits varied significantly by year and management practices. Among ML models, LASSO and ElasticNet achieved the highest predictive accuracy for crude protein (R² = 0.90) and amylose content (AMLS, R² = 0.99; AMLG, R² = 0.92). Bayesian Ridge was most effective for lysine from protein (R² = 0.64), while Partial Least Squares (PLS) excelled in starch content prediction (R² = 0.80). The correlation between grain composition (LysP, CF) and photosystem II efficiency (PhiPS2) indicated that enhanced photosynthesis and yield promote their accumulation. However, Partial Dependence Plots (PDPs) revealed strong non-linear effects, where slight variations in leaf temperature (Tleaf) and stomatal conductance (gsw) were associated with significant shifts in amylose content. Discussion This study highlights the role of genotype × management interactions in sorghum breeding and demonstrates the value of integrating ML-driven models to enhance grain quality and precision agriculture strategies.
Why it matches plant phenotyping methods穀粒の生化学的形質を少数の測定値から推定する機械学習モデルの開発・検証が研究の中心であり、単なる農業実験の routine 測定ではない。
abstractreducing laboratory cost by identifying a minimal set of post-harvest measurements required to estimate other grain composition traits accurately
Reproduction assets foundThe article's data availability statement points to a Figshare deposit containing the study's datasets (agronomic, physiological, UAV-based, and grain composition data used for ML modeling). No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicith weather data acquisition.
Edited by: Filipe Matias , University of Wisconsin-Madison, United States
Reviewed by: Xiaolong Yang , Nantong University, China
David Mojaravscki , State University of Campinas, Brazil
Data availability statement
The datasets presented in this study can be found in online repositories, on Figshare https://figshare.com/s/2765f89c7ea840e5c6be?file=59367320 . The names of the repository/repositories and accessionnumber(s) can be found in the article/ Supplementary Material .
Author contributions
BG: Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing. MC: Conceptualization, Data Open asset ↗Figsharelines:471-515Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
We propose a lightweight Multi-Head Self-Attention (MHSA) mechanism for plant phenotypic feature extraction, which integrates cross-species transfer learning with dynamic head pruning to improve efficiency without compromising accuracy. The primary challenge stems from minimizing redundant computations without compromising the model's capacity to generalize over varied plant species, an issue intensified by the substantial dimensionality of attention mechanisms in Vision Transformers. Our solution, the Transferable Attention Head Alignment (TAHA) framework, operates in three stages: pre-training on a source species, cross-species alignment via a Domain Alignment Loss (DAL), and head pruning based on a transferability score. The framework selects and keeps solely the attention heads with the highest transferability, thus diminishing model intricacy without compromising the ability to distinguish phenotypic traits. Furthermore, the pruned MHSA module is smoothly combined with standard Transformer backbones, which makes efficient deployment on edge devices possible. Experiments were conducted on real edge hardware (Raspberry Pi 4, NVIDIA Jetson Nano) and GPU platforms, showing our approach attains accuracy similar to full-head models yet cuts computational expenses by as much as 40% (14.1 ms inference latency on Raspberry Pi 4, 519 M parameters). The method holds special importance for scalable plant phenotyping, in situations where computational capacity is frequently constrained yet generalization across species is essential. Moreover, the repeated alignment and pruning procedure permits gradual adjustment to novel species without complete retraining, which increases feasibility for agricultural applications in practical settings. Supplementary experiments on phylogenetically distant species (Arabidopsis → pine) demonstrate the framework's generalization limits, with a 7.2% F1-score drop compared to close-species transfer (Arabidopsis → maize), highlighting the need for trait-specific head adaptation in distant transfers. The proposed method improves lightweight feature extraction by merging transfer learning and attention head optimization, achieving a balanced compromise between performance and efficiency.
Why it matches plant phenotyping methods植物表現型特徴抽出のためのVision Transformer剪定・転移学習手法を開発し、複数種およびエッジデバイスで精度と計算効率を検証しており、表現型取得・抽出法が研究の中心である。
abstractWe propose a lightweight Multi-Head Self-Attention (MHSA) mechanism for plant phenotypic feature extraction
The canopy characteristics of crops are essential aspects for assessing crop growth status and conducting phenotype analysis. As one of the key indicators to measure crop growth situation, accurate canopy coverage assessment can provide a strong foundation for crop growth and yield monitoring. Considering plant growth differences, this study investigated the statistical method for assessing canopy coverage using visual technology, focusing on lettuce as the research subject. Firstly, a multi-variety and multi-growth stage hydroponic lettuce image dataset was constructed, which lays a data foundation for the construction of a semantic segmentation model. Secondly, in order to ensure the precision of semantic segmentation, this study proposed a Channel-Axial-Spatial attention mechanism module from the perspective of feature enhancement. To satisfy the lightweight demands of practical model deployment, this study replaced the original backbone network of PSPNet with MobileNetv3, greatly reduced model complexity while minimizing model performance degradation. Finally, we developed a group lettuce canopy coverage acquisition system by employing Python in conjunction with PyQt5 and embedded the pre-trained models CAS-PSPNet and MobileNetv3-PSPNet into the system for effectiveness verification. By integrating the proposed attention mechanism module with PSPNet, the integrated model outperformed FCN, Unet, SegNet, Deeplabv3+, GCN, ExFusion, ENet, BiseNet, FusionNet, LinkNet, RefineNet, LWRefineNet, and PSPNet in semantic segmentation of lettuce plant groups, achieving a Mean Intersection over Union of 0.9832. The Mean Intersection over Union of PSPNet based on lightweight improvement is 0.9717, and the model size is 9.3M. The results show that the proposed semantic segmentation method can accurately capture the crop canopy coverage, offering a feasible solution for real-time crop growth monitoring.
Why it matches plant phenotyping methodsレタス群落のキャノピー被覆率という植物形質を画像セグメンテーションで推定する手法を開発し、データセット構築、モデル比較、システム実装・検証まで行っており、フェノタイピング手法が研究の中心である。
abstractthis study investigated the statistical method for assessing canopy coverage using visual technology
This study investigates the relationship between maize leaf carotenoid content and spectral reflectance, evaluates existing carotenoid estimation indices, and develops new spectral indices and machine learning models for improved prediction. A strong positive correlation was observed between carotenoid and chlorophyll content, highlighting carotenoids' role in both light harvesting and photoprotection. Spectral analysis revealed that carotenoid concentration significantly affects leaf reflectance in the visible range, particularly between 500-650 nm. Existing carotenoid indices exhibited limited predictive performance for the studied samples, prompting the development of nine new indices based on principal component analysis. Among these, CAR 7 , CAR 8 , and CAR 9 demonstrated superior predictive ability across different training (2021-2022: R 2 = 0.72-0.76, NRMSE = 15-16%, 2021-2023: R 2 = 0.60-0.62, NRMSE = 11-12%, 2022-2023: R 2 = 0.42-0.49, NRMSE = 18.3-18.5%) and testing periods (2023: R 2 = 0.44-0.50, NRMSE = 14-19%, 2022: R 2 = 0.65-0.72, NRMSE = 13-16%, 2021: R 2 = 0.81-0.83, NRMSE = 18.28-24.65%). Machine learning models further improved carotenoid estimation, with REPTree providing the most reliable and balanced performance during testing (R 2 = 0.79, NRMSE = 13.84%). The findings suggest that the combination of targeted spectral indices and appropriate machine learning approaches enables accurate, non-destructive estimation of maize carotenoid content, offering potential for practical applications in crop monitoring and stress assessment.
Why it matches plant phenotyping methodsトウモロコシ葉のカロテノイドという植物形質を、反射スペクトル指標と機械学習で非破壊推定する手法の開発・評価が研究の中心であるため。
abstractdevelops new spectral indices and machine learning models for improved prediction
Introduction Key rice panicle traits (grain number, panicle length, grain dimensions, maturity) determine yield and quality, and high-precision/high-throughput measurement is critical for rice breeding. Traditional methods are. Methods A dataset of 5300 rice panicle images (loose/normal/dense types; milk/dough/full maturity/over-ripe stages) was constructed, with 3290 for training, 940 for validation, and 470 for testing. A deep learning pipeline integrating. Results The panicle length extraction achieved R²=0.9583, RMSE=5.69 mm. Grain counting R² values were 0.9799 (loose), 0.9551 (normal), 0.9278 (dense). Grain length R²=0.8823, grain width MAPE=6.64%. OPG-YOLOv8. Discussion This study provides a comprehensive, automated tool for rice panicle phenotyping, addressing occlusion challenges and bridging the gap between advanced models and breeding applications.
Why it matches plant phenotyping methodsイネ穂の画像から粒数・穂長・粒形などの形質を抽出する深層学習パイプラインを開発・評価しており、フェノタイピング手法が研究の中心です。
titleDeep learning-based methods for phenotypic trait extraction in rice panicles.
Tomato ( Solanum lycopersicum ) is a globally cultivated horticultural crop, yet its productivity is severely constrained by foliar and insect-vectored diseases that reduce its quality and production. Early and accurate diagnosis of these diseases, along with sustainable biocontrol strategies, is essential for improving crop health and reducing economic losses. This review synthesizes and evaluates the recent progress in lightweight deep learning models and edge AI for tomato disease detection, highlighting their potential for practical deployment in precision agriculture. A comprehensive survey of recent literature was conducted, which covers convolutional neural networks, transformer-based models, optimization techniques including pruning, quantization, and knowledge distillation, and use of explainable AI tools to enhance transparency and trust. In addition, experimental validation was performed by utilizing MobileNetV2 and EfficientNetB0 on a subset of tomato diseases that are most common and prevalent in Tamil Nadu. The test performance of both the models resulted in an overall accuracy of 99.9% and macro-F1 nearly 0.99. Further, a unique framework that combines AI-powered diagnosis with microbial biocontrol recommendations is proposed offering a solution to manage diseases in both eco-friendly and region-specific way. Overall, this work provides a roadmap for combining sustainable methods with AI-driven diagnosis, promoting resilient, scalable, and farmer-friendly agricultural systems.
Why it matches plant phenotyping methodsトマト葉の病害状態を画像から推定する深層学習手法をレビューし、モデル性能も実験検証しているため、植物フェノタイピング手法が中心です。
abstractThis review synthesizes and evaluates the recent progress in lightweight deep learning models and edge AI for tomato disease detection
The growth status of Kenaf ( Hibiscus cannabinus L.) seedlings directly impacts its yield and quality. Addressing the challenges of inefficient monitoring and quantitative assessment of Kenaf seedlings under saline-alkali conditions, this study developed an automated method for plant identification and canopy coverage estimation during the seedling stage. This approach leverages high-resolution visible light imagery captured by unmanned aerial vehicles (UAVs) combined with deep learning semantic segmentation techniques. First, a UAV imagery dataset of Kenaf seedlings was constructed through geometric and radiometric calibration, image cropping, and sample annotation. Subsequently, three classical semantic segmentation models-FCN, U-Net, and DeepLabV3+-were trained and compared using image enhancement strategies. Model performance was quantitatively evaluated using metrics including Intersection over Union (IoU), accuracy, precision, and F1 score. Results indicate that all three models effectively segmented Kenaf plants from soil backgrounds. U-Net demonstrated optimal overall accuracy and detail retention, DeepLabV3+ exhibited advantages in small-scale object recognition, while FCN offered high computational efficiency, making it suitable for applications demanding real-time processing. Building upon this, the U-Net architecture was enhanced by incorporating a Self-Attention (SE) channel mechanism, further improving model performance to achieve an IoU of 85.99% and an Dice of 92.44%. Based on segmentation results from the enhanced UNet, plant canopy coverage during the Kenaf seedling stage was calculated. Combined with measured dry bark yield per mu, this enabled analysis of growth performance across varieties under saline-alkali conditions, identifying Xiao 3, K5, and Xiao 2 as materials exhibiting strong saline-alkali adaptability. The study demonstrates that this method enables high-precision identification and quantitative analysis of Kenaf seedlings, providing effective technical support for monitoring seedling growth and variety selection in saline-alkali soils.
Why it matches plant phenotyping methodsUAV画像と意味論的セグメンテーションを用いてケナフ幼苗を識別し、キャノピー被覆率という植物形質を推定する手法を開発・比較・改良しており、方法が研究の中心です。
abstractthis study developed an automated method for plant identification and canopy coverage estimation during the seedling stage.
In natural planting environments, pest and disease detection on eggplant fruits is characterized by small lesion sizes, weak edge feature information, significant scale variations, and complex backgrounds. Particularly, fruit borer holes, fruit rot lesions, and melon thrips bite marks exhibit obvious differences in size, edge structure, and spatial distribution, posing considerable challenges for real-time accurate detection. This paper proposes the DFSNet, a lightweight improved network for pest and disease detection on eggplant fruits in natural scenes. First, PConv is introduced in the P1, P2 shallow feature extraction stages of the baseline model's backbone network to enhance the modeling capability for fine-grained directional textures and weak edge information. Subsequently, an MSDA (Multi-Scale Directional Aggregation) module is designed and embedded into the feature enhancement modules at the P3, P4, and P5 layers of the backbone, which effectively improves the perception capability for insect hole edges and lesion contours through multi-directional depthwise separable convolution and Directional Edge Enhancer (DEE). Furthermore, a CSP-MSLA structure is introduced into the neck network, combining multi-scale linear attention mechanism with cross-stage partial connections to achieve selective enhancement of key pest and disease regions while maintaining low computational complexity. Finally, an SDDH (Shape-based Dynamic Detection Head) is introduced, which enhances the model's adaptive capability to different pest and disease geometric features and scale variations by introducing Scale-based Dynamic Loss. Experimental results demonstrate that the model achieves Precision of 81.0%, Recall of 78.3%, and mAP@50 of 80.5% on a self-constructed eggplant pest and disease dataset under natural scenes, representing improvements of 6.9, 8.8%, and 7.8% percentage points respectively compared to the baseline model. Meanwhile, the model parameters and computational cost are compressed to 1.8M and 5.4G respectively, with an inference speed of up to 378.13 FPS. The proposed method effectively improves small target detection accuracy and robustness under complex backgrounds while ensuring real-time performance, demonstrating particularly significant advantages in detecting small targets such as fruit borer holes and melon thrips bite marks, proving that this model is an efficient and robust real-time detection model for eggplant fruit pests and diseases.
Why it matches plant phenotyping methods卵果実の病斑・食害痕を画像から検出する深層学習手法を開発し、データセット上で性能評価しており、植物の病害状態の取得が中心的です。
abstractThis paper proposes the DFSNet, a lightweight improved network for pest and disease detection on eggplant fruits in natural scenes.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
In the recent era, the growth of deep learning is inevitable. Various models such as convolutional neural networks (CNNs) and transformers are used widely in images for high classification accuracy. Since the invention of transformers, researchers have widely used novel approaches using transformers to achieve an impressive accuracy. In spite of this, this paper proposes a novel custom lightweight CNN model called Attentive and Lightweight Network (ALNet). ALNet consists of three major blocks: stem, core, and head. The core part is the novel classifier built as an inspiration from various pre-trained models such as ResNet, SENet (Squeeze and Excitation Network), EfficientNet, SqueezeNet, and ShuffleNet. The main objective is to build a model that has a high classification accuracy while reducing the number of parameters. This reduces the size of the model and hence makes it easy to deploy on cloud platforms and use in edge devices. The model was evaluated using 5-fold cross-validation on three different datasets. The primary dataset was a grapevine dataset with an accuracy of 99.78 percent and 100 percent in multi-class and binary classification respectively. To test the robustness of the model, a multi-class classification using the apple dataset achieved an accuracy of 99.95 percent and a binary classification with the cherry dataset achieved an accuracy of 100 percent. ALNet uses only 0.17 million parameters which is 18 times less parameters than the lightest model (SqueezeNet) and it takes only 14 seconds to train each epoch while pretrained models take 17–31 seconds. ALNet requires only 151.98 MFLOPs with a model size of 677.20 KB, making it approximately 18 times smaller than SqueezeNet. On the whole, ALNet is a highly accurate, lightweight model for plant leaf diseases prediction.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類する軽量CNNを開発・検証しており、病害表現型の画像ベース抽出手法が研究の中心である。
abstractthis paper proposes a novel custom lightweight CNN model called Attentive and Lightweight Network (ALNet).
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
3D phenotyping refers to the quantitative characterization of a plant's structural and morphological traits in three-dimensional space, allowing for a detailed analysis of plant architecture and growth patterns. In recent years, rapid advancements in non-destructive, high-throughput 3D imaging technologies have enabled the precise measurement of these traits. Initially focused on single-plant traits under controlled conditions, the field has now expanded towards robust applications in real-world field environments, enabling large-scale analyses of plant canopies and complex structures. This study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies. It compares sensor technology and its application in controlled environments (Chamber-Crop Phenotyping, CCP) and field conditions (Field-Crop Phenotyping, FCP). Technologies such as Multiview stereo (MVS) reconstruction, LiDAR, and laser triangulation have enhanced plant phenomics by enabling high-throughput, non-destructive measurements of key traits such as canopy structure, leaf area, and stem diameter. This review highlights the strengths of the CCP, where environmental variables and flexibility are tightly controlled, facilitating precise trait measurement, and contrasts it with the challenges of the FCP, where unpredictable factors, such as occlusion, wind, light variability, and terrain complexity, complicate data acquisition. Various sensor platforms, including ground-based robotic systems and unmanned aerial vehicles (UAVs), have been discussed regarding their ability to overcome occlusion and limited sensor range in real-world conditions. The need to transition these technologies from laboratory environments to real-world agricultural applications is emphasized, highlighting their potential to improve crop management and plant breeding through accurate phenotypic trait extraction. Finally, current research gaps and future directions for integrating advanced sensor platforms and analytical techniques in both CCP and FCP settings are identified, emphasizing the need to enhance the scalability and robustness of 3D phenotyping for field applications.
Why it matches plant phenotyping methods3D作物フェノタイピングのセンサー技術、点群処理、対象形質、検証上の課題を中心に扱う方法論レビューであり、植物形質の取得手法が明確に中心である。
abstractThis study focuses on the recent advancements in 3D crop phenotyping using point cloud technologies.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Efficient and non-destructive cotton organ extraction is crucial for automatic cotton phenotyping. However, limited by leaf occlusion, large model parameters, and inefficient manual observation, it fails to meet current high-throughput phenotyping demands. To address these challenges, this paper propose ELGCot3D, a lightweight 3D point cloud-based cotton organ segmentation method, enabling high-precision segmentation in resource-constrained environments. First, a new module called ELG3D replaces traditional Set Abstraction structures, enhancing local cotton data learning capability via multi-mechanism feature fusion and boosting segmentation accuracy. Second, a cotton-specific feature enhancement module is proposed to secondary optimize the features output from the Feature Propagation layer. This module significantly increases feature discriminability while substantially reducing redundant and high consumption network layers, achieving a balance between performance and efficiency. Finally, a cotton point cloud-adapted training strategy improves model training stability and prediction accuracy. Experimental results on the Crops3D dataset show ELGCot3D achieves 76.7% mIoU and 86.1% OA for cotton segmentation, Meanwhile, the number of parameters and computational complexity are reduced by 50.1% and 50.7%, respectively, demonstrating the model's lightweight characteristics. Notably, it performs well in segmenting other Crops3D crops and exhibits strong generalization on the other cotton point cloud datasets. The proposed method offers a reliable approach for cotton phenotyping and precision agriculture. Future work will extend its high-throughput extraction capability for individual plant organs in large cotton fields, providing breeders with accurate data to support efficient breeding and new variety development.
Why it matches plant phenotyping methods綿花器官の3D点群セグメンテーション手法を開発し、精度・計算量・汎化性能を評価しており、植物表現型取得の方法が中心である。
abstractEfficient and non-destructive cotton organ extraction is crucial for automatic cotton phenotyping.
The integration of unmanned aerial vehicle (UAV)-assisted remote sensing with the Internet of Things (IoT) and Internet of Everything (IoE) offers a robust platform for optimizing precision agriculture by capturing spatiotemporal variability in crop growth. In this context, the present study aimed to predict the bulb yield of rainy-season onion crops across four staggered planting dates using UAV-based multispectral imagery. Canopy reflectance mosaics acquired at key growth stages, along with vegetation indices (VIs), viz. NDVI, NDRE, SAVI, LAI, NORM2, and GNDVI, were extracted for yield modeling. Yield prediction models at three onion growth stages were developed and assessed using five machine learning algorithms: linear regression (lm), random forest (rf), support vector machine with radial kernel (svmRadial), gradient boosting (gbm), and elastic net regression (glmnet), with model training and evaluation performed using 10-fold cross-validation. Among these, random forest consistently outperformed the other models at all growth stages, showing promising results at the bulb development stage, with a training R 2 = 0.944, RMSE = 1.919 t ha -1 , MAE = 1.523 t ha -1 , and a validation R² = 0.755, RMSE = 3.824 t ha -1 , and MAE = 3.11 t ha -1 . The support vector machine also demonstrated strong generalization (training R² = 0.787; validation R 2 = 0.716), highlighting its predictive capability. Year-wise evaluation revealed notable interannual variability in model performance, with models trained on data from 2024 outperforming those from 2023. Overall, these results demonstrate the efficacy of UAV-derived multispectral sensing, combined with machine learning, as an effective, scalable, and timely approach for reliable onion yield prediction and decision support in rainy-season onion crops under varying agronomic conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からタマネギの収量を推定する取得・解析ワークフローと機械学習モデルを開発・評価しており、植物表現型の測定が中心である。
abstractthe present study aimed to predict the bulb yield of rainy-season onion crops across four staggered planting dates using UAV-based multispectral imagery.
Rapid and non-destructive estimation of soluble solids content (SSC) is essential for tomato quality evaluation, yet the generalization ability of many existing spectral models remains limited when applied across multiple cultivars. In this study, hyperspectral reflectance was combined with a genetic algorithm (GA)-based optimization strategy to develop a robust SSC prediction framework applicable to diverse tomato types. Spectral reflectance and SSC (°Brix) were measured for 152 fruits representing 13 cultivars, including large red, medium red, red cherry, and yellow cherry types. To overcome the structural rigidity of conventional fixed-form spectral indices, reflectance spectra were linearly transformed to construct three novel indices: the linearly transformed difference spectral index (ltDSI), linearly transformed normalized difference spectral index (ltNDSI), and linearly transformed ratio spectral index (ltRSI). For each index, GA was employed to simultaneously optimize wavelength combinations and transformation coefficients. Under identical calibration and validation datasets, the GA-optimized indices consistently outperformed conventional two-band spectral indices as well as full-spectrum partial least squares models, while exhibiting markedly reduced sensitivity to tomato type. Across all validation datasets, the proposed models achieved coefficients of determination of approximately 0.80, with root mean square errors around 0.6°Brix and mean relative errors close to 10%. These results demonstrate that joint optimization of spectral index structure and parameters is an effective strategy for improving model robustness and transferability. The proposed framework provides a scalable solution for non-destructive SSC assessment and offers practical guidance for the development of low-cost, field-deployable spectral sensing tools for fruit quality phenotyping across cultivars and growing conditions.
