Plant phenotyping methods.

Early diagnosis of cadmium stress in rice by intelligent profiling of multiple response indicators with portable Raman SERS and deep learning. →

Cadmium contamination severely affects rice growth, yield, and quality, making early stress monitoring essential for agricultural management and food safety. However, traditional detection methods are cumbersome and time-consuming, limiting their applicability to early stress diagnosis. This study developed a rapid and accurate approach for discriminating cadmium stress levels in rice. Arginine-modified flower-like silver nanoparticles (Ag NPs-Arg) were synthesized to enhance Raman signals associated with three stress-response indicators: salicylic acid (SA), malondialdehyde (MDA), and peroxidase (POD) activity. Quantitative prediction models for these physiological indicators and a stress-level discrimination model were established. Among the evaluated models, the CNN-Transformer model achieved the best predictive performance, with Rp 2 values of 0.889, 0.832, and 0.802 for SA, MDA, and POD activity, respectively. An objective weighting method was used to integrate the three biochemical reference indicators, providing a multi-indicator physiological basis for comprehensive stress assessment. The resulting stress-level assessment model achieved an accuracy of 95.83%, demonstrating its ability to capture cadmium-induced physiological changes and assess stress levels in rice.

Why it matches plant phenotyping methods携帯型Raman SERSと深層学習を開発し、イネの生理指標とカドミウムストレスレベルを推定・判別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に該当する。

abstractThis study developed a rapid and accurate approach for discriminating cadmium stress levels in rice.

Deep learning technique for rice leaf disease classification and severity level identification via Hybrid ResConvolutional Neural Network →

Rice leaf diseases significantly reduce agricultural yield and pose a major challenge for sustainable food production, particularly owing to the limitations associated with manual and visual inspection methods that are subjective and often ineffective in early-stage detection. This investigation proposes a new Hybrid ResConvolutional Neural Network (HyResCN-Net) for effective classification and severity identification of rice plant leaf diseases. The proposed framework integrates an Internet of Things (IoT) -based data acquisition and routing simulation using CrowWhale Energy Trust Routing (CrowWhale-ETR) for efficient data handling. Initially, preprocessing is done by an averaging filter to reduce noise. Then, plant leaves are segmented using the Eff-UNet++ method. Augmentation techniques like rotation, scaling, and color change are applied to expand the dataset. Features, like entropy with Gradient Directional Pattern (GDP), Complete Local Binary Pattern (CLBP), and histogram features, are extracted to enhance feature representation. These features are then used within the proposed HyResCN-Net model, which integrates Parallel Convolutional Neural Network (PCNN) and ResNeXt to improve discriminative learning for disease classification and severity estimation. Experimental evaluation is conducted on the Rice Leaf Bacterial and Fungal Disease Dataset. Considering a k-value of 8, the HyResCN-Net gains an accuracy of 94.258%, a True Positive Rate (TPR) of 96.479%, a True Negative Rate (TNR) of 92.898%, a precision of 91.312% and an F1-score of 93.824% compared to existing methods. The HyResCN-Net efficiently enhances rice leaf disease identification and severity analysis, supporting its applicability in precision agriculture applications.

Why it matches plant phenotyping methodsイネ葉の病害分類と重症度という植物の状態を、画像取得・分割・特徴抽出・深層学習により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中心である。

abstractThis investigation proposes a new Hybrid ResConvolutional Neural Network (HyResCN-Net) for effective classification and severity identification of rice plant leaf diseases.

Plant disease identification through Explainable AI: A deep learning architecture using fine-tuned EfficientNet for sustainable agriculture →

Problem: Agriculture plays a pivotal role in the Indian economy, where crop production quality and quantity directly impact the livelihoods of millions. Climate variability, emerging plant diseases, and improper pesticide application contribute significantly to agricultural losses. Early and accurate disease detection is crucial for mitigating crop damage and ensuring food security. Methodology: This study presents a novel deep learning framework based on EfficientNet architecture, enhanced with Progressive Fine-Tuning Strategy for automated plant disease detection. The proposed methodology was evaluated on two benchmark datasets: the Plant Village dataset comprising 20,639 images of tomato, potato, and bell pepper with 15 disease varieties; and a drone-captured rice plant dataset containing 4432 samples from public repositories. The model’s performance was assessed using multiple metrics, including classification accuracy, precision, recall, and F1-score. To strengthen the validation of high accuracy results, additional statistical analyses like class imbalance ratio, Entropy, Chi-Square test, convergence curve, ANOVA test, Tukey’s post hoc HSD test, confidence interval, Cohen’s Kappa result, fold-wise dispersion analysis, mean, std deviation are included in the manuscript. Result: Experimental results demonstrate that the fine-tuned EfficientNetV2-B1 architecture achieved exceptional performance with 99.7% classification accuracy on the PlantVillage dataset and 99.03% accuracy on the drone-based rice disease dataset, significantly outperforming existing state-of-the-art transfer learning models. Model explainability techniques further validated the reliability and interpretability of the predictions, confirming the model’s focus on disease-relevant features.

Why it matches plant phenotyping methods植物画像から病害状態を推定する深層学習手法を開発し、複数データセットで性能検証しており、植物フェノタイピング手法が中心である。

abstractThis study presents a novel deep learning framework based on EfficientNet architecture, enhanced with Progressive Fine-Tuning Strategy for automated plant disease detection.

YOLOv12-RSLW: An Efficient Detection and Severity Grading Framework for Rice False Smut via Count-Area Calibration →

Rice false smut is a major panicle disease that affects rice yield and grain quality and is an important target for resistance evaluation in breeding programs. Accurate field phenotyping is important for disease assessment and resistance screening, yet current assessment relies heavily on manual visual scoring and smut ball counting, which are laborious and subject to evaluator variation. In close-range single-panicle images, false smut balls are often small, dense, occluded, adhered, making automatic detection and severity grading difficult. To address these challenges, we developed a YOLOv12-RSLW detector by integrating RepGhost, SimAM, LSCD, and WIoU into YOLOv12. Detection boxes were then used to guide the Segment Anything Model for panicle and lesion mask extraction, allowing calculation of the lesion-to-panicle area ratio as a supplementary indicator for count-based severity grading. A total of 1911 original field images were collected. After augmentation, the dataset contained 5663 images, including 4531 training images, 566 validation images, and 566 test images. Detection performance was evaluated on the test set, while SAM segmentation was assessed using 80 manually annotated original images. YOLOv12-RSLW achieved 92.06% mAP@0.5, 91.46% precision, and 87.01% recall, with 3.45 M parameters and 6.0 GFLOPs. Compared with the baseline YOLOv12, mAP@0.5 and recall increased by 3.60 and 4.37 percentage points, respectively. Within the augmented dataset, 41.2% of samples initially assigned to Grade 1 and 28.1% of those assigned to Grade 2 met the area-ratio criteria for potential reassignment to higher grades. The framework provides a quantitative approach to rice false smut severity phenotyping and may support future resistance breeding after further validation.

Why it matches plant phenotyping methodsイネいもち病の病徴を画像から検出・分割し、病斑面積比に基づく重症度を定量化する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ある。

abstractTo address these challenges, we developed a YOLOv12-RSLW detector by integrating RepGhost, SimAM, LSCD, and WIoU into YOLOv12.

Autonomous Quadcopter Flight Path Generation via MAVLink and Ground Control Station Architecture for Precision Agricultural Crop Monitoring →

This paper presents an integrated system design for autonomous quadcopter flight path generation using the MAVLink protocol and a custom Ground Control Station (GCS) for precision agricultural crop monitoring. The system combines three coverage path algorithms (Boustrophedon, Spiral, and Energy-Optimized), a Pixhawk 4 / ArduPilot flight stack, a MicaSense RedEdge-P multispectral payload, and a ROS2-based GCS for mission planning, telemetry, and vegetation-index-based crop health assessment. The 2.8 kg quadcopter (450 mm frame, 4-cell LiPo) achieves 22–25 minutes of flight time. Across five field sizes (0.5–10 ha), the Energy-Optimized path achieved 96.5% coverage efficiency with 4.2% overlap and a 12.4% energy reduction over the Boustrophedon baseline. NDVI-based crop segmentation achieved pixel accuracy of 92.5% (maize), 94.1% (rice), and 90.8% (wheat), and four-class crop-health classification achieved a weighted F1-score of 90.0%. MAVLink 2.0 command latency averaged 15.8 ms with 99.3% packet delivery at ranges up to 800 m. An ablation study showed additional gains of 1.5–3.1% coverage from wind compensation and 2.1–2.8% from terrain-following.

Why it matches plant phenotyping methods自律ドローン、マルチスペクトル撮像、NDVIセグメンテーションによる作物健康状態推定を統合し、飛行・画像解析性能を定量評価しているため、植物状態の取得・抽出が技術的に実質的な構成要素である。

abstractThe system combines three coverage path algorithms (Boustrophedon, Spiral, and Energy-Optimized), a Pixhawk 4 / ArduPilot flight stack, a MicaSense RedEdge-P multispectral payload, and a ROS2-based GCS for mission planning, telemetry, and vegetation-index-based crop health assessment.

Rice aboveground biomass estimation based on three-dimensional dry matter distribution integration model →

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

Counting of rice panicles using drone mounted RGB sensor and deep learning approaches →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsドローン搭載RGBセンサーと深層学習によりイネの穂数を計測する手法が題名で明示されており、植物形質の取得・抽出が研究の中心である。

titleCounting of rice panicles using drone mounted RGB sensor and deep learning approaches

From detection accuracy to safety assurance in intelligent plant health early warning systems →

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.

A Vision-Based Deep Learning Framework enables High-Accuracy Prediction of Geng Rice Eating Quality and Facilitates QTL Mapping. →

Northeast China's Geng rice (Oryza sativa subsp. japonica) dominates the high-value rice markets in China due to its superior eating quality. However, current evaluation methods rely on either labor-intensive, subjective sensory protocols or low-accuracy, calibration-heavy near-infrared spectroscopy (NIRS), constraining breeding for high eating quality and market development. Here, we report a vision-based deep learning framework combining multi-population fine-tuning with industrial vision-language model (VLM) pre-training for Geng rice eating quality prediction. Trained on natural and recombinant inbred (RI) population datasets, our optimal model (Model 4) showed high cross-population stability. It achieved R 2 values of 0.98, 0.57, and 0.61 in a natural population validation set (35 cultivars), an independent DA-RI population (201 lines), and a randomly collected set (30 Northeast and 28 Southern cultivars), respectively, consistently outperforming the widely used Satake STA1B analyzer. Furthermore, our approach enabled the mapping of a novel, robust quantitative trait locus, qIVOE7, for Geng rice eating quality on Chromosome 7. Further analysis suggested that Model 4 appears to rely on the Hue dimension of the HSV color space for its predictions. This framework provides a high-accuracy prediction model and an easy-to-use tool for rice eating quality evaluation, accelerating high-quality rice breeding as well as the development of the high-quality rice market.

Why it matches plant phenotyping methodsコメの食味という植物（種子）形質を画像ベースの深層学習で推定する手法を開発し、複数集団で検証・既存分析器と比較しており、フェノタイピング手法が研究の中心である。

abstractwe report a vision-based deep learning framework combining multi-population fine-tuning with industrial vision-language model (VLM) pre-training for Geng rice eating quality prediction.

Genomic prediction vs. gene-based crop models: a case study on rice trait prediction. →

Conventional breeding for ideotypes in target environments remains challenging due to genotype-by-environment interactions and the genetic complexity of key agronomic traits. Traditional multi-environment field trials are costly and time-consuming, limiting rapid genetic gain. These challenges highlight the need for digital tools to support rice breeding. However, two major approaches, genomic prediction (GP) and gene-based crop models (GBCMs), have distinct advantages. In this study, a dataset derived from a natural rice population comprising 210 genotypes, genotyped with about 650,000 markers, rice dry matter, and yield across three environments, was used to develop two genomic prediction models, genomic best linear unbiased prediction (GBLUP) and a convolutional neural network (CNN), together with a gene-based crop modeling framework. The effectiveness of these models in predicting rice traits and assisting in breeding selection was subsequently evaluated. Prediction results indicated that biomass and yield could be effectively predicted by all models, with Normalized Root Mean Square Error (NRMSE) values ranging from 10.60% to 18.59% and 9.93% to 18.19%, respectively. In terms of predictive accuracy, parameter-based crop models achieved the highest predictive accuracy, although it was confined to theoretical simulations. This was followed by the GBCM and CNN, whereas the GBLUP exhibited the lowest performance. Furthermore, GGE biplot analysis revealed the predictions of the GBCM aligned more closely with field observations than those of the CNN, emphasizing the potential of GBCM as a practical surrogate for digital breeding. These results provide valuable insights into modeling genotype-by-environment interactions and support the development of data-informed breeding strategies for future rice improvement.

Why it matches plant phenotyping methodsイネの乾物量・収量という植物形質を予測する複数の計算モデルを開発・比較評価しており、形質推定手法が研究の中心である。

abstractIn this study, a dataset derived from a natural rice population comprising 210 genotypes, genotyped with about 650,000 markers, rice dry matter, and yield across three environments, was used to develop two genomic prediction models, genomic best linear unbiased prediction (GBLUP) and a convolutional neural network (CNN), together with a gene-based crop modeling framework.

Development and Verification of an Automatic Tower-Based SIF Observation System Based on Narrow Field-of-View Scanning and DOAS Atmospheric Correction →

Sun-induced chlorophyll fluorescence (SIF) is an effective proxy for vegetation photosynthesis, but tower-based retrieval suffers from atmospheric path interference under humid and variable conditions. We present a DOAS-based SIF retrieval algorithm that operates in Fraunhofer lines (680–686 nm, 745–758 nm) and water vapour-sensitive bands (717–727 nm). It constructs an adaptive reference spectrum from SCOPE simulations and PCA and incorporates H2O absorption cross-sections into the fitting process for active atmospheric correction. The algorithm is implemented in a dedicated tower-based system integrating a 1° scanning gimbal with a high-resolution spectrometer. Validation with simulated and field data demonstrates the following: (1) the algorithm retrieves SIF with high fidelity (correlation coefficients >0.9 across all windows); (2) it exhibits lower water-vapour sensitivity and greater cloudy-sky stability than FLD, 3FLD, and SFM, achieving the lowest coefficient of variation (CV = 0.356); (3) over a complete wheat–rice rotation, the retrieved SIF tracks crop growth and phenological stages. This work provides a reliable solution for automated, high-precision tower-based SIF observation under complex atmospheric conditions.

Why it matches plant phenotyping methods植物の光合成状態を示すSIFを取得するタワー型分光観測システムとDOAS補正アルゴリズムを開発し、シミュレーションおよび圃場データ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。

abstractWe present a DOAS-based SIF retrieval algorithm

CED-RTDETR: a contour-aware evidence-guided decoupled network for rice leaf disease detection. →

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.

Genome-wide association study of image-based traits reveals genetic architecture of salt tolerance in rice. →

Soil salinity is a major constraint on rice ( Oryza sativa L.) production, particularly during the yield-determining reproductive stage. Utilizing a high-throughput RGB platform, we non-destructively phenotyped a diverse panel of 294 rice accessions over two consecutive years. By extracting 60 dynamic image-based traits (i-traits) reflecting canopy architecture and stay-green capacity, and four seed-setting rate-related traits, our genome-wide association study (GWAS) identified 95 significant loci, 35.8% of which precisely co-localized with previously reported QTLs. We further prioritized OsSLT1 ( LOC_Os01g05790 ) as a candidate gene at a reproducible suggestive locus associated with leaf-rolling-related image traits. Transgenic evaluations confirmed that it acts as a positive regulator of salt tolerance at the seedling stage. OsSLT1 was mainly detected in the nucleus, and no significant changes in Na + or K + accumulation were observed in flag leaves under the tested salt-stress condition, suggesting that OsSLT1 may regulate salt tolerance through mechanisms beyond classical shoot ion accumulation. Natural variations in the OsSLT1 promoter were associated with transcriptional divergence. The Hap2 promoter haplotype showed significantly higher stress-induced transcriptional activity. Hap2 was rare in modern indica accessions, suggesting that it may represent a potentially useful genetic resource for future salt-tolerance improvement. Supplementary information The online version contains supplementary material available at 10.1007/s11032-026-01704-2.

Why it matches plant phenotyping methodsRGB高スループット基盤による非破壊画像計測と、60種の動的画像形質の抽出が研究の主要なデータ取得・解析手法として明示されているため、GWAS中心の応用研究でも植物フェノタイピング手法の実質的応用に該当する。

abstractUtilizing a high-throughput RGB platform, we non-destructively phenotyped a diverse panel of 294 rice accessions over two consecutive years.

Development of a Sheath Inoculation Protocol to Screen Rice Varieties for Resistance to Cercospora janseana . →

Cercospora janseana (Racib.) O. Const. is a re-emerging fungal pathogen that causes Cercospora net blotch on rice. Previous research on resistance to C. janseana has primarily focused on foliar symptoms. Subsequently, sheath infection remains poorly characterized which hinders disease management efforts. This study developed and validated a reproducible sheath inoculation protocol under controlled conditions. Three inoculation methods (agar disc, spray, and drop) were evaluated with and without mechanical wounding. Lesions only formed with inoculation methods using wounding and the agar disc method produced the most consistent and uniform symptom development. Time-course analysis in the susceptible variety Cheniere revealed earlier lesion onset, more rapid expansion, and lower variability in the agar disc method compared to spray, confirming its suitability for phenotypic screening. The optimized protocol was applied across five independent trials involving four rice varieties. DG263L consistently exhibited minimal lesion development, confirming its resistance, while Cheniere showed extensive lesion growth, indicating high susceptibility. PVL03 and LaGrue displayed moderately susceptible reactions, with PVL03 developing significantly higher lesion lengths and AUDPC values than LaGrue in one-month-old plants. Although lesion onset was delayed in 45-day-old plants, disease progressed more rapidly once established. AUDPC analysis corroborated these trends, further distinguishing varietal responses. The protocol effectively discerned resistant, intermediate, and susceptible phenotypes, supporting its use in resistance screening. To our knowledge, this is the first controlled sheath inoculation method developed for Cercospora net blotch, offering a standardized approach for evaluating sheath-specific resistance and advancing the characterization of the C. janseana -rice pathosystem.

Why it matches plant phenotyping methodsイネ葉鞘の病斑を用いた抵抗性表現型の取得プロトコルを開発・検証し、品種間の病害表現型を再現性よく識別しているため、植物フェノタイピング手法が中心である。

abstractThis study developed and validated a reproducible sheath inoculation protocol under controlled conditions.

DisQuan: a hybrid quantum–classical architecture for plant disease detection in staple and specialty crops →

Image-based plant disease identification is essential for advancing smart agriculture, particularly for staple crops such as rice and economically significant crops such as tea, which are cultivated under complex environmental conditions. Furthermore, these crop groups present distinct challenges: rice leaf disease data typically exhibits clear pathological structures but necessitates large-scale deployment on resource-limited devices, whereas tea leaf disease data is complicated by variable lighting, diverse backgrounds, and high biodiversity. Although current deep learning models achieve high accuracy, they predominantly utilize deep convolutional neural network (CNN) architectures with millions of parameters, which hinders practical deployment on edge devices and increases the risk of overfitting when field data is scarce. In response, this study introduces DisQuan, a hybrid architecture that integrates classical deep learning with quantum machine learning (QML) to balance accuracy and resource efficiency. In particular, DisQuan combines the lightweight DisNet feature-extraction network with a variablequantum neural network to compress and refine feature representations in quantum space, yielding a model with only 0.09 million parameters. Experimental results on rice and tea leaf disease datasets indicate that DisQuan achieves the highest accuracy on the rice dataset and performance comparable to deep CNN models with significantly more parameters on the tea dataset, while maintaining a compact and stable structure. Overall, these findings suggest that DisQuan provides a practical compromise between performance and model complexity, and highlight the potential of quantum-classical hybrid architectures for plant disease detection in real-world agricultural settings and on resource-constrained devices.

Why it matches plant phenotyping methods植物葉の病害状態を画像から識別する量子・古典ハイブリッド手法を開発し、複数データセットで性能とモデル規模を評価しており、表現型取得・推定が中心である。

abstractExperimental results on rice and tea leaf disease datasets indicate that DisQuan achieves the highest accuracy on the rice dataset and performance comparable to deep CNN models with significantly more parameters on the tea dataset

A System for the Recognition of Some Selected Grain Plant Leaves Using Deep Learning Algorithms →

Manual inspection of grain plant leaves for defects is subjective and labor-intensive. Few studies have compared deep learning methods on a combined multi-crop dataset. The study collected locally 5,640 leaf images from rice, maize, and guinea corn farms in Nigeria and grouped them into six classes representing defective and healthy leaves for each crop. Three models were trained: YOLOv8 for end-to-end detection and classification, EfficientNetB0 for standalone image classification, and a hybrid that used YOLOv8 for leaf detection followed by EfficientNetB0 for patch classification. The hybrid achieved 99.85% accuracy on the test set, slightly above EfficientNetB0 (99.82%) and YOLOv8 (mAP 0.995). The hybrid also supplies bounding box locations, helping farmers identify exactly where damage appears. This system offers a reliable, field-deployable tool for monitoring grain crop health.

Why it matches plant phenotyping methods穀物葉の健全・欠損状態を画像から検出・分類する深層学習システムの開発とモデル比較が中心であり、植物の病害・損傷状態を直接推定するため、植物フェノタイピング手法に該当する。

abstractManual inspection of grain plant leaves for defects is subjective and labor-intensive.

GCT-BCLN: a bidirectional closed-loop network for nondestructive detection of rice seed vigor using hyperspectral imaging. →

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.

Optimizing cell segmentation and downstream processing for plant probe-based spatial transcriptomics →

Abstract Probe-based spatial transcriptomics platforms use predefined oligonucleotide panels to detect selected RNAs in tissue sections while preserving transcript spatial coordinates. Accurate cell segmentation is required for reliable transcript-to-cell assignments. This analytical process is affected in plant tissues by cell walls, large vacuoles, and strong autofluorescence, which often reduce boundary contrast and elevate background. Nucleus-only segmentation with fixed-distance expansion can be an alternative approach, but it underestimates cellular area and morphology and reduces the number of assignable transcripts per cell. Here, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics. Using the soybean nodule, soybean seed, rice root, and wheat inflorescence, we demonstrate the applicability of our workflow across species, tissues, and technological platforms. In brief, candidate cell masks are generated from available fluorescence signals and then selected and corrected using two napari plugins. Transcript-informed refinement with Baysor is included as an optional step. Upon benchmarking our approach using a collection of metrics (assignment yield, background/negative controls, and per-cell transcript/gene distributions) and linked segmentation choices to expression-matrix quality and downstream clustering, we demonstrate the potential of our workflow to support the analysis of plant probe-based spatial transcriptomics.

Why it matches plant phenotyping methods植物組織の細胞セグメンテーションとトランスクリプト割当てを改善する実用ワークフローを開発し、複数種・組織でベンチマークしている。植物形態そのものの測定ではないが、細胞レベルの空間状態を抽出する解析手法が中心である。

abstractHere, we present a practical workflow for segmentation and downstream processing in plant probe-based spatial transcriptomics.

Phenomics and High-Throughput Phenotyping of Photosynthetic Traits for Improving Abiotic Stress Resilience in Wheat and Rice →

Photosynthesis is the fundamental biological process underlying plant growth, crop productivity, and global food security. However, its efficiency is highly vulnerable to abiotic stresses, which disrupt chlorophyll biosynthesis, electron transport, carbon assimilation, stomatal regulation, and photoprotective mechanisms, ultimately reducing crop yield. Improving photosynthetic resilience under adverse environments has therefore become a major objective of modern crop improvement. Recent advances in phenomics and high-throughput phenotyping (HTP) have transformed the evaluation of photosynthesis-related traits by enabling rapid, non-destructive, and large-scale assessment across diverse environments, while facilitating quantitative characterization of structural, physiological, biochemical, and thermal responses to abiotic stress. Technologies including chlorophyll fluorescence, gas-exchange analysis, thermal imaging, hyperspectral imaging, LiDAR, and UAV-based sensing provide comprehensive insights into plant physiological responses and stress adaptation. Integration of these phenomic approaches with genomic information and artificial intelligence (AI)-driven analytical frameworks has strengthened genomic and phenomic prediction, enabling more accurate identification of candidate genes, selection of superior genotypes, and accelerated genetic gain. This review critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding, highlighting current challenges, knowledge gaps, and future opportunities for developing climate-resilient wheat and rice cultivars and promoting sustainable crop production.

Why it matches plant phenotyping methods植物の光合成形質を対象に、HTP技術やセンサー手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 critically synthesizes recent advances in photosynthesis-related traits, phenomics, HTP technologies, and their integration with genomics and AI-assisted breeding

Global-Local Feature-Based Rice Leaf Disease Classification Using Two-Stream Deep Neural Network Feature Fusion →

Rice leaf diseases significantly affect crop health and yield potential, creating a need for accurate and timely disease diagnosis. Many existing approaches still rely on single-stream feature extraction architectures, which may limit the ability to simultaneously capture global contextual information and fine-grained disease characteristics. Moreover, limited interpretability and decision-support capability hinder their practical deployment in real-world rice farming. To address these limitations, we employed a framework consists of two parallel feature extraction streams designed to capture different characteristics of disease patterns. The first stream uses a Swin Transformer to learn global contextual information and long-range spatial relationships across the leaf image. The second stream employs ConvNeXt to extract local texture features, including lesion details, spots, and color variations. By combining these complementary representations, the proposed framework effectively integrates global semantic information with local disease-specific features for improved classification performance. The extracted features from the two streams are fused through concatenation followed by an attention-based feature refinement module, enabling adaptive weighting of discriminative features. The refined representation is then used by a fully connected classifier for disease prediction. To enhance model interpretability, Grad-CAM visualization is incorporated to highlight disease-relevant regions and provide visual explanations for the model decisions. Furthermore, an LLM-based advisory module is integrated as a post-diagnosis decision-support component to provide contextualized disease management information and suggestions based on the predicted disease category. The generated suggestions are intended to support, rather than replace, expert agronomic recommendations and should be validated by agricultural professionals before practical application. The proposed framework was evaluated on two rice leaf disease datasets, achieving accuracies of 99.55% and 97.06% on Dataset-1 and Dataset-2, respectively, which are higher than those reported in previous studies. Additionally, five-fold cross-validation on Dataset-1 achieved an average accuracy of 98.91% ± 0.47, demonstrating the stability of the proposed approach. Cross-dataset evaluation using nine common disease classes across both datasets achieved 90.80% accuracy, indicating improved generalization across different data distributions. The proposed framework provides an accurate and explainable approach for rice leaf disease diagnosis in smart agriculture applications.

Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定する二流路深層学習手法を開発・評価しており、植物病害表現型の取得・分類が中心である。

titleGlobal-Local Feature-Based Rice Leaf Disease Classification Using Two-Stream Deep Neural Network Feature Fusion

Rice evapotranspiration estimation and irrigation optimization based on coupling UAV multispectral and thermal infrared imagery with the FAO-56 model →

Abstract China's rice production and environmental sustainability are largely dependent on the cold black soil region in Northeast China, where precise water and nitrogen management is challenged by water scarcity and high carbon emissions. To overcome the limitations of conventional empirical management and improve the accuracy of evapotranspiration (ET) estimation in controlled-irrigation paddy fields, this study proposes a novel framework integrating unmanned aerial vehicle (UAV) multispectral and thermal infrared observations, the FAO-56 dual crop coefficient approach, and the NSGA-II multi-objective optimization model. To parameterize and validate this methodology, field data comprising four lower limit thresholds for controlled irrigation and four nitrogen fertilizer application rates were acquired from the Rice Research Site of Farm 856, Heilongjiang Province, China. This integrated approach was used to systematically evaluate rice growth, water consumption, resource use efficiency, and greenhouse gas emissions under different water-nitrogen treatments. Based on these evaluations, an irrigation optimization scheme was developed using daily crop evapotranspiration (ETc). The results indicated that water, nitrogen, and their interaction significantly affected rice yield, irrigation water use efficiency (IWUE), partial factor productivity of nitrogen (PFPN), and global warming potential (GWP). Treatments W3N2 (80%+155 kg/ha N) and W3N3 (80%+200 kg/ha N) achieved the highest yields, 11,883.51 and 11,436.82 kg/ha, respectively, whereas W2N1 (70%+110 kg/ha N) exhibited the best comprehensive performance, with a TCQ value of 0.65. Among the tested vegetation indices, the normalized difference vegetation index (NDVI) showed the strongest correlation with the basal crop coefficient, with an R²of 0.85. The NDVI -crop water stress index ( CWSI ) coupled model achieved the highest ET c estimation accuracy (R 2 = 0.89, RMSE = 0.39 mm/day), reducing the RMSE by 10.3% compared to the traditional, Multi-objective optimization revealed obvious trade-offs among high yield, water saving, high nitrogen efficiency, and low emissions. Scenario S5 was identified as the optimal solution, with an irrigation amount of 669.94 mm, a nitrogen rate of 117.48 kg/ha, a yield of 11,473.43 kg/ha, and the highest coordination degree of 0.86. These results demonstrate that coupling UAV multispectral and thermal infrared imagery with the FAO-56 model can effectively improve ETc estimation and provide reliable data support for water-nitrogen multi-objective optimization in cold-region rice production.

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱赤外画像とFAO-56を結合し、イネの蒸発散量を推定する手法を開発・検証しており、ETc推定精度も定量評価しているため、単なる灌漑試験ではなく植物状態の計測手法が中心です。

abstractthis study proposes a novel framework integrating unmanned aerial vehicle (UAV) multispectral and thermal infrared observations, the FAO-56 dual crop coefficient approach, and the NSGA-II multi-objective optimization model.

Time-Resolved Phenotyping Reveals Heterogeneous Rice Seed Germination Dynamics in Shallow-Water Culture →

Germination percentage is an endpoint measure and therefore does not describe when an individual seed begins visible growth or how rapidly its radicle and plumule expand. We developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture. A single industrial camera moved along a 1 m rail and imaged three culture boxes at 1 h intervals for up to 80 h. The archive comprised 1,062 full-frame images and 6,372 seed-level repeated observations under the six-seed field-of-view configuration. A physical grid maintained seed identity through time and enabled individual regions of interest to be extracted. Whole-seed foregrounds were obtained with a pretrained U 2 -Net, and a masked RGB intensity rule separated newly emerging tissue from the darker hull. For each tracked seed, projected emerging-tissue area and interval growth rate were calculated. Three representative normally germinating seeds first showed measurable tissue at 48 h, yet subsequently followed distinct trajectories: final projected areas ranged from 2,605 to 4,700 pixels and peak interval growth rates ranged from 106.88 to 287.92 pixels h −1 . B-1 accumulated 63.71% of its final visible area during 72–80 h, whereas B-3 accumulated 73.51% during 60–72 h. Thus, seeds with the same observed emergence interval can differ substantially in the timing and magnitude of post-emergence expansion. The workflow converts repeated images into biologically interpretable temporal phenotypes and provides a basis for nondestructive studies of rice seed vigor and germination heterogeneity.

Why it matches plant phenotyping methods連続画像から個々のイネ種子の発芽・組織面積・成長速度を抽出する時間分解フェノタイピングワークフローを開発しており、表現型取得と解析手法が研究の中心である。

abstractWe developed a time-resolved phenotyping workflow to quantify rice seed germination continuously in shallow-water culture.

Structured Multi-Kernel Heteroscedastic Gaussian Process for Crop Straw-to-Grain Ratio Prediction and Uncertainty Quantification →

Crop straw-to-grain ratio (SGR) estimation underpins regional straw resource assessment, yet national inventories rely on fixed coefficients that ignore structured variation across variety, environment, and phenotype. We introduce a Structured Multi-Kernel Heteroscedastic Gaussian Process (GP) framework that models SGR variation through three additive kernels heuristically motivated by the genotype–environment–phenotype (G+E+P) framework—capturing variety-associated variation, spatially structured variation, and environmental and management covariates—and employs an input-dependent noise model for prediction-specific uncertainty quantification. To prevent information leakage, target encoding and feature scaling are recomputed within each cross-validation fold. Evaluated via internal leave-one-out cross-validation on 80 rice samples (42 varieties, six Chinese provinces), the model achieves R2=0.541 with a prediction interval coverage probability of 0.95. Ablation identifies variety-associated variation as the largest contributor among the modeled factors (ΔR2=−0.024) and the multi-kernel design, by incorporating variety-specific information, substantially improves upon a covariate-only RBF GP (ΔR2=0.103). On point-prediction accuracy, Gradient Boosting achieves R2=0.58, slightly ahead of the Heteroscedastic GP (R2=0.54), underscoring that the primary advantage of the GP lies in its input-dependent uncertainty quantification. However, leave-one-county-out validation yields R2≈0 (with σ escalating to 24.4), confirming that the model does not yet generalize to unsampled counties; all reported performance is therefore internal to the nine sampled counties. The framework couples an agronomically motivated additive kernel structure with input-dependent uncertainty quantification, offering a path toward uncertainty-aware prediction from small field datasets.