Why it matches plant phenotyping methodsトマト果実のSSCという植物形質を対象に、ハイパースペクトル反射とGA最適化による新規推定指標を開発・検証しており、形質取得手法が研究の中心である。
abstracthyperspectral reflectance was combined with a genetic algorithm (GA)-based optimization strategy to develop a robust SSC prediction framework applicable to diverse tomato types.
Tobacco transplanting quality inspection is crucial for tobacco production, as it directly affects crop yield and quality of tobacco leaves. Accurate transplanting status detection and assessment provide essential support for replanting decisions and transplanting machine optimization. Traditional methods rely on manual inspection, which suffer from high cost, low efficiency, and unstable results. To tackle the aforementioned issues, this paper proposes a Deep Learning and Digital Twin driven Online Quality Inspection Method for Tobacco Transplanting, which consists of four core modules: Transplanting Status Detection, Multi-sensor Data Fusion, Digital Twin Visualization, and Operational Optimization Feedback. This paper proposes a lightweight improved YAN-YOLO11 algorithm capable of assessing normal, exposed-root, and buried seedlings. By fusing GNSS positioning data with visual detection results, the system estimates in-row spacing and performs status assessment for missed planting and double planting. The system establishes a virtual-real interactive closed-loop of "collection-detection-mapping-feedback" via the digital twin. By visualizing operational status in real-time and generating replanting path suggestions, it provides guidance for operation management and significantly improves inspection efficiency. Field experiments demonstrate that, compared with YOLO11n, YAN-YOLO11 improves precision and recall by 2.4% and 2.5%, respectively; mAP@50 increased by 3% to 80.9% ± 1.4%, and mAP@0.5:0.95 increased by 5.8% to 54.2% ± 1.0%, while significantly reducing model complexity. The system achieves a real-time performance of 30 FPS in the field, with an overall recognition accuracy of 90.74%, meeting practical application requirements. This study effectively enhances the digitalization, automation, and refined management of tobacco transplanting operations, providing a theoretical foundation and practical solution for the intelligent transformation of transplanting machinery and precision crop management.
Why it matches plant phenotyping methodsタバコ苗の植栽状態、株間、欠株・二株植えを画像とセンサーで推定する手法を開発・検証しており、植物状態の取得が研究の中心である。
abstractThis paper proposes a lightweight improved YAN-YOLO11 algorithm capable of assessing normal, exposed-root, and buried seedlings.
Reproduction assets foundThe paper's authors explicitly state that the demonstration video and code for the tobacco transplanting quality inspection system are publicly available via a Zenodo DOI link, which matches an allowed URL. This is a paper-specific, publicly actionable code asset. The arXiv URLs are cited prior work, not paper assets.Code · publicThe related demonstration video and code has been made publicly available on GitHub: https://doi.org/10.5281/zenodo.17075402.Open asset ↗GitHub · 10.5281/zenodo.17075402html-lines:294-405Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction The potato is one of the most consumed vegetable crops worldwide. However, the environmental changes and various crop diseases have a significant impact on potato production, indicating severe damage to yield quality and quantity. Farmers mostly employ manual disease classification methods in agriculture, which have limitations in detecting subtle disease symptoms, are time-intensive, and often necessitate specialized expertise, which may not be accessible in all farming communities. Therefore, automated systems are designed for accurate and rapid disease classification, mitigating the risks of misdiagnoses and delayed treatments. However, differences in the size, mass, and structure of the diseased areas of potato leaf diseases, combined with complex environmental conditions, complicate the effective identification of these diseases. Methods Therefore, to address the existing issues, we propose an improved deep learning approach, namely the PotatoGuardNet, which is an Inception-ResNet-V2-based Faster-RCNN model, for locating and classifying various potato leaf diseases. Precisely, the InceptionResNet-V2 approach is employed as the base network to capture the visual attributes of the samples, which are later recognized and classified by the 2-stage detector of the Faster-RCNN model. Results The model is tested on huge and complex data samples of potato plants from the PlantVillage dataset and reported a classification accuracy of 99.41%, along with an mAP of 0.9556. Further, the core working of the proposed method is evaluated by generating the heatmaps to show its explanatory power. Discussion Extensive experiments and comparative analyses against several recent state-of-the-art approaches confirm the effectiveness and reliability of PotatoGuardNet for potato leaf disease detection. The results demonstrate that the proposed framework successfully captures disease-specific visual patterns and provides accurate localization and classification, indicating its potential for practical deployment in automated agricultural disease monitoring systems.
Why it matches plant phenotyping methodsジャガイモ葉の病徴を画像から局在化・分類する深層学習手法を提案し、精度とmAPによる評価および比較実験を行っており、植物病害状態の表現型取得が中心である。
abstractwe propose an improved deep learning approach, namely the PotatoGuardNet, which is an Inception-ResNet-V2-based Faster-RCNN model, for locating and classifying various potato leaf diseases.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
MRI / PETX-ray / CTRootMorphology / geometry measurementRoot system architectureStress response / toleranceWater status / transpiration
Strategic optimisation of Root System Architecture (RSA) represents a critical frontier for stabilising crop productivity amid increasingly unpredictable moisture-deficit regimes. Understanding key root traits underlying effective drought response is necessary to harness the genetic diversity associated with root growth patterns and environmental adaptations. Many functionally significant root architectural traits have been reported, and the mechanistic importance of some of the anatomical ideotypes, such as the increased metaxylem vessel diameter to reduce axial hydraulic resistance to maintain leaf water potential and change in root growth angle to promote geotropic deep-soil moisture foraging, are discussed in this review. Despite the identification of these characteristics, the knowledge gap in their integration into predictive breeding frameworks remains. This review addresses this fragmentation by critically evaluating how the bottleneck of the ‘phenotyping’ process is being broken down through non-invasive high-throughput phenotyping modalities. Dynamic root-soil interfaces can be spatio-temporally quantified in situ using non-destructive technologies such as X-ray computed tomography and MRI, which can detect developmental plasticity masked by destructive sampling. Artificial Intelligence (AI), especially Convolutional Neural Networks, enables automated extraction of high-dimensional topological parameters from complex digital rhizograms. Present review integrates recent advances in phenotyping with molecular regulatory mechanisms, bridging two traditionally disparate fields. By focusing on the DRO1/qSOR1 loci and ABA-auxin crosstalk, we establish critical connections between molecular regulation and field-scale architectural performance. The resulting multi-scale roadmap may help in targeted selection of climate-resilient cultivars to maximize resource use efficiency.
Why it matches plant phenotyping methods根系構造の非破壊・ハイスループット表現型解析技術を中心に、X線CT、MRI、AIによる根系形質抽出をレビューしており、植物フェノタイピング手法が中核です。
abstractThis review addresses this fragmentation by critically evaluating how the bottleneck of the ‘phenotyping’ process is being broken down through non-invasive high-throughput phenotyping modalities.
This study addresses the challenge of organ-level instance segmentation in cotton point clouds, which arises from significant morphological variations and leaf occlusion across growth stages. To achieve high-precision leaf extraction, a hybrid framework integrating PointNeXt and DBSCAN is proposed. A dataset containing 1,065 cotton plants from seedling to boll-opening stages was constructed via multi-view image reconstruction and augmented through random rotation and scaling. Methodologically, a two-stage pipeline was designed: semantic segmentation was first performed using the PointNeXt network, where its residual MLP blocks enhanced edge and local feature learning; instance segmentation was then conducted by applying density-adaptive DBSCAN clustering to the semantic results, effectively mitigating over-segmentation in emerging leaves. Experimental results indicate that the semantic segmentation achieved an mIoU of 0.9846, representing a 7.2% improvement over PointNet++. The subsequent instance segmentation attained an ARI of 0.983, reduced the over-segmentation rate by 63%, and maintained an error below 3% for leaves smaller than 5 cm 2 . The framework provides reliable technical support for the automated extraction of key phenotypic traits such as leaf area index and leaf inclination distribution.
Why it matches plant phenotyping methods綿花葉の点群から器官レベルの葉を自動抽出するセグメンテーション手法を開発・評価し、葉面積指数や葉傾斜分布などの表現型形質への応用を示しているため、方法が中心的である。
abstractTo achieve high-precision leaf extraction, a hybrid framework integrating PointNeXt and DBSCAN is proposed.
Introduction High-throughput and accurate phenotyping is critical for enhancing crop breeding efficiency by enabling rapid identification of superior cultivars within large populations. For soybean [ Glycine max (L.) Merr. ], maturity group is a key determinant of geographic adaptation and influences yield potential. Consequently, accurate assessment of physiological maturity dates is essential for selecting lines suited to specific environments. This study evaluated the feasibility of three transfer learning techniques in improving the generalizability of models developed using historical data to predict the maturity dates of soybean breeding lines across new environments. Methods Our dataset included five breeding trials conducted in two sites from 2018 to 2021. Maturity dates were visually assessed at the R8 stage, and multispectral imagery from an unmanned aerial vehicle (UAV) was collected within each trial. Seven image features served as predictors in the models. Transfer learning techniques, namely pre-training and fine-tuning, single-source and multiple-source domain adaptation, were evaluated using the multiple-year datasets. Results When models were trained on data from three prior years and tested on two independent trials, the pre-training and fine-tuning technique demonstrated the best performance, with the highest agreement with visual ratings (coefficient of determination R 2 = 0.74 and 0.79) and root mean square errors of 1.70 and 1.96 days, respectively. The quantity for fine-tuning samples had minimal influence on the prediction accuracy for previously unseen data. Discussion These findings provide a reference for leveraging accumulated knowledge to generalize deep learning models for future practical utilization.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からダイズ成熟日を推定するモデルについて、転移学習とドメイン適応の性能・汎化性を評価しており、植物形質取得手法が中心です。
abstractThis study evaluated the feasibility of three transfer learning techniques in improving the generalizability of models developed using historical data to predict the maturity dates of soybean breeding lines across new environments.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
With the development of artificial intelligence (AI) in complicated imaging and remote sensing technologies, plant research is transitioning from manual measurements to automated data collecting. High-throughput image-based phenotyping enables the precise and automated acquisition of traits across various spatial and temporal scales, ranging from controlled laboratory settings to intricate field. Furthermore, AI facilitates the combination of satellite observations, unmanned aerial vehicle (UAV) imaging, soil and climate data, and spatiotemporal information to enhance the precision of trait monitoring and yield prediction. These advances enhance the ability to evaluate and predict crop performance under variable environmental conditions. This paper offers a cross-disciplinary paradigm for accurate and sustainable modern agriculture by merging AI methodologies with plant phenotyping and yield forecasting.
Why it matches plant phenotyping methods画像ベース・リモートセンシングによる植物形質取得とAI解析を中心に扱う方法論的レビューであり、植物フェノタイピング手法が中心的である。
abstractThis paper offers a cross-disciplinary paradigm for accurate and sustainable modern agriculture by merging AI methodologies with plant phenotyping and yield forecasting.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Plant disease segmentation in real-world agricultural environments poses significant technical challenges, including complex backgrounds, diverse lesion morphologies, and extreme class imbalance. Methods In this paper, we propose an integrated solution, STAR-Net, which combines a novel network architecture with a dynamic training strategy. The architecture features an innovative Heterogeneous Branch Attention Aggregation (HBAA) module to robustly represent multi-scale and multi-morphology features. The training strategy employs a Dynamic Phase-Weighted Loss (DPW-Loss) to navigate the complexities of imbalanced data. Results Our method achieves a state-of-the-art average mIoU of 93.36% on the NLB dataset. This result demonstrates its superior ability to precisely segment diseases with specific elongated morphologies. Furthermore, the model obtains a competitive average mIoU of 41.13% on the highly challenging PlantSeg dataset. This result validates its robustness in complex 'in-the-wild' scenarios. Discussion Our work presents a powerful, well validated, and synergistic solution for plant disease segmentation. It also paves the way for practical applications in precision agriculture.
Why it matches plant phenotyping methods植物病徴を画像からセグメンテーションする新規ネットワークと学習法を開発し、複数データセットで検証しているため、病害状態の表現型抽出が中心である。
abstractwe propose an integrated solution, STAR-Net, which combines a novel network architecture with a dynamic training strategy.
Far-red light (FR, 700-800 nm) can enhance photosynthesis by stimulating photosystem I (PSI). However, during chlorophyll fluorescence (CF) measurements using pulse-amplitude modulation (PAM) fluorometry, unusually high quantum yields of photosystem II ( Φ PSII ) have been observed under high FR light intensities, raising concerns about measurement artifacts. To test this, we constructed light response curves for sweet basil ( Ocimum basilicum L.) grown under light-emitting diode (LED) light (R:G:B = 44%:18%:38%) with varying photosynthetic photon flux densities (PPFD, 0-1,000 μmol m -2 s -1 ) and FR fractions (0, 0.26, 0.45, and 0.63). FR treatments consistently increased Φ PSII , but when total photon flux density (TPFD, 400-800 nm) exceeded 1,000 μmol m -2 s -1 , Φ PSII rose abruptly. Nonfluorescent reference tests using white and black paper confirmed that FR induced spurious fluorescence signals, likely due to spectral overlap between FR photons and the PAM detection range (680-760 nm). Tilting the LED panel to reduce reflected FR eliminated the abrupt Φ PSII peak but introduced unexpectedly increased Φ PSII across treatments, likely due to probe-induced shading. These findings demonstrate that high-intensity FR can confound PAM-based CF measurements by producing spurious signals unrelated to plant physiology. Accurate and reliable assessment of photosynthetic performance under extended spectral lighting conditions requires careful management of lighting geometry and FR intensity.
Why it matches plant phenotyping methodsPAMによる光合成量子収率測定のアーティファクトを検証し、照明条件と測定 geometry が植物生理形質の取得精度に与える影響を評価しているため、測定法の技術的検証が中心である。
abstractThese findings demonstrate that high-intensity FR can confound PAM-based CF measurements by producing spurious signals unrelated to plant physiology.
Fruit recognition and ripeness detection are crucial steps in selective harvesting. To better address the difficulties of fruit recognition and ripeness detection techniques in complex facility environments, a novel lightweight tomato ripeness detection network model based on an improved YOLO v8s is proposed (called TRD-Net). Here, a tomato dataset including 3,330 images from real scenarios was constructed, and an accurate lightweight tomato ripeness detection model trained on the captured images was developed. The TRD-Net model achieves efficient detection of tomatoes affected by overlapping occlusions, lighting variations, and capture angles, offering swifter detection speeds and lower computational demands. Specifically, the feature extraction module of YOLO v8s was refactored by employing spatial and channel reconstruction convolution (SCRConv) and adding the SimAM attention mechanism. The CIoU loss function was replaced by the MPDIoU loss function. The performance of the novel TRD-Net was comprehensively investigated. The proposed TRD-Net achieved an mAP@0.5 of 0.9581 with an improvement of 4.32 percentage points, and the model size decreased from 22.5 M to 17.6 M with an inference time of 8.7 ms per image. The number of model parameters and floating-point operations per second (FLOPs) decreased by 19.69% and 22.03%, respectively. Compared with state-of-the-art models, the proposed TRD-Net is notably promising for real-time tomato recognition and maturity detection. The study contributes to the establishment of a machine vision sensing system for a selective harvesting robot in a complex gardening environment.
Why it matches plant phenotyping methodsトマト果実の成熟状態を画像から推定する軽量検出ネットワークを開発し、データセット上で性能評価しているため、植物状態の取得・抽出手法が中心である。
abstracta novel lightweight tomato ripeness detection network model based on an improved YOLO v8s is proposed
Introduction Accurate detection of Taiqiu sweet persimmon in orchards is essential for estimating yield, planning harvest operations, and supporting intelligent management in precision agriculture. However, current fruit-detection approaches for this cultivar, especially during the color-transition period, suffer from highly subjective and inefficient manual inspection and from poor adaptability of existing deep-learning models to complex field scenes. Methods In this study, we propose an improved YOLO11-based detector, YOLO11-FC2T, for robust detection under conditions with strong color-background coupling, small or adherent fruits, and uneven illumination. YOLO11-FC2T introduces four key architectural modifications: (1) a C3k2_FasterBlock to improve gradient-efficient feature learning; (2) a C2PSA_CGA module to enhance channel-spatial focus via coordinate-guided aggregation; (3) a three-layer Dysample-T structure to strengthen multi-scale representation; and (4) a cross-scale attention fusion module, CAFMAttention, to better decouple fruits from cluttered backgrounds. To further enhance generalization in complex orchard scenes without additional labeling cost, we introduced the DiffuseMix data-augmentation method and apply it to color-transition images. Results Experiments show that YOLO11-FC2T clearly outperforms the YOLO11 baseline. The model achieves a precision of 91.7% (+1.0%), recall of 86.7% (+2.8%), mAP@0.5 of 94.8% (+1.6%), and mAP@0.5-0.95 of 81.2% (+4.0%), where mAP@0.5 uses an IoU threshold of 0.50. On a challenging tail-case set of 537 images, the false detection rate is 1.30%, with a 45.2% reduction in errors relative to YOLO11. In the performance evaluation stage, we first perform causal-effect analysis based on the Average Treatment Effect (ATE) to quantify the independent and joint contributions of each architectural component and of DiffuseMix; at the same time, the efficiency of the model is analyzed by the number of parameters (Params, M) and per-image inference latency (ms). in addition, we construct and use a dedicated tail-case dataset as a supplementary experiment to further verify the robustness and effectiveness of these improvements in the most difficult scenes. Finally, we introduced cross-condition test set to further validate the generalization capability of YOLO11-FC2T. The above results indicate that YOLO11-FC2T not only improves the indicators, but also possesses reliable generalization ability and stability. Discussion Overall, YOLO11-FC2T addresses key detection challenges during the color-transition period and provides a practical, portable solution for automated fruit identification and counting in precision agriculture. The above results indicate that YOLO11-FC2T not only improves the indicators, but also possesses reliable generalization ability and stability.
Why it matches plant phenotyping methods果実の検出・計数を目的とする画像ベース手法を開発し、比較評価・頑健性検証を行っており、植物器官の表現型取得が中心である。
abstractwe propose an improved YOLO11-based detector, YOLO11-FC2T, for robust detection
Introduction Leaf water potential (Ψ leaf ) is a fundamental physiological metric quantifying tree water status and forest drought stress, yet its measurement remains labor-intensive and destructive. Hyperspectral techniques show great promise for retrieving plant physiological traits; however, robust Ψ leaf estimation remains limited by three critical factors: unbalanced data distributions, the need for global-local feature synergy, and inherent uncertainty in point-based regression. Methods Here, we propose a deep learning framework (CIDL) that integrates: (1) a conditional generative adversarial network (CGAN) to generate balanced synthetic samples across the full Ψ leaf domain; (2) a feature extractor that combines Inception-ResNet with ACmix (IRAC) to capture local absorption features and long-range spectral dependencies jointly; and (3) a distribution-aware regression network (DARN) to explicitly model the target-variable distribution, thereby enhancing predictive reliability. The model was trained and evaluated using a dataset derived from dehydration experiments on leaves of young Populus euramericana 'I-214' trees, comprising 229 paired Ψ leaf and hyperspectral reflectance measurements, which were further augmented with 500 CGAN-generated synthetic samples to improve model robustness. Results CIDL achieved a prediction accuracy of R 2 = 0.78 and RMSE = 0.27 MPa on the test set, clearly outperforming traditional machine learning methods (mean R 2 = 0.66, mean RMSE = 0.34 MPa) and yielding a modest yet consistent improvement over mainstream deep learning approaches (mean R 2 = 0.76, mean RMSE = 0.28 MPa). Discussion These results demonstrate that the proposed CIDL framework provides a generalizable solution for small-sample physiological hyperspectral analysis and offers a reliable, non-destructive pathway for tree water-stress monitoring, with strong potential for applications in smart forestry management.
Why it matches plant phenotyping methods葉のハイパースペクトル反射から葉水ポテンシャルという植物生理形質を推定する深層学習フレームワークを開発し、既存手法と比較検証しているため、植物フェノタイピング手法が中心である。
abstractHere, we propose a deep learning framework (CIDL)
This study explores the use of hyperspectral imaging (HSI) combined with machine learning to detect physiological alterations in cassava leaves caused by Xanthomonas phaseoli pv. manihotis (Xpm), a bacterial plant disease that causes significant yield losses worldwide. Therefore, the use of hyperspectral images associated with machine learning can provide information rapidly and accurately, aiming to support decision-making. HSI captures spectral data that reflects biochemical changes in infected plant tissues. An image set of cassava healthy and symptomatic leaves (402 and 450, respectively) were imaged using a hyperspectral camera across wavelengths from 400 to 1000 nm, with image calibration and spectral normalization to improve data quality. Spectral parameters, such as mean reflectance and spectral differences (healthy vs. infected), were analyzed. Six machine learning models were tested for classification: Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP). SVM performed best, achieving the highest accuracy (91.41%), followed by MLP (87.89%), XGBoost (79.69%), and RF (77.34%). DT and KNN had the lowest accuracy (71.88% and 70.31%, respectively). The results suggest that HSI, particularly when combined with SVM, offers a rapid and accurate method for diagnosing cassava bacterial blight, with potential for large-scale field applications.
Why it matches plant phenotyping methodsカッサバ葉の病徴・生理変化をハイパースペクトル画像と機械学習で直接推定する診断手法を開発・比較しており、植物表現型取得が研究の中心である。
abstractThis study explores the use of hyperspectral imaging (HSI) combined with machine learning to detect physiological alterations in cassava leaves caused by Xanthomonas phaseoli pv. manihotis (Xpm)
Introduction: Accurately segmenting cotton seedling organs from 3D point clouds is fundamental for high-throughput plant phenotyping and digital breeding. However, cotton seedling segmentation remains challenging due to fine-scale and complex organ morphology, uneven point density with noise, and the lack of high-quality annotated datasets. Methods: To address these issues, we propose DCSFormer, a tailored extension of Point Transformer V3 designed for cotton seedling point cloud segmentation. The model introduces the DCS Block, which leverages dynamic sparse expert routing and dual-channel attention to adaptively capture global semantic dependencies and subtle local geometric variations, thereby improving stem-leaf boundary discrimination. In addition, the proposed CLFSkip replaces traditional skip connections with a cross-layer fusion strategy, effectively integrating multi-scale features while preserving organ-level details. We also constructed an annotated cotton seedling dataset to support training and evaluation. Results and Discussion: Experimental results show that DCSFormer achieves 93.67% mIoU, 95.83% mPrec, 97.35% mRec, and 96.56% mF1, outperforming multiple comparison models. Furthermore, when evaluated against baseline models on two public datasets, Crops3D and Pheno4D, DCSFormer exceeds the baseline across all four metrics, further validating its effectiveness and generalizability. This work provides an effective solution for precise cotton seedling organ segmentation.