Why it matches plant phenotyping methods作物のわら・穀粒比という植物関連形質を対象に、不確実性定量化を備えた予測手法を開発・検証しており、単なるルーチン測定ではなく計算的な形質推定が中心である。

abstractWe introduce a Structured Multi-Kernel Heteroscedastic Gaussian Process (GP) framework that models SGR variation

Measurement of Net NH 4 + Fluxes Using the Non-invasive Micro-Test Technology (NMT) System in Rice. →

Ammonium (NH 4 + ) is the primary inorganic nitrogen source for rice ( Oryza sativa L.). Substantial progress has been made in characterizing the functions of ammonium transporters (AMTs) in roots; however, the regulatory dynamics governing subcellular ammonium compartmentation after its entry into cells, particularly its vacuolar sequestration and efflux back to the external environment, remain poorly understood. This knowledge gap stems mainly from two factors: the difficulty of applying conventional detection methods at the organellar scale and interference caused by nonspecific ion adsorption to the cell wall of intact roots. To address these challenges, we present a detailed and reproducible protocol for real-time measurement of net NH 4 + fluxes in rice roots, root protoplasts, and isolated vacuoles using non-invasive micro-test technology (NMT). The protocol covers the preparation of protoplasts and vacuoles from rice roots, the configuration and calibration of the NMT system, and the step-by-step measurement of net NH 4 + fluxes at three distinct biological levels (intact roots, protoplasts, and vacuoles). By employing a unified sample preparation and measurement strategy, this protocol enables quantification of net uptake fluxes across the plasma membrane, characterization of net efflux dynamics under specific conditions, and indirect estimation of vacuolar sequestration capacity using the isolated vacuole system. Overall, this protocol provides a flexible and robust framework for studying NH 4 + homeostasis in plants and is readily adaptable to different crop species, treatment conditions, and experimental objectives. Owing to its modular design and compatibility with standard NMT equipment, it can be readily adopted by laboratories seeking to investigate nitrogen transport mechanisms in plants. Key features • Allows for testing of NH 4 + fluxes in roots, protoplasts, and vacuoles. • Applicable to plants grown under different culture systems, including Arabidopsis thaliana grown in dishes and rice grown in hydroponic systems. • Supports both long-term and transient stress treatments. • Real-time monitoring.

Why it matches plant phenotyping methods植物根・プロトプラスト・液胞のNH4+フラックスをリアルタイム定量するNMT測定プロトコルが研究の中心であり、植物の生理状態を取得する方法を詳細に開発・標準化している。

abstractwe present a detailed and reproducible protocol for real-time measurement of net NH 4 + fluxes in rice roots, root protoplasts, and isolated vacuoles using non-invasive micro-test technology (NMT).

Ozone suppresses rice photosynthesis and yield in China's middle-lower yangtze plain: satellite evidence from SIF and panel regression. →

Ground-level ozone (O 3 ) adversely affects rice physiology and is associated with yield reductions. This study developed a high-resolution assessment framework integrating multi-source satellite remote sensing with econometric methods to quantify the impacts of O 3 on rice production in China's primary rice-growing region-the Middle and Lower Reaches of the Yangtze River (MLYR)-from 2019 to 2023. We fused Sentinel-5P TROPOMI total ozone column (TOC) data, a harmonized multi-satellite solar-induced chlorophyll fluorescence (SIF) product (LHSIF), high-precision rice distribution maps, and ERA5 meteorological reanalysis data. In addition to SIF, we examined multiple vegetation indicators (chlorophyll content, leaf area index, and vegetation indices) to capture broad physiological responses. A bidirectional fixed-effects panel model was employed to control for spatiotemporal confounders, revealing a significant inhibitory effect of O 3 on photosynthesis (β = -1.334 × 10 -5 , p 3 concentrations would increase regional SIF by 36.36%, while a commensurate 10% reduction in annual exposure could elevate rice yields by approximately 8.4%. This spaceborne remote sensing approach provides a robust and transferable methodology for the precise regional monitoring of ozone stress and for informing targeted mitigation strategies to safeguard crop productivity.

Why it matches plant phenotyping methods衛星リモートセンシングによるSIF等の植物生理指標を用いてイネのオゾンストレスを地域スケールで推定する評価フレームワークが研究の中心であり、単なる生物学的実験の routine 測定ではない。

abstractThis study developed a high-resolution assessment framework integrating multi-source satellite remote sensing with econometric methods to quantify the impacts of O 3 on rice production

Hyperspectral–machine learning framework enables early and non-destructive prediction of plant resistance to pest →

The brown planthopper ( Nilaparvata lugens ) is one of the most destructive pests of rice and poses a threat to yield stability and food security. Although host-plant resistance is the most sustainable strategy for BPH management, conventional resistance phenotyping remains labor-intensive, destructive, and poorly suited for large-scale breeding. Here, we combined hyperspectral reflectance profiling of 50 rice varieties with an interpretable machine learning framework to enable non-destructive prediction of resistance phenotypes. Using post-infestation spectral profiles, we established classification models that captured resistance states shaped by constitutive traits and inducible defense responses. Among 13 evaluated algorithms, a radial basis function support vector machine achieved the best performance on full-spectrum data within the sampled variety panel, with an average accuracy of 0.939 ± 0.015 and a maximum of 0.972. Predictive wavelengths were concentrated in the green, red-edge, and near-infrared regions, corresponding to variation in pigment regulation, canopy structure, and water status. Spectral and network analyses showed that resistant genotypes exhibited more complex but less stable spectral co-occurrence networks, consistent with physiological trade-offs associated with defense. We also tested whether resistance could be predicted before pest infestation. Pre-infestation spectra retained significant predictive power, with accuracies of 0.572 ± 0.021 for five-class classification and 0.667 ± 0.021 for binary classification, indicating that constitutive defense-associated physiological states are optically detectable before visible damage occurs. Together, our results show that hyperspectral reflectance encodes both inducible responses after infestation and constitutive defense baselines present beforehand. This work establishes a scalable, non-invasive phenotyping strategy for early resistance screening within evaluated germplasm panels, while future validation across independent and variety-level held-out populations will be required before broader deployment.

Why it matches plant phenotyping methodsイネの害虫抵抗性という植物状態を、ハイパースペクトル計測と機械学習で非破壊推定する方法を開発・評価しており、表現型取得が研究の中心である。

abstractwe combined hyperspectral reflectance profiling of 50 rice varieties with an interpretable machine learning framework to enable non-destructive prediction of resistance phenotypes.

CEG-YOLO: A lightweight edge-optimized framework for in-field rice panicle counting →

Rice panicle number per unit area is a key determinant of yield, but manual counting remains time-consuming and labor-intensive. This study proposes CEG-YOLO, a lightweight deep learning model for in-field rice panicle detection and counting using consumer-grade RGB imaging devices. The model introduces three improvements to YOLOv11s to address specific challenges in field scenarios: C2f-Fast replaces standard convolutions with depthwise convolutions to reduce computational cost for edge deployment; SPPF-ECA integrates an attention mechanism to suppress complex background interference; and GhostConv reduces feature redundancy to improve detection of dense panicles. A dataset of 5,175 images was collected from four rice cultivars planted at three densities using an iPhone 12. The proposed model achieved 93.9% average precision (AP) on the test set, outperforming YOLOv11s which achieved 89.1%, while reducing parameters to 7.8 million and floating-point operations (FLOPs) to 16.5 billion. Robustness evaluation yielded coefficients of determination (R²) values of 0.91 to 0.94 for lighting, 0.89 to 0.94 for planting density, and 0.90 to 0.94 for cultivar. A cross-year field test using an NVIDIA Jetson Orin NX edge device on 120 quadrats in 2025 achieved R² of 0.91, root mean square error (RMSE) of 4.0, and mean absolute error (MAE) of 3.3 at 20 frames per second, confirming practical deployability. This study demonstrates that smartphone-based proximal phenotyping with an optimized deep learning model can provide accurate, low-cost rice panicle counting for breeding and production applications.

Why it matches plant phenotyping methodsイネ穂数という植物形質をRGB画像から推定する深層学習モデルを開発し、精度・頑健性・実地展開性能を検証しており、フェノタイピング手法が研究の中心である。

abstractThis study proposes CEG-YOLO, a lightweight deep learning model for in-field rice panicle detection and counting using consumer-grade RGB imaging devices.

Precisely tracking and counting of field rice seedlings based on UAV platform with DMNP-YOLO and Improved-Bytetrack →

Accurate and non-destructive counting of rice seedlings is crucial for yield estimation and precision agriculture, yet remains challenging in UAV videos due to dense distribution and strong temporal appearance similarity. This study proposes an efficient tracking-based rice seedling counting framework that integrates an improved Yolov11n detector with a robust multi-object tracking strategy to achieve reliable video level counting. The proposed detector, termed DMNP-YOLO, enhances feature representation, localization robustness, and computational efficiency through Dynamic Snake Convolution, a multi-scale feature attention module, Shape-IoU combined with Normalized Wasserstein Distance, and BatchNorm scaling factor based structured channel pruning, resulting in reductions of 40.5% in Params and 15.2% in GFLOPs while achieving a precision of 0.901 and an mAP@0.5 of 0.921. Building upon accurate frame-level detections, a trajectory based counting mechanism is realized by embedding an Anchor–Angle–Distance association strategy into ByteTrack, which explicitly enforces geometric and temporal consistency across frames, significantly improving tracking stability in dense seedling scenes. As a result, Multi-Object Tracking Accuracy is increased by 5.3 percentage points, identity switches are reduced by 33.3%, and counting accuracy is improved by 3.7 percentage points. Extensive experiments demonstrate that the proposed tracking-based counting framework achieves a mean absolute error of 16.47, a mean absolute percentage error of 6.48%, and an R² of 0.95969. Field scale validation further confirms its practical applicability, achieving an overall rice seedling counting accuracy of 93.4% and demonstrating strong robustness in real world agricultural environments.

Why it matches plant phenotyping methodsUAV画像と検出・追跡アルゴリズムにより圃場のイネ幼苗数を推定する手法を開発し、精度検証と実圃場検証を行っており、植物表現型の取得方法が研究の中心である。

titlePrecisely tracking and counting of field rice seedlings based on UAV platform with DMNP-YOLO and Improved-Bytetrack

High-Throughput Panicle Counting of Wild Rice Accessions for Germplasm Evaluation: An AI-Driven UAV Phenotyping Framework →

Wild rice (Oryza spp.) harbors abundant genetic variation and represents an important germplasm resource for improving yield-related traits in cultivated rice. Panicle number is a key phenotypic trait for evaluating tillering capacity and yield potential in wild rice. However, existing approaches for acquiring panicle-number phenotypes remain limited by low efficiency, high dependence on manual operation, and cumbersome matching between plant targets and accession identifiers. In this study, we proposed an AI-driven UAV phenotyping framework for high-throughput panicle counting of wild rice accessions for germplasm evaluation. The framework integrates field plant localization, accession identifier binding, flight route planning, plant-by-plant video acquisition, spatiotemporal registration, video slicing, and panicle detection and tracking, enabling structured panicle-number outputs indexed by accession identifier. To address the small scale, loose structure, morphological variation, and wind-induced swaying of wild rice panicles in UAV imagery, a wild rice panicle detection model was constructed, and WRPD-Tracker was developed for cross-frame identity association and non-redundant counting. The wild rice panicle detection model achieved an AP@50 of 91.56%, representing a 6.16-percentage-point improvement over the DEIM baseline, with 3.70 M parameters and 6.55 G FLOPs, while WRPD-Tracker achieved a HOTA of 65.1% and a MOTA of 79.0%, representing a 5.8-percentage-point improvement in HOTA over the baseline tracker. At the final counting level, UAV-based counts were highly consistent with manual ground counts, with an R² of 0.992 and an MAE of 0.37 panicles. This framework enables batch acquisition of panicle-number phenotypes in wild rice and provides quantitative support for germplasm evaluation, panicle-number trait comparison, and subsequent yield-related phenotypic studies.

Why it matches plant phenotyping methodsUAV画像とAIによるイネ穂数の取得・追跡・計数手法を開発し、手動計数と技術検証しており、植物フェノタイピング手法が研究の中心です。

abstractwe proposed an AI-driven UAV phenotyping framework for high-throughput panicle counting of wild rice accessions for germplasm evaluation.

Using hyperspectral reflectance to explore the responses of rice canopy chlorophyll fluorescence to water stress →

【Objective】Chlorophyll fluorescence is a physiological indicator reflecting crop photosynthesis and water stress. Non-destructively monitoring the changes in chlorophyll fluorescence under water stress is critical for improving irrigation management. This paper explores the applicability of canopy hyperspectral reflectance for elucidating the response of rice canopy chlorophyll fluorescence to water stress.【Method】The experiment was conducted in pots and the measurements were taken during the booting stage of rice. Three water treatments were set, including continuous flooding irrigation (CK), mild drought (MS) and severe drought (HS). Canopy hyperspectral reflectance and chlorophyll fluorescence were synchronously measured using a high-throughput phenotyping platform, from which we analysed the responses of chlorophyll fluorescence traits to soil water change. Prediction models were developed to estimate chlorophyll fluorescence traits using partial least squares regression (PLSR) and backpropagation neural network (BPNN), based on characteristic spectral bands.【Result】①The chlorophyll fluorescence traits Fv/Fm, Y(II), qL and Y(NPQ) varied with water stress, with significant changes observed 3-4 days after cessation of irrigation, and detectable variation identified up to day 6 after terminating irrigation. On day 6 after irrigation cessation, the HS treatment reduced Fv/Fm, Y(II) and qL by 41.3%, 46.9% and 53.1%, respectively, whereas increased Y(NPQ) by 117.5% compared with CK. ②Savitzky-Golay smoothing and multiplicative scatter correction (MSC) preprocessing effectively reduced the scattering effects on canopy hyperspectral data induced by structural variation. The characteristic spectral bands selected from the hyperspectral data were mainly distributed in the blue (400-500 nm), red and near-infrared regions. ③Compared with PLSR, the BPNN was more effective in capturing the nonlinear relationships between hyperspectral data and chlorophyll fluorescence traits. The BPNN was most accurate for estimating Y(NPQ) and qL, with the associated R2 values being 0.867 and 0.845, respectively, and less accurate for estimating Fv/Fm.【Conclusion】Canopy hyperspectral data can be used to estimate rice chlorophyll fluorescence traits. This approach provides a rapid, cost-effective, and non-destructive method for monitoring crop physiological responses to water stress.

Why it matches plant phenotyping methodsイネのクロロフィル蛍光という生理形質を、キャノピー分光反射から推定するセンサー計測・予測モデルを開発し、精度評価しており、フェノタイピング手法が中心である。

abstractCanopy hyperspectral reflectance and chlorophyll fluorescence were synchronously measured using a high-throughput phenotyping platform

Machine Learning-Based Crop Growth Diagnosis System Using Spatiotemporal Relative Analysis of Vegetation Indices via a Quartile-Based Method →

Japanese agriculture faces pressing challenges, including a declining and aging farming population and the need to adapt to climate change. To address these issues, Smart Agriculture is being introduced to improve production efficiency. Among these, unmanned aerial vehicles (UAVs) have gained attention for their ability to rapidly monitor entire fields. We proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values. The experimental site consisted of five paddy fields within an 80 m × 50 m plot in Iwate Prefecture, Japan, equipped with weather and water sensors. Ground-truth data (overall length, culm length, panicle number, and stem number) were collected approximately one week before harvest. UAV monitoring was conducted four times using a multispectral camera, and growth analysis was performed with six VIs. Correlation analysis revealed a positive relationship between crop growth and the daily average water level during the drainage period, and a negative relationship with the daily temperature range in mid-June. A combined cluster-label representation, constructed from clustering results of all VIs for each mesh, enabled integrated analysis and visualization of multi-index patterns. Grid size optimization showed no significant differences in correlation trends between 1 m × 1 m and 5 m × 5 m resolutions. For non-crop area removal, a comparison of three image segmentation methods demonstrated that the Otsu Method achieved the highest performance. Finally, to facilitate practical use in the field, we prototyped a report interface for the diagnosis system. Future work will focus on developing a comprehensive field diagnosis system to clarify field environments, with the aim of addressing fragmentation and enclaves in Japanese farms.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植生指数、画像分割、クラスタリングを統合した作物生育診断システムの開発・評価が中心であり、作物形質との相関検証や実用インターフェースも扱っている。

abstractWe proposed a machine learning-based crop growth diagnosis system that generates spatiotemporal data for multiple vegetation indices (VIs) using the quartile method and diagnoses crop growth based on patterns of change in these values.

Field-scale rice yield prediction using UAV imagery and machine learning in a developing country context →

• DL model trained on MS data achieved the highest accuracy with an R 2 of 75.29% • Linear Regression Coefficient-Based feature selection with SVM and PCA with Linear Regression significantly improved model performance. • NIR and red-edge bands in the MS dataset consistently outperformed the RGB dataset • More represented rice variety (Sona) achieved a strong R 2 of 80.21% on MS data Accurate crop yield prediction is critical for agricultural planning, food security assessment, and farm-level decision-making. In Nepal, however, rice yield estimation is still predominantly based on traditional approaches, where local agricultural extension offices collect field-level observations that are subsequently aggregated at district, provincial, and national scales, often limiting spatial detail and timeliness. This study aims to develop a field-scale rice yield estimation framework by integrating Unmanned Aerial Vehicle (UAV)-derived remote sensing data with machine learning (ML) and deep learning (DL) techniques. High-resolution multispectral (MS) and RGB UAV imagery were used to evaluate the influence of Vegetation Indices (VIs), including HUE and VNDVI from RGB data and RGBVI and Simple Ratio (SR) from MS data, along with plant characteristics and farm management practices (e.g., application of Zyme and Zinc Potash) on rice yield. The predictive performance of Support Vector Machines (SVM), Linear Regression (LR), Decision Trees (DT), Random Forests (RF), and deep neural network models were systematically assessed. Data preprocessing included feature selection based on importance ranking, Yeo–Johnson power transformation, and Principal Component Analysis (PCA) to improve model stability and performance. Among conventional ML models, LR combined with PCA achieved a coefficient of determination (R²) of 69.09% using MS data, while SVM yielded the best performance using RGB data (R² = 68.27%). Overall, deep neural networks outperformed other models, achieving R² values of 75.29% and 64.60% for MS and RGB data, respectively. Model performance varied notably across rice varieties; the Sona variety (n = 127) achieved the highest coefficient of determination (R² = 80.21% for MS and 76.34% for RGB), whereas varieties with fewer samples exhibited lower predictive performance. Results further indicate that ranking features by importance, rather than eliminating them, enhances predictive accuracy, particularly when using LR-derived feature importance, which proved critical for improving the performance of both LR and SVM models.