Why it matches plant phenotyping methods綿花幼苗の3D点群から器官を抽出する手法を開発し、アノテーション済みデータセットの構築と複数データセットでの性能検証を行っており、植物表現型取得が中心である。
abstractAccurately segmenting cotton seedling organs from 3D point clouds is fundamental for high-throughput plant phenotyping and digital breeding.
Reproduction assets foundThe authors constructed an annotated cotton seedling point cloud dataset (100 samples with semantic/instance organ labels and ground-truth traits) used for training and evaluating DCSFormer, and the data availability statement points to a public Kaggle repository containing it. No author analysis code or trained model/Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.kaggle.com/datasets/tengfeiliu333/dcsformer-cotton/croissant/download .Open asset ↗Kaggle · tengfeiliu333/dcsformer-cottonlines:957-1015Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
As an important leafy vegetable, pakchoi ( Brassica chinensis L.) frequently suffers from pests and diseases in field environments. These symptoms are often localized on specific leaf regions, resulting in substantial losses in yield and quality. To achieve efficient and accurate detection of pakchoi pests and diseases, this study proposes an improved lightweight object detection model, termed YOLOv8n-DBW, based on the YOLOv8n framework. First, the original C2f module in the backbone network is replaced with a novel C2f-PE module, which integrates Partial Convolution (PConv) and an Efficient Multi-Scale Attention (EMA) mechanism to enhance high-level semantic feature extraction and multi-scale information fusion. Second, a Weighted Bidirectional Feature Pyramid Network (BiFPN) is introduced into the neck network to strengthen multi-scale feature fusion while improving model generalization and lightweight performance. Finally, the original CIoU loss in the regression branch is replaced with the Wise-IoU (Weighted Interpolation of Sequential Evidence for Intersection over Union) bounding box loss function, which improves bounding box regression accuracy and significantly enhances the detection of small and irregular pest and disease targets. Experimental results on a field-collected pakchoi pest and disease dataset demonstrate that the proposed YOLOv8n-DBW model reduces the number of parameters and model size by 33.3% and 31.8%, respectively, while improving precision and mean average precision (mAP) by 5.0% and 7.5% compared with the baseline YOLOv8n model. Overall, the proposed method outperforms several mainstream object detection algorithms and provides an efficient and accurate solution for real-time pakchoi pest and disease detection, showing strong potential for deployment on embedded systems and mobile devices.
Why it matches plant phenotyping methods圃場画像からパクチョイの病害症状を検出する軽量画像解析モデルを開発・評価しており、植物の病害状態の取得が研究の中心である。
titleAn improved YOLOv8n model for in-field detection of pests and diseases in pakchoi.
In order to realize the rapid detection of soybean seed germination potential, this study designed a fusion model to solve the problem that the single model was insufficient in spectral feature analysis and the prediction performance was limited. The model combines the advantages of the Partial Least Squares Regression (PLSR) and the Multilayer Perceptron (MLP), and utilizing principal components extracted by PLSR as the input features for MLP to construct a soybean seed germination potential prediction model with both linear and nonlinear modeling capabilities. The PLSR module accurately extracts the linear features of the spectrum, and the MLP network further captures the nonlinear relationship between the spectral data and the target variable, which significantly improves the generalization ability of the model. The experimental results show that the prediction performance of the proposed PLSR-MLP fusion model (R p 2 = 0.9534, RMSEP = 7.3821) is significantly improved compared with the single PLSR model (R p 2 = 0.7284, RMSEP = 17.8154) and the single MLP model (R p 2 = 0.7935, RMSEP = 15.5335). In the prediction of soybean germination potential, the PLSR-MLP model also outperforms other single models (Support Vector Machine, SVM; Random Forest, RF) and other fusion models such as PLSR-SVM and PLSR-RF. The PLSR-MLP fusion model effectively addresses the limitations of a single model's performance enhancement potential and the susceptibility to overfitting. It provides a new method for the efficient evaluation of seed germination potential. It also has practical application value for precision seed selection in agriculture and offers a new idea for near-infrared spectrum modeling.
Why it matches plant phenotyping methods大豆種子の発芽能力という植物状態を近赤外スペクトルとPLSR-MLP融合モデルで推定する手法の開発・比較検証が研究の中心である。
abstractthis study designed a fusion model to solve the problem that the single model was insufficient in spectral feature analysis and the prediction performance was limited.
Plant diseases remain a significant challenge in global agricultural production. Achieving efficient and accurate disease detection is essential for reducing crop losses, controlling agricultural costs, and improving yields. As agriculture rapidly advances toward digitalization and intelligent transformation, the application of artificial intelligence technologies has become a key pathway to enhancing industrial competitiveness. In this study, Chat Demeter, a multi-agent system for plant disease diagnosis based on deep learning. The system captures real-time leaf images through camera devices. It employs a CNN-Transformer model to perform instance segmentation and object detection, thereby enabling automatic identification of diseased leaves and classification of disease types. To enhance interactivity and practical value, the system incorporates a natural language interface, allowing users to upload images and receive automated diagnostic results and treatment suggestions. Experimental results demonstrate that the system achieves an accuracy of 99.50% and an AUC o f 99.91% on the validation dataset, highlighting its superior performance. Overall, Chat Demeter provides an effective tool for crop health monitoring and disease intervention, while offering a feasible pathway and developmental direction for integrating and optimizing future agricultural multi-agent systems.
Why it matches plant phenotyping methods植物葉の画像から病葉をセグメンテーション・分類する診断システムが研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractChat Demeter, a multi-agent system for plant disease diagnosis based on deep learning.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe original dataset can be accessed at https://www.kaggle.com/code/anshulm257/rice-disease-detection-using-cnn , which includes four distinct datasets to ensure diversity in data sources: https://www.kaggle.com/datasets/nirmalsankalana/rice-leaf-disease-imageOpen asset ↗Kaggle · nirmalsankalana/rice-leaf-disease-imagelines:304-312Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
TomatoGreenhouseLeafPhysiological trait estimationWater status / transpiration
Introduction Accurate crop water status monitoring is crucial for optimized irrigation in controlled environments, but traditional approaches relying on damaging measurements or sporadic sampling frequently restrict real-time evaluation. Methods This study explored the non-invasive leaf patch clamp pressure (LPCP) probe to evaluate the water status of drip-irrigated tomato and celery. Leaf turgor dynamics analysis enabled the characterization of the LPCP probe's output parameter (P p ) and its environmental drivers, and the development of predictive machine learning models. Results The results indicated that diurnal patterns of P p in drip-irrigated tomato and celery exhibited two distinct states: State I (unimodal) and State II (troughed), corresponding to moisture conditions with no or mild stress, and severe stress, respectively. The soil water content (SWC) thresholds for State I were set at SWC > 20% (tomato) and SWC > 19% (celery), whereas those for State II were set at SWC p was positively associated with solar radiation but negatively associated with SWC (in tomato) and wind speed (in celery). For State II, the associations between P p and environmental parameters were less than those in State I. Interestingly, compared to full irrigation, non-full irrigation treatments not only showed a higher proportion of State II but also resulted in an increase in both P p,max and P p,min by 15.39%-138.39% in tomato and 3.44%-94.02% in celery. These analytical results yielded four model parameter combinations based on the inclusion of SWC and the management of distinct P p states. The prediction model that integrated Combination 4 (substate P p prediction based on meteorological factors and SWC) with the random forest approach exhibited the highest accuracy (R 2 = 0.995, MSE = 2.419, RMSE = 1.540, and MAE = 0.531), with SWC identified as its key feature parameter. Discussion These findings provide a scientific foundation for optimizing the precision irrigation of greenhouse vegetables in drip systems.
Why it matches plant phenotyping methodsLPCPプローブによる葉の膨圧動態（水分状態）の非破壊測定と、機械学習による予測モデル開発が研究の中心であり、植物生理状態を抽出するフェノタイピング手法に該当する。
abstractThis study explored the non-invasive leaf patch clamp pressure (LPCP) probe to evaluate the water status of drip-irrigated tomato and celery.
Introduction Soybean diseases pose a significant threat to global crop yield and food security, necessitating rapid and accurate identification for effective management. While deep learning offers promising solutions for plant disease recognition, existing models often struggle with the complexities of in-field soybean disease identification, particularly due to high intra-class variations and subtle inter-class differences. Methods To address these challenges, we propose a novel region-specific feature decoupling and adaptive fusion network (RFDAF-Net) designed for robust and precise soybean disease recognition under real-world field conditions. The core of RFDAF-Net consists of two key components: a region-specific feature decoupling (RFD) module that enhances discriminative patterns and suppresses redundant information through a dual-pathway design, explicitly separating shallow, intermediate, and deep features; and a region-specific feature adaptive fusion (RFAF) module that dynamically integrates these multi-scale features via learned spatial attention. This hierarchical feature decomposition effectively isolates discriminative disease signatures while suppressing irrelevant variations. The architecture is flexible, enabling seamless integration with various backbone networks including both convolutional neural networks and Transformers. Results We evaluate RFDAF-Net extensively on a comprehensive soybean disease dataset containing images captured in diverse field environments. Experimental results show that our method significantly outperforms current state-of-the-art models across multiple architectures, achieving a top accuracy of 99.43% when implemented with a Swin-B backbone. Discussion The proposed framework offers an interpretable and field-ready solution for precision crop protection, demonstrating strong generalization ability and practical utility for real-world agricultural applications.
Why it matches plant phenotyping methods圃場画像からダイズ病害を識別する深層学習ネットワークを新規開発し、データセット上で広範に評価しているため、植物病害状態の取得・推定手法が研究の中心である。
abstractwe propose a novel region-specific feature decoupling and adaptive fusion network (RFDAF-Net) designed for robust and precise soybean disease recognition under real-world field conditions.
Introduction Ginseng, as a precious medicinal plant, requires precise classification of its seeds, which directly impacts production processes and the stability of herbal quality. Furthermore, this classification plays a critical role in advancing ginseng breeding and the modernization of the industry. Current research indicates that systematic automated precision classification technologies for ginseng seeds remain underdeveloped, necessitating breakthroughs in technical bottlenecks. Methods This study innovatively proposes a smart classification method based on multimodal data fusion. It employs recursive feature elimination (RFE) to select morphological features from images, followed by competitive adaptive reweighted sampling (CARS) to extract spectral bands from hyperspectral data within the 350~2500 nm range. Morphological and spectral features are then integrated to construct a random forest (RF) classification model optimized using an enhanced, red-billed blue magpie optimization (RBMO) algorithm. To address the RBMO algorithm's tendency to converge to local optima, the hybrid optimization framework is constructed by integrating three mechanisms: the improved Circle chaotic map, the golden sine search strategy, and the adaptive simulated annealing perturbation mechanism. Results Experimental results demonstrate that the proposed model outperforms the baseline model RF, achieving 4.69%、4.79%、4.69 and 4.74% improvements in classification accuracy, precision, recall, and F1-score on test datasets, respectively. Discussion The established multimodal data fusion classification system not only provides theoretical and technical foundations for industrial-scale ginseng seed classification but also offers a transferable intelligent decision-making paradigm for non-destructive testing in traditional Chinese medicine.
Why it matches plant phenotyping methods画像由来の形態特徴とハイパースペクトル特徴を統合し、種子を非破壊分類する手法自体が研究の中心であり、植物器官（種子）の観測可能な状態を推定しているため。
abstractThis study innovatively proposes a smart classification method based on multimodal data fusion.
Introduction Ensuring smarter and more sustainable farming practices is a critical challenge in modern agriculture. Agentic Artificial Intelligence (AAI), combined with Precision Agriculture (PA) and Federated Learning (FL), has the potential to enhance decision-making, optimize resource utilization, and reduce environmental impact. Methods This study proposes an AAI based framework for precision agriculture that integrates distributed sensing devices, intelligent agents, and federated learning to enable real time monitoring and decision support at the farm level. A practical deployment architecture is outlined, detailing inter-device communication and localized intelligence. The proposed model is evaluated across two distinct datasets tomato disease classification and weed detection. The model is designed to have DenseNet121, MobileNetV2, EfficientDet-D0, and YOLOv8 as local models within a federated learning environment. Results The federated global model achieved an accuracy of 96.4%, outperforming individual client models, with DenseNet121 and MobileNetV2 attaining accuracies of 95.0% and 93.9%, respectively. For weed species detection, EfficientDet-D0 demonstrated superior performance, achieving an mAP@0.5 of 0.978, average precision of 0.865, and an F1-score of 0.961, compared to YOLOv8 with an mAP@0.5 of 0.956 and an F1-score of 0.935. Discussion The results confirm the feasibility and effectiveness of integrating AAI with federated learning for intelligent precision agriculture. A SWOT analysis highlights the strengths of the proposed approach, along with deployment challenges and constraints. Overall, this study establishes a roadmap for future research, emphasizing sustainable intelligent farming systems.
Why it matches plant phenotyping methods植物病害分類を含む連合学習・エージェント型AI基盤を提案し、データセット上で性能評価しているため、植物の病害状態を推定する計算的フェノタイピング手法が中心である。雑草検出も含まれるが、病害分類の技術評価が明示されている。
abstractThis study proposes an AAI based framework for precision agriculture that integrates distributed sensing devices, intelligent agents, and federated learning to enable real time monitoring and decision support at the farm level.
Reproduction assets foundThe paper evaluates its federated learning phenotyping models on two publicly available Kaggle image datasets (tomato disease classification and weedcrop detection), explicitly linked in the data availability statement and references. No author code or models are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: The datasets used in the current study are openly accessible at https://www.kaggle.com/datasets/ashishmotwani/tomato and https://www.kaggle.com/datasets/vinayakshanawad/weedcrop-image-dataset/data .Open asset ↗Kaggle · ashishmotwani/tomatolines:838-838Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: The datasets used in the current study are openly accessible at https://www.kaggle.com/datasets/ashishmotwani/tomato and https://www.kaggle.com/datasets/vinayakshanawad/weedcrop-image-dataset/data .Open asset ↗Kaggle · vinayakshanawad/weedcrop-image-datasetlines:838-838Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Genebanks serve as critical repositories for preserving the genetic diversity of plant species, including crops, forages, and their wild relatives, which is essential for adapting to climate change, enhancing food security, and improving agricultural sustainability. Seed phenotyping, the process of evaluating observable seed traits influenced by genetics and environmental factors, plays a pivotal role in characterizing and utilizing this diversity. Traditional phenotyping methods, however, are labor-intensive and inadequate for the vast collections housed in genebanks. This paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum-from gamma rays to radio waves-to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor. We highlight the integration of advanced imaging systems (e.g., hyperspectral, X-ray, and thermal imaging) to enrich genebank datasets, facilitating trait discovery and crop improvement. Despite challenges like cost, scalability, and data standardization, opportunities arise from collaborative initiatives between genebanks and phenomics facilities through organizations such as International Plant Phenotyping Network. Our conclusions underscore how phenomics can revolutionize genebank operations, ensuring the efficient conservation and deployment of genetic resources to address global agricultural demands.
Why it matches plant phenotyping methods種子形質を対象とする高スループットセンサー・画像フェノタイピング技術を総説しており、フェノタイピング手法が中心である。
abstractThis paper explores the transformative potential of high-throughput phenomics technologies, leveraging the electromagnetic spectrum-from gamma rays to radio waves-to enable rapid, precise, and non-invasive assessment of seed traits such as size, shape, biochemical composition, and vigor.
Introduction In precision agriculture, accurate measurement of maize stem diameter during the jointing stage is crucial for lodging resistance assessment and yield prediction. However, existing methods have certain limitations: manual measurement is time-consuming and highly subjective, while two-dimensional image recognition can only capture local features and fails to reconstruct the true three-dimensional structure of the stem. Therefore, there is a critical need for an accurate and automated three-dimensional stem diameter measurement approach. Methods This study proposes a three-dimensional stem diameter measurement method that integrates an improved PointNet++ segmentation network with structural feature fitting, focusing on the position of the second above-ground internode of maize plants. Specifically, multi-view image reconstruction is employed to generate three-dimensional point clouds of maize stems, and Relative Position Encoding, the Local Group Rearrangement Module, and the Local Region Self-Attention mechanism are incorporated into the PointNet++ network to achieve precise segmentation of stems from the ground. On this basis, a structural feature fitting strategy is applied, where principal axis analysis and ellipse fitting are utilized to extract cross-sectional features, thereby obtaining the major axis and minor axis parameters for stem diameter estimation. Results Experimental results demonstrate that the proposed method maintains high accuracy under complex field conditions, achieving a mean absolute error (MAE) of 1.27 mm (R² = 0.87) for major-axis stem diameter and 1.38 mm (R² = 0.82) for minor-axis stem diameter. Discussion The proposed method effectively overcomes the limitations of traditional manual and two-dimensional measurement techniques. It provides a robust and accurate solution for maize stem diameter measurement during the jointing stage. This approach offers technical support for intelligent maize growth monitoring, lodging resistance analysis, and three-dimensional phenotypic trait extraction.
Why it matches plant phenotyping methodsトウモロコシ茎径という植物形態形質を、3D再構成、点群セグメンテーション、構造特徴フィッティングで自動推定する手法が研究の中心であり、精度検証も行っている。
abstractThis study proposes a three-dimensional stem diameter measurement method that integrates an improved PointNet++ segmentation network with structural feature fitting
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
The apple (Malus × domestica), a globally significant perennial fruit crop, faces immense pressure from climate change, evolving pathogens, and consumer demand for novel traits. Also, remains constrained by slow trait selection despite technological advances. Further, the traditional breeding methods are slow and resource-intensive, hampered by the apple's long juvenile period and high heterozygosity. This systematic literature review (SLR) synthesizes the state of the art in advanced data-driven technologies for accelerating apple breeding and genetic modification. Following the PRISMA-EcoEvo protocol, 47 selected studies were analyzed from databases including Web of Science, Scopus, and PubMed. Our thematic synthesis reveals a paradigm shift towards a "digital breeding" model, characterized by the convergence of three core technological pillars. First, high-throughput phenotyping (HTP), which leverages sensor modalities such as RGB-D, hyperspectral imaging, and LiDAR, is automating the collection of trait data at an unprecedented scale. Second, machine learning (ML) and deep learning (DL) algorithms are being deployed for diverse applications, including cultivar identification with over 96% accuracy, non-destructive quality prediction, and genomic selection, thereby boosting predictive ability for key traits by up to 18%. Third, precise and efficient genome editing, predominantly using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9), is enabling the rapid introduction of desirable traits, such as disease resistance, enhanced shelf life, and improved nutrient uptake. Demonstrated transgene-free editing protocols are accelerating the path to commercialization. We further explore the integration of these pillars through the agricultural internet of things (AIoT) and discuss emerging frontiers, including federated learning for data privacy, explainable AI (XAI) for model transparency, and the implications of recent regulatory frameworks. This review identifies critical research gaps, including the need for standardized open-access datasets and integrated end-to-end system validation. It concludes that the synergistic application of these technologies is poised to revolutionize the speed, precision, and resilience of apple improvement programs worldwide.
Why it matches plant phenotyping methodsリンゴ育種におけるデータ駆動技術の系統的レビューであり、高スループット表現型解析のセンサー技術と技術統合・検証課題を主要に扱っている。
abstractThis systematic literature review (SLR) synthesizes the state of the art in advanced data-driven technologies for accelerating apple breeding and genetic modification.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Agriculture is one of the major backbones of the Indian economy, where rice is the most prominent staple crop across the country. However, rice production has been significantly affected due to the occurrence of various plant diseases. Deep learning and machine learning have emerged as powerful solutions for computer vision-based problems. Methods This work identifies some of the key diseases and addresses these prominent ones using a state-of-the-art deep learning model. It proposes a novel multiclass rice leaf disease recognition model named GRG-ViT, which integrates Vision Transformer (ViT), Generative Artificial Intelligence (GenAI), and Explainable Artificial Intelligence (XAI) techniques for better outcomes. The Vision Transformer-based framework is designed to capture long-range spatial dependencies in leaf images, which enhances the model’s ability to identify the subtle disease patterns. Since the dataset portrayed considerable class imbalance, a GenAI-based synthetic data generation approach is equipped in this model to create balanced training samples, which in turn improves the model’s robustness. This model also proposes a hybrid Rectified Linear Unit (ReLU)–Gaussian Error Linear Unit (GELU)-based activation mechanism to attain effective feature representation. Results and discussion The obtained experimental results exhibit that the proposed GRG-ViT model reaches close to an overall accuracy of 96%, which outperforms conventional approaches. The incorporation of XAI methods like Gradient-weighted Class Activation Mapping (Grad-CAM) provides both interpretability and transparency by emphasizing the regions impacting the model’s actions. This research showcases the blended power of ViT, GenAI, and XAI in producing reliable and high-performing results for rice disease detection in precision agriculture.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定する画像解析モデルを開発し、データ拡張・分類性能・説明可能性を評価しており、病害フェノタイピング手法が中心である。
abstractIt proposes a novel multiclass rice leaf disease recognition model named GRG-ViT, which integrates Vision Transformer (ViT), Generative Artificial Intelligence (GenAI), and Explainable Artificial Intelligence (XAI) techniques for better outcomes.
Introduction Automatic and accurate segmentation of cherry tomato maturity in natural environment is the foundation for automatic picking. Lacking of significant differences in adjacent maturity and the problem of mutual occlusion between fruits usually affect the picking process. According to the changes in phenotypic characteristics of cherry tomato during its mature period and the Chinese national standard GH/T 1193-2021, a lightweight maturity instance segmentation method of cherry tomato with 5 levels, including green, turning, pink, light red and red was proposed based on improved YOLOv8n-Seg model, named as MobileViTv3-SK-WIoU-YOLOv8n-Seg (MSW-YOLOv8n-Seg). Methods In this model, MobileViTv3 was introduced into the original YOLOv8 model as backbone for feature extraction to reduce the parameters of the original model; selective kernel (SK) attention module was added to the neck part to improve the feature expression ability of the model; the complete intersection over union (CIoU) loss function in the original head part was replaced with wise intersection over union (WIoU), which can effectively filter low-quality samples and improve the stability and reliability of the model in complex scenes. The proposed model can better balance the relationship between segmentation speed, accuracy, and model computational complexity. Results The experimental results show that the bounding box precision, recall and mean average precision (mAP)@0.5 of the improved model on the test sets were 90.8%, 86.3% and 83.9% respectively, and the model size was 6.0 MB. Compared with YOLOv7-Mask, YOLOv8n-Seg, YOLOv9s-Seg, YOLO11n-Seg, Mask R-CNN (Mask region-based convolutional neural network) and Mask2Former, the bounding box precision increased by 9.6%, 5.2%, 5.7%, 12.3%, 13.3% and 5.0%, the recall increased by 7.8%, 7.4%, 8.8%, 13.1%, 13.9% and 0.1%, and the mAP@0.5 increased by 10.5%, 3.0%, 0.9%, 15.0%, 13.8% and 1.4% respectively. In terms of inference speed, the MSW-YOLOv8n-Seg has the highest inference speed, with FPS of up to 52.9 f·s -1 and latency of only 18.2ms, which demonstrates its real-time processing capability. Discussion The results show that the improved MSW-YOLOv8n-Seg model is optimal, and it suitable for instance segmentation scenarios with high real-time performance and can provide effective exploration for automated cherry tomato fruit picking.