Why it matches plant phenotyping methodsUAV画像からイネ収量を推定する手法・フレームワークの開発と、複数の機械学習モデルの系統的評価が研究の中心であり、単なる収量のルーチン測定ではない。

abstractThis study aims to develop a field-scale rice yield estimation framework by integrating Unmanned Aerial Vehicle (UAV)-derived remote sensing data with machine learning (ML) and deep learning (DL) techniques.

Genomic Selection Integrated with High-Throughput Phenotyping and Speed Breeding for Smart and Greener Rice ( Oryza sativa ) Improvement. →

Background: Rice breeding requires faster development of high-yielding, climate-resilient, resource-efficient, and high-quality cultivars for production systems exposed to environmental variability and increasing input constraints. Genomic selection offers an opportunity to predict breeding value before extensive field evaluation, although its effectiveness depends on the integration of genomic, phenotypic, and environmental information. Methods: This narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding. Results: Genome-wide marker data can support early ranking of breeding materials for grain yield, grain quality, disease resistance, drought tolerance, salinity tolerance, and nutrient-use efficiency. Prediction performance is influenced by trait architecture, marker density, training-population size, genetic relatedness between training and candidate populations, phenotypic data quality, and genotype-by-environment interaction. Red-green-blue, multispectral, hyperspectral, thermal, and light detection and ranging platforms can generate temporal traits associated with plant architecture, biomass, water status, nutrient status, and stress responses, which may improve prediction under suitable population and validation designs. Speed-breeding systems shorten generation intervals and facilitate rapid advancement, recurrent selection, and recycling of superior parental lines. Conclusions: Integrated breeding pipelines that combine genomic prediction, high-throughput phenotyping, environmental data, and speed breeding can improve selection efficiency and shorten rice improvement cycles. Wider adoption will require affordable technology platforms, standardized data systems, multi-environment validation, breeder capacity development, and collaborative data-sharing frameworks for smart and greener agriculture.

Why it matches plant phenotyping methods高スループット表現型解析をゲノム選抜との統合という方法論的主題の一部として批判的にレビューしており、各種画像・センサープラットフォームと形質抽出を扱うため、表現型手法レビューに該当する。

abstractThis narrative review critically examines recent advances in genomic selection for rice and its integration with high-throughput genotyping, high-throughput phenotyping, machine learning, multi-environment prediction, and speed breeding.

A comparative analysis of 3D point clouds and crop surface models for rice plant height estimation using UAV-SfM →

Accurate estimation of crop plant height using unmanned aerial vehicles (UAVs) is essential for field-scale crop monitoring and phenotyping. Most previous studies using UAV-based structure-from-motion (SfM) photogrammetry have relied on raster-based crop surface models (CSMs) and have evaluated their performance using accuracy metrics such as the coefficient of determination ( R 2 ) and root mean square error (RMSE). However, such evaluations provide limited insight into how estimation behavior varies across space and time, particularly during dynamic crop growth stages. To address this gap, this study conducted a time-series comparison of rice plant height estimates derived from UAV-SfM-generated dense point clouds (DPCs) and raster-based CSMs in farmer-managed paddy fields in Cambodia, which are characterized by heterogeneous micro-environmental conditions. Rice plant height was measured throughout the growing season and UAV-derived estimates were evaluated using regression analysis, analysis of covariance, and canopy cover dynamics. In the pooled analysis, both approaches achieved high overall accuracy, with R 2 = 0.92 and RMSE = 7.2 cm for the CSM-based approach and R 2 = 0.90 and RMSE = 8.8 cm for the DPC-based approach. However, time-series analyses revealed that CSM-derived plant height estimates exhibited strong location-dependent variability and sensitivity to early-stage canopy development, whereas DPC-based estimates showed more consistent performance across locations and growth stages. Regression coefficients derived from CSM-based estimates varied significantly among locations, whereas those from DPC-based estimates did not, suggesting that point-based representations may provide more spatially consistent estimation behavior under heterogeneous field conditions. By explicitly considering temporal dynamics, canopy development, and data representation, this study highlights the limitations of current raster-based UAV-SfM workflows for structurally complex crop canopies and suggests that DPC-based approaches may offer a useful complementary representation for crop monitoring and phenotyping, particularly when spatial consistency across heterogeneous field conditions is important.

Why it matches plant phenotyping methodsUAV-SfMによるイネの草丈推定手法を、3D点群と作物表面モデルで時系列比較・検証しており、表現形式と技術性能の評価が研究の中心です。

titleA comparative analysis of 3D point clouds and crop surface models for rice plant height estimation using UAV-SfM

A Knowledge-Guided Multi-Task Framework for Robust and Interpretable Rice Disease Diagnosis in Open-Field Scenarios →

Abstract Deep learning has achieved remarkable success in rice disease diagnosis; however, existing methods often suffer from limited interpretability and poor robustness against open-world environmental noise. To address these challenges, this study proposes the Knowledge-Guided Multi-Task Rice Network (MTRNet) built upon a ResNet-50 backbone. Unlike conventional "black-box" models, MTRNet employs expert knowledge injection via a phytopathological matrix to explicitly disentangle disease features into Shape, Color, and Location attributes within a multi-head architecture. Furthermore, to mitigate false positives in complex field scenarios, a non-parametric Cascade Inference System (CIS)—comprising a biological grayscale filter and a visual consistency check—is introduced for robust Out-of-Distribution (OOD) detection and anomaly rejection. Experiments on a benchmark dataset of 5,932 field images, which primarily comprises four main rice diseases (Rice Leaf Blast, Brown Spot, Bacterial Leaf Blight, and Tungro), demonstrate that MTRNet achieves a diagnostic accuracy of 99.83%. Crucially, in an open-world robustness evaluation involving 1,000 non-agricultural noise samples, the proposed system achieved an 81.80% OOD rejection rate. By balancing diagnostic accuracy with structural transparency, this framework effectively narrows the gap between laboratory benchmarks and real-world agricultural applications.

Why it matches plant phenotyping methodsイネ病害の画像から病徴を診断する深層学習・OOD検出手法を提案し、実画像データで性能評価しており、植物の病害状態の取得・推定が研究の中心である。

abstractthis study proposes the Knowledge-Guided Multi-Task Rice Network (MTRNet) built upon a ResNet-50 backbone.

A Review of Plant Leaf Disease Identification Using Deep Learning: Recent Advances, Challenges, and Future Directions →

Agriculture plays a pivotal role in ensuring global food security, economic stability, and sustainable development. Plant diseases significantly reduce agricultural productivity, resulting in substantial economic losses and threatening food supply worldwide. Early and accurate identification of plant leaf diseases enables timely intervention, minimizes crop damage, and enhances agricultural yield. Traditional disease diagnosis relies heavily on visual inspection by agricultural experts, making the process labor-intensive, subjective, and unsuitable for large-scale deployment. Recent advances in artificial intelligence, particularly deep learning, have transformed plant disease diagnosis by enabling automatic feature extraction and highly accurate image-based classification. This review presents a comprehensive analysis of recent developments in deep learning techniques for plant leaf disease identification. Various convolutional neural network (CNN) architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, Inception, and Xception, are critically reviewed along with modern transformer-based models such as Vision Transformer (ViT), Swin Transformer, and hybrid CNN–Transformer frameworks. The paper also examines transfer learning strategies, object detection methods including YOLO and Faster R-CNN, and semantic segmentation approaches such as U-Net and DeepLabV3+. Publicly available benchmark datasets, including PlantVillage, PlantDoc, AI Challenger, Cassava Leaf Disease, and Rice Leaf Disease datasets, are discussed in terms of dataset diversity, annotation quality, and practical applicability. Furthermore, image preprocessing techniques, data augmentation methods, evaluation metrics, and deployment considerations for mobile and edge devices are comprehensively reviewed. The paper identifies current research challenges, including dataset imbalance, environmental variability, model interpretability, computational complexity, and limited real-world generalization. Finally, emerging research directions such as explainable artificial intelligence, federated learning, multimodal learning, self-supervised learning, lightweight architectures, and edge AI are discussed to provide future research opportunities. This review serves as a valuable resource for researchers, practitioners, and agricultural technologists interested in developing robust, scalable, and intelligent plant disease identification systems.

Why it matches plant phenotyping methods植物葉の病害状態を画像から識別する深層学習手法を中心に、モデル、データセット、評価、展開を体系的にレビューしており、植物表現型計測手法のレビューに該当する。

abstractThis review presents a comprehensive analysis of recent developments in deep learning techniques for plant leaf disease identification.

Explainable and Robust Rice Leaf Disease Classification Across Heterogeneous Datasets Using Class Harmonization and ConvNeXt →

Rice leaf diseases pose a major threat to crop productivity and global food security. Therefore, rapid and accurate disease diagnosis is essential for effective crop management. Despite the advancements made by deep learning algorithms in automated disease detection, the majority of current deep learning-based models are built and evaluated on single-source datasets. Therefore, the generalization capability of these models is still questionable. In order to overcome this problem, this paper presents an interpretable deep learning approach for classifying diseases in rice leaves using ConvNeXt-Tiny and Grad-CAM. The proposed semantic class normalization technique is used to harmonize the heterogeneous data classes to form a seven-class classification system. The model performance was analyzed based on two available public datasets for rice leaf disease and a combination of both. After performing five individual runs, the average classification accuracies were calculated to be 99.13±0.37%, 95.28±0.63%, and 97.27±0.41% for Datasets 1, 2, and the combined dataset, respectively. The confusion matrix analysis indicated minor misclassification errors in the form of false positives for the disease classes which have similarities in appearance. The training and validation curves showed consistent learning with minimum overfitting. Moreover, Grad-CAM analysis confirmed the focus of the model on the disease-specific regions. These results collectively show that the combination of transfer learning, semantic class harmonization, and explainable AI is a stable platform to conduct automated rice disease detection. It is evident from these outcomes that reliable performance can be achieved even under heterogeneous imaging conditions. Consequently, there exists great potential for this approach in agricultural decision-support systems.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類・推定する深層学習手法を開発し、異質データセットで性能評価しているため、植物フェノタイピング手法が中心です。

abstractthis paper presents an interpretable deep learning approach for classifying diseases in rice leaves using ConvNeXt-Tiny and Grad-CAM.

ResMDCL-PDM: An IoT-Enabled Multi-Task Deep Learning Framework for Precision Pest and Disease Management in Maize and Rice Production →

Abstract Pests and diseases are major constraints to cereal production, reducing crop yield, farm profitability, and food security worldwide. Timely detection of crop health threats and accurate assessment of infection severity are essential for effective crop protection, yet conventional field scouting remains labor-intensive, subjective, and unsuitable for real-time decision-making. Although recent advances in the Internet of Things (IoT) and deep learning have enhanced automated crop monitoring, most existing approaches focus on single-task disease classification and provide limited support for severity-aware management. This study proposes ResMDCL-PDM (Residual Network with Multi-Dimensional Compensation Layer for Pest and Disease Management), an IoT-enabled multi-task deep learning framework for precision pest and disease management in maize and rice production. The framework combines field-based environmental sensing with a modified ResNet-50 architecture enhanced by a Multi-Dimensional Compensation Layer (MDCL) to jointly identify crop species, classify pest and disease categories, and estimate infection severity. Field images collected from maize and rice farms at the Federal University of Agriculture, Abeokuta, Nigeria, were integrated with publicly available benchmark datasets. Following preprocessing and data augmentation, 8,556 annotated images were used for model development and evaluation. The proposed framework achieved an overall classification accuracy of 97.8% , outperforming AlexNet, VGG16, MobileNetV3, DenseNet121, EfficientNet-B0, and the baseline ResNet-50. High precision, recall, and F1-score, together with ablation analysis, confirmed the effectiveness of the proposed MDCL. The results demonstrate that integrating IoT-enabled monitoring with multi-task deep learning provides reliable, severity-aware decision support for targeted crop protection and offers a practical, scalable solution for sustainable precision agriculture.

Why it matches plant phenotyping methods植物画像から病害・害虫カテゴリーと感染重症度を推定するIoT・深層学習フレームワークの開発と評価が中心であり、感染植物の状態を直接測定する方法論的研究である。

abstractThis study proposes ResMDCL-PDM (Residual Network with Multi-Dimensional Compensation Layer for Pest and Disease Management), an IoT-enabled multi-task deep learning framework for precision pest and disease management in maize and rice production.

A multimodal geospatial foundation model anticipates crop stress and yield failure across climates and species →

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.

Artificial Intelligence and Machine Learning for Genomic Prediction, High-Throughput Phenotyping and Climate-Adaptive Breeding In Maize and Rice: A Comprehensive Review →

Climate change is intensifying abiotic stresses such as drought and heat, posing significant threats to global food security and the productivity of staple crops including maize (Zea mays L.) and rice (Oryza sativa L.). Conventional breeding approaches are often constrained by the complex genetic architecture of stress-adaptive traits and lengthy breeding cycles, highlighting the need for more efficient, data-driven strategies. This review summarizes recent advances in artificial intelligence (AI) and machine learning (ML) for genomic prediction, high-throughput phenotyping (HTP), and climate-adaptive breeding in maize and rice. We discuss the applications of machine learning architectures, including multilayer perceptron (MLP), convolutional neural networks (CNN), random forest (RF), deep neural networks (DNN), gradient boosting methods, and explainable artificial intelligence (XAI), in improving genomic selection and capturing complex genotype–environment interactions. The review further explores the integration of AI with HTP technologies, including autonomous robotic platforms, drones, hyperspectral imaging, and LiDAR, to enable rapid, accurate, and non-destructive phenotypic assessment. In addition, we examine the role of AI-driven predictive models in identifying stress-responsive genes, improving trait prediction, and accelerating the development of climate-resilient crop varieties. Current challenges, including data heterogeneity, computational demands, model interpretability, and biological validation, are also discussed alongside emerging solutions such as multi-view learning, transfer learning, and intelligent precision design breeding. Overall, the convergence of AI, ML, multi-omics, and advanced phenotyping technologies represents a transformative framework for next-generation crop improvement, offering new opportunities to accelerate sustainable breeding programs and strengthen global food security under changing climatic conditions.

Why it matches plant phenotyping methodsAI・MLを用いた高スループット植物表現型解析と、ロボット、ドローン、ハイパースペクトル、LiDARによる表現型評価を中心的にレビューしているため。

abstractThis review summarizes recent advances in artificial intelligence (AI) and machine learning (ML) for genomic prediction, high-throughput phenotyping (HTP), and climate-adaptive breeding in maize and rice.

Deep learning based groundnut and paddy leaf disease classification using dual attention network. →

Plant diseases are crucial for improving crop yield and ensuring sustainable agricultural practices, particularly for staple crops such as groundnut and paddy leaf. However, existing methods often suffer from limited feature discrimination, inadequate attention to disease-affected regions, and reduced performance under real-world conditions. To address these limitations, this research introduces a novel deep learning (DL)-based GOPI-NET framework for precise groundnut and paddy leaf disease classification. Initially, the input leaf images are enhanced using Bilateral Filtering (BF) and Contrast Limited Adaptive Histogram Equalization (CLAHE) to reduce noise and improve contrast. Subsequently, HSV color space segmentation is employed to precisely isolate disease-affected regions. The proposed Dual Attention Network (DuAtNet) integrates channel and spatial attention mechanisms within a ConvNeXt backbone to capture discriminative disease-specific features. An efficient Fuzzy Extreme Learning Machine (FELM) classifier is then utilized for final categorization into Healthy, Leaf Spot, Bacterial Wilt, and Leaf Blight classes. The effectiveness of the GOPI-NET is evaluated using precision, recall, specificity, accuracy, and F1-score. The experimental results demonstrate that GOPI-NET achieves an overall accuracy of 98.32%. The GOPI-NET improves classification accuracy by 1.29%, 1.40%, and 2.23% compared to GLDICCNN, DNN-CSA, and LeafNet respectively.

Why it matches plant phenotyping methods植物葉画像から病斑領域を抽出し、病害状態を分類する深層学習手法が研究の中心であり、植物病害表現型の取得・推定に該当する。

abstractthis research introduces a novel deep learning (DL)-based GOPI-NET framework for precise groundnut and paddy leaf disease classification.

A UNIFIED MULTI-MODAL FRAMEWORK FOR CROP STRESS DETECTION: COMBINING TABULAR ENVIRONMENTAL DATA AND LEAF-IMAGE CLASSIFICATION →

Accurate crop stress detection is essential for precision agriculture; however, most existing approaches rely on binary labels that collapse distinct stress processes water deficit, nutrient deficiency, disease, and pest damage into a single "stressed" category.We demonstrate empirically that this binary formulation is the primary barrier to classification performance: five model architectures achieve ROC-AUC values within ±0.01 of the random baseline (0.50) on binary stress classification, regardless of feature engineering strategy. Decomposing the binary label into stress-type-specific categories enables anXGBoost classifier to achieve 91.4% accuracy and a macro-averaged F1-score of 0.93 using the same underlying features.To extend coverage to visual disease symptoms, we train a MobileNetV2-based CNN on paddy leaf images, achieving 93.7% binary accuracy (healthy vs. disease_stress) with 100% healthy recall.We combine both modalities in a fusion ensemble that merges tabular and image predictions through rule-based priority logic, achieving 94.6% accuracy on the evaluated image subset.

Why it matches plant phenotyping methods葉画像から健全・病害ストレス状態を推定するCNNと、画像・表形式データの融合分類法が研究の中心であり、植物状態の取得・推定手法を評価している。

abstractTo extend coverage to visual disease symptoms, we train a MobileNetV2-based CNN on paddy leaf images, achieving 93.7% binary accuracy (healthy vs. disease_stress) with 100% healthy recall.

An End-to-End Precision Phenotyping Framework: Rice Panicle Detection and Counting in Complex Fields via Lightweight DETR →

Accurate, high-throughput quantification of rice panicles is important for yield estimation and breeding-oriented rice phenotyping. However, unmanned aerial vehicle (UAV)-based panicle detection remains challenging because flight-altitude variation produces large target-scale changes. Flooded paddy backgrounds, leaf occlusion, and illumination fluctuations further obscure small panicle targets. To address these challenges, we constructed a composite multi-altitude dataset covering UAV imagery acquired from 3 to 20 m under varying field conditions. We then propose Panicle-DETR, a lightweight detection and counting framework based on a frequency-aware Cross Stage Partial (CSP) backbone. Rather than treating the Fast Fourier Transform (FFT) as a filter by itself, the proposed FasterFD module uses frequency-domain representations with learnable frequency-response reweighting to enhance panicle-related texture cues and reduce redundant background responses. A Lossless Feature Encoder is designed to preserve fine spatial information for small targets across altitude-induced scale changes, while a composite metric loss based on Normalized Gaussian Wasserstein Distance (NWD) and Inner-IoU improves localization for adherent and overlapping panicle clusters. On the composite dataset, Panicle-DETR achieved a Precision of 90.97%, a Mean Absolute Error of 4.28, and an R2 of 0.957 for single-frame panicle counting. With 13.78 M parameters and 53.0 GFLOPs, the framework achieved 16.9 FPS with 1.96 GB peak GPU memory in a battery-powered notebook benchmark, supporting its potential for resource-constrained field-side UAV image analysis.

Why it matches plant phenotyping methodsUAV画像からイネ穂の検出・計数という植物形質推定を中心に、マルチ高度データセットと専用解析フレームワークを開発・評価しているため。

abstractAccurate, high-throughput quantification of rice panicles is important for yield estimation and breeding-oriented rice phenotyping.

Smart Plant Disease Diagnosis via MERN Stack Interface and PyTorch Deep Learning Models →

Context: Early identification of plant diseases plays a crucial role in enhancing crop productivity and promoting sustainable agricultural practices. Advances in artificial intelligence and web-based technologies have paved the way for smart systems capable of automatically diagnosing diseases in crops like rice and sugarcane. Objective: This research focuses on developing a smart plant disease diagnosis system that integrates deep learning techniques with a MERN (MongoDB, Express.js, React.js, Node.js) stack to provide accurate, real-time classification of rice and sugarcane leaves diseases through a user-friendly web interface. Method: The proposed framework employs a Convolutional Neural Network (CNN) built with PyTorch and trained using a carefully curated dataset of diseased rice and sugarcane leaf images. The developed model was incorporated into a web application built using the MERN stack to enable seamless frontend-backend communication and real-time disease prediction. The model’s effectiveness was assessed using evaluation metrics such as precision, recall, F1-score, and confusion matrix analysis. Results: The CNN model achieved high classification performance, with an average class accuracy of 95.92%, overall classification accuracy of 91.83%, average precision of 91.85%, average recall of 92.05%, and average F1-score of 91.86%. Confusion matrix analysis further validated the model’s efficiency in accurately recognizing rice and sugarcane leaves diseases. The integrated web platform demonstrated efficient and user-friendly real-time disease prediction capabilities. Conclusions: The developed AI-based plant disease detection system highlights the effectiveness of integrating deep learning techniques with modern web technologies to support scalable agricultural solutions. The system provides a practical solution for farmers and agronomists seeking early and accurate crop disease detection. Future enhancements may include multilingual support, mobile application integration, and agronomic advisory modules to further advance precision agriculture initiatives.

Why it matches plant phenotyping methods葉画像から植物病害状態を分類するCNNモデルとリアルタイムWeb基盤の開発・評価が研究の中心であり、植物表現型取得手法に該当する。

abstractThis research focuses on developing a smart plant disease diagnosis system that integrates deep learning techniques with a MERN (MongoDB, Express.js, React.js, Node.js) stack to provide accurate, real-time classification of rice and sugarcane leaves diseases through a user-friendly web interface.

A hybrid Conv1D-GRU model with spectral augmentation for non-destructive rice seed vigor detection. →

Seed quality is closely associated with rice yield and grain quality, and seed vigor is a key indicator for seed quality evaluation. High-vigor seeds usually show stronger resistance to environmental and biotic stresses, thereby improving germination and seedling establishment. Thus, rapid and accurate detection of rice seed vigor is essential for breeding, storage management, and crop production. In this study, a non-destructive rice seed vigor detection method based on near-infrared (NIR) spectroscopy, spectral augmentation, and Conv1D-GRU classification was developed. Rice seed samples with different vigor levels were prepared through artificial aging, and seed-level NIR spectra were acquired using a NIR spectrometer. Spectral preprocessing was applied to reduce noise, enhance relevant spectral features, and correct scattering effects. Sparse representation and dictionary learning were used to augment the training spectra and improve sample diversity. In the Conv1D-GRU classifier, the Conv1D layers extracted local spectral features from adjacent wavelength regions, while the GRU layer captured wavelength-order contextual information across the spectral sequence. The key hyperparameters of the classifier were optimized using an integrated population search algorithm. Experimental results showed that the proposed method achieved test accuracies of 0.9844, 0.9740, and 0.9818 for conventional japonica rice, indica-japonica hybrid rice, and japonica glutinous rice, respectively. Compared with PLS-DA, SVM, XGBoost, 1D-CNN, and GRU models, the Conv1D-GRU classifier showed better overall performance under the current experimental conditions. These results indicate that the proposed NIR spectroscopic method provides a promising non-destructive approach for rice seed vigor detection and has potential for seed quality evaluation and agricultural production management.

Why it matches plant phenotyping methodsイネ種子の活力という植物形質を、NIR分光・スペクトル拡張・Conv1D-GRU分類で非破壊推定する手法の開発と比較評価が中心である。

abstracta non-destructive rice seed vigor detection method based on near-infrared (NIR) spectroscopy, spectral augmentation, and Conv1D-GRU classification was developed.

Smartphone-based paddy leaves SPAD value prediction from RGB contact imaging using ensemble deep learning model →

Assessment of chlorophyll content is important to understand plant nitrogen status in precision agriculture. Traditional destructive methods for chlorophyll quantification are time-consuming, labor-intensive, and unsuitable for high-throughput phenotyping applications. The SPAD meter (Soil Plant Analysis Development) provides a rapid and non-destructive alternative by measuring leaf greenness as a proxy for chlorophyll content. Recent technological advances in imaging sensors and computational methods have enabled the development of low-cost approaches for predicting SPAD values. In this study, we propose an ensemble deep learning model-based Android application ( SPAD Predictor ) that was developed for predicting the SPAD value from RGB contact imaging. A total of 34 features, including color space features, RGB-derived features, and vegetation indices, were used to develop the model. The model consists of a lightweight Multi-Layer Perceptron (MLP) and Random Forest (RF) Regressor layer with stacking ensemble architecture. A linear regression was used as a meta-model to ensemble the MLP and RF layers. A permutation-based feature importance analysis showed that the a* channel, ExGR, RG, NRI and VARI indices played the most important roles in predicting SPAD value. The proposed ensemble deep learning model yielded R 2 t r a i n i n g of 0.987 and R 2 t e s t i n g of 0.89, RMSE of 3.25. The developed application was successfully deployed and was able to perform image submission, backend communication, prediction generation, result display, and history management. Field-level validation of the developed application yielded R 2 of 0.848, RMSE of 3.068, and MAE of 2.544. These findings indicate that the developed system has practical potential as a low-cost, field-applicable tool for estimating paddy leaf SPAD.

Why it matches plant phenotyping methodsRGB画像からイネ葉のSPAD値を推定するアプリと深層学習モデルを開発し、フィールド検証も実施しており、植物表現型取得手法が研究の中心である。

abstractwe propose an ensemble deep learning model-based Android application ( SPAD Predictor ) that was developed for predicting the SPAD value from RGB contact imaging.

UAV remote sensing for yield prediction in staple crops: a review. →

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.

Mathematical modeling with RGB data for comparative analysis of rice lineages and distribution of breaking points. →

Rice (Oryza sativa L.) is a crucial food crop, supplying a significant portion of the global population's caloric intake. With the shift from traditional breeding methods to digital approaches, image analysis is becoming essential for distinguishing between rice cultivars. However, the optimal growth stages for effectively utilizing image analysis to classify rice varieties remain uncertain. This study aimed to evaluate 102 rice cultivars through non-destructive image processing and RGB ratio analysis. Images were captured every two days throughout the growth period, and an RGB ratio formula was developed, excluding background pixels to focus on plant characteristics. Regression analysis identified critical time points for differentiation, with the red (R) channel being most effective at 55 and 75 days post-transplanting, and the green (G) channel at 60 and 80 days. Hierarchical clustering of slopes from piecewise regression categorized the 102 cultivars into three distinct clusters, representing their ecological types. These findings provide a precise and efficient method for classifying rice cultivars, offering breeders key insights into the most effective stages for variety differentiation. By optimizing image analysis techniques, this research enhances the efficiency of rice breeding programs and supports the targeted management of genetic resources for improved trait selection.

Why it matches plant phenotyping methodsイネの画像から背景を除去してRGB比を算出し、時系列画像解析と回帰・クラスタリングにより品種識別に有効な時点と特徴量を開発・評価しており、植物表現型取得・抽出手法が中心である。

abstractThis study aimed to evaluate 102 rice cultivars through non-destructive image processing and RGB ratio analysis.

Rice yield prediction using UAV-based multispectral imagery and AutoGluon across regions and field scales →

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.

Lodicule Isolation and Morphometric Analysis During Rice Floret Opening. →

Rice lodicules are specialized floral organs located at the base of the ovary that undergo dynamic morphological changes during the flowering period. Water uptake-driven swelling and subsequent dehydration-induced shrinkage of the lodicules trigger floret opening and closure, respectively. Although lodicules play a central role in floret movement, standardized methods for quantitatively monitoring their temporal morphological changes remain limited. Here, we describe a detailed and reproducible workflow for lodicule sampling, dissection, imaging, and quantitative morphometric analysis. Florets are collected at predefined clock time points during the flowering period, and lodicules are carefully isolated under a stereomicroscope. High-resolution imaging is performed under consistent acquisition settings, followed by precise measurement of lodicule length, width, and thickness using image analysis software. This protocol emphasizes positional consistency in sampling, uniform imaging parameters, and standardized data analysis to enhance reproducibility. This method is suitable for evaluating the effects of genetic background or environmental conditions on lodicule morphology. By providing a standardized analytical framework, this protocol enables accurate and quantitative morphometric analysis of rice lodicules during floret opening. Key features • Standardized time-point sampling minimizes variability caused by diurnal fluctuations and handling during lodicule morphometric analysis. • Enables reproducible isolation and imaging of rice lodicules while preserving native morphology and preventing dehydration-induced artifacts. • Time-resolved workflow enables analysis of rapid morphological changes associated with floret opening and closure. • Applicable for comparing genetic and environmental effects on lodicule morphology under controlled experimental conditions.

Why it matches plant phenotyping methodsイネ小花器官の形態を標準化された採取・撮像・画像解析で定量する再現可能な表現型測定プロトコルが中心である。

abstractwe describe a detailed and reproducible workflow for lodicule sampling, dissection, imaging, and quantitative morphometric analysis.

YOLO-DC: A Crop Detection and Counting Network for UAV-Based Agricultural Scenes →

Crop targets in UAV aerial images are typically characterized by small scale, dense distribution, severe mutual occlusion, and complex backgrounds, which often lead to low detection accuracy and large counting errors for existing deep learning models. To address these issues, this study proposes an improved YOLOv12-based crop detection and counting model, named YOLO-DC. By introducing an attention mechanism (LGCB-AM) and a multi-scale detection head (MS-DH), the proposed model effectively enhances local texture extraction, global modeling, foreground–background contrast, and boundary perception for dense small objects. Subsequently, a series of comparative experiments, ablation studies, and transfer experiments were conducted on the wheat and rice datasets. The results show that YOLO-DC achieves a favorable balance among detection accuracy, counting error, and model efficiency and overall outperforms the other comparison models. Ablation studies further verify the effectiveness of the proposed design, showing that LGCB-AM is the key contributor to the performance improvement, while the boundary branch and repulsion branch play critical roles in dense-target discrimination. In addition, an appropriate module insertion strategy can effectively balance high-level semantic enhancement and feature fusion stability. Transfer experiments demonstrate that pretraining on the wheat dataset and fine-tuning on the rice dataset significantly outperform training from scratch, indicating strong cross-crop transfer potential. Overall, the proposed YOLO-DC provides an effective solution for high-precision crop detection and counting in agricultural scenarios.

Why it matches plant phenotyping methodsUAV画像から作物個体を検出・計数する手法を中心に、モデル開発、比較、アブレーション、転移検証を行っており、植物個体数という観測可能な形態・集団特性を抽出するため、植物フェノタイピング手法として適格です。

abstractthis study proposes an improved YOLOv12-based crop detection and counting model, named YOLO-DC.

Structural Volume Composition of Internodes is a Key Morphological Factor Contributing to Culm Non-structural Carbohydrate Accumulation in Rice. →

Non-structural carbohydrates (NSC) stored in the stem play a crucial role in supporting yield formation in rice. However, internode morphological factors associated with NSC accumulation remain unclear. This study aimed to clarify the relationship between internode morphology and NSC accumulation and to identify a robust morphological indicator for evaluating NSC accumulation capacity. Two years of field experiments were conducted using multiple cultivars. The NSC content was quantified for individual internodes and at the whole-plant culm level, and its relationships with internode morphological traits were analyzed. Since the upper internodes (UIN; first and second internodes) and lower internodes (LIN; third and subsequent internodes) exhibited contrasting roles in NSC accumulation, a novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm. The VCR of UIN/LIN showed the strongest correlation with culm NSC and high reproducibility across years, outperforming simple morphological traits. In addition, plant growth regulator treatments that altered VCR were accompanied by changes in culm NSC accumulation. Accordingly, the VCR of UIN/LIN serves as a robust morphological indicator of culm NSC accumulation capacity, providing a practical framework for improving stem carbohydrate storage capacity in rice.

Why it matches plant phenotyping methodsイネ茎の形態からNSC蓄積能力を評価する新規指標VCRを導入し、複数年で再現性と既存形態形質との性能を検証しており、形態表現型の抽出・評価法が中心である。

abstracta novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm.

Comparative evaluation of YOLO algorithms for detection and classification of rice leaf diseases using field-collected datasets →

Rice is the staple food for more than half of the global population. However, the productivity and quality of rice or grain dropped significantly due to leaf diseases. These diseases are difficult to identify through manual processes, which are time-consuming, labour-intensive and often inaccurate, particularly in rural farming communities. With recent advances in computer vision technology, object detection algorithms, namely the you only look once (YOLO) family, can provide high-speed, high-accuracy solutions for automated plant disease detection. This study evaluates four YOLO variants, such as YOLOv5, YOLOv7, YOLOv8 and YOLOv11, using about 1500 field images collected in Bangladesh. The data represent four major rice leaf diseases, such as bacterial leaf blight, brown spot, leaf blast and sheath blight. Data pre-processing, including image annotation and data augmentation, was conducted before model training and was followed by the training of the YOLO models. All the models were trained with the same hyperparameters and their performance was evaluated using standard metrics, such as F1 scores, precision, recall and mean average precision (mAP). According to experimental findings, the YOLOv7 recorded the highest performance based on F1 score of 0.77 and mAP of 0.85 in comparison with the rest of the variants. The results suggest that YOLOv7 will be the most appropriate to use instead of other models in the detection of rice leaf disease in real-time, which can be utilised in precision agriculture and mobile-based disease management systems.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から検出・分類するYOLO手法を複数比較し、フィールド画像データセット上で性能検証しているため、植物フェノタイピング手法が中心である。

abstractThis study evaluates four YOLO variants, such as YOLOv5, YOLOv7, YOLOv8 and YOLOv11, using about 1500 field images collected in Bangladesh.