Why it matches plant phenotyping methodsチェリートマト果実の成熟度という植物状態を画像から推定するインスタンスセグメンテーション手法を開発し、精度・速度・モデルサイズを比較検証しており、表現型取得法が中心である。
abstracta lightweight maturity instance segmentation method of cherry tomato with 5 levels, including green, turning, pink, light red and red was proposed based on improved YOLOv8n-Seg model
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) was merged into 5-levels based on the actual growth and peel color changes of cherry tomatoes.Open asset ↗lines:279-289Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Introduction Plant diseases and weeds are among the leading biological threats to global crop production. While deep learning has advanced automated analysis, existing approaches often fail under challenges like large multi-scale variations and blurred boundaries. Methods To address this, we propose SEAFEC (Spatial-Edge Adaptive Feature Enhancement Convolution), a novel convolutional module that jointly enhances scale adaptivity and boundary precision. SEAFEC employs a dual-branch design: the SCARF branch dynamically adjusts receptive fields, while the MEFE branch explicitly strengthens edge features. Results Across three representative tasks—plant disease classification, corn leaf disease detection, and sugarcane-weed segmentation—SEAFEC achieved consistent improvements (+1.8% accuracy, +2.5% mAP, +3.4% mIoU), with notable gains in boundary-sensitive cases. Discussion These results highlight SEAFEC as a general-purpose enhancement module, providing a unified solution for tackling scale-boundary challenges in agricultural imagery to support reliable disease diagnosis and precision weed management.
Why it matches plant phenotyping methods植物病害画像を対象に、マルチスケール・境界認識のための新規畳み込みモジュールSEAFECを開発し、病害分類・検出で技術性能を評価しているため、植物状態の画像ベース推定手法が中心である。
abstractwe propose SEAFEC (Spatial-Edge Adaptive Feature Enhancement Convolution), a novel convolutional module that jointly enhances scale adaptivity and boundary precision.
Tobacco leaf diseases significantly affect yield and quality, underscoring the need for rapid and non-destructive diagnostic tools. Although hyperspectral imaging (HSI) has been applied in tobacco pathology, most existing studies focus on single diseases and lack generalized, interpretable frameworks for multi-class identification. In this study, hyperspectral images of healthy leaves and four major diseases-brown spot, wildfire, Tobacco Mosaic Virus (TMV), and Potato virus Y (PVY)-were collected to construct a balanced, leaf-independent dataset. Pixels were grouped by leaf ID, and the entire dataset was strictly partitioned at the leaf level to prevent pixel-level data leakage and ensure generalization to unseen leaves. Multiple preprocessing techniques, wavelength-selection methods, and machine-learning classifiers were systematically compared. A compact ANN model integrating Savitzky-Golay preprocessing and SPA-based wavelength selection achieved the best overall performance while requiring only a small number of informative wavelengths. A Transformer model provided slightly stronger predictive capacity but depended on full-spectrum inputs and substantially higher computational cost. Pixel-level predictions enabled lesion-area-based severity estimation for the two leaf-spot diseases. SHAP analysis highlighted physiologically meaningful spectral regions associated with pigment absorption and structural variation. Overall, this study presents an efficient and interpretable HSI framework for multi-disease tobacco diagnosis, supporting the development of practical hyperspectral or multispectral systems.
Why it matches plant phenotyping methodsタバコ葉の病徴をハイパースペクトル画像と機械学習で識別し、病斑面積に基づく重症度推定まで行う診断フレームワークの開発が中心である。
abstractthis study presents an efficient and interpretable HSI framework for multi-disease tobacco diagnosis
Introduction Soybean mosaic virus (SMV) is one of the major pathogens affecting global soybean yield and quality, and its early and accurate detection is essential for disease warning and precision management. This study proposes a non-invasive early detection method by integrating portable Raman spectroscopy with artificial intelligence algorithms. Methods Raman spectra of leaves from both resistant and susceptible soybean cultivars were collected at different infection stages (0, 2, 4, and 6 days post-inoculation), and preprocessed using Savitzky-Golay (S-G) smoothing and adaptive iteratively reweighted penalized least squares (Air-PLS) baseline correction. Four classification models-1D-CNN, SVM, KNN, and BP-ANN-were developed to classify samples from different infection stages. Results Spectral feature analysis revealed significant changes in carotenoid levels caused by viral infection, and distinct spectral responses between resistant and susceptible cultivars during disease progression. Among the four classification models, the 1D-CNN model achieved the highest prediction accuracy of 90%. In addition, principal component analysis (PCA) indicated that the Raman spectroscopy-based method significantly advanced the early detection of SMV (SC3) to 4 days post-inoculation, compared to 7-10 days required by conventional methods. Discussion This evidences the superior capability of Raman spectroscopy for monitoring the dynamics of SMV infection and its potential to considerably reduce the duration of diagnosis. This study confirms the feasibility and efficiency of Raman spectroscopy combined with deep learning for in situ early detection of plant viral diseases and provides a promising reference for non-destructive diagnosis of early-stage foliar infections.
Why it matches plant phenotyping methods携帯型ラマン分光と機械学習を統合し、感染葉の病態を非破壊・早期検出する方法を開発および評価しており、植物病害状態の表現型取得が中心である。
abstractThis study proposes a non-invasive early detection method by integrating portable Raman spectroscopy with artificial intelligence algorithms.
Introduction Accurate identification of crop diseases is crucial for ensuring crop quality and yield. However, existing deep learning models for crop disease identification lack robustness in complex field environments and suffer from large model parameter sizes, which makes them difficult to deploy on resource-constrained devices. This gap between laboratory models and practical field applications necessitates the development of a lightweight and robust identification model. Methods To address these challenges, this paper proposes a lightweight YOLO-CGA model for sunflower disease identification and deploys it on a Raspberry Pi for field application. The model incorporates three key improvements based on YOLOv8n-cls: (1) A CBAM_ADown module is designed, which integrates attention mechanisms with asymmetric downsampling to enhance feature extraction and noise suppression in complex image backgrounds; (2) The C2f module of YOLOv8n-cls is replaced with the C3Ghost module, which utilizes ghost convolution to reduce parameter count while preserving fine-grained features; (3) An AFC_SPPF module is constructed, which aggregates multi-scale disease features through a multi-branch adaptive fusion structure to improve recognition performance for diverse lesions. Results Experimental results on three major datasets show that the proposed YOLO-CGA model achieves high identification accuracy: 98.48% on the BARI-Sunflower dataset, 98.32% on the Cotton Disease Dataset, and 91.11% on the FGVC8 dataset. Meanwhile, the model maintains a lightweight property with only 0.92M parameters, which is significantly fewer than that of other comparative models. Discussion The deployment of the YOLO-CGA model on the Raspberry Pi end device effectively bridges the gap between laboratory models and field applications, fulfilling the demand for real-time and on-site crop disease identification. The integration of attention mechanisms, ghost convolution, and multi-scale feature fusion enables the model to balance accuracy, robustness, and lightweight performance, making it suitable for resource-limited field scenarios.
Why it matches plant phenotyping methodsヒマワリ病害を画像から識別する軽量モデルを開発・評価し、Raspberry Piへ実装しており、植物の病害状態を推定する方法が研究の中心である。
abstractExperimental results on three major datasets show that the proposed YOLO-CGA model achieves high identification accuracy
Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often fail to represent. However, despite its potential, the adoption of DL in breeding programs remains limited due to its computational demands and the lack of accessible tools for users without extensive programming experience. This study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction. The package supports data preparation, hyperparameter optimization, model training, and DL-based evaluation. To assess its performance, MTMEGPS was applied to the two default datasets included in the package: Maize (genomic data) and Eucalyptus (near-infrared spectroscopy, NIR, data), as well as to an independent publicly available multi-environment validation dataset. Across most scenarios, MTMEGPS showed superior predictive ability compared with all benchmark models, particularly under UT for the internal datasets and MT for the independent multi-environment dataset. Mean squared error (MSE) values were similar across models, all falling within a moderate range. Overall, these results demonstrate the efficiency and practical utility of MTMEGPS for genomic and phenomic selection, even in scenarios where prediction errors remain moderate.
Why it matches plant phenotyping methods植物の複雑形質を予測するゲノム・フェノミック選抜用Rパッケージを開発し、データ準備からモデル評価までの再利用可能なワークフローを提供・検証しているため、フェノタイピング関連ソフトウェアとして中心的です。
abstractThis study introduces the MTMEGPS (Multi-Trait and Multi-Environment Genomic and Phenomic Selection), an R package that provides a streamlined end-to-end workflow for Uni- and Multi-Trait (UT and MT, respectively) and Uni- and Multi-Environment (UE and ME, respectively) genomic and phenomic prediction.
Reproduction assets foundThe paper's authors publicly released the MTMEGPS R package (analysis code/workflow) on GitHub, and the independent multi-environment maize validation dataset (phenotypes and genotypes) is publicly available via the Genomes to Fields initiative DOI. Both are paper-specific, public, and actionable.Dataset · publicnal phenotypic information.
2.2
Independent multi-environment maize validation dataset
The datasets analyzed in this study were obtained from the Genomes to Fields (G2F) initiative ( www.genomes2fields.org ). The dataset comprises 135 unique maize hybrids evaluated across nine experimental sites during the 2018 growing season ( https://doi.org/10.25739/anqq-sg86 ). Phenotypic measurements were collected following standardized protocols provided by the G2F consortium, as detailed in the accompanying documentation available on the project website.
The traits evaluated in this study included plant height (distance from the plant base to the ligule of the flag leaf), ear height (distance fOpen asset ↗10.25739/anqq-sg86lines:51-61Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Introduction Jackfruit cultivation is highly affected by leaf diseases that reduce yield, fruit quality, and farmer income. Early diagnosis remains challenging due to the limitations of manual inspection and the lack of automated and scalable disease detection systems. Existing deep-learning approaches often suffer from limited generalization and high computational cost, restricting real-time field deployment. Methods This study proposes CNNAttLSTM, a hybrid deep-learning architecture integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) units, and an attention mechanism for multi-class classification of algal leaf spot, black spot, and healthy jackfruit leaves. Each image is divided into ordered 56×56 spatial patches, treated as pseudo-temporal sequences to enable the LSTM to capture contextual dependencies across different leaf regions. Spatial features are extracted via Conv2D, MaxPooling, and GlobalAveragePooling layers; temporal modeling is performed by LSTM units; and an attention mechanism assigns adaptive weights to emphasize disease-relevant regions. Experiments were conducted on a publicly available Kaggle dataset comprising 38,019 images, using predefined training, validation, and testing splits. Results The proposed CNNAttLSTM model achieved 99% classification accuracy, outperforming the baseline CNN (86%) and CNN-LSTM (98%) models. It required only 3.7 million parameters, trained in 45 minutes on an NVIDIA Tesla T4 GPU, and achieved an inference time of 22 milliseconds per image, demonstrating high computational efficiency. The patch-based pseudo-temporal approach improved spatial-temporal feature representation, enabling the model to distinguish subtle differences between visually similar disease classes. Discussion Results show that combining spatial feature extraction with temporal modeling and attention significantly enhances robustness and classification performance in plant disease detection. The lightweight design enables real-time and edge-device deployment, addressing a major limitation of existing deep-learning techniques. The findings highlight the potential of CNNAttLSTM for scalable, efficient, and accurate agricultural disease monitoring and broader precision agriculture applications.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する深層学習手法を提案・比較評価しており、病害フェノタイピングの方法開発が中心である。
abstractThis study proposes CNNAttLSTM, a hybrid deep-learning architecture integrating Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) units, and an attention mechanism for multi-class classification of algal leaf spot, black spot, and healthy jackfruit leaves.
Reproduction assets foundThe paper's core phenotyping input is a publicly available Kaggle jackfruit leaf disease image dataset (38,019 images) explicitly linked in the data availability statement; no author code or model checkpoints are deposited.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/shuvokumarbasak4004/jackfruit-leaf-diseases .Open asset ↗Kaggle · shuvokumarbasak4004/jackfruit-leaf-diseaseslines:823-830Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
To accelerate the pace of wheat ( Triticum aestivum L.) improvement worldwide, desired seed-level characteristics and seed quality receive a growing attention as they directly impact early seedling establishment, seed longevity, and grain quality. Nevertheless, the throughput and accuracy of seed-level phenotyping and analysis have become a key limiting factor in this research domain, requiring new solutions to relieve this bottleneck. In this study, we first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds. Then, using 493 lines selected from the NIAB Diverse MAGIC (NDM) population, we applied the pipeline to segment individual seeds from MSI seed-lot images. This enabled us to perform seed-level measurement of sixteen morphological (e.g. seed size, length, width, and roundness) and spectral traits, ranging from ultraviolet (i.e. 375 nm, correlating with crude protein) to near-infrared (e.g. 975 nm, for assessing water content) wavelengths. After verifying these seed quality related traits (R2 ≥ 0.949; p < 0.001), we applied genome-wide association studies (GWAS) to link the computationally derived traits to genetic loci and identified eleven significant loci. Some of the loci were previously reported, with two unknown loci valuable for further assessment. Taken together, we believe this integrated MSI analysis pipeline provides a powerful solution for seed research and crop improvement in wheat, enabling us to bridge MSI, seed-level analysis, and genetic mapping to assess seed morphology, seed quality, and their underlying genetic architectures effectively.
Why it matches plant phenotyping methods自動マルチスペクトル画像と機械学習・コンピュータビジョンを統合し、個々の小麦種子の形態・スペクトル形質を高スループットに抽出するパイプラインが研究の中心である。
abstractwe first combined automated multispectral seed imaging (MSI; i.e. the VideometerLab 4 and Autofeeder systems) with a variety of machine learning and computer vision techniques to establish a high-throughput pipeline to analyse wheat seeds.
Reproduction assets foundThe paper's data availability statement names authors' public source code for the multispectral seed imaging analysis pipeline on GitHub (allowed URL), qualifying as a paper-specific public code asset. The multispectral imagery deposit (BioImage Archive S-BIAD2408, DOI 10.6019/S-BIAD2408) is also paper-specific and perCode · publicSource codes that support the results of this paper is available at https://github.com/The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipeline/releases .Open asset ↗The-Zhou-Lab/Videometer_Seed_Imaging_Analytic_Pipelinelines:562-570Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Plant leaf spectrophotometry has been used successfully as a means to detect stress, and it has been complemented by fluorescence analysis. This identification can be achieved in the ultraviolet (UV), visible (red, green, blue; RGB), near-infrared (NIR), and infrared (IR) spectral regions. Hyperspectral (measuring continuous wavelength bands) and multispectral (measuring discrete wavelength bands) imaging modalities can provide detailed information concerning the physiological well-being of plants, often diagnosing them at an earlier stage than visual or other more traditional biochemical assays. Because hyperspectral methods are highly sensitive and accurate, they cost a lot and produce vast quantities of data, which demand sophisticated computing software, and compared to multimedia, multispectral, and RGB cameras, they are less expensive and easier to carry but have reduced spectral resolution. Such methods are justified by thermal and fluorescence images revealing variations in the temperature and efficiency of photosynthesis of the leaves in response to stress. New digital imaging, thermal imaging, and optical filter technologies, and advancements in smartphone cameras have rendered low-cost, field-deployable platforms to monitor plant stress in real time feasible. Machine learning also supports these techniques by automating feature extraction, classification, and prediction to reduce the use of expensive instrumentation and human skill. But also problems like sensor calibration in a changing field, low model generalization across species and environments, and large, annotated datasets are needed. Beyond highlighting the relative strengths of the conventional and contemporary sensing approaches, the paper also examines the possibility of applying machine learning to multimodal images, as well as the growing impact of smartphone- based solutions in supplying inexpensive agricultural diagnostics. It concludes by overviewing the current limitations and limits to future research into scalable, cost-effective, and generalizable plant stress models.
Why it matches plant phenotyping methods植物ストレスを対象としたマルチモーダル画像・分光・熱・蛍光センシングと機械学習による表現型抽出を中心に扱う方法レビューであり、植物フェノタイピング手法の範囲に明確に該当する。
titlePlant stress detection using multimodal imaging and machine learning: from leaf spectra to smartphone applications.
Introduction Understanding plant responses to abiotic stress requires an insight into plant redox activity. This study proposes a novel and cost-effective method for assessing the redox state of plants. Methods The method utilizes the electrochromic properties of polyoxometalate phosphomolybdic acid hydrate (PMA). PMA is reduced proportionally by glutathione (GSH) and ascorbic acid (AsA), which results in a measurable color change. The validity of this method was confirmed through empirical experimentation in Arabidopsis thaliana under conditions of salinity and UV radiation. Results Salinity treatments revealed a non-significant, two-phase trend in redox activity with an increase at moderate levels followed by a decrease. UVC radiation led to a substantial decrease in redox activity, indicating distress. In contrast, UVA promoted resilience, also known as eustress. Notably, UVB significantly increased redox activity, suggesting the activation of an emergency antioxidant response. Discussion A demonstrable correlation has been identified between the redox activity of plants and various stress types. This correlation facilitates the classification of responses into two distinct categories: adaptive eustress and detrimental distress. This advancement contributes to the enhancement of plant metabolic and stress tolerance evaluation.
Why it matches plant phenotyping methods植物のレドックス状態を測定する新規手法を開発し、シロイヌナズナでストレス条件下の妥当性を検証しており、表現型取得が研究の中心である。
abstractThis study proposes a novel and cost-effective method for assessing the redox state of plants.
Reproduction assets foundThe paper's Data availability statement points to a public Zenodo deposit (DOI 10.5281/zenodo.17795112) containing the study's datasets, which underpin the PMA-based redox activity measurements (absorbance at 852 nm) in Arabidopsis thaliana under salinity and UV stress. No author analysis code or trained models are attDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://doi.org/10.5281/zenodo.17795112 .Open asset ↗zenodo · 10.5281/zenodo.17795112lines:416-483Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Early warning of overgrowth in strawberry seedlings is essential to balance vegetative and reproductive growth. However, existing monitoring methods face major challenges, including subtle visual symptoms and limited abnormal samples. To address this, we propose MM-CAPNet, a multimodal fusion framework for early detection of seedling overgrowth. We first developed a representative sample collection of strawberry seedlings through a systematic induction experiment, integrating historical environmental time-series data with contemporaneous plant images. The MM-CAPNet architecture uses a dual-stream design to process these inputs, with a Transformer encoder for environmental sequences and a MobileNetV2 encoder for images. A critical component of the proposed framework lies in the image-guided Cross-Attention mechanism, which uniquely treats the current phenotype as an active query to adaptively retrieve and aggregate the most diagnostically relevant segments of past environmental data. Experiments show MM-CAPNet outperforms baselines, reaching 87.6% accuracy and 0.901 AUC, with strong discriminative ability for early overgrowth categories. Ablation studies confirm its interpretability by linking visual phenotypes to key environmental drivers. This work provides growers with a proof-of-concept framework to regulate fertilization, irrigation, and light management during the nursery stage, thereby reducing the risk of excessive vegetative growth. The proposed framework supports precision cultivation strategies that enhance resource efficiency and crop resilience.
Why it matches plant phenotyping methods画像と環境時系列を統合し、イチゴ苗の過繁茂という植物状態を早期推定する新規マルチモーダル手法を開発・評価しており、表現型取得・判定が研究の中心である。
abstractwe propose MM-CAPNet, a multimodal fusion framework for early detection of seedling overgrowth.
Introduction Plant lesion segmentation aims to delineate disease regions at the pixel level to support early diagnosis, severity assessment, and targeted intervention in precision agriculture. However, the task remains challenging due to large variations in lesion scale—ranging from minute incipient spots to coalesced regions—and ambiguous, low-contrast boundaries that blend into healthy tissue. Methods We present GARDEN, a Gradient-guided boundary-Aware Region-Driven Edge-refiNement network that unifies multi-scale context modeling with selective long-range boundary refinement. Our approach integrates a Multi-Scale Context Aggregation (MSCA) module to harvest contextual cues across diverse receptive fields, forming scale-consistent lesion priors to improve sensitivity to tiny lesions. Additionally, we introduce a Boundary-aware Selective Scanning (BASS) module conditioned on a Gradient-Guided Boundary Predictor (GGBP). This module produces an explicit boundary prior to steer a Mamba-based 2D selective scan, allocating long-range reasoning to boundary-uncertain pixels while relying on local evidence in confident interiors. Results Validated across two public plant disease datasets, GARDEN achieves state-of-the-art results on both overlap and boundary metrics. Specifically, the model demonstrates pronounced gains on small lesions and boundary-ambiguous cases. Qualitative results further show sharper contours and reduced spurious responses to illumination and viewpoint changes compared to existing methods. Discussion By coupling scale robustness with boundary precision in a single architecture, GARDEN delivers accurate and reliable plant lesion segmentation. This method effectively addresses key challenges in the field, offering a robust solution for automated disease analysis under challenging real-world conditions.
Why it matches plant phenotyping methods植物病斑を画像から分割し、病害状態・重症度を推定する新規手法を開発し、公開データセットで検証しているため、植物フェノタイピング手法が中心である。
abstractPlant lesion segmentation aims to delineate disease regions at the pixel level to support early diagnosis, severity assessment, and targeted intervention in precision agriculture.
Reproduction assets foundThe paper uses two public plant disease segmentation datasets as its phenotyping inputs, both explicitly linked in the data availability statement: the Leaf Disease Segmentation Dataset (Kaggle) and the PlantSeg dataset (Zenodo record 13762907). No author code or model release is mentioned.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/fakhrealam9537/leaf-disease-segmentation-datasetOpen asset ↗Kaggle · leaf-disease-segmentation-datasetlines:767-820Dataset · publicThis data can be found here: https://www.kaggle.com/datasets/fakhrealam9537/leaf-disease-segmentation-dataset https://zenodo.org/records/13762907 .Open asset ↗Zenodo · 13762907lines:767-820Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Diameter at breast height (DBH) is a key parameter for assessing tree growth, carbon storage, and ecological functions. Traditional ground surveys are inefficient, labor-intensive, and terrain-limited, making them unsuitable for large-scale monitoring. Airborne LiDAR, as an advanced remote sensing tool, provides an efficient and non-destructive method for DBH estimation. However, most existing LiDAR-based models overlook the influence of genotype differences, limiting prediction accuracy. Methods In this study, we used data from 2,899 Catalpa bungei trees of different genotypes to develop a nonlinear mixed-effects (NLME) model that incorporates genotype as a random effect. This approach improved model generalizability by using LiDAR-derived tree height (LH) and LiDAR-derived crown diameter (LCD) as core predictors. Multiple sampling strategies were also evaluated to assess their impact on model performance. Results The results showed that, considering genotype effects, the proposed NLME model outperformed both traditional regression models and dummy-variable models (R 2 = 0.8624, RMSE = 1.1330, TRE = 3.9555), demonstrating the important role of genotype differences in improving model accuracy. Random sampling further improved prediction accuracy while effectively reducing measurement costs. Discussion This research introduces a new framework for integrating genotype variability into DBH prediction models and offers valuable insights for future LiDAR-based studies in genetically heterogeneous plantations. The findings provide technical support for forest management and ecosystem monitoring, as well as a methodological foundation for predicting tree growth under varying site and genetic conditions.