Design of the "SRIKANDI" Image Processing Application for Detecting Rice Leaf Diseases using the NASNetMobile Convolutional Neural Network Architecture →

Rice leaf diseases can cause a significant decrease in productivity if not treated early, while currently still using a manual diagnosis process that is often slow, inconsistent, and dependent on extension workers. In this study, the SRIKANDI application was developed, a mobile application for image processing for rice leaf diseases using the NASNetMobile Convolutional Neural Network (CNN) architecture. This system is designed using five labels, namely bacterial, blast, brownspot, leafsmut, and healthy leaves. The dataset used consists of 2500 images collected from Kaggle, Mendeley Data, and taken directly. All images go through preprocessing stages of resizing, pixel normalization, and augmentation, then divided into 80% train, 10% test, and 10% validation. The model training was carried out in two stages, namely, 40 epochs of fine-tuning with a learning rate of 0.0008 followed by 20 epochs of fine-tuning with a learning rate of 1e-5, the results obtained by the model with a test set accuracy rate of 96.40%. The trained model is then saved in TFLite format to be integrated into the SRIKANDI mobile application so that it can help farmers detect rice leaf diseases in real-time via camera or taken from the gallery.

Why it matches plant phenotyping methodsイネ葉の画像から病害状態を推定するCNNとモバイルアプリの開発が研究の中心であり、植物の病徴を直接評価するフェノタイピング手法に該当する。

abstractthe SRIKANDI application was developed, a mobile application for image processing for rice leaf diseases using the NASNetMobile Convolutional Neural Network (CNN) architecture.

Physiology-informed high-throughput phenotyping of grain moisture dynamics provides enhanced insights into rice grain weight formation. →

Grain filling is the decisive period for rice grain weight formation. However, traditional static traits fail to capture its complex, nonlinear dynamics, while direct panicle weighing is hindered by canopy occlusion. Given the intrinsic synchronization between grain filling and dehydration from anthesis to physiological maturity, monitoring grain moisture content (GMC) dynamics serves as a robust proxy for characterizing the filling process. Here, we propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration. Leveraging a 4-year dataset across 135 cultivar-environment combinations, we demonstrate that the GMC threshold for physiological maturity is relatively stable (≈25%). Concurrently, we developed 2 image-based models for GMC estimation, achieving high accuracies (R2 = 0.82 and 0.86). Integrating this physiological threshold with GMC estimation models enabled the successful reconstruction of the dehydration process. Validation on 26 independent cultivars across 2 sowing dates predicted physiological maturity with a root mean square error of 2.4 to 3.3 d. Traits extracted from these dehydration profiles accounted for 42% of the variance in grain weight, doubling the explanatory power of traditional traits. These gains are largely attributed to a new integrated trait, the moisture maintenance index, which showed a higher and more stable correlation with thousand-grain weight (r = 0.6). This framework offers a scalable approach for monitoring large-scale dehydration dynamics to deepen our understanding of grain weight formation, facilitating the genetic improvement of the filling process to enhance crop yield.

Why it matches plant phenotyping methods穀粒含水率の画像推定モデルと生理学的閾値を統合し、脱水動態や成熟期などの植物形質を高スループットに抽出・検証する枠組みが研究の中心である。

abstractwe propose a high-throughput, physiology-informed phenotyping framework to monitor dehydration

Advanced Progressive Graph Convolutional Networks for Early Detection and Monitoring of Plant Infections and Disease Progression in Smart Agriculture →

ABSTRACT Plant disease is a physiological or structural problem caused by pathogens such as fungi, bacteria, viruses, or environmental factors, which disrupts plant development, yield, and overall health. Furthermore, the formation of new and more aggressive diseases complicates disease control, making it harder for farmers to preserve their crops while ensuring consistent food production. In this manuscript, to advance Progressive Graph Convolutional Networks enable early detection and continuous monitoring of plant infections in smart agriculture (PGCN‐EDM‐PID) is proposed. Initially, input images of food grains such as rice, wheat, and maize are collected from internet sources. To implement this, the input image is preprocessed using the Adaptive Two‐Stage Unscented Kalman Filter (ATSUKF), which performs resizing, sharpening, cropping, contrast enhancement, brightness adjustment, and Gaussian blurring on the images from the dataset. Then the preprocessed images are augmented based on horizontal flip, width shift, height shift, vertical flip, rotation range, shear, zoom and brightness. Additionally, Make Sense AI is proposed to annotate the images in the dataset under each class. Then the preprocessed and augmented images are fed to Progressive Graph Convolutional Networks (PGCN) to detect and classify the plant diseases. Generally, PGCN does not show adapting optimization approaches to find ideal factors to assure accurate plant disease detection. Therefore, the Augmented Red Panda Optimizer (ARPO) was proposed to optimize the weight parameter of PGCN, which accurately detects the plant disease. Then the proposed PGCN‐EDM‐PID is executed in Python and the performance metrics such as Accuracy, Precision, False Positive Rate (FPR), True Positive Rate (TPR), Specificity, Recall, F1‐score, Mean Squared Error (MSE), Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) are analyzed. Performance of the PGCN‐EDM‐PID approach attains high accuracy, high Precision, high Recall when analyzed through existing techniques like Real‐time plant disease dataset improvement and detection of plant disease utilizing DL (PDD‐DPD‐CNN), Detection of plant leaf diseasesusing deep convolutional neural network methods (DPLD‐DCNN), New DL algorithm for cross‐crop detection of plant disease: A generalized model for detecting unhealthy leaves (CPDD‐SVM) methods respectively.

Why it matches plant phenotyping methods植物画像から病害を検出・分類する画像解析手法の開発と性能評価が研究の中心であり、感染状態という植物表現型を直接推定している。

abstractProgressive Graph Convolutional Networks enable early detection and continuous monitoring of plant infections in smart agriculture (PGCN‐EDM‐PID) is proposed.

Drone-Based Crop Health Analysis and Precision Agriculture System →

Agriculture remains the backbone of global food security, yet crop diseases, nutrient deficiencies, water stress, and pest infestations cause annual yield losses estimated at 20–40% worldwide. Conventional field scouting methods are labour-intensive, time-consuming, and fail to capture the spatial heterogeneity of large farms. This paper presents a Drone-Based Crop Health Analysis and Precision Agriculture System (DBCHAPS) that integrates multi-spectral and RGB imaging drones, deep learning-based crop disease detection, vegetation index analysis, variable-rate prescription mapping, and autonomous precision spraying. A DJI Matrice 300 RTK drone equipped with a MicaSense RedEdge-MX multi-spectral camera captures high-resolution aerial imagery across five spectral bands (Blue, Green, Red, Red-Edge, Near Infrared). The captured data is processed through a custom-trained YOLOv8-based convolutional neural network (CNN) pipeline to detect 18 distinct crop diseases and stress conditions across rice, wheat, and cotton crops. Concurrently, vegetation indices (NDVI, NDRE, GNDVI, SAVI) are computed to generate prescription maps for site-specific fertilizer and pesticide application. Experimental evaluation on a 120 acre farm in Thanjavur, Tamil Nadu over two crop seasons demonstrates a disease detection accuracy of 96.3%, early stress detection 8–12 days before visible symptoms, and a 31% reduction in agrochemical usage through variable-rate application. The system achieves an end-to-end field analysis time of under 45 minutes for 100 acres. Keywords — UAV, Precision Agriculture, Crop Disease Detection, Multi-Spectral Imaging, NDVI, YOLOv8, Deep Learning, Variable-Rate Application, Remote Sensing, Smart Farming.

Why it matches plant phenotyping methodsドローンのマルチスペクトル/RGB画像とYOLOv8を用いて作物の病害・ストレス状態を推定するシステムを開発し、精度と運用性能を評価しており、植物フェノタイピング手法が中心である。

abstractThis paper presents a Drone-Based Crop Health Analysis and Precision Agriculture System (DBCHAPS) that integrates multi-spectral and RGB imaging drones, deep learning-based crop disease detection, vegetation index analysis, variable-rate prescription mapping, and autonomous precision spraying.

Real-Time Crop Stress Monitoring and Early Warning System for Paddy and Maize Using Multi-Temporal Sentinel-2 Data and Deep Learning in Semi-Arid Regions →

Semi-arid regions with a high potential for rice and maize cultivation have become some of the most actively farmed areas. They now face the challenge of achieving food security despite the threats of crop water stress, nutrient loss, and environmental changes. In this paper, we develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models in Mahabubabad district, Telangana, India. Different types of crop stresses such as water stress, nutrient deficiency, and phenological anomalies were detected and classified using a hybrid CNN-LSTM architecture with an attention mechanism. The methodology was based on 874 field polygons with extensive in-situ data collection during 2023-24, incorporating multi-temporal spectral indices (NDVI, EVI, NDWI, REP), weather variables, and soil characteristics. The total classification accuracy reached 89.4% for paddy and 87.2% for maize over all stress types, showing that stress detection from satellite images is quite reliable. Water stress was the category that was detected most accurately (92.1% for paddy and 89.8% for maize), followed by nutrient stress (88.7% and 86.3%) and phenological stress (85.2% and 83.9%). The warning system made it possible to identify the problem 15-25 days before there were visible symptoms, making it possible for the farm management to respond in time. Activities of the farm that were most vulnerable to detection were air and water temperatures, precipitation, and crop growth stages for water stress 45-60 days after sowing, 30-45 days for nutrient stress, and during the reproductive phase for phenological stress. The system could be extended for industrial crop stress monitoring across the semi-arid agricultural systems which might lead to precision agriculture and climate-resilient farming practices.

Why it matches plant phenotyping methods衛星画像と深層学習を用いて作物の水ストレス・栄養ストレス・生育異常を直接推定し、精度検証と早期検出性能を評価しているため、植物表現型取得法が中心である。

abstractwe develop a real-time crop stress monitoring and early warning system that utilizes multi-temporal Sentinel-2 images and deep learning models

Performance Evaluation and Cross-Dataset Validation of Machine Learning Algorithms for Crop Disease Identification Using Rice and Maize Leaf Images →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsイネとトウモロコシの葉画像による病害識別について、機械学習アルゴリズムの性能評価とデータセット間検証が主題であり、植物病害状態の画像ベース推定手法を検証している。

titlePerformance Evaluation and Cross-Dataset Validation of Machine Learning Algorithms for Crop Disease Identification Using Rice and Maize Leaf Images

A physics-informed neural network for continuous rice canopy thermal monitoring and forecasting from sparse UAV observations →

Continuous monitoring of canopy temperature (Tc), a key indicator of crop water-heat stress and physiological dynamics, using unmanned aerial vehicle (UAV) imagery is inherently limited by temporal discontinuity and the limited physical realism of purely data-driven models. This study develops a physics-informed neural network (PINN) framework to transform temporally sparse UAV thermal observations into continuous hourly rice Tc reconstruction and 48 h forecasting products. The model leverages sparse UAV thermal measurements as supervisory signals while integrating them with continuous meteorological forcing and daily UAV-derived crop phenotypic features. Validated through a comprehensive season-long rice field experiment using walk-forward cross-validation, the proposed PINN framework demonstrated superior performance. It achieved R 2 values of 0.92 for reconstruction and 0.90 for forecasting, with RMSE of 0.71 °C and 0.82 °C, respectively. Ablation analysis further showed that crop phenotypic variables contributed more strongly than temporal descriptors, reducing predictive uncertainty by approximately 4.8–14.3 %, while the integration of SEB physical constraints and uncertainty modeling improved R 2 by 8.4–9.5 % and reduced Total STD by 28.4–37.7 %. The model successfully captures diurnal dynamics, spatial variability, and canopy thermal hysteresis while maintaining physical consistency through improved energy closure. This framework bridges sparse aerial observations with continuous physiological monitoring and highlights its potential to support precision irrigation, early stress detection, and high-throughput phenotyping in smart agriculture.

Why it matches plant phenotyping methodsUAV熱画像による疎な観測からイネ群落温度を連続再構成・予測するPINNを開発し、交差検証とアブレーション分析で性能評価しており、表現型取得・推定手法が研究の中心である。

abstractThis study develops a physics-informed neural network (PINN) framework to transform temporally sparse UAV thermal observations into continuous hourly rice Tc reconstruction and 48 h forecasting products.

Use of machine learning techniques to detect and classify selected fungal diseases in rice crop using hyperspectral imaging →

Fungal diseases cause significant yield losses in rice, making early detection and accurate classification essential for effective disease management. In this study, hyperspectral imaging technique was used to acquire the spectral signatures of three major fungal diseases viz., brown spot, blast and sheath blight in rice. The acquired hyperspectral images were pre-processed using Standard Normal Variate (SNV) transformation and Savitzky-Golay filtering, followed by pixel-wise spectral data extraction. Principal Component Analysis (PCA) was used to investigate spectral variability among healthy and diseased leaf samples. Subsequently, machine learning models including artificial neural networks (ANN), support vector machines (SVM) and random forests (RF) were employed to classify these diseases based on the acquired and pre-processed spectral signature data. The results indicated that the ANN model outperform the others, achieving an accuracy of 98%, followed by SVM at 94%, and RF at 88%. Among the three models, the ANN exhibited the highest accuracy, precision and recall, making it the most effective model for disease detection and classification. Hyperspectral imaging, combined with machine learning, offers an affordable and efficient solution for large-scale detection and assessment of fungal diseases in rice crops.

Why it matches plant phenotyping methodsイネ葉の病害状態をハイパースペクトル画像から取得し、機械学習で検出・分類する手法が研究の中心であり、植物病害表現型の技術評価に該当する。

abstracthyperspectral imaging technique was used to acquire the spectral signatures of three major fungal diseases viz., brown spot, blast and sheath blight in rice.

Rice Disease Detection Using an Optimized Modified Lightweight Convolutional Neural Network →

Rice is an important staple food crop in the world, but the yield is dramatically lowered by fungal diseases including brown spot, leaf blast and neck blast. Thus, disease detection algorithms that are not only computationally efficient but also accurate enough are crucial for the real-time application of precision agriculture systems. In this paper, an Optimized Convolutional Neural Network from Modified Lightweight Weight Convolutional Neural Network (MLWCNN) is proposed to multi-class rice disease classification for the purpose of achieving high accuracy with low computational complexity. The proposed MLWCNN is tested on a Kaggle public data set, which comprises the rice leaf disease dataset as well as sub-images of healthy and diseased leaves with 3-Fold and 5-Fold cross-validation for robustness and unbiased performance evaluation. Experimental results show that they converge consistently well on objective and generalize well under various validation settings. With the strictest evaluation setup (i.e., 5-Fold cross-validation and a training stage of 30 epochs) the novel model demonstrated a validation accuracy just under around 94%. A comparison with state-of-the-art deep learning networks such as wide VGG16, InceptionV3, ResNet50, ResNet152 SqueezeNet and MobileNetV2 on typical datasets proves that the proposed MLWCNN achieves competitive classification accuracy with a higher speedup. Analyzing the class‐wise performance using confusion matrix, balanced prediction characteristics throughout all disease categories are observed. Moreover, computational cost analysis demonstrates that the proposed model needs much less floating-point operations (0.000095 GFLOPs) than deep models, which is ideal for deployment in resource-limited mobile, edge and embedded agricultural applications. The experimental results demonstrate that the MLWCNN achieved high accuracy, thereby providing a practical and scalable solution for automated rice disease detection in precision agriculture.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類するCNN手法を開発し、公開データセットと交差検証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。

abstractan Optimized Convolutional Neural Network from Modified Lightweight Weight Convolutional Neural Network (MLWCNN) is proposed to multi-class rice disease classification

Rapid rice seed vigor assessment: A machine learning and deep learning framework with multi-time-point image analysis. →

Automated rice seed vigor classification provides a non-invasive and scalable solution for improving agricultural decision-making. This study proposed an image-based framework to compare traditional machine learning and deep learning approaches for classifying individual rice seed vigor using standard RGB images. Machine learning models were developed using hand-crafted morphological and color features, while convolutional neural networks were employed to automatically extract visual patterns related to seed quality. Both single-time-point and multi-time-point image analysis strategies were investigated. Models trained on images captured at individual growth stages were compared with a multi-time-point ensemble approach that integrated visual information across multiple developmental stages. The ensemble approach achieved superior performance, highlighting the importance of incorporating temporal growth dynamics into vigor classification. Notably, traditional machine learning models performed comparably to deep learning models when informative features were carefully engineered. To improve transparency and reliability, interpretability techniques were applied to better understand model decisions. Overall, the findings demonstrate the practical potential of data-driven, image-based seed vigor assessment.

Why it matches plant phenotyping methodsRGB画像と機械学習・深層学習を用いてイネ種子の活力を自動推定する枠組みを開発・比較しており、表現型取得・抽出手法が研究の中心である。

abstractThis study proposed an image-based framework to compare traditional machine learning and deep learning approaches for classifying individual rice seed vigor using standard RGB images.

Multi-task Deep Learning Pipeline for Rice Field Classification and Growth Monitoring Using Drone Imagery →

A booming population around the world raises the concern of shortages of food resources in this new era. Thus, monitoring and managing crop production is extremely essential, especially rice crops, as they are the fundamental food source for most countries. Several challenges need to be addressed in this case, such as the classification of farmland from various land usages, precise monitoring of rice seedlings, and segmentation of rice growth. By leveraging advanced technologies such as drone imagery and machine learning, this paper proposed a new integrated pipeline for rice field classification and growth monitoring: a combination of convolutional neural networks (CNNs), You Only Look Once (YOLO), and modified U-Net models. These models were used in stages, specifically for paddy field classification, rice seedling detection, and rice growth segmentation. Substantial measurements and analysis have been carried out to verify the performance of the proposed system, including an accuracy of at least 85%, low classification/segmentation loss below 0.35, and high detection recall above 0.9. Thus, the findings highlight how combining different machine learning models with aerial photography can revolutionise conventional farming methods for better efficacy.

Why it matches plant phenotyping methodsドローン画像とCNN・YOLO・改良U-Netを統合し、イネ苗の検出および生育セグメンテーションを行う技術パイプラインが中心で、性能検証も実施している。農地分類は除外対象になり得るが、植物の生育状態を直接抽出する手法部分が十分に実質的である。

abstractThese models were used in stages, specifically for paddy field classification, rice seedling detection, and rice growth segmentation.

High-Throughput Phenotyping: Status and Applications in Rice Breeding. →

The reliance on traditional or manual phenotyping creates significant operational bottlenecks in rice breeding due to its resource-intensive and time-consuming nature. This review focused on the significance of high-throughput phenotyping (HTP) as a promising technology that enables rapid, accurate, and non-destructive phenotyping of large populations. HTP has great potential to accelerate rice breeding by revolutionizing phenomics. This review examined the various applications of HTP in rice research, phenomics, and breeding. The use of HTP in rice has been substantiated through a range of cutting-edge technologies, such as drones, imaging systems, and sensor networks, that facilitate precise monitoring of key traits at various growth stages, assessment of responses to biotic and abiotic stresses, and the identification of genes or quantitative trait loci (QTLs) associated with essential characteristics. Also, this review discussed HTPs' contributions to current rice breeding programs and documented notable challenges in scaling them. This review offers insights into optimizing HTP strategies to advance rice research, phenomics, and rice breeding.

Why it matches plant phenotyping methodsイネのハイスループット表現型解析技術の応用、技術、課題を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。

abstractThis review focused on the significance of high-throughput phenotyping (HTP) as a promising technology that enables rapid, accurate, and non-destructive phenotyping of large populations.

DESENVOLVIMENTO DE APLICATIVO COM APRENDIZADO DE MÁQUINA PARA PREDIÇÃO DO IMPACTO DE RIZOBACTÉRIAS NO CRESCIMENTO DE PLANTAÇÕES DE ARROZ →

O uso de rizobactérias promotoras de crescimento de plantas (RPCPs) apresenta-se como alternativa sustentável para a agricultura, porém a predição de seus efeitos envolve múltiplas variáveis. Este trabalho teve como objetivo desenvolver um aplicativo móvel apoiado por aprendizado de máquina para análise preditiva do impacto de RPCPs no crescimento de arroz. A metodologia abrangeu quatro fases: levantamento de requisitos com especialista, análise exploratória de uma base de dados com 6.038 registros experimentais, desenvolvimento e avaliação de modelos de classificação e implementação do sistema. Foram comparados os algoritmos KNN, Random Forest e XGBoost, sendo este último selecionado por apresentar maior acurácia (0,945) e menor desvio padrão (0,010) na validação cruzada. A arquitetura Cliente-Servidor integrou um aplicativo Android em Kotlin com Jetpack Compose a uma API RESTful em FastAPI, operando em duas modalidades: não destrutiva, baseada em medições de campo, e destrutiva, com métricas de biomassa seca. Os resultados indicam que a ferramenta pode auxiliar a tomada de decisão ao reduzir a necessidade de coletas destrutivas em determinadas situações, contribuindo para práticas agrícolas mais sustentáveis.

Why it matches plant phenotyping methodsイネの生育影響という植物形質を、非破壊測定および乾物バイオマスから機械学習で予測するアプリケーションの開発・評価が研究の中心であり、単なる生育実験ではない。

abstractdesenvolver um aplicativo móvel apoiado por aprendizado de máquina para análise preditiva do impacto de RPCPs no crescimento de arroz

Three-dimensional root architectural plasticity in rice: mechanistic responses to water deficit stress. →

Background Understanding root architectural plasticity under water deficit is essential for improving rice drought tolerance. However, whether drought-tolerant and drought-sensitive cultivars differ in qualitative spatial strategies or merely in the magnitude of plastic responses remains unresolved, and conventional destructive phenotyping cannot capture three-dimensional dynamics. Results We developed WSroots, an L-system-based three-dimensional model, and quantified root development in drought-tolerant HY73 and drought-sensitive Longliangyou Huazhan (LLYHZ) under polyethylene glycol-6000 (PEG6000) osmotic stress at 0, 50, 125 and 200 g kg -1 for 28 days in hydroponic culture. HY73 maintained 25-30% of root length at 35-60 cm depth with only 25.2% total length reduction at 200 g kg -1 PEG6000, whereas LLYHZ concentrated 65-70% of roots in the 0-15 cm surface layer with 39.6% reduction. Root diameter declined less in HY73 (9.3%) than in LLYHZ (14.8%), indicating superior structural resilience. Calibration accuracy reached a coefficient of determination (R 2 ) of 0.986 (HY73) and 0.949 (LLYHZ). Conclusion The two cultivars employ qualitatively distinct strategies - deep exploration versus shallow expansion - rather than quantitative gradients of the same response. Deep-rooting maintenance is therefore a key target for drought-resilient rice breeding. WSroots provides a transferable framework for virtual phenotyping and irrigation design that can be extended to soil-based systems through water potential equivalence. © 2026 Society of Chemical Industry.

Why it matches plant phenotyping methodsWSrootsという3次元L-systemモデルを開発し、根系構造を定量化・校正しており、植物表現型取得と計算推定が研究の中心である。

abstractWe developed WSroots, an L-system-based three-dimensional model, and quantified root development

Next-Generation Crop Breeding: Harnessing Genomics, Phenomics and Machine Learning: A Review →

Global food security requires crop improvement strategies that can respond to population growth, climate variability and increasing constraints on agricultural resources. Conventional plant breeding has contributed substantially to crop productivity, yet long selection cycles and dependence on extensive field evaluation can limit the rate of genetic gain. This review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding, with emphasis on their combined contribution to selection accuracy and breeding efficiency. Key genomic approaches discussed include whole-genome sequencing, reference and pan-genome resources, genome-wide association studies, genomic selection and CRISPR-Cas-based genome editing. The review also examines high-throughput phenotyping platforms, including controlled-environment systems, ground-based robots, UAV-based remote sensing and root phenotyping tools. Machine learning approaches, ranging from random forest and support vector machines to convolutional neural networks, recurrent networks, transformers and explainable artificial intelligence, are considered in relation to genomic prediction, image analysis and breeding decision support. Multi-omics integration, data management, FAIR principles and an integrated genomics-phenomics-ML breeding pipeline are reviewed as enabling components for practical deployment. Crop-specific examples from wheat, rice, maize, soybean and legumes illustrate the potential and constraints of these technologies. The review further identifies key challenges, including phenotyping bottlenecks, genotype-environment interaction, data governance, model interpretability and regulatory uncertainty.

Why it matches plant phenotyping methods植物フェノタイピング手法を中心に、ハイスループット計測プラットフォーム、画像解析、機械学習、UAV・ロボット・根系計測などをレビューしているため。

abstractThis review synthesises advances in genomics, phenomics and machine learning for next-generation crop breeding

Editorial: Plant phenotyping for agriculture →

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。

titleEditorial: Plant phenotyping for agriculture

Intelligent framework for automated rice leaf disease diagnosis using a Simplicial Finite-Element-Informed Neural Network optimised by the MCA →

Rice, a staple food for more than half of the global population, faces significant yield and quality losses due to various diseases and environmental stresses. This paper presents a new Simplicial Finite-Element-Informed Neural Network based on the Musical Chairs Optimisation Algorithm (SFEINN-MCOA) to detect rice leaf disease precisely and efficiently. The first step involves the acquisition of the RGB image from the Rice Leaf Disease dataset. The Iterative Robust Peak-Aware Guided Filter (IRPAGF) is used to remove noise and enhance contrast, thus improving the image. The Graph-Based Soft-Balanced Fuzzy Clustering (GSBFC) method is used to separate diseased regions and then analyse them. The Self-Distillated Masked Autoencoder (SMA) is used to perform feature extraction and capture important attributes of leaves. The SFEINN classifies data under the category of healthy and diseased leaves, and the MCOA optimises the model parameters to achieve maximum accuracy and minimum error. Experimental findings reveal that the SFEINN-MCOA model has an accuracy of 99.9% and an F1-score of 98.9%, which is better and stronger. This smart system offers a secure, automatic, and effective system for early disease identification of rice and helps farmers to enhance crop health and yield sustainability.

Why it matches plant phenotyping methodsイネ葉の病斑領域を画像から抽出し、健全・罹病状態を自動判定する画像解析フレームワークが研究の中心であり、植物病害状態の表現型推定に該当する。

abstractThis paper presents a new Simplicial Finite-Element-Informed Neural Network based on the Musical Chairs Optimisation Algorithm (SFEINN-MCOA) to detect rice leaf disease precisely and efficiently.

Quantitative light element profiling in plant tissues with monochromatic X-ray fluorescence analysis: a new frontier for abiotic stress studies. →

Determining elemental concentrations in plant tissues is essential for physiological studies on abiotic stress. However, high-throughput routine analysis of light elements (sodium to calcium) in plants is challenging due to the need for complete sample dissolution and expensive and time-consuming inductively coupled plasma-mass-spectrometry (ICP-MS). Ion chromatography and ion-selective electrodes are low-cost methods but suffer from major drawbacks, including limited throughput and time-consuming sample preparation. This study reports on a new methodology for quantitative analysis of light elements in plants using monochromatic X-ray fluorescence (MXRF) analysis. We quantitatively assessed sodium and potassium uptake in Arabidopsis thaliana, Oryza sativa and Lactuca sativa in salinity treatments. The new method provides reliable results from samples as small as 1 mg, making it suitable for analysis at the seedling stage. This is enabled by the high sensitivity of the system and optimized sample preparation that ensures sufficient signal even at low sample masses. We tested the accuracy and precision of the technique for other light elements to demonstrate its broad applicability. The results show that the method delivers rapid, non-destructive, and extraction-free light element analysis on small samples highly correlating with ICP-MS. The monochromatic XRF method provides accurate measurements and reproducible results for studying salinity tolerance ideally suited for investigating elemental composition of early plant developmental stages, offering new possibilities for research into early stimuli responses.

Why it matches plant phenotyping methods植物組織中の元素濃度という生理形質を測定するMXRF法の開発と、ICP-MSとの相関、精度・再現性評価が研究の中心であるため。

abstractThis study reports on a new methodology for quantitative analysis of light elements in plants using monochromatic X-ray fluorescence (MXRF) analysis.

Quantifying Canopy Closure Dynamics Using UAV Imagery and Semantic Segmentation in Rice Breeding Trials. →

The canopy closure stage is a critical phase of rice ( Oryza sativa L.) development that influences canopy structure and final grain yield. Accurate and continuous monitoring of canopy closure dynamics is therefore essential for variety screening and cultivation optimization. This study combines unmanned aerial vehicle (UAV) remote sensing technology with deep learning-based semantic segmentation to establish an efficient framework for quantifying rice canopy closure dynamics. UAV RGB images were acquired for 198 hybrid rice varieties during early growth stages and used to build a canopy segmentation dataset. Three semantic segmentation models, i.e., DeepLabv3+, U-Net, and PSPNet, were systematically evaluated. Results show that DeepLabv3+ performed the best and enabled precise extraction of rice canopy features, obtaining a mean intersection over union (mIoU) of 0.86. Based on the extracted canopy coverage, the Gompertz model was utilized to characterize temporal canopy closure trajectories for all varieties, achieving an average R 2 of 0.978. Subsequently, five key dynamic indicators were derived, including canopy closure limit value ( K ), initial growth coefficient ( a ), growth rate coefficient ( b ), maximum instantaneous growth rate ( MGR ), and days to maximum growth rate ( Tm ). K-means clustering analysis was performed on these indicators to categorize all rice varieties into three clusters, disclosing pronounced differences in early-stage canopy development characteristics. Correlation analysis further demonstrated that canopy closure dynamics were closely associated with grain yield. Overall, while acknowledging the limitations of a single-season and single-site dataset, this study provides a scalable and objective framework for quantifying rice canopy closure dynamics, offering valuable support for variety selection, cultivation optimization, and high-yield rice production.

Why it matches plant phenotyping methodsUAV画像と深層学習セマンティックセグメンテーションを用いてイネのキャノピー閉鎖動態を定量化する手法を構築・評価しており、植物形質抽出が研究の中心である。

abstractThis study combines unmanned aerial vehicle (UAV) remote sensing technology with deep learning-based semantic segmentation to establish an efficient framework for quantifying rice canopy closure dynamics.

Precise estimation of rice leaf macro and micro nutrients from multi-spectral images using neural architecture search with polynomial approximation functions →

Estimating the nutritional status of rice leaves is crucial for efficient nutrient management and yield enhancement. Traditional wet lab analyses are time-consuming and labor-intensive. This study presents a novel deep learning-based approach utilizing multispectral images captured by unmanned aerial vehicles (UAVs) to estimate macro/micro nutrients in rice leaves. The proposed framework integrates a differentiable neural search technique using polynomial function approximators and an adaptive activation mechanism, which not only provides improved predictive performance but also deals efficiently with limited training data. The model performance is evaluated across different treatments and crop growth stages using mean absolute error (MAE) and [Formula: see text] values. Experiments were conducted at the Punjab Agricultural University. The results demonstrate that the proposed model achieves MAE values in the range of 0.06-0.11 for SAS-I and 0.06-0.16 for SAS-II across eleven leaf macro/micro nutrients. To further evaluate the reliability of the predicted nutrients beyond the prediction error analysis, uncertainty estimation of nutrients is also performed. Comparative analysis shows that the proposed framework outperforms conventional deep learning baselines and machine learning methods in terms of accuracy and robustness. Furthermore, the t-SNE visualization of learned feature representations effectively clusters similar nutrient values while separating dissimilar ones. The robustness of the proposed framework is further validated through ablation studies, treatment-wise and plot-wise cross-validation, highlighting the contribution of individual components and their performance under varying field conditions. These findings highlight the proposed NAS-based framework for precise and reliable nutrient assessment in precision agriculture.

Why it matches plant phenotyping methods稲葉のマクロ・微量栄養素という植物状態をマルチスペクトル画像から推定する深層学習手法を開発し、比較検証・不確実性評価・アブレーション試験まで行っており、表現型取得・推定法が中心である。

abstractThis study presents a novel deep learning-based approach utilizing multispectral images captured by unmanned aerial vehicles (UAVs) to estimate macro/micro nutrients in rice leaves.

Image-based Phenotyping and Machine Learning Prediction of Rice Genotypes to Combined Drought-Salinity Stresses →

Abstract Simultaneous stresses of salinity and drought often coincide during rice-growing seasons in coastal areas due to insufficient water resources and inadequate irrigation infrastructure. Consequently, combined salinity-drought stress poses a major threat to rice production. To investigate the effects of combined salinity-drought stress, a two-season study was conducted utilizing soil media. The first season involved screening 58 rice genotypes, while the second season focused on validating the consistency of response in 20 selected tolerant and susceptible genotypes. These included established tolerant checks (Pokkali and Salumpikit) and susceptible checks (IR 29 and IR 20). Both drought and salinity treatments were given at an electrical conductivity (EC) of 10 dSm⁻¹ and 75% field capacity at the seedling stage. The experimental design was arranged in a modified lattice design in each season, with six blocks and three replications in the first season and two blocks and five replications in the second season. The data collected are leaf symptoms, biomass weight, and shoot length. A number of 330 images captured by a smartphone camera. Machine learning models were employed to predict drought-salinity tolerance criteria. The study revealed that XGBoost model achieved an accuracy of 90.62%. The study identified two genotypes, IR18A1925-SKI-0 and Inpari 30, that exhibited insignificance to Pokkali, based on assessment of shoot length, biomass, and leaf symptoms. These two genotypes were also consistently clustered with Salumpikit. These findings highlight potential of machine learning techniques in predicting rice tolerance to combined salinity-drought stress, with the XGBoost model demonstrating superior predictive capability in this study.