Why it matches plant phenotyping methodsUAV LiDARと非線形混合効果モデルを用いて個体樹木のDBHを推定する手法を開発・比較検証しており、植物形質取得が研究の中心である。
abstractAirborne LiDAR, as an advanced remote sensing tool, provides an efficient and non-destructive method for DBH estimation.
Introduction Cassava is one of the most widely cultivated crops worldwide, renowned for its rich natural ingredients and numerous nutritional benefits. However, the complex interdependencies among its features often pose challenges in image restoration and segmentation, particularly when identifying disease regions. In previous work, this manifested as higher false positives and misidentification of non-relevant areas, leading to a decline in precision and accuracy. Methods To address these issues, this study proposed an efficient artificial intelligence-powered image analysis system that leverages optimal feature selection with a HyperCapsInception-ResNet-V2-CNN model to enhance disease detection accuracy. Initially, the dataset was collected from the Kaggle repository, its name was Cassava Leaf Disease Classification, and it comprised 21,367 different images. Our approach began by normalizing cassava plant disease data using adaptive Gaussian Otsu thresholding. Histogram color evaluation and iterative clustering fragmentation were then applied to better isolate disease variations and improve precision. Subsequently, Cascaded Canny Edge Segmentation (CCES) was used to effectively segment the disease region. The disease variation properties were further evaluated using the Optimal Spider Swarm Intelligence Technique (OSSIT) to reduce irrelevant feature dimensions. For classification, the HyperCapsInception-ResNet-V2-CNN model was employed to categorize cassava diseases, including cassava bacterial blight (CBB), cassava mosaic disease (CMD), cassava green mite (CGM) disease, and cassava brown streak disease (CBSD), along with regular and abnormal leaf states. Results The proposed method's simulation results achieved 98.15% accuracy, a 97.22% F1-score, and 96.02% precision, outperforming other traditional methods such as EfficientNetB3, AlexNet, Faster-RCNN, and InceptionV3. Discussion Both optimized feature selection with OSSIT and hybrid HyperCapsInception-ResNet-V2-CNN architecture significantly enhanced the detection reluctance and the classification of the data. These findings indicate that the proposed system is effective in the automated detection of cassava disease and has a high potential of being practical in agricultural practices especially in precision farming and early detection of diseases.
Why it matches plant phenotyping methodsカッサバ葉画像から病変領域を分割・抽出し、病害状態を分類する画像解析手法の開発が研究の中心であるため、植物表現型手法として採用。
abstractCascaded Canny Edge Segmentation (CCES) was used to effectively segment the disease region.
Reproduction assets foundThe paper uses the public Kaggle 'Cassava Leaf Disease Classification' dataset (21,367 cassava leaf images) as its phenotyping input; the dataset is publicly downloadable at the authors' stated URL, which matches an allowed URL.Dataset · publicThe Cassava Leaf Disease Classification dataset is available on Kaggle and comprises 21,367 images. The images have an average resolution of 512 × 512 pixels. The data are split into training and test sets, enabling machine learning algorithms to be trained and tested to accurately detect diseases. The data are available for download from Kaggle: https://www.kaggle.com/datasets/nirmalsankalana/cassava-leaf-disease-classification .Open asset ↗Kaggle · cassava-leaf-disease-classificationlines:523-601Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Introduction Three-dimensional (3D) point clouds acquired by LiDAR are fundamental for applications such as autonomous navigation, mobile robotics, infrastructure inspection, and cultural-heritage documentation. However, environmental disturbances and sensor limitations often yield incomplete or noisy point clouds, degrading downstream performance. This study addresses robust, high-fidelity point cloud completion under such practical conditions. Methods We propose an unsupervised deep learning framework, Multi-Resolution Completion Net (MRC-Net), which builds on ShapeInversion by integrating a Generative Adversarial Network (GAN) inversion strategy with multi-resolution principles. The architecture comprises an encoder for feature extraction, a generator for completion, and a discriminator to assess geometric integrity and detail. Two key designs enable strong performance without supervision: (i) a multi-resolution degradation mechanism that guides reconstruction across coarse-to-fine scales, and (ii) a multi-scale discriminator that captures both global structure and local details. Results Extensive experiments on multiple datasets demonstrate that MRC-Net achieves accuracy comparable to leading supervised approaches. On virtual datasets (e.g., CRN), MRC-Net attains an average Chamfer Distance (CD) of 8.0 and an F1 score of 91.3. On a custom dataset targeting agricultural scenarios, the model preserves object integrity across varying complexity: for regular cartons, it achieves CD 3.3 and F1 97.3; for structurally complex simulated plants, it maintains overall shape while delivering average CD 8.6 and F1 88.1. Discussion These results indicate that MRC-Net advances unsupervised point cloud completion by balancing global shape consistency with fine-grained detail. The method provides a reliable data foundation for downstream tasks—including autonomous navigation, high-precision 3D modeling, and agricultural robotics—thereby contributing to improved data quality in precision-agriculture and related domains.
Why it matches plant phenotyping methods植物3D LiDAR点群の欠損補完を主題とする手法開発であり、植物形状の再構成性能を実験的に検証しているため、植物形態フェノタイピングに再利用可能な中心的手法研究と判断する。
titleStructure-aware completion of plant 3D LiDAR point clouds via a multi-resolution GAN-inversion network
Hyperspectral remote sensing has shown great promise for early detection of plant diseases, yet its adoption is often hindered by spectral variability, noise, and distribution shifts across acquisition conditions. In this study, we present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection, combining pixel-wise correction, curve-wise normalization and smoothing, and channel-wise standardization. The pipeline was evaluated on an experiment on early detection of stem rust ( Puccinia graminis f. sp. tritici Eriks. and E. Henn.) of wheat ( Triticum aestivum L.). The pipeline implementation enhanced the classification models accuracy raising F1-scores of logistic regression, support vector machines and Light Gradient Boosting Machine from 0.67-0.75 (raw spectra) to 0.86-0.94. Notably, it enabled reliable detection of asymptomatic infections as early as 4 days after inoculation, which was not achievable without preprocessing. The framework demonstrates potential for generalization beyond plant pathology, suggesting applicability to a range of hyperspectral remote sensing tasks such as vegetative health monitoring, environmental assessment, and material classification through improved signal interpretability and robustness. This work lays the groundwork for advancing hyperspectral image processing by proposing a reproducible, scalable pipeline that could be adapted for integration into unmanned and satellite imaging systems.
Why it matches plant phenotyping methods小麦茎锈病の無症状感染を対象に、ハイパースペクトル画像の前処理パイプラインを開発・評価し、植物病害状態の早期推定性能を検証しているため、植物フェノタイピング手法が中心である。
abstractwe present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection
Reproduction assets foundThe paper's data availability statement points to a public Google Drive repository containing the study's hyperspectral datasets used for wheat stem rust early detection. No separate author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:616-634Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Field / plotLeafMorphology / geometry measurementLeaf traits
Quantifying inequality in the leaf area distribution within a single module is critical for elucidating plant resource allocation strategies, but the accuracy of theoretical Gini coefficients derived from statistical distributions remains poorly validated against observed values. To resolve this gap, we analyzed 9,242 leaves from 121 culms of the bamboo Semiarundinaria densiflora , a model system with minimal ontogenetic noise and moderate leaf counts (36-187 leaves per culm) that enables robust Lorenz curve construction. Four candidate distributions were tested: the normal, log-normal, two-parameter Gamma, and two-parameter Weibull distributions. The parameters of the normal and log-normal distributions were estimated directly from sample statistics, whereas the parameters of the Gamma and Weibull distributions were estimated using the maximum likelihood method. Goodness of fit was assessed using the Kolmogorov-Smirnov (K-S) test for distributional validity, and the Akaike's information criterion (AIC) for model selection. Although the Gamma distribution passed the K-S test for a slightly higher percentage of culms (99%) than the Weibull distribution (97.5%), the Weibull distribution was selected as the superior model because it yielded significantly lower AIC values. Crucially, the theoretical Gini coefficients of the Gamma and Weibull distributions (denoted as G G and G W , respectively) were tested against the observed Gini coefficients ( G P ) calculated nonparametrically using the polygon method. Linear regression demonstrated that G W predicted G P with near isometric accuracy: the intercept's 95% confidence interval included zero (-0.006 to 0.017) and the slope's 95% confidence interval included unity (0.929 to 1.039). In contrast, G G exhibited significant bias. Notably, pooling leaves across culms violated all distributions due to microhabitat driven multimodality, confirming that intra-culm inequality assessments require organism level analysis. This work provides an empirical validation that the Weibull shape parameter reliably quantifies intra-culm leaf area inequality. By bridging theoretical distribution models with field-derived inequality metrics, our approach provides insights into canopy efficiency, photosynthetic optimization, and hydraulic trade-offs. Future work should test this approach in other grass species and assess its generalizability in plants with contrasting canopy architectures.
Why it matches plant phenotyping methods竹の葉面積不均一性を定量化する統計的フェノタイピング手法を、観測Gini係数との比較で検証しており、方法論が研究の中心です。
abstractThis work provides an empirical validation that the Weibull shape parameter reliably quantifies intra-culm leaf area inequality.
Introduction Ground-based Global Navigation Satellite System Reflectometry (GNSS-R) has recently emerged as a low-cost, continuous, and high-resolution technique for monitoring crop growth. However, conventional GNSS-R approaches that rely on signal-to-noise ratio (SNR) observables are limited by data availability, particularly from legacy receivers, and the effectiveness of observable combination methods in this context has not yet been established. Methods This study presents the first successful attempt to retrieve wheat height using ground-based GNSS-R with code pseudorange and dual-frequency carrier phase observables. Six observable combination schemes from GPS and BDS were evaluated through a field experiment at the Fengqiu Agro-ecology Experimental Station in China. A GDD-parameterized Logistic growth model was employed as a continuous reference. A multi-system, multi-satellite fusion strategy was developed, incorporating principal frequency power weighting within each system and residual reciprocal weighting across systems. Results The observable combination method effectively captured wheat growth dynamics. The best-performing combinations-GPS C5I+L5I+L2P and BDS C2I+L2I+L6I-achieved correlation coefficients (R) of 0.935 and 0.957, and RMSE values of 0.081 m and 0.086 m, respectively. Dual-system fusion further enhanced retrieval accuracy, reducing RMSE by 22.6% compared with the best single-system combination and by 34.6% relative to an SNR-based method. Discussion These findings demonstrate the feasibility and superiority of pseudorange and dual-frequency carrier phase combinations for SNR-independent GNSS-R crop monitoring. The proposed strategy offers a robust, scalable, and accessible tool for precision agriculture and continuous crop growth tracking, particularly in contexts where SNR data are unavailable or unreliable.
Why it matches plant phenotyping methodsGNSS-Rを用いてコムギ高さと成長動態を推定する観測量の組合せ・融合戦略を開発し、既存のSNR法と精度比較しており、植物形質取得手法が中心である。
abstractThis study presents the first successful attempt to retrieve wheat height using ground-based GNSS-R with code pseudorange and dual-frequency carrier phase observables.
The date palm ( Phoenix dactylifera L.) is a vital crop in arid and semi-arid regions, contributing over $13 billion annually to the global economy. However, it faces significant yield losses due to pests, such as the red palm weevil, and diseases, including Bayoud and Black Scorch. Currently, expert visual inspection is the primary method of management, but it is time-consuming, subjective, and unsuitable for detecting large-scale or early-stage damage. Automated approaches based on classical machine learning offer limited improvements due to their lack of generalizability and environmental sensitivity. Recent deep learning methods, such as CNNs and Vision Transformers, have improved classification accuracy, but treat tasks like classification, detection, segmentation, and severity estimation as separate. This paper proposes an integrated Reveal-Aware Hybrid Vision-Language and Transformer-based AI framework that combines GAN-based augmentations for feature generation, CLIP for multimodal classification, PaliGemma2 for text-based detection, Grounding DINO + SAM 2.1 for zero-shot segmentation, and a Vision Transformer regression model for severity prediction. This end-to-end explainable diagnostic pipeline achieved 98% classification accuracy, 95.8% precision, 91.3% recall, and 94.2% F1-score across two datasets: nine classes of infected date palm leaves and three classes of date palm diseases. The proposed framework demonstrated detection accuracy of 94-98%, high-quality segmentations, and reliable severity estimates. This integrated approach highlights the potential of combining AI, vision-language models, and transformers for scalable, accurate, and sustainable plant disease management.
Why it matches plant phenotyping methods植物病害の分類・検出・セグメンテーション・重症度推定を統合した画像ベース手法の開発と評価が中心であり、感染植物の状態を直接推定するため収録対象。
abstractThis paper proposes an integrated Reveal-Aware Hybrid Vision-Language and Transformer-based AI framework
Reproduction assets foundThe paper analyzes two publicly available date palm disease image datasets (Kaggle and Mendeley Data), both explicitly named in the data availability statement and used directly for the paper's phenotyping measurements. No author analysis code or trained model checkpoints are disclosed.Dataset · public1)Kaggle Dataset: The date palm leaf disease dataset used for this study is available on Kaggle and can be accessed via the following link: https://www.kaggle.com/datasets/hadjerhamaidi/date-palm-data. This dataset contains images of date palm leaves categorized into three classes: healthy leaves, brown spot disease, and white scale infection.Open asset ↗Kaggle · hadjerhamaidi/date-palm-datahtml-lines:510-532Dataset · public2)Mendeley Dataset: Additionally, the second dataset, which includes images of date palm leaves with eight types of diseases, can be accessed via Mendeley Data: https://data.mendeley.com/datasets/g684ghfxvg/2. This dataset was collected from 10 date farms in Madinah, Saudi Arabia.Open asset ↗Mendeley Data · g684ghfxvg/2html-lines:510-532Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Accurate detection of crop diseases from unmanned aerial vehicle (UAV) imagery is critical for precision agriculture. This task remains challenging due to the complex backgrounds, variable scales of lesions, and the need to model both fine-grained spot details and long-range spatial dependencies within large field scenes. To address these issues, this paper proposes a novel Multiscale CNNState Space Model with Feature Fusion (MSCNN-VSS). The model is specifically designed to hierarchically extract and integrate multi-level features for UAVbased analysis: a dilated multi-scale Inception module is introduced to capture diverse local lesion patterns across different scales without sacrificing spatial detail; a Visual State Space (VSS) block serves as the core component to efficiently model global contextual relationships across the canopy with linear computational complexity, effectively overcoming the limitations of Transformers on high-resolution UAV images; and a hybrid attention module is subsequently applied to refine the fused features and accentuate subtle diseased regions. Extensive experiments on a UAV-based crop disease dataset demonstrate that MSCNN-VSS achieves state-of-the-art performance, with a Pixel Accuracy (PA) of 0.9421 and a mean Intersection over Union (mIoU) of 0.9152, significantly outperforming existing CNN and Transformer-based benchmarks. This work provides a balanced and effective solution for automated crop disease detection in practical agricultural scenarios.
Why it matches plant phenotyping methodsUAV画像から作物の病斑・病害領域を抽出する新規画像解析モデルを開発し、データセット上で性能比較・検証しているため、植物病害状態のフェノタイピング手法が中心である。
abstracta dilated multi-scale Inception module is introduced to capture diverse local lesion patterns across different scales without sacrificing spatial detail
Introduction Perennial orchard systems are emerging as important yet underrepresented carbon sinks within the AFOLU sector, which contributes 20-24% of global GHG emissions. Many countries still rely on Tier 1 default values that fail to capture the structural and management characteristics of orchard species. Accurate biomass and carbon estimation, particularly through species-specific allometric equations, is essential for improving Tier 2-3 GHG reporting and recognizing orchards as meaningful contributors to climate-smart land management. Methods A systematic literature review was conducted using five major databases (2008-2024), following PRISMA guidelines. From 240 initial records, 53 studies met the inclusion criteria. These were categorized into three domains: (i) biometric modeling of fruit-tree biomass, (ii) species-specific allometric equation development, and (iii) carbon-sequestration assessments. Methodological trends, model performance, and research gaps were synthesized to inform an IPCC-aligned framework for orchard-specific emission and removal factors. Results Most studies were concentrated in Asia and the Mediterranean and focused on citrus, mango, apple, grape, and olive systems. Power-law allometric models dominated and generally showed high predictive performance (R² > 0.90) with variables such as diameter, height, and crown dimensions. However, major gaps remained: limited data for belowground biomass, juvenile trees, grafted architectures, vineyards, and uncertainty quantification-all of which restrict Tier 2-3 applicability. Discussion Based on these findings, this review proposes a standardized methodological framework linking biometric measurements, species-specific allometric modeling, remote-sensing integration, and uncertainty analysis to derive orchard-specific emission and removal factors consistent with IPCC guidance. Broader adoption of such protocols would improve transparency and accuracy in national AFOLU inventories and strengthen recognition of perennial orchards as viable nature-based climate solutions that support national net-zero targets.
Why it matches plant phenotyping methods果樹・ブドウ樹のバイオマスという植物形質を推定するアロメトリック手法を体系的にレビューし、計測・モデル化・リモートセンシング・不確実性分析を統合した標準化フレームワークを提案しており、手法が中心である。
abstractA systematic literature review was conducted using five major databases (2008-2024), following PRISMA guidelines.
This study investigates the potential of utilizing nonparametric, nonlinear machine learning (ML) algorithms, in conjunction with vegetation indices (VIs) derived from unmanned aerial vehicles (UAVs), to estimate the height-to-node ratio and the fourth internode length in cotton plants. The objective was to enhance the monitoring of these traits, thereby providing more accurate guidance on the optimal timing of plant growth regulator (PGR) applications. Data was collected from eight plots in our experimental field, with six plots used for model training and two for testing. During model development, the performance was assessed using nested 5-fold cross-validation, repeated three times with different partitions. For each algorithm, hyperparameters were tuned on the inner folds via Bayesian optimization with a Gaussian process surrogate, and the tuned model was evaluated on the corresponding outer test fold. We evaluated the performance of the ML algorithms using the Friedman test and interpreted their result using the Wilcoxon signed-rank test. The results demonstrate that VIs, combined with ML algorithms, can reliably estimate both the height-to-node ratio and the length of the fourth internode. Additionally, among the tested ML algorithms, Support Vector Regression (SVR) demonstrated superior performance for predicting height-to-node ratio, with an R² value of 0.8257 (95% CI: 0.7404 - 0.9110), RMSE value of 0.0998 (95% CI: 0.0953 - 0.1044), and rRMSE value of 5.51 (95% CI: 5.30 - 5.7). Meanwhile, the CatBoost demonstrated higher performance in estimating the fourth internode length, with an R² value of 0.799 (95% CI: 0.7570 - 0.8415), an RMSE of 0.1788 (95% CI: 0.1631 - 0.1945), and a rRMSE of 10.64 (95% CI: 9.90 - 11.38). Furthermore, using the Shapley Additive exPlanations (SHAP) approach, we revealed the contribution of each of the VI to the model's prediction. Overall, the findings demonstrate that UAV-derived VIs, combined with a machine learning algorithm, can consistently estimate these cotton traits. Additionally, this approach can replace traditional field-based measurements, thereby supporting more efficient monitoring and precise PGR management decisions.
Why it matches plant phenotyping methodsUAV由来の植生指数と機械学習により綿花の節間長・節位比を推定し、交差検証やアルゴリズム比較で性能を評価しており、形質取得手法が研究の中心である。
abstractThis study investigates the potential of utilizing nonparametric, nonlinear machine learning (ML) algorithms, in conjunction with vegetation indices (VIs) derived from unmanned aerial vehicles (UAVs), to estimate the height-to-node ratio and the fourth internode length in cotton plants.
Introduction Cotton is a vital global economic crop and textile material, yet its yield and quality are threatened by leaf diseases such as brown spot, verticillium wilt, wheel spot, and fusarium wilt. Methods We propose ViTKAB, a cotton disease recognition model based on an enhanced Vision Transformer that integrates a Kolmogorov-Arnold network and a BiFormer module. The model optimizes the Vision Transformer architecture to improve inference speed, employs nonlinear feature representation to better capture complex disease characteristics, and incorporates sparse dynamic attention to enhance robustness and accuracy. Results Experiments show that ViTKAB achieves an average recognition accuracy of 98.05% across four cotton leaf diseases, outperforming models such as CoAtNet-7, CLIP, and PaLI. Conclusions This method offers valuable insights for advancing intelligent crop disease detection systems and exhibits strong potential for deployment on edge devices.
Why it matches plant phenotyping methods綿花葉の病害状態を画像から認識する深層学習モデルを開発・評価しており、植物病害フェノタイピング手法が中心である。
abstractWe propose ViTKAB, a cotton disease recognition model based on an enhanced Vision Transformer that integrates a Kolmogorov-Arnold network and a BiFormer module.
Introduction The morphological characteristics of grafting seedlings affect the quality of automatic grafting. Because of the non-uniform and unstable lighting conditions in greenhouses, it is difficult to implement targeted control over seedlings. In contrast, plant factories are able to cultivate grafted seedlings in a more optimal environment by adjusting environmental factors like light. This research aims to propose an intelligent control method for seedling growth, in order to precisely cultivate seedlings that meet the requirements of different grafting machines. Methods This research established an evaluation method for tomato seedlings (suitable for automatic grafting) and scored seedlings that underwent light recipe transitions at different time points. Based on the comprehensive weighting of tomato seedlings suitable for automatic grafting, combined with the growth data of seedlings under different light environments, six machine learning algorithms were used to establish growth prediction models. Results The results indicate that the length of the hypocotyl and the diameter of the stem are crucial factors influencing whether the seedling can be mechanically grafted. And the transition of light recipes during cultivation can regulate seedling quality. XGBoost achieved the best accuracy for predicting rootstock and scion growth, with R 2 values of 0.9253 and 0.9334, respectively. A smart light control system was established and grafting experiments were conducted. The results showed that the automatic grafting success rate and post-grafting survival rate of light- regulated seedlings were 8.3% and 1.4% higher than those of commercially available seedlings, respectively. Discussion This demonstrates the feasibility of the model and highlights the practical application of the system in precision agriculture.
Why it matches plant phenotyping methodsトマト苗の接ぎ木適性を評価する方法と、胚軸長・茎径などの形質を予測する機械学習モデルを開発し、光制御へ適用しているため、表現型取得・推定手法が中心である。
abstractThis research established an evaluation method for tomato seedlings (suitable for automatic grafting)
The accurate quantification of plant organ color remains a major challenge in plant variety identification, particularly when adjacent expression states exhibit subtle visual differences in color. This study addressed this challenge by integrating colorimetric, phenotypic, and genomic analyses of 137 eggplant germplasm resources to characterize their fruit color. The CIELAB color parameters accurately represented fruit coloration and exhibited strong correlations with DNA fingerprinting results, while also aligning with the visual description of color characteristics based on Distinctness, Uniformity, and Stability (DUS) test guidelines. The color transition from harvest maturity to physiological ripeness was effectively captured by shifts in these values. Furthermore, the purple fruits at harvest maturity were subdivided into violet and red subcategories based on their CIELAB parameter distributions, and the yellow, ochre, and brown fruits at physiological ripeness were clearly separated using K-means clustering. Consequently, this study defined precise CIELAB ranges for each color category, offering a robust, multidimensional approach for the objective identification of eggplant varieties and enhancing the reproducibility of color-based DUS evaluations.
Why it matches plant phenotyping methodsナス果実という植物器官の色をCIELAB値とクラスタリングで定量・分類し、品種識別とDUS評価の再現性を高める方法が研究の中心である。
abstractThe accurate quantification of plant organ color remains a major challenge in plant variety identification
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
Fruit agriculture is undergoing a profound transformation driven by multi-omics, high-throughput phenotyping, and machine learning-driven bioinformatics. However, we demonstrate that this technological revolution has paradoxically created a 'valley of death' where most of genomic discoveries fail to reach farmers' fields. While we can now identify beneficial alleles in days and edit genomes in weeks, it still takes 10 years and 14,5 million euros to deliver a single improved cultivar to European markets - the same timeline as 30 years ago. This review exposes how data abundance has shifted, not eliminated, the fundamental bottlenecks in fruit crop improvement. We critically assess how these tools reshape genetic and metabolic diversity, emphasizing both their transformative promises and structural limitations. We highlight three persistent gaps: the challenge of integrating heterogeneous multi-omics datasets, the phenotyping bottleneck for complex traits, and the tension between innovation and biodiversity conservation. By framing fruit breeding as a "data-to-decisions" challenge, we outline the systemic changes needed for sustainable, resilient, and high-quality fruit production.
Why it matches plant phenotyping methods果樹育種におけるハイスループット表現型解析とデータ統合の課題を批判的にレビューしており、表現型解析が中心的な方法論テーマの一つである。
abstractThis review exposes how data abundance has shifted, not eliminated, the fundamental bottlenecks in fruit crop improvement.
Problems Tomato Spotted Wilt Virus (TSWV) severely affects tobacco yield and quality, creating an urgent need for accurate, rapid, non-destructive monitoring to support disease management. While existing TSWV detection methods perform well at the leaf scale, their field-scale application remains challenging. Due to complex crop canopy structures, spectral characteristics at the field level differ significantly from leaf-level observations, and TSWV-sensitive spectral features are still unclear. This study therefore aims to develop a field-scale TSWV identification model using UAV-based hyperspectral imaging to enable targeted disease control. Methodology A UAV-mounted hyperspectral camera (400-1000 nm) was deployed to capture imagery of tobacco plants at the rosette stage, enabling comparative spectral analysis between healthy and infected specimens. To identify sensitive features associated with tobacco plants infected with TSWV, six distinct feature extraction methodologies encompassing traditional statistical approaches (spectral ratio, correlation analysis, and principal component analysis [PCA]), machine learning-based techniques (relevant features [Relief], successive projections algorithm) and vegetation indices were utilized. Subsequently, we conducted a systematic evaluation of 18 classification models developed using three machine learning algorithms-support vector machine (SVM), k-nearest neighbors, and extreme gradient boosting -with the derived feature variables. Results This study demonstrates that while all integrated models combining Relief- and Correlation- selected feature bands with three machine learning algorithms delivered excellent performance, the SVM-Relief model achieved the most outstanding results (OA = 97.3%, AUC = 0.994, Kappa=0.947). Based on the SVM-Relief combination, a proposed method called RPR -which integrates PCA with recursive feature elimination- was further employed to reduce the number of feature indicators from 15 to 4 (775.6/772.9/781.1/756.4 nm). The resulting SVM-RPR combination model achieved performance (OA = 97.3%, AUC = 0.990, Kappa=0.947) comparable to that of the SVM-Relief model. Contribution This indicated that red-edge bands were of significant value in distinguishing healthy and TSWV-infected tobacco plants. Our study indicates the significant potential of integrating UAV-based hyperspectral imaging with machine learning techniques for rapid, non-destructive detection of tobacco TSWV at the field scale. The proposed approach offers a novel and efficient pathway for remote sensing-based monitoring of viral diseases in crops, with implications for precision agriculture and plant disease management.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習により、タバコ個体のウイルス感染状態を圃場規模で推定する手法の開発・評価が研究の中心であるため。
abstractThis study therefore aims to develop a field-scale TSWV identification model using UAV-based hyperspectral imaging to enable targeted disease control.
Reproduction assets foundThe paper states its tobacco TSWV UAV hyperspectral dataset is publicly available on GitHub, matching an allowed URL.Dataset · publicThe datasets are available in the GitHub repository ( https://github.com/smith22357/hyperspectral-dataset : tobacco TSWV hyperspectral-dataset).Open asset ↗smith22357/hyperspectral-dataset · tobacco TSWV hyperspectral-datasetlines:369-377Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction The real-time, accurate detection and classification of rice seeds are crucial for improving agricultural productivity, ensuring grain quality, and promoting smart agriculture. Although significant progress has been made using deep learning, particularly convolutional neural networks (CNNs) and attention-based models, earlier methods such as threshold segmentation and single-grain classification faced challenges related to computational efficiency and latency, especially in high-density seed agglutination scenarios. This study addresses these limitations by proposing an integrated intelligent analysis model that combines object detection, real-time tracking, precise classification, and high-accuracy phenotypic measurement. Methods The proposed model utilizes the lightweight YOLOv11-LA for real-time grain segmentation, which builds upon the YOLOv11 architecture. YOLOv11-LA incorporates several enhancements over YOLOv11, including separable convolutions, CBAM (Convolutional Block Attention Module) attention mechanisms, and module pruning strategies. These modifications not only improve detection accuracy but also significantly reduce the number of parameters by 63.2% and decrease computational complexity by 51.6%. For classification, the model employs a custom-designed, lightweight RiceLCNN classifier. Additionally, the DeepSORT algorithm is employed for real-time multi-object tracking, and sub-pixel edge detection along with dynamic scale calibration mechanisms are applied for precise phenotypic feature measurement. Results Compared to YOLOv11, the YOLOv11-LA model increases the mAP@0.5:0.95 score by 1.9%, showcasing its superior detection performance while maintaining lower computational overhead. The RiceLCNN classifier achieved classification accuracies of 89.78% on private datasets and 96.32% on public benchmark datasets. The system demonstrated high accuracy in measuring phenotypic features such as seed size and roundness, with measurement errors kept within 0.1 millimeters. The DeepSORT algorithm effectively managed multi-object tracking, reducing duplicate identifications and frame loss in real-time. Discussion Experimental validation confirmed that the YOLOv11-LA model outperforms the original YOLOv11 in terms of both detection speed and accuracy, while also maintaining low computational complexity. The integration of the YOLOv11-LA, RiceLCNN, and DeepSORT algorithms, combined with advanced measurement techniques, underscores the model's potential for industrial applications, particularly in enhancing smart agricultural practices.
Why it matches plant phenotyping methodsイネ種子画像からサイズや真円度を抽出するリアルタイム画像解析手法を開発し、精度・速度・測定誤差を検証しており、表現型取得が研究の中心です。
abstractThe proposed model utilizes the lightweight YOLOv11-LA for real-time grain segmentation
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository (RiceLCNN) containing the study's rice seed datasets and analysis code. The supplementary material link is generic and not confirmed to contain paper-specific assets.Dataset · publicang , Southwest Forestry University, China
Guodong Sun , Beijing Forestry University, China
Xiaofei Fan , Hebei Agricultural University, China
Data availability statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/5120191452/RiceLCNN .
Author contributions
DZ: Methodology, Software, Writing – original draft. SS: Funding acquisition, Resources, Writing – review & editing. JL: Validation, Writing – review & editing. WX: Data curation, Resources, Writing – review & editing. NX: Formal Analysis, Visualization, Writing – review & editing.
Conflict of interest
ThOpen asset ↗https://github.com/5120191452/RiceLCNN · RiceLCNNlines:619-662Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
MaizeSeed / grainObject detectionGrowth / development / phenology
With the rapid development of precision agriculture, seed germination detection is crucial for crop monitoring and variety selection. Existing fully supervised detection methods rely on large-scale annotated datasets, which are costly and time-intensive to obtain in agricultural scenarios. To tackle this issue, we introduce a knowledge distillation-enhanced semi-supervised germination detection framework (KD-SSGD) that requires no pre-trained teacher and supports end-to-end training. Built on a teacher-student architecture, KD-SSGD introduces a lightweight distilled student branch and three key modules: Weighted Boxes Fusion (WBF) to optimize pseudo-label localization, Feature Distillation Loss (FDL) for deep semantic knowledge transfer, and Branch-Adaptive Weighting (BAW) to stabilize multi-branch training. On the Maize-Germ (MG) open-access dataset, KD-SSGD achieves 47.0% mAP with only 1% labeled data, outperforming Faster R-CNN (35.6%), Mean Teacher (41.9%), Soft Teacher (45.1%), and Dense Teacher (45.0%), and reaches 59.3%, 62.8%, and 65.1% mAP at 2%, 5%, and 10% labeled ratios. On the Three Grain Crop (TGC) open-access dataset, which achieves 73.3%, 75.3%, 75.6%, and 76.1% mAP at 1%, 2%, 5%, and 10% labels, surpassing mainstream semi-supervised methods and demonstrating robust cross-crop generalization. The results indicate that KD-SSGD could generate high-quality pseudo-labels, effectively transfer deep knowledge, and achieve stable high-precision detection under limited supervision, providing an efficient and scalable solution for intelligent agricultural perception.
Why it matches plant phenotyping methods発芽という植物状態を画像から検出する半教師あり手法を開発し、複数データセットで性能比較・検証しており、表現型取得手法が研究の中心である。
abstractwe introduce a knowledge distillation-enhanced semi-supervised germination detection framework (KD-SSGD)
Tomato growth is highly susceptible to diseases, making accurate identification crucial for timely intervention. While deep learning models like the YOLO family have demonstrated success in detecting diseases in agricultural settings, they typically assume that training and testing data are independently and identically distributed (i.i.d.), which often doesn't hold in real-world scenarios. When pre-trained models are applied to new environments, performance can degrade due to domain shifts. To address this, we propose CTTA-DisDet, a continuous test-time domain adaptation framework for tomato disease detection that adapts models to evolving environments during testing, improving generalization in unseen domains. CTTA-DisDet utilizes a teacher-student architecture where both models share the same structure. Dynamic data augmentation is introduced, involving explicit and implicit augmentations. Explicit augmentation corrupts input images, while implicit augmentation uses large language models (LLMs) to generate new domain data. The teacher model learns generalized knowledge, and the student model mimics the teacher to distill domain-specific information. During testing, pseudo-labels generated by the teacher update the student model. To prevent catastrophic forgetting, a subset of neurons is randomly restored to their original weights during each test-time iteration. The teacher model is continuously updated via exponential moving average (EMA). Experimental results demonstrate that CTTA-DisDet achieves an impressive 67.9% performance in continuously changing cross-domain environments, significantly benefiting practical applications in non-stationary settings.
Why it matches plant phenotyping methodsトマトの病徴を画像から検出するためのドメイン適応手法を提案し、異なる環境で性能評価しており、植物病害状態の表現型取得が中心である。
abstractwe propose CTTA-DisDet, a continuous test-time domain adaptation framework for tomato disease detection that adapts models to evolving environments during testing, improving generalization in unseen domains.
Reproduction assets foundThe article's data availability statement says the required data was deposited in a public Zenodo repository with a DOI matching an allowed URL. The deposit is paper-specific (the data used for the tomato disease detection experiments), though the statement does not detail contents (e.g., whether code/models are also)Dataset · publicThe required data has been deposited in a public repository. It is available on Zenodo at the following https://doi.org/10.5281/zenodo.17659449.Open asset ↗Zenodo · 10.5281/zenodo.17659449html-lines:1047-1068Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Introduction The traditional strawberry picking operation has long relied on manual work. With the aging trend of the population becoming more and more obvious, the application of intelligent picking technology has become an irreversible trend. However, existing recognition methods still face bottlenecks such as suboptimal recognition accuracy and low computational efficiency. To address these issues, this study constructs a lightweight detection model, LBS-YOLO, based on an improved YOLOv11n architecture, significantly the model's accuracy and interference robustness while greatly compressing the parameter quantity. Methods The LBS-YOLO model is built upon YOLOv11n as the baseline network. In order to enhance the ability of backbone network feature representation, the model designs a lightweight LAWDS module. This design combines channel attention with spatial reconstruction operation to optimize the information retention efficiency in the down-sampling process, thus effectively enhancing the multi-scale feature representation ability and gradient flow propagation performance. Then in the feature fusion stage, the model introduces a Bidirectional Feature Pyramid Network (BiFPN), which not only enables cross-scale feature fusion but also achieves adaptive weighting through a learnable weight allocation mechanism. At last, adopts the C3k2_Star module to replace the conventional C3K2 for improved feature representation. Results On the used strawberry dataset, the LBS-YOLO model reached 88.6% mAP@0.5 and 75.8% mAP@0.5:0.95, which were 2.2 and 1.3 percentage points higher than YOLOv11n, respectively. The LBS-YOLO model improves the recall rate from 83.2% of YOLOv11n to 86.4%, and the F1-score from 81.2% to 82.9%. Its computational complexity is 6.6 GFLOPs and its reasoning speed is 260.7 FPS. Even better, LBS-YOLO only needs 3.4MB of storage space and 1.6 million parameters, which are 34.6% and 38% less than YOLOv11n respectively. Discussion The experiment demonstrates that, the LBS-YOLO model can significantly reduce the number of parameters and effectively improve the detection accuracy and operation efficiency. It successfully alleviated the problems of false detection and missed detection, thereby providing reliable technical support for strawberry growth monitoring, maturity identification and automatic picking.
Why it matches plant phenotyping methodsイチゴの成熟度という植物状態を画像から推定する軽量検出モデルを開発・評価しており、フェノタイピング手法が中心である。
abstractthis study constructs a lightweight detection model, LBS-YOLO, based on an improved YOLOv11n architecture
Reproduction assets foundThe paper uses a public strawberry image dataset from Baidu AI Studio (Paddle) as its phenotyping input, with an explicit public URL provided in the article text. No author analysis code or trained model checkpoints are stated as publicly available.Dataset · publicThe dataset used in this study is a publicly available dataset from Baidu Paddle. Detailed dataset information can be found at: https://aistudio.baidu.com/aistudio/datasetdetail/147119 . A total of 3,000 strawberry images are included here.Open asset ↗Baidu Paddle · 147119lines:317-334Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Plant diseases are a significant challenge to sustainable farming resulting in drastic losses of crop quality and quantity. Conventional diagnostic procedures like manual examination and single-model deep learning-based methods tend to be ineffective in identifying overlapping appearances, detailed textures of leaves, and environmental changes, which results in inconsistent performance. In order to address these issues, this paper presents an ensemble transformer framework that incorporates the segmentation, classification and optimization to identify multi-diseases in plants accurately. The framework has a two phase design. At the initial stage, U-Net and Swin Transformer V2 detect the disease-affected leaf areas with high accuracy, and the important features are correctly captured. In the second stage, classification is carried out using CoAtNet and its enhanced variant, which combine convolutional feature extraction with transformer-based global context learning. To further improve decision-making, a meta-heuristic fusion strategy based on the Levy Flight Honey Badger Algorithm dynamically weights classifier outputs, enhancing robustness and reducing misclassifications. Model interpretability is enhanced through GRAD-CAM visualizations, providing clear insights into the regions influencing disease classification. The framework was extensively evaluated on the PlantVillage dataset containing 54,305 images across 38 classes. Results demonstrate outstanding performance, with 99.31% accuracy, 99.32% precision, 99.31% recall, 99.32% specificity, and 99.31% F1-score. The ensemble segmentation approach exhibits a statistically significant 7.34% improvement over single-method implementations. Moreover, the heterogeneous ensemble model achieves 8.43% and 14.59% superiority over homogeneous ensembles and individual models, respectively. The integration of segmentation, hybrid transformer architectures, and meta-heuristic decision fusion delivers a powerful, interpretable, and highly reliable solution for early plant disease detection, offering strong potential for real-world agricultural deployment.
Why it matches plant phenotyping methods植物病害の症状領域を画像からセグメンテーションし、病害を分類する手法の開発・評価が中心であり、植物の病害状態を直接推定するため対象範囲に含まれる。
abstractthis paper presents an ensemble transformer framework that incorporates the segmentation, classification and optimization to identify multi-diseases in plants accurately.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Introduction Advances in automation and AI/ML offer new opportunities for plant science, including design, modeling, and analysis. This study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools. Methods The EcoBOT platform was developed, which consists of sterile containers (EcoFABs) for growing plants and imaging for monitoring plant growth and health. Brachypodium distachyon was grown on the EcoBOT, and its response to nutrient limitation and copper stress was evaluated. Results The results showed that Brachypodium distachyon grown in the EcoBOT maintained sterility and responded to nutrient limitation and copper stress. Analysis of over 6,500 root and shoot images revealed varying sensitivity and response rates to copper. Bayesian Optimization was used to improve model accuracies relating copper concentrations to plant biomass via sequential experiments, resulting in a >30% improvement. Discussion The findings of this study demonstrate the potential of the EcoBOT platform for researching plant responses to environmental factors. Future experiments could focus on relating other chemical stresses and microbial interactions to create generalized models of plant responses.
Why it matches plant phenotyping methodsEcoBOTという自動化プラットフォームを開発し、画像による植物成長・健康状態のモニタリングと、画像に基づくバイオマス推定を中核としているため、植物フェノタイピング手法として採用する。
abstractThis study aimed to develop an automated platform for researching small model plants under axenic conditions and integrate it with AI/ML tools.
Introduction Microplastics (MPs), ubiquitous and insidious pollutants pervading agricultural systems, pose an escalating threat to global food security. This makes the development of nondestructive methods for the early detection of MPs stress in rice seedling an urgent scientific imperative. Method Rice seedlings were cultivated under exposure to polyethylene terephthalate (PET), polystyrene (PS), and polyvinyl chloride (PVC) MPs at concentrations of 0 (control), 10, and 100 mg/L. Based on the stress-induced alterations in root exudates composition, a novel detection method for MPs stress in rice seedlings was developed using excitation-emission matrix fluorescence (EEMF) spectra combined with deep learning. Results Analysis of the original EEMF spectra revealed discernible differences. Feature extraction was performed using both the peak method and the PARAFAC method. Spectral changes in seedlings exposed to the low MP concentration (10 mg/L) were relatively minor compared to the control group. In contrast, exposure to the high concentration (100 mg/L) induced significant alterations in humic acid-like and amino acid-like substances. Subsequently, enhanced Vision Transformer (VIT) models were developed utilizing three distinct data representations: full EEMF spectra, emission spectra at specific excitation wavelengths, and extracted characteristic fluorescence values. The optimal model achieved 100% classification accuracy. Furthermore, SHapley Additive exPlanations (SHAP) analysis was employed to evaluate feature importance, identifying both humic acid-like and marine humic acid-like components as major contributors to the model's predictions. Conclusion In summary, this study establishes a novel, non-destructive, and interpretable framework for the early detection of MPs stress in rice seedlings based on EEMF spectra of root exudates combined with deep learning.
Why it matches plant phenotyping methodsイネ幼苗のマイクロプラスチックストレス状態を、根圏滲出物の蛍光スペクトルと深層学習から非破壊推定する手法の開発が中心であり、単なる生物学的測定ではない。
abstractthe development of nondestructive methods for the early detection of MPs stress in rice seedling
Problem Accurate diagnosis of plant diseases is crucial for ensuring crop yield and food safety. This study aims to explore a deep learning based intelligent recognition methods for plant leaf diseases to solve the automatic recognition problem of various pea leaf diseases. Methodology We propose a novel deep learning framework called TSSC. First, a three-neighbor channel attention is designed to promote the effectiveness of feature extraction. Second, a complementary squeeze and excitation mechanism is introduced to enhance the ability to extract key features. Finally, a split attention module is embedded to reduce model complexity. Results The experimental results demonstrate that the proposed model achieves an overall classification accuracy of 99.61% and outperforms other excellent deep learning models. Contribution The currently proposed system provides an effective solution for image recognition of complex plant diseases and has reference value for the development of mobile disease detection equipment.
Why it matches plant phenotyping methodsエンドツーエンドの葉画像から植物病害を識別する新規深層学習モデルを開発・評価しており、植物の病害状態を直接推定する方法が中心である。
abstractThis study aims to explore a deep learning based intelligent recognition methods for plant leaf diseases to solve the automatic recognition problem of various pea leaf diseases.
Introduction Real-time diagnosis of strawberry diseases plays a key role in sustaining yield and improving field management. However, achieving reliable recognition remains challenging. Lesions often display irregular shapes and appear at different scales, which complicates detection. Field images also contain cluttered backgrounds, while many diseases look visually alike, making differentiation more difficult. In addition, collecting data under real conditions is not easy, resulting in small datasets on which deep learning models tend to overfit and fail to generalize. Methods To address these issues, this study introduces ENet-CAEM, a redesigned EfficientNetB0 framework equipped with modules tailored for disease recognition. The Channel Context Module helps the network capture key lesion features while suppressing background noise. The Multi-Scale Efficient Channel Attention module applies multiple one-dimensional filters of varying sizes in parallel, enabling the model to highlight critical patterns, tell apart similar diseases, and adapt to lesions of different scales. A lightweight version of Atrous Spatial Pyramid Pooling is further integrated, allowing the network to perceive features at multiple spatial ranges. To balance local detail with global context, a mixed pooling strategy is adopted, enhancing robustness when lesion shapes change. Finally, Learnable DropPath and label smoothing are applied as regularization strategies, reducing overfitting and improving generalization on limited data. Results Experiments show that ENet-CAEM achieves 85.84% accuracy on a self-built dataset, outperforming the baseline by 4.29%. On a public strawberry dataset, the model reaches 97.39%, surpassing existing approaches. Discussion The proposed ENet-CAEM model shows superior accuracy and robustness over existing methods, providing an effective solution for strawberry disease recognition in practical field environments.
Why it matches plant phenotyping methodsイチゴ葉の病斑・病害状態を画像から認識する深層学習手法を開発し、複数データセットで性能評価しているため、植物病害表現型の取得・推定が中心である。
abstractthis study introduces ENet-CAEM, a redesigned EfficientNetB0 framework equipped with modules tailored for disease recognition.
Lettuce ( Lactuca sativa ), a widely cultivated leafy vegetable, is highly susceptible to bacterial and fungal infections that severely reduce yield and quality. Rapid and accurate disease identification is therefore essential for precision agriculture and sustainable crop management. This study proposes Efficient-FBM-FRMNet, a modular deep learning framework for automated lettuce disease detection. The model integrates EfficientNetB4 with dilated convolutions, a Feature Bottleneck Module (FBM) for redundancy reduction, a Reasoning Engine for higher-order semantic inference, and a Feature Refinement Module (FRM) for enhanced generalization. The framework was trained and validated on a publicly available dataset of 2,813 lettuce leaf images (bacterial, fungal, and healthy classes) using stratified 5-fold cross-validation. The proposed Efficient-FBM-FRMNet achieved an overall accuracy of 97.5%, outperforming baseline CNNs such as EfficientNetB4, ResNet50, and DenseNet121. It demonstrated superior precision (96.0%), recall (96.6%), and F1-score (97.0%), confirming its robustness and consistency across multiple folds. Statistical significance analysis (p
Why it matches plant phenotyping methodsレタス葉画像から病害状態を推定する深層学習フレームワークを開発し、公開データセット上で交差検証・ベースライン比較により性能を評価しており、植物表現型取得手法が中心である。
abstractThis study proposes Efficient-FBM-FRMNet, a modular deep learning framework for automated lettuce disease detection.
Reproduction assets foundThe paper's phenotyping measurements are based entirely on a public Kaggle lettuce plant disease image dataset (2,813 images, bacterial/fungal/healthy), cited with an explicit public URL matching an allowed URL. No author code or model checkpoints are stated as available.Dataset · publiceelwal P.
Dhiman P.
Gulzar Y.
Kaur A.
Wadhwa S.
Onn C. W.
( 2024 ).
A systematic review of deep learning applications for rice disease diagnosis: current trends and future directions
. Front. Comput. Sci.
6 . doi:
10.3389/fcomp.2024.1452961
Shaha, S.
.(n.d.) Lettuce plant Disease Dataset [Data set]. Kaggle. Available online at: https://www.kaggle.com/datasets/santoshshaha/lettuce-plant-disease-dataset (Accessed March 12, 2025 ).
Shoaib M. A.
Lai K. W.
Chuah J. H.
Hum Y. C.
Ali R.
Dhanalakshmi S.
. ( 2022 ).
Comparative studies of deep learning segmentation models for left ventricle segmentation
. Front. Public Health
10 , 981019 . doi:
10.3389/fpubh.2022.981019
, PMID:
36091529
PMC9453312
SuOpen asset ↗Kaggle · lettuce-plant-disease-datasetlines:990-1174Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Introduction High-resolution plant imagery is vital for phenotyping, disease monitoring, and precision agriculture. However, image acquisition in real-world conditions is frequently limited by sensor resolution, cost, and environmental noise, resulting in low-quality images. While deep learning-based super-resolution (SR) approaches show promise, they often fail to recover fine structural details that are essential for plant science applications. Methods We propose a plug-and-play high-frequency feature enhancement (HF-FE) module that can be seamlessly integrated into existing SR architectures. The module selectively amplifies high-frequency information, thereby improving the reconstruction of subtle details such as leaf venation, lesion boundaries, and texture patterns, while maintaining computational efficiency. Performance was evaluated on three diverse plant datasets: an oil palm dataset for large-scale plantation imagery, the UAV-based AqUAVPlant dataset for aquatic plants, and the Plant Pathology 2020 dataset for crop disease imagery. Results Across all datasets, models incorporating the HF-FE module achieved consistent improvements over state-of-the-art (SOTA) baselines, with notable gains in PSNR and SSIM. Visual assessments further confirmed enhanced clarity of fine structural features, particularly in challenging plant imaging scenarios. Discussion The proposed HF-FE module provides a flexible and effective enhancement strategy for plant image SR. By improving the fidelity of reconstructed plant imagery, it supports more accurate visualization and analysis, offering a methodological advancement that contributes to intelligent plant sensing, supports digital agriculture, and facilitates sustainable crop management.
Why it matches plant phenotyping methods植物画像の超解像度復元手法を開発し、複数の植物画像データセットで性能評価しているため、表現型取得を支える計算手法が中心です。
abstractIntroduction High-resolution plant imagery is vital for phenotyping, disease monitoring, and precision agriculture.
This work demonstrates the use of optical coherence tomography (OCT) for studying a plant’s long-range signaling in real time, in vivo , and non-invasively. This feat is achieved using OCT as a novel technique to visualize minute cellular displacements and deformations within the plant’s leaves. The use of bespoke registration algorithms enables tracking displacements with a precision greater than 0.1 μm. This measurement precision is one order of magnitude better than the typical ~1-μm optical resolution of OCT images. In the present work, OCT is used to analyze the time evolution of deformations incurred by wounding. The use of OCT enabled to 1) visualize, in real time, the propagation and evolution of the morphological changes associated with slow wave potentials (onset, peak, and recovery); 2) compute propagation speeds (~0.07 cm s −1 ); and 3) distinguish the type of deformation incurred (transient bending of the leaf due to changes in turgor cell pressure). This proof-of-concept study thus exemplifies the potential of OCT as a convenient and complementary tool to study the plant’s response mechanisms in vivo and in real time.
Why it matches plant phenotyping methodsOCT画像と登録アルゴリズムを用いて葉内の微小変位・形態変形を非侵襲的に取得・定量する手法が研究の中心であり、植物表現型の測定法として明確に該当する。
abstractThis work demonstrates the use of optical coherence tomography (OCT) for studying a plant’s long-range signaling in real time, in vivo , and non-invasively.
India is an agro-based country. The major goal of agriculture is to produce disease-free healthy crops. For Indian agronomists, cotton is a profitable commercial and fibre crop, it is the world's second-biggest export crop after China. Cotton production is also affected in a negative way by high use of water, authority of soil erosion and the practice of using dangerous fertilizers and pesticides. The two greatest threats to the rapid growth of the crop are the sucking bugs and cotton diseases. Prompt detection and accurate identification of diseases is vital to ensure healthy crop growth and achieve better yields. The primary objective of this research is to build a model by implementing deep learning-based approaches to spot infections in cotton crops. Deep learning is used because of its exceptional results in classification and image processing tasks. To address this issue, we developed CottonNet-MHA a novel deep learning framework to identify pathological symptoms in cotton leaves. The model employs multi-head attention mechanisms to strengthen feature learning and highlight the diseased-affected regions. To evaluate the performance of the proposed model, five pretrained transfer learning architectures-VGG16, VGG19, InceptionV3, Xception, and MobileNet were used as benchmark models. Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM) visualization was applied to enhance the trustworthiness and interpretability of the model. A web-based application was developed to deploy the trained model for real-world applicability. The performance analysis is carried out on the developed model based on the conventional models and the results indicate that CottonNet-MHA dominates the conventional models with respect to its accuracy as well as efficiency in the detection of diseases. The use of attention mechanisms approach strengthens the model's diagnostic accuracy and overall reliability. Grad-CAM results further demonstrated that the model effectively targets diseased areas, enhancing interpretability and reliability. Discussion: The study shows that CottonNet-MHA not only automates disease detection but also enhances interpretability through Grad-CAM analysis. The developed web platform allows the model to be applied in real-world environments, supporting live disease monitoring. The proposed framework not only improves the accuracy of cotton disease diagnosis but also offers potential for extension to other crop disease detection systems.
Why it matches plant phenotyping methods綿花葉の病徴を画像から検出・分類する深層学習手法を開発し、既存モデルとの比較検証とGrad-CAMによる病変領域の解釈を行っており、植物病害状態の表現型取得が中心である。
abstractThe primary objective of this research is to build a model by implementing deep learning-based approaches to spot infections in cotton crops.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset used in this work is downloaded from Akash Zade (Data Scientist) which is openly accessible and can be found at: https://drive.google.com/drive/folders/1vdr9CC9ChYVW2iXp6PlfyMOGD-4Um1ue.Open asset ↗Akash Zadehtml-lines:312-354Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Introduction Excessive fertilization not only causes environmental pollution and degrades water and soil quality but also increases production costs and reduces agricultural sustainability. Methods Based on two consecutive years of field experiments, this study developed a two-step data assimilation strategy for nitrogen (N) topdressing recommendations for winter wheat. First, a data assimilation system was established by minimising the discrepancy between aboveground dry biomass (AGB) estimated from remote sensing and that simulated by the crop growth model using a particle swarm optimization approach. Second, target yields under varying growth conditions were constructed using the DSSAT model and N economic return curves to enable optimised N fertilization recommendations. Results AGB monitoring model was developed, achieving satisfactory results in both the calibration and validation datasets, with determination coefficient (R²) (normalised root mean square error (nRMSE)) values of 0.94 (13.62%) and 0.82 (15.42%), respectively. Based on the data assimilation system, the data assimilation stability for AGB and yield are relatively high. The nRMSE values for AGB are 11.20% and 19.44% for the training and validation datasets, respectively. The nRMSE values for yield are 6.35% and 11.22% for the training and validation datasets, respectively. The data assimilation-based recommended fertilization shows a negative power-law relationship with AGB at the jointing stage (R² = 0.65). Under different yield levels, fertilization was reduced by 6.69%-34.08% compared with that under high yield levels. Conclusion This study balances yield and production costs by developing a data assimilation strategy for N fertilization recommendations, which can maintain high productivity and sustainability.
Why it matches plant phenotyping methodsリモートセンシングから小麦の地上部乾物量を推定するモデルを開発・検証し、データ同化に組み込んでいるため、施肥推薦が主目的でも植物形質取得法が実質的に中心的役割を持つ。
abstractFirst, a data assimilation system was established by minimising the discrepancy between aboveground dry biomass (AGB) estimated from remote sensing and that simulated by the crop growth model using a particle swarm optimization approach.
Introduction The Leaf Area Index (LAI) is a critical biophysical parameter for assessing crop canopy structure and health. Unmanned Aerial Vehicles (UAVs) equipped with multispectral sensors offer a high-throughput solution for LAI estimation, but flight altitude compromises between efficiency and image resolution, ultimately impacting accuracy. This study investigates the integration of super-resolution (SR) image reconstruction with multi-sensor data to enhance LAI estimation for soybeans across varying UAV flight altitudes. Methods RGB and multispectral images were captured at four flight altitudes: 15 m, 30 m, 45 m, and 60 m. The acquired images were processed using several SR algorithms (SwinIR, Real-ESRGAN, SRCNN, and EDSR). Texture features were extracted from the RGB images, and LAI estimation models were developed using the XGBoost algorithm, testing data fusion strategies that included RGB-only, multispectral-only, and a combined RGB-multispectral approach. Results (1) SR performance declined with increasing altitude, with SwinIR achieving superior image reconstruction quality (PSNR and SSIM) over other methods. (2) Texture features from RGB images showed strong sensitivity to LAI. The XGBoost model leveraging fused RGB and multispectral data achieved the highest accuracy (relative error: 4.16%), outperforming models using only RGB (5.25%) or only multispectral data (9.17%). (3) The application of SR techniques significantly improved model accuracy at 30 m and 45 m altitudes. At 30 m, models incorporating Real-ESRGAN and SwinIR achieved an average R 2 of 0.86, while at 45 m, these methods yielded models with an average R 2 of 0.77. Discussion The results demonstrate that the fusion of SR-reconstructed imagery with multi-sensor data can effectively mitigate the negative impact of higher flight altitudes on LAI estimation accuracy. This approach provides a robust and efficient framework for UAV-based crop monitoring, enhancing data-driven decision-making in precision agriculture.
Why it matches plant phenotyping methodsUAV画像の超解像・マルチセンサー融合・機械学習を用いて、植物キャノピー形質であるダイズLAIの推定手法を開発・評価しており、フェノタイピング手法が中心です。
abstractThis study investigates the integration of super-resolution (SR) image reconstruction with multi-sensor data to enhance LAI estimation for soybeans across varying UAV flight altitudes.
TomatoMultimodalStereoFruitObject detectionVisualization / data managementGrowth / development / phenologyFruit / seed / panicle traitsYield / yield components
Introduction The advancement of smart agriculture has witnessed increasing applications of computer vision in crop monitoring and management. However, existing approaches remain challenged by high computational complexity, limited real-time capability, and poor multi-task coordination in tomato cultivation scenarios. Methods To address these limitations, an intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm. The lightweight architecture of the GAE-YOLO framework is achieved through the replacement of standard convolutional layers with Ghost Convolution (GhostConv) modules, while detection accuracy is significantly improved by the integration of both AReLU activation functions and Effective Intersection over Union (E-IoU) loss optimization. The system, implemented on a Jetson TX2 embedded platform, also incorporates ZED stereo vision for 3D localization and a PyQt6-based visualization platform. Results When implemented on Jetson TX2, the system achieving 93.5% mean Average Precision at 50% intersection over union (mAP@50) at 10.2 frames per second (FPS), which can be optimized to 27 FPS by employing TensorRT acceleration and 720p resolution for scenarios demanding higher throughput. Furthermore, it establishes standardized assessment systems for tomato maturity and yield prediction, and offers integrated modules for disease diagnosis and agricultural large language model consultation. Discussion This work establishes a new paradigm for edge computing in agriculture while providing critical technical support for smart farming development.
Why it matches plant phenotyping methodsトマトの成熟度・収量予測および病害診断を含む画像・3Dビジョン基盤を開発し、エッジ環境で性能評価しているため、植物表現型取得が中心的な研究である。
abstractan intelligent tomato management system is proposed based on the Ghost-based Adaptive Efficient You Only Look Once (GAE-YOLO) algorithm
Reproduction assets foundThe paper's data availability statement explicitly states that the data and code supporting the study are publicly available on GitHub at the authors' repository (GAE-YOLO), which matches an allowed URL. This qualifies as a paper-specific public code asset for the tomato detection/phenotyping analysis.Code · publicThe data and code supporting this study are publicly available at GitHub under the following links: https://github.com/NSSCk/GAE-YOLO .Open asset ↗NSSCk/GAE-YOLOlines:756-834Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Growth chamberLeafClassificationCountingObject detectionGrowth / development / phenologyLeaf traits
Climate change, shrinking arable land, urbanization, and labor shortages increasingly threaten stable crop production, attracting growing attention toward AI-based indoor farming technologies. Accurate growth stage classification is essential for nutrient management, harvest scheduling, and quality improvement; however, conventional studies rely on time-based criteria, which do not adequately capture physiological changes and lack reproducibility. This study proposes a phenotyping-based and physiologically grounded growth stage classification pipeline for basil. Among various morphological traits, the number of leaf pairs emerging from the shoot apex was identified as a robust indicator, as it can be consistently observed regardless of environmental variations or leaf overlap. This trait enables non-destructive, real-time monitoring using only low-cost fixed cameras. The research employed top-view images captured under various artificial lighting conditions across seven growth chambers. YOLO automatically detected multiple plants, followed by K-means clustering to align positions and generate an individual dataset of crop images–leaf pairs. A regression model was then trained to predict leaf pair counts, which were subsequently converted into growth stages. Experimental results demonstrated that the YOLO model achieved high detection accuracy with mAP@0.5 = 0.995, while the A convolutional neural network regression model reached MAE of 0.13 and R² of 0.96 for leaf pair prediction. Final growth stage classification accuracy exceeded 98%, maintaining consistent performance in cross-validation. In conclusion, the proposed pipeline enables automated and precise growth monitoring in multi-plant environments such as plant factories. By relying on low-cost equipment, the pipeline provides a technological foundation for precision environmental control, labor reduction, and sustainable smart agriculture.
Why it matches plant phenotyping methodsバジルの葉対数という植物形質を低コストカメラ画像から自動推定し、生育段階へ分類する画像解析パイプラインを開発・評価しており、フェノタイピング手法が中心である。
abstractThis study proposes a phenotyping-based and physiologically grounded growth stage classification pipeline for basil.
As an important economic crop, peanut is frequently affected by leaf diseases during its growth period, which severely threaten its yield and quality. Therefore, early and accurate disease detection is critical. However, existing lightweight deep learning methods often struggle to balance model size, real-time detection accuracy, and edge device deployment, limiting their widespread application in large-scale agricultural scenarios. This study proposes a lightweight real-time detection model, YOLO-PLNet, designed for edge deployment. The model is based on YOLO11n, with lightweight improvements to the backbone network and Neck structure. It introduces a Lightweight Attention-Enhanced (LAE) convolution module to reduce computational overhead and incorporates a Channel-Spatial Attention Mechanism (CBAM) to enhance feature representation for small lesions and edge-blurred targets. Additionally, the detection head adopts an Asymptotic Feature Pyramid Network (AFPN), leveraging staged cross-level fusion to improve detection performance across multiple scales. These improvements significantly enhance the detection accuracy of peanut leaf diseases under complex backgrounds while improving adaptability for edge device deployment. Experimental results show that YOLO-PLNet achieves a parameter count, computational complexity, and model size of 2.13M, 5.4G, and 4.51MB, respectively, representing reductions of 18.07%, 16.92%, and 15.70% compared to the baseline YOLO11n. The mAP@0.5 and mAP@0.5:0.95 reach 98.1% and 94.7%, respectively, improving by 1.4% and 1.7% over YOLO11n. When deployed on the Jetson Orin NX platform with real-time video input from a CSI camera, the model achieves a latency of 19.1 ms and 28.2 FPS at FP16 precision. At INT8 precision, latency is reduced to 11.8 ms, with real-time detection speed increasing to 41.3 FPS, while GPU usage and power consumption are significantly reduced with only a slight decrease in detection accuracy. In summary, YOLO-PLNet achieves high detection accuracy and robust edge deployment performance, providing an efficient and feasible solution for intelligent monitoring of multiple categories of peanut leaf diseases.
Why it matches plant phenotyping methods落花生葉の病徴を画像から検出する軽量モデルを開発し、精度・計算量・エッジ実装性能を評価しており、植物状態の取得手法が中心である。
abstractThis study proposes a lightweight real-time detection model, YOLO-PLNet, designed for edge deployment.
The canopy volume of fruit trees is an important basis for precise orchard management. However, current methods for predicting canopy volume cannot accurately identify and exclude canopy porosity, resulting in a larger prediction than the actual volume. To address this issue, this study proposes a calculation method of canopy effective volume (EV) for fruit tree based on LiDAR point cloud data. In this method, the fruit tree canopy model is first reconstructed using an improved alpha-shape algorithm, and its volume is calculated. Then, the canopy effective volume coefficient was constructed, and the product of the two was used as the canopy effective volume. To evaluate the accuracy and applicability of the proposed method, both simulated fruit tree and orchard experiments were conducted and compared with the prediction results of alpha-shape by slices (ASBS), convex hull by slices (CHBS), and voxel-based (VB) methods. The results show that the best model prediction performance is achieved when the voxel size is the average nearest neighbor distance of the point cloud and the partition size is five times the voxel size. The method achieved an R² of 0.9720, an RMSE of 0.0203 m 3 , and an MAE of 0.0192. Compared with the prediction results of the ASBS, CHBS, and VB methods, the volume reduction rates were 0.5101, 0.6953, and 0.6213, respectively. The EV method can accurately quantify the canopy effective volume after removal of canopy porosity and provide decision support for precise orchard management.
Why it matches plant phenotyping methodsLiDAR点群から果樹キャノピーの有効体積を抽出する計算法を開発し、複数手法との比較検証を行っており、植物形態形質の取得が研究の中心である。
abstractthis study proposes a calculation method of canopy effective volume (EV) for fruit tree based on LiDAR point cloud data.
Tea flowers play a crucial role in taxonomic research and hybrid breeding of tea plants. As traditional methods of observing tea flower traits are labor-intensive and inaccurate, TflosYOLO and Tea Flowering Stage Classification (TFSC) models were proposed for tea flowering quantification, which enable the estimation of flower count and flowering period. In this study, a highly representative and diverse dataset was constructed by collecting flower images from 29 tea accessions in 2 years. Based on this dataset, the TflosYOLO model was built on the YOLOv5 architecture and enhanced with the Squeeze-and-Excitation (SE) network, Adaptive Rectangular Convolution, and Attention Free Transformer, which is the first model to offer a viable solution for detecting and counting tea flowers. The TflosYOLO model achieved a mean Average Precision at 50% IoU (mAP50) of 0.844, outperforming YOLOv5, YOLOv7, and YOLOv8. Furthermore, the TflosYOLO model was tested on 31 datasets encompassing 26 tea accessions and five flowering stages, demonstrating high generalization and robustness. The correlation coefficient (R 2 ) between the predicted and actual flower counts was 0.964. Additionally, the TFSC model-a seven-layer neural network-was designed for the automatic classification of the flowering period. The TFSC model was evaluated for 2 years and achieved an accuracy of 0.738 and 0.899. Using the TflosYOLO+TFSC model, the tea flowering dynamics were monitored, and the changes in flowering stages were tracked across various tea accessions. The framework provides crucial support for tea plant breeding programs and the phenotypic analysis of germplasm resources.
Why it matches plant phenotyping methods茶花画像から花数と開花期を推定するモデルを開発・検証しており、植物表現型の取得・抽出手法が研究の中心である。
abstractTflosYOLO and Tea Flowering Stage Classification (TFSC) models were proposed for tea flowering quantification, which enable the estimation of flower count and flowering period.
Reproduction assets foundThe paper's data availability statement explicitly deposits the tea flower datasets and models in a public GitHub repository (sufie-mi/tea-flower-model), which directly supports this paper's tea flower phenotyping measurements and models. The labelImg repository is a generic third-party annotation tool, not a paper-ownDataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/sufie-mi/tea-flower-model .Open asset ↗https://github.com/sufie-mi/tea-flower-model · tea-flower-modellines:764-781Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Early identification of grapevine diseases is critical for reducing yield losses and ensuring sustainable viticulture. CNNs trained on benchmark datasets such as PlantVillage often achieve near-perfect accuracy, yet this performance fails to translate to real-world field conditions where lighting, backgrounds, and lesion appearance vary widely. To address challenges of data scarcity and imbalance, this study introduces VitiForge, a novel procedural synthetic imagery pipeline for generating realistic synthetic grape leaf textures representing healthy, Black Rot, Esca, and Leaf Blight conditions. VitiForge is systematically evaluated against GAN-based augmentation through a data ablation study on PlantVillage and FieldVitis, a curated field dataset, using MobileNetV2, InceptionV3, and ResNet50V2 classifiers. Results show that VitiForge significantly improves performance in low-data regimes, enabling model training even without real samples, whereas GAN augmentation proves more effective once sufficient real data is available. On field imagery, VitiForge often matched or surpassed GAN-based methods, particularly when paired with MobileNetV2. These findings highlight the complementary roles of procedural and GAN-based synthetic data: VitiForge offers flexibility and scalability under cross-domain and data-scarce conditions, while GANs enhance realism and variability when ample data exists. Together, they support the development of robust and generalizable models for automated grape disease detection in precision agriculture.
Why it matches plant phenotyping methodsブドウ葉の病徴を画像から識別するための合成画像生成パイプラインを開発し、GAN augmentationと比較評価しているため、植物病害状態のフェノタイピング手法が中心である。
abstractthis study introduces VitiForge, a novel procedural synthetic imagery pipeline for generating realistic synthetic grape leaf textures representing healthy, Black Rot, Esca, and Leaf Blight conditions.
Reproduction assets foundThe paper introduces FieldVitis, a curated field grapevine leaf image dataset assembled from public sources, explicitly deposited on Zenodo with a DOI matching an allowed URL. No explicit public availability of the VitiForge pipeline code or trained models is stated in the supplied blocks.Dataset · publicThe introduction of FieldVitis, a curated dataset of grapevine leaves collected from multiple public sources to reflect the real-world variability of vineyard imagery, providing a valuable benchmark for evaluating model generalization under realistic field conditions. It is available in Zenodo at https://doi.org/10.5281/zenodo.17307846 .Open asset ↗Zenodo · 10.5281/zenodo.17307846lines:310-320Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Introduction Agriculture is crucial to human survival. The growing of biotic rice plants is very helpful for feeding a lot of people around the world, especially in places where rice is a main food. The detection of rice leaf disease is critical to increasing crop productivity. Methods To improve the accuracy of rice leaf disease prediction, this paper proposes a hybrid Vision Transformer (ViT) with pre-trained ResNet18 models (ViT-ResNet18). In general, the input images apply to the pre-trained ViT and ResNet18 models independently. The output features of these two models are combined and fed into the final Fully Connected (FC) layer, followed by a Softmax layer for final classification. Results The output of rice leaf diseases from the FC layer of the proposed hybrid ViT with ResNet18 model achieved 94.4% accuracy, a precision of 0.948, a recall of 0.944, an F1-Score of 0.942, and an Area Under Curve (AUC) of 0.985. Discussion The proposed hybrid model ViT-ResNet18 shows a 5%, 1%, and 1% improvement in accuracy compared to VGG16 with Neural Network, Inception V3 with Neural Network, and SqueezeNet with Neural Network classifier, respectively.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する深層学習手法の提案・比較評価が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractthis paper proposes a hybrid Vision Transformer (ViT) with pre-trained ResNet18 models (ViT-ResNet18).
Reproduction assets foundThe paper trains a hybrid ViT-ResNet18 model for rice leaf disease classification on public rice leaf disease image datasets. Two public image datasets are cited in the references with explicit URLs: the Kaggle rice diseases image dataset and the Mendeley rice leaf diseases dataset. No authors' analysis code or trainedDataset · publicly represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
( 2024 ). Rice diseases image dataset . Available online at: https://www.kaggle.com/datasets/minhhuy2810/rice-diseases-image-dataset (Accessed November 5, 2024).Open asset ↗kaggle · minhhuy2810/rice-diseases-image-datasetlines:512-537Dataset · public( 2023 ). Rice leaf diseases dataset . Available online at: https://data.mendeley.com/datasets/dwtn3c6w6p/1:~:text=Overview%3A%20The%20Rice%20Life%20Disease,and%20Leaf%20Smut%20(LS) (Accessed November 5, 2024 ).
Abasi A. K.
Makhadmeh S. N.
Alomari O. A.
Tubishat M.
Mohammed H. J.
( 2023 ).
Enhancing rice leaf disease classification: a customized convolutional neural network approach
. Sustainability
15 , 15039 . doi:
10.3390/su152015039
Aggarwal M.
Khullar V.
Goyal N.
Singh A.
Tolba A.
Thompson E. B.
(Open asset ↗mendeley · dwtn3c6w6p/1lines:538-750Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Introduction Nepeta cataria L. (NcL), a perennial medicinal plant, is used to dispel wind-heat, remove blood stasis, and reduce swelling. However, soil salinization and heavy metal pollution have severely impacted its growth and quality. Current quality assessments, based solely on pulegone (PG) content in the final product, neglect cultivation process controls. Methods We applied biophoton detection to differentiate stress types in fresh NcL leaves and explored the correlation between biophoton emission and plant quality. Spontaneous photon emission (SPE) and delayed luminescence (DL) were measured under NaCl and K 2 Cr 2 O 7 stress. Key parameters, including counts per second (CPS) and initial intensity (I 0 ), were analyzed. PG content was determined via ultra-high performance liquid chromatography, alongside physiological and oxidative stress indexes. Results Stress conditions significantly inhibited NcL growth and quality, leading to marked differences in biophoton emission, physiological and biochemical indicators, oxidative stress indexes and PG content between stress and control groups. For instance, in the 120 mmol/L NaCl group, the CPS decreased by 54.27%, while I 0 dropped by 54.12%. PG content in high-salinity groups was 1.22 mg/g, 40.19% lower than controls (2.04 mg/g). CPS and I 0 strongly correlated with PG content (r = 0.89 and r = 0.85, respectively), as well as with physiological and oxidative indicators. These relationships were clearly visualized. Discussions Our study demonstrates the potential of biophoton detection for quality evaluation during NcL cultivation. We identified specific biophoton parameters that reflect plant quality, offering a new approach for process-based quality control.
Why it matches plant phenotyping methods植物葉のストレス状態・品質を推定するためのバイオフォトン検出を中心に、測定パラメータと植物生理・品質指標との関係を評価しており、単なる routine measurement ではない。
abstractWe applied biophoton detection to differentiate stress types in fresh NcL leaves and explored the correlation between biophoton emission and plant quality.
In modern agricultural production, accurate monitoring of maize growth and leaf counting is crucial for precision management and crop breeding optimization. Current UAV-based methods for detecting maize seedlings and leaves often face challenges in achieving high accuracy due to issues such as low spatial-resolution, complex field environments, variations in plant scale and orientation. To address these challenges, this study develops an integrated detection and visualization software, DP-MaizeTrack, which incorporates the DP-YOLOv8 model based on YOLOv8. The DP-YOLOv8 model integrates three key improvements. The Multi-Scale Feature Enhancement (MSFE) module improves detection accuracy across different scales. The Optimized Spatial Pyramid Pooling-Fast (OSPPF) module enhances feature extraction in diverse field conditions. Experimental results in single-plant detection show that the DP-YOLOv8 model outperforms the baseline YOLOv8 with improvements of 3.9% in Precision (95.1%), 4.1% in Recall (91.5%), and 4.0% in mAP50 (94.9%). The software also demonstrates good accuracy in the visualization results for single-plant and leaf detection tasks. Furthermore, DP-MaizeTrack not only automates the detection process but also integrates agricultural analysis tools, including region segmentation and data statistics, to support precision agricultural management and leaf-age analysis. The source code and models are available at https://github.com/clhclhc/project.
Why it matches plant phenotyping methodsUAV画像からトウモロコシ個体数と葉数を抽出するソフトウェアを開発しており、植物形質取得が研究の中心です。
abstractthis study develops an integrated detection and visualization software, DP-MaizeTrack
Reproduction assets foundThe paper explicitly states that the authors' source code and trained models for DP-MaizeTrack/DP-YOLOv8 are publicly available on GitHub. No public dataset deposit is stated; the UAV image dataset is described but not declared publicly available.Code · publicThe source code and models are available at https://github.com/clhclhc/project .Open asset ↗https://github.com/clhclhc/projectlines:224-300Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Background Rapidly estimating multiple trait indicators simultaneously, nondestructively, and with high precision is an important means of accurate diagnosis in modern phenomics. Increasing the accuracy of estimation models for rice yield-related trait indicators (leaf nitrogen concentration, LNC; leaf area index, LAI; aboveground biomass, AGB; and grain yield, GY) through a strategy of "spectral data + texture data + dimensionality reduction + machine learning" is highly important. Methods Between 2022 and 2023, hyperspectral canopy images, the LNC, LAI, AGB, and GY were collected synchronously. Then, dimensionality reduction was performed on the preprocessed spectral data using the Pearson correlation coefficient method, the successive projections algorithm (SPA), and competitive adaptive reweighted sampling (CARS) to select sensitive wavelengths. Estimation models were constructed using artificial neural networks (ANNs), support vector machine regression, one-dimensional convolutional neural networks, and long short-term memory networks. By extracting the texture features corresponding to sensitive wavelengths, high-precision estimation models were constructed using a "spectral data + texture data + dimensionality reduction + machine learning" method. Results SPA-ANN provided the best prediction for LNC (R 2 = 0.82, RMSE = 3.68 g/kg) and LAI (R 2 = 0.75, RMSE = 0.47), while CARS-ANN was optimal for AGB (R 2 = 0.90, RMSE = 79.05 g/m2) and GY (R 2 = 0.63, RMSE = 0.59 t/ha). Adding texture features increased R 2 by up to 9.9% and reduced RMSE by up to 27.2%. Conclusion The optimized method can significantly increase the accuracy of estimation models. The results provide a scientific basis and technical data for the precise diagnosis of rice yield-related traits.
Why it matches plant phenotyping methods水稲の収量関連形質を、ハイパースペクトル画像・テクスチャ特徴・次元削減・機械学習で非破壊推定する手法が研究の中心であり、植物フェノタイピング手法として明確に該当する。
abstractRapidly estimating multiple trait indicators simultaneously, nondestructively, and with high precision is an important means of accurate diagnosis in modern phenomics.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Introduction Diseases of plants remain one of the greatest threats to sustainable agriculture, with a direct adverse effect on crop productivity and threatening food security worldwide. Conventional detection methods rely heavily on manual detection and laboratory analysis, which are time-consuming, subjective, and unsuitable for large-scale monitoring. The use of the most recent progress in computer vision and artificial intelligence has opened up a prospect of automated, scalable, and precise disease diagnosis. Methods This paper introduces a feature-efficient hybrid model that trains classical Machie Learning (ML) classifiers with Deep Neural Network (DNN) using ResNet-based feature extraction and Principal Component Analysis (PCA). The PlantVillage dataset with mixed crop-disease pairs is used to implement and thoroughly test five hybrid models. Results Wide-ranging experiments proved that the Logistic Regression (LR)+DNN hybrid resulted in the best classification accuracy of 96.22% as compared to other models and available benchmarks. Besides being able to outperform other techniques in terms of predictive power, the framework displayed good training stability and robustness to class imbalance as well as a higher degree of interpretability based on LIME-based analysis. Discussion The obtained results confirm the hybrid ML+DNN paradigm as a safe, transparent, scalable disease recognition framework when applied to plant diseases. Providing opportunities for timely and accurate disease detection, the proposed framework can help with precision agriculture, where pesticide use can be reduced, consequently, and a significant contribution to sustainable farming can be achieved.
Why it matches plant phenotyping methods植物病害を対象に、ResNet特徴抽出・PCA・ML/DNNを組み合わせた画像ベースの病害認識手法を開発・評価しており、植物状態の推定方法が研究の中心である。
abstractThis paper introduces a feature-efficient hybrid model that trains classical Machie Learning (ML) classifiers with Deep Neural Network (DNN) using ResNet-based feature extraction and Principal Component Analysis (PCA).
Introduction The cigar leaves moisture content (CLMC) is a critical parameter for controlling curing barn conditions. Along with the continuous advancement of deep learning (DL) technologies, convolutional neural networks (CNN) have provided a way of thinking for the non-destructive estimation of CLMC during the air-curing process. Nevertheless, relying merely on single-perspective imaging makes it difficult to comprehensively capture the complementary morphological features of the front and back sides of cigar leaves during the air-curing process. Methods This study constructed a dual-view image dataset covering the air-curing process, and proposes a regression framework named CADFFNet (channel attention weight-based dual-branch feature fusion network) for the non-destructive estimation of CLMC during the curing process based on dual-view RGB images. Firstly, the model utilizes two independent and parallel ResNet as its backbone structure to capture the heterogeneous features of dual-view images. Secondly, the Dual Efficient Channel Attention (DECA) module is introduced to dynamically adjust the channel attention weights of the features, thereby facilitating interaction between the two branches. Lastly, a Multi-scale convolutional feature fusion (MSCFF) module is designed for the deep fusion of features from the front and back images to aggregate multi-scale features for robust regression. Results On five-fold cross-validation, CADFFNet attains R2 of 0.974±0.007 and mean absolute error (MAE) of 3.80±0.37%. On an independent cross-region, cross-variety testing set, it maintains strong generalization (R2=0.899, MAE=5.82%), compared with the classic CNN models ResNet18, GoogLeNet, VGG19Net, DenseNet121, and MobileNetV2, its R2 value has increased by 0.047, 0.041, 0.055, 0.098, and 0.090 respectively. Discussion Generally, the proposed CADFFNet offers an efficient and convenient method for non-destructive detection of CLMC, providing a theoretical basis for automating the air-curing process. It also provides a new perspective for moisture content prediction during the drying process of other crops, such as tea, asparagus, and mushrooms.
Why it matches plant phenotyping methods葉の水分含量という植物状態を、二視点RGB画像と新規深層学習回帰モデルで非破壊推定する手法を開発し、交差検証および独立試験で性能評価しており、植物フェノタイピング手法が中心である。
abstractproposes a regression framework named CADFFNet (channel attention weight-based dual-branch feature fusion network) for the non-destructive estimation of CLMC during the curing process based on dual-view RGB images.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Crop phenology is one of the most critical physiological attributes of agricultural crops, serving as a direct indicator of growth status throughout the developmental cycle. With the advancement of phenological research, satellite remote sensing has emerged as a primary monitoring tool due to its large spatial coverage and convenient data acquisition. However, high-resolution remote sensing satellites, which are essential for precise phenological observations, often have long revisit intervals. Additionally, adverse atmospheric conditions such as cloud cover frequently compromise the usability of images on multiple dates. As a result, high-resolution time-series data for crop phenology monitoring are typically sparse, limiting the ability to capture rapid phenological changes during the growing season.To address this challenge, this study focuses on paddy and dryland fields as experimental sites and proposes a novel method for filling temporal gaps in remote sensing data using generative image processing techniques. Specifically, a lightweight super-resolution Generative Adversarial Network (GAN) is developed for image reconstruction. Using the reconstructed dataset, dense time-series monitoring and phenological metric extraction were conducted throughout the crop growing season.(1) The proposed super-resolution reconstruction method achieves structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) values of 0.834 and 28.69, respectively, outperforming mainstream approaches in reconstructing heterogeneous remote sensing data.(2) Following temporal reconstruction, the revisit intervals of remote sensing imagery for the two test sites improved from 6.40 and 6.63 days to 5.70 and 5.88 days, respectively. To further analyze phenological metrics, four smoothing techniques were applied, among which Savitzky–Golay filtering yielded the most accurate and robust results. Although discrepancies were observed between the results obtained using the reconstructed data and those based on the original datasets, the proposed method demonstrated smaller deviations from benchmark datasets. Compared with conventional interpolation-based gap-filling approaches, the framework demonstrated marked improvements in the accuracy of phenological extraction, while also delivering superior spatial resolution and robustness relative to the Harmonized Landsat and Sentinel (HLS) dataset. Experimental results confirm that the proposed approach effectively fills temporal gaps in satellite imagery, enhances data continuity, accurately captures key phenological turning points, and enables precise crop phenology monitoring at high spatial and temporal resolution.
Why it matches plant phenotyping methods衛星リモートセンシング画像の超解像・時系列補間手法を開発し、作物フェノロジー指標の抽出精度を検証しており、植物表現型取得手法が研究の中心である。
abstractthis study focuses on paddy and dryland fields as experimental sites and proposes a novel method for filling temporal gaps in remote sensing data using generative image processing techniques.
Introduction Cyclic loads caused by natural factors such as strong winds are common in plant growth environments. Prolonged exposure to such loads can compromise the anchorage performance of plants. This study examines how cyclic loading influences the root anchorage of Betula platyphylla, a prominent tree species in northern China. Methods A series of pull-out tests were performed on soil-embedded roots, including monotonic pull-out tests and 100 cycles of loading and unloading. Results The research results show that under different cyclic load amplitudes, the peak bearing capacity is negatively correlated with the load amplitude. Energy dissipation in the root system increases with higher load amplitudes but decreases as the number of cycles increases. From the initial cycle to the 25th cycle, energy dissipation decreased substantially, with no further significant reduction observed between the 25th and 100th cycles. To more effectively capture the nonlinear hysteretic behavior of roots, an enhanced Bouc-Wen model was developed and successfully fitted to the force-displacement curves. The model accurately replicated the hysteresis loops and characterized the damage progression in root anchorage under cyclic loading. Discussion These findings offer valuable insights into the mechanical stability of plant roots under repeated environmental stresses and provide a robust framework for modeling root anchorage performance in natural settings.
Why it matches plant phenotyping methods根系引抜試験によるアンカレッジ性能の測定と、力–変位曲線を用いた拡張Bouc-Wenモデルの開発・検証が研究の中心であり、植物の機械的形質を抽出・モデル化している。
abstractTo more effectively capture the nonlinear hysteretic behavior of roots, an enhanced Bouc-Wen model was developed and successfully fitted to the force-displacement curves.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Net Primary Productivity (NPP) is a vital indicator for evaluating the carbon source and sink capacities of ecosystems, significantly influencing assessments of agricultural productivity and carbon cycle studies. Accurately estimating NPP in the agricultural sector, however, remains challenging. This research addresses the challenge by refining the estimation of the Fraction of Photosynthetically Active Radiation (FPAR) within the CASA model, introducing a novel methodology that significantly improves the accuracy of NPP estimation and, when applied to remote sensing imagery covering a broad region, demonstrates strong potential for large-scale crop NPP monitoring. We employed high-resolution Sentinel-2 satellite imagery and the Recursive Feature Elimination algorithm to extract FPAR-related features from 15 vegetation indices. The FPAR was subsequently estimated using a Convolutional Neural Network, leading to a dramatic decrease in the Root Mean Square Error (RMSE) from 0.2040 to 0.0020. The prediction errors for the improved model ranged from 0.0001 to 0.0092, with a mean absolute error (MAE) below 0.01. These values reflect the distribution of absolute residuals and indicate a substantial enhancement in accuracy over traditional methods. This improved FPAR estimation method was subsequently integrated into the CASA model. Compared to field-measured NPP data, the optimized model reduced the Mean Absolute Percentage Error (MAPE) from 28.92% to 20.31%. The MAPE values across the test samples ranged between 15% and 25%, indicating a significant improvement in model reliability. The optimized CASA model performs well in estimating net primary productivity (NPP) of crops, providing strong support for agricultural decision-making and future research on large-scale productivity and carbon cycling.
Why it matches plant phenotyping methods作物NPPおよびFPARを衛星画像とCNNで推定する手法の開発・精度検証が研究の中心であり、作物の生産性という植物状態を直接評価している。
abstractThis research addresses the challenge by refining the estimation of the Fraction of Photosynthetically Active Radiation (FPAR) within the CASA model, introducing a novel methodology that significantly improves the accuracy of NPP estimation
Plant diseases pose a severe threat to global agricultural production, significantly challenging crop yield, quality, and food security. Therefore, accurate and efficient disease detection is crucial. Current detection methods have clear limitations: CNN-based methods struggle to model long-range dependencies effectively and have weak generalization abilities. Transformer-based methods, while adept at long-range feature modeling, face issues with large parameter sizes and inefficient calculations due to the quadratic complexity of the self-attention mechanism in relation to image size. To address these challenges, this paper proposes the MamSwinNet model. Its core innovation lies in: using the Efficient Token Refinement module with an overlapping space reduction method, relying on depthwise separable convolutions designed with “stride + 3” convolution kernels to expand the image block overlap area and fully preserve boundary spatial structure. This generates high-quality tokens and converts them into a fixed number of latent tokens, reducing computational complexity while maximizing the retention of key features. It integrates the Spatial Global Selective Perception (SGSP) module and the Channel Coordinate Global Optimal Scanning (CCGOS) module. The SGSP module uses a dual-branch structure (the spatial modeling branch introduces 2D-SSM to scan four directions for capturing long-range dependencies, and the residual compensation branch supplements features to prevent loss; the two branches are combined using Hadamard product to enhance spatial detail modeling). The CCGOS module combines channel and spatial attention by embedding positional information through global average pooling in the height and width dimensions, using the Mamba block for channel-selective scanning and generating an attention map, enabling precise association of key channel features like color with spatial distribution. Experimental results show that the model achieves F1 scores of 79.47%, 99.52%, and 99.38% on the PlantDoc, PlantVillage, and Cotton datasets, respectively. The model has only 12.97M parameters (52.9% less than the Swin-T model) and a computational cost as low as 2.71GMac, significantly improving computational efficiency. This study provides an efficient and reliable intelligent solution for large-scale crop disease detection.
Why it matches plant phenotyping methods植物病害を画像から検出・判定するMamSwinNetモデルの開発とデータセット評価が研究の中心であり、感染植物の病態を直接推定する画像ベース表現型解析に該当する。
abstractExperimental results show that the model achieves F1 scores of 79.47%, 99.52%, and 99.38% on the PlantDoc, PlantVillage, and Cotton datasets, respectively.
Reproduction assets foundThe paper's phenotyping inputs are three public plant-disease image datasets (PlantDoc, PlantVillage, Cotton Disease) used for all experiments. No author analysis code, trained model checkpoints, or paper-specific supplements are disclosed.Dataset · publicwhile enhancing its practical relevance. Owing to these characteristics, PlantDoc has become a key benchmark dataset for evaluating the robustness and applicability of plant disease detection models.
Figure 5
illustrates several representative samples from the dataset. The dataset is publicly accessible via the following link: https://github.com/pratikkayal/PlantDoc-Dataset
Figure 5
Sample images from the PlantDoc dataset.
Grid of images showing various diseased leaves. Top row: Apple rust leaf with red spots, apple scab leaf with black lesions, bell pepper leaf with dark spots. Middle row: Corn gray leaf with discolored areas, two corn leaves with blight showing yellow and brown patterns. BOpen asset ↗PlantDoc-Datasetlines:151-174Dataset · publicility and distinct disease features, PlantVillage is frequently employed for model pre-training and performance benchmarking, and has become an important reference dataset in plant disease detection research.
Figure 6
presents several representative samples from this dataset. PlantVillage can be accessed via the following link: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset
Figure 6
Sample images from the PlantVillage dataset.
Grid of nine leaves showing various plant diseases. Top row: Apple Black rot, Apple scab, Grape Leaf blight. Middle row: Peach Bacterial spot, Potato Early blight, Squash Powdery mildew. Bottom row: Tomato Early blight, Tomato Septoria Leaf spot, TOpen asset ↗plantvillage-datasetlines:151-174Dataset · publicdataset’s class design not only covers the major and prevalent diseases in cotton production but also provides a reliable benchmark for evaluating models in multi-class disease classification tasks.
Figure 7
presents several representative image samples from this dataset. The dataset is publicly available at the following link: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease .
Figure 7
Sample images from the cotton disease dataset.
Nine images of leaves show different conditions: three with aphids, showing yellowing and damage; two with bacterial blight, displaying dark spots; three healthy with vibrant green; and three with powdery mildew, covered in white residue. Each conditioOpen asset ↗cotton-plant-diseaselines:151-174