Why it matches plant phenotyping methodsスマートフォン画像から葉症状・バイオマス・草丈などの表現型を取得し、機械学習で複合ストレス耐性を予測する手法の適用が研究の中心である。

titleImage-based Phenotyping and Machine Learning Prediction of Rice Genotypes to Combined Drought-Salinity Stresses

Automatic measurement of rice tiller angle from unmanned aerial vehicle images →

Abstract Rice ( Oryza sativa L.) tiller angle is an important trait that influences plant architecture, canopy light interception, and yield potential. In this study, we proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery. Our method leverages keypoint detection models to estimate tiller angles efficiently and accurately. We collected and annotated a dataset of UAV‐captured rice plant images for tiller angle estimation. We demonstrated that our approach provides scalable and precise measurement for keypoints under real‐world field conditions, achieving a mean average precision (mAP)@50 of 0.982 and a mAP@50:95 of 0.859 on the test set. Predicted tiller angle distribution aligns well with human annotations, with a mean absolute error of 5.3° across a range of 10.7°–27.8° and a Pearson's correlation coefficient of 0.64, offering acceptable accuracy for tiller angle measurement in real‐world agricultural settings. Additionally, the predicted plant base width ranges from 2.8 to 5.5 cm, with a mean absolute error of 1.02 cm compared to human annotations, highlighting the model's capability for precise spatial analysis. Significant differences in tiller angle and plant base width were detected among 27 rice genotypes. These results validate the proposed pipeline's potential for accurate and efficient differentiation of plant architecture traits. This research is the first to measure the rice tiller angle directly from UAV images. It lays a foundation for automated phenotyping of plant architecture traits and has the potential for integration into plant phenotyping frameworks to further promote artificial intelligence‐driven rice research and production.

Why it matches plant phenotyping methodsUAV画像と深層学習により、イネの分げつ角度・株元幅という植物形態形質を自動推定する手法を開発・検証しており、フェノタイピング手法が研究の中心である。

abstractwe proposed a deep learning‐based pipeline for automated measurement of rice tiller angle and plant base width using unmanned aerial vehicle (UAV) imagery.

Characteristic wavelength selection for rice blast based on hyperspectral remote sensing and deep convolutional neural networks →

Background Hyperspectral remote sensing technology is one of the key technical methods for detecting rice blast in the field, but existing hyperspectral dimensionality reduction methods still suffer from information redundancy and insufficient feature interpretability. This study aimed to develop a feature wavelength selection method integrating deep learning and model attribution analysis to extract key spectral features across different disease severity levels. Results A residual network model Dilated Convolution and Deformable Convolution-Residual Network (DCR-ResNet) combining dilated convolution and deformable convolution was constructed to deeply mine spectral features across varying disease severities. Meanwhile, the Integrated Gradient (IG) and Gradient-weighted Class Activation Mapping (Grad-CAM) methods were combined to enable the selection of spectral wavelengths. The effectiveness of the proposed method was validated using statistical analysis (transformed divergence, within-class scatter) and modeling analysis. Findings reveal that the spectral feature wavelengths identified by DCR-ResNet in conjunction with the IG-GradCAM approach exhibit excellent inter-class separability and intra-class compactness. Furthermore, when benchmarked against conventional dimensionality reduction techniques such as Successive Projections Algorithm, Random Frog, and Competitive Adaptive Reweighted Sampling, the Support Vector Machine, Extreme Learning Machine, and Random Forest models developed using IG-GradCAM-selected feature wavelengths demonstrate superior classification performance. The overall accuracy reaches 85.9%, 85.5% and 86.2%, with kappa values of 81.3%, 80.6% and 81.6%, respectively. Conclusion The feature wavelength selection method combining DCR-ResNet with IG-GradCAM not only improves the accuracy of hyperspectral feature extraction but also provides an efficient and feasible approach for the precise identification of rice blast. © 2026 Society of Chemical Industry.

Why it matches plant phenotyping methodsイネいもち病という植物の病害状態を対象に、ハイパースペクトル特徴波長の選択手法を開発し、複数手法との比較検証を行っており、フェノタイピング手法が研究の中心である。

abstractThis study aimed to develop a feature wavelength selection method integrating deep learning and model attribution analysis to extract key spectral features across different disease severity levels.

Dynamic monitoring of rice plant height during the early growth stage using UAV-LiDAR and GWAS analysis of growth rate →

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

Mamba-YOLO: A Hybrid Architecture with Linear-Complexity Selective Scan Mechanisms for Enhanced Microscopic Rice Disease Detection →

Abstract While automated visual inspection facilitates large-scale crop disease management, its deployment in field environments remains challenging. The morphological similarity of early-stage symptoms, combined with severe canopy occlusion, frequently degrades model accuracy. When applied to these unconstrained datasets, standard lightweight Convolutional Neural Networks (e.g., the YOLOv5-v11 series) tend to overfit, yielding an accuracy of only around 46% mAP@0.5 on microscopic targets. Even advanced NMS-free architectures like YOLO26 struggle to capture the long-range spatial dependencies required to decouple highly ambiguous, discrete lesions like early-stage Rice Blast.We address this fundamental structural limitation by introducing Mamba-YOLO. This hybrid architecture integrates the Visual State Space Model (VMamba) directly into a lightweight YOLO26-Nano baseline. We replaced standard deep bottleneck layers with Visual State Space (VSS) modules, leveraging a Selective Scan Mechanism (SSM) to model global context with linear computational complexity (𝑂(𝑁)). Our network perceives fragmented pathological features across the entire image without the massive quadratic computational burden typical of Vision Transformers.Our empirical evaluations on a multi-class rice disease dataset yield compelling insights. Mamba-YOLO achieves a highly competitive overall mAP@0.5 of 92.36%, performing on par with the heavily optimized pure-CNN baseline (92.41%). More importantly, under the strictly penalized mAP@0.5:0.95 metric, our architecture establishes a new peak of 55.6%. We recorded a critical +0.9% accuracy breakthrough for Rice Blast, the most challenging microscopic category. Beyond static accuracy, analysis of the training dynamics proves that the selective scan mechanism acts as a robust global regularizer, effectively collapsing the massive generalization gap that plagues traditional lightweight detectors.We achieve these structural breakthroughs with near-zero overhead. Mamba-YOLO maintains an ultra-low computational footprint of 5.9 GFLOPs and requires only 2.69 million parameters. This Pareto-optimal balance positions our architecture as a highly robust, field-ready solution for deploying high-precision diagnostics on resource-constrained agricultural edge devices.

Why it matches plant phenotyping methodsイネ病害の症状・病斑を画像から検出する新規深層学習アーキテクチャを開発し、複数の評価指標で性能検証しているため、植物表現型取得手法が中心である。

titleMamba-YOLO: A Hybrid Architecture with Linear-Complexity Selective Scan Mechanisms for Enhanced Microscopic Rice Disease Detection

Physiology-informed LSTM framework integrating crop model and Sentinel-2 time series for rice nitrogen status estimation. →

Accurate assessment of plant dry matter (PDM) and plant N accumulation (PNA) provides essential indicators for precision nitrogen (N) management in rice production. However, purely data-driven models struggle to generalize due to the spatial scarcity of ground-truth physiological data. To address this, a physiology-informed long short-term memory (PI-LSTM) framework was developed for robust regional N diagnosis and variable-rate fertilization. First, the model was pretrained to internalize crop growth dynamics using a DSSAT-based simulation library, which spanned 2000 representative fields and 700 management scenarios to provide physiologically consistent pseudo-labels. Subsequently, the framework was fine-tuned using multi-year field observations (2020, 2023, 2024), Sentinel-2 time-series data, and meteorological inputs. The proposed LSTM framework outperformed conventional machine learning approaches in estimating PDM and PNA, achieving five-fold cross-validation R 2 values of 0.87 and 0.83, respectively. Based on these biophysical estimations, the N nutrition index (NNI) diagnosis achieved a 67.3% overall classification accuracy. Furthermore, by integrating the critical N dilution curve, the critical PNA and accumulated N deficiency (AND) were quantified, which served as the basis for developing the AND-based N recommendation algorithm (ANDA). Finally, variable-rate topdressing field experiments conducted across seven sites in 2024 and 2025 demonstrated that the ANDA reduced N input by 13.4% compared with farmers' practices, while maintaining or increasing yield and improving N partial factor productivity by 18.6%. This study provides a reliable, physically consistent decision-support framework for regional-scale precision N management.

Why it matches plant phenotyping methodsSentinel-2時系列とLSTMにより、イネの乾物量および窒素蓄積量という植物形質を推定する方法の開発・検証が研究の中心であり、施肥管理への応用も技術評価として記述されている。

abstracta physiology-informed long short-term memory (PI-LSTM) framework was developed for robust regional N diagnosis and variable-rate fertilization.

VE-MLM: A variable endmember-based multilinear mixing framework for crop FAPAR estimation using UAV multispectral imagery →

The fraction of absorbed photosynthetically active radiation (FAPAR) is critical for characterizing crop photosynthetic capacity and growth status. Remote sensing technology based on unmanned aerial vehicles (UAVs) enables efficient estimation of FAPAR, but multiple scattering and transmission in the complex and dynamically changing crop canopy and background limit the accuracy of vegetation index (VI)-based methods. This study proposed an adaptive spectral unmixing framework VE-MLM for the multi-layer mixed scenarios, comprising three modules: (1) Variable Endmember Extraction , building a spectral library of foreground (crop) and background endmembers, by extracting pure pixels on the R-NIR feature space and reducing redundancy using k-means and iterative endmember selection algorithm; (2) Iterative Unmixing , iterating over foreground-background endmember combinations as input of the multilinear mixing model (MLM) pixel by pixel; (3) Optimal Selection , selecting the optimal combination according to RMSE and outputting corresponding canopy abundance A f . Taking sorghum and rice as study objects, this study collected UAV multispectral images and field-measured FAPAR at multiple periods to validate the advantages of VE-MLM. The results demonstrated that compared to fixed-endmembers and linear/bilinear mixing models, VE-MLM always achieved excellent unmixing performance, effectively quantifying canopy contributions. The derived A f mitigated the saturation and background interference that commonly existed in VI-based regression models and exhibited a higher correlation with FAPAR (sorghum: R 2 = 0.900, rRMSE = 7.753%; rice: R 2 = 0.807, rRMSE = 2.200%). In conclusion, VE-MLM has a great potential to address spectral variability, dynamic changes, and scene complexity in crop growth scenarios, providing a more accurate and generalizable approach for sorghum and rice FAPAR estimation in precision agriculture.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物キャノピーのFAPARを推定するスペクトルアンミキシング手法を開発し、ソルガムとイネで実測値により検証しており、植物表現型取得が中心である。

abstractThis study proposed an adaptive spectral unmixing framework VE-MLM for the multi-layer mixed scenarios

Hyperspectral phenotyping reveals the genetic basis of grain quality in rice. →

Rice ( Oryza sativa ) grain quality is an important breeding target, yet its genetic basis remains incompletely understood. In this study, we integrated hyperspectral phenotyping with genome-wide association study (GWAS) to investigate apparent amylose content (AAC) and protein content (PC) in 241 modern rice varieties. Using a visible-shortwave infrared hyperspectral system combined with optimized preprocessing and machine-learning pipelines, we achieved accurate predictions for AAC ( R 2 = 0.97) and PC ( R 2 = 0.92). Hyperspectral-based GWAS identified both known loci and previously unreported genetic associations. For AAC, qAAC (780.791nm) -1-3 was mapped to the Green Revolution gene SD1 , showing that the sd1 allele increases AAC while conferring high yields. For PC, we identified qPC (1998.98nm) -5-1 and confirmed GW5 as the causal gene, linking the high-yielding gw5 allele with high grain PC. Hyperspectral features outperformed traditional measurements, enhancing the detection of genetic signals. This study provides an efficient strategy for elucidating the genomic architecture of complex grain-quality traits.

Why it matches plant phenotyping methodsイネ穀粒の品質形質を対象に、ハイパースペクトル計測、前処理、機械学習による形質推定を開発・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。

abstractUsing a visible-shortwave infrared hyperspectral system combined with optimized preprocessing and machine-learning pipelines, we achieved accurate predictions for AAC ( R 2 = 0.97) and PC ( R 2 = 0.92).

Developing Quick Screening Method to Identify Rice Cultivars with Unique Aromatic Features. →

Aroma is a primary determinant of rice quality and market value, yet its evaluation in breeding programs remains constrained by labor-intensive milling, cooked-grain sensory methods, binary screening assays, and the limited seed availability of early generation selection. Moreover, aromatic rice breeding has historically focused narrowly on 2-acetyl-1-pyrroline-mediated popcorn aroma, potentially overlooking valuable alternative aromatic profiles. In this study, we developed a rapid sensory phenotyping approach for paddy rice that enables quantitative assessment of the aroma intensity and qualitative aroma characterization without milling or cooking. A diverse panel of 126 rice genotypes was evaluated using 1 g of ground paddy rice heated under controlled conditions coupled with sensory analysis and targeted HS-SPME-GC-MS/MS quantification of 164 volatile compounds. The method discriminated the aroma intensity and enabled characterization of aroma quality. Hierarchical clustering integrating sensory and chemical data resolved five distinct aroma classes, including popcorn-dominant, fruity-floral, nutty-grainy, woody-floral, and oxidation-driven phenotypes. While 2AP showed the strongest association with popcorn aroma and overall intensity ( r = 0.50), several high-intensity genotypes exhibited minimal 2AP, yet strong aroma perception driven by esters, alcohols, indole, and ketones. Interestingly, two genotypes (R125 and R126) showed strong popcorn perception despite much lower 2AP than typical aromatic rice, indicating the contribution of non-2AP popcorn-like aroma drivers. Conversely, genotypes with elevated lipid oxidation aldehydes exhibited high volatile abundance but poor aroma quality characterized by rancid, phenolic, and musty notes. These results demonstrate that superior rice aroma is a multivariate trait and is not related to only 2AP. The rapid phenotyping framework presented here provides breeding programs with an employable, information-rich tool for early generation screening, accelerating the identification of aromatic rice cultivars with expanded sensory diversity.

Why it matches plant phenotyping methodsイネ籾の香気を迅速・定量的に評価する感覚フェノタイピング手法を開発し、遺伝子型間で検証・適用した研究であり、表現型取得法が中心である。

abstractwe developed a rapid sensory phenotyping approach for paddy rice that enables quantitative assessment of the aroma intensity and qualitative aroma characterization without milling or cooking.

C 4 photosynthetic pathway fluxes in transgenic rice plants →

ABSTRACT Most land plants photosynthesize using the C 3 pathway, in which ribulose bisphosphate carboxylase/oxygenase (Rubisco) fixes CO 2 into 3-carbon acids. The C 4 pathway, a biochemical CO 2 -concentrating mechanism that operates in the context of specialized leaf anatomy to concentrate CO 2 around Rubisco, is more efficient. Introduction of the C 4 pathway into the C 3 crop rice could increase yield by 50%. Expression of five C 4 enzymes in transgenic rice previously led to flux through the first step. However, there was no evidence for flux later in the cycle. Here we developed new transgenic rice lines and novel protocols to detect C 4 cycle activity: CO 2 fixation into C 4 acids by carboxylation of a C 3 compound, decarboxylation, refixation of CO 2 by Rubisco, and regeneration of the C 3 donor. We demonstrate that these four core C 4 reactions are operating in rice, establishing the in vivo flux framework needed to progress towards a functional carbon-concentrating mechanism.

Why it matches plant phenotyping methodsトランスジェニックイネのC4光合成フラックスを検出する新規プロトコルを開発し、植物内での生理状態を測定・実証しており、フェノタイピング手法が研究の中心である。

abstractHere we developed new transgenic rice lines and novel protocols to detect C 4 cycle activity

RSCM: A Bayesian remote sensing-integrated crop model software framework for yield estimation →

RSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization. This approach automates estimation of leaf area index, aboveground dry matter, and grain yield without extensive ground-based calibration. The system couples a Python data interface with a high-performance C simulation engine, enabling efficient regional-scale processing. Validation using independent datasets for rice, wheat, and maize demonstrated robust performance: yield Model Efficiency reached 0.99, with a minimum ME of 0.67 for wheat. The Bayesian prior regularization constrained parameter estimates while maintaining predictive accuracy. Regional applications in South Korea, North Korea, and the U.S. Corn Belt captured spatial yield gradients and inter-annual variability across millions of pixels. RSCM provides a computationally efficient tool bridging process-based modeling and remote sensing for precision agriculture and food security monitoring.

Why it matches plant phenotyping methods衛星データと作物モデルを統合し、LAI・地上部乾物量・収量という植物形質を推定するソフトウェア手法を開発・検証しており、形質取得・推定法が研究の中心である。

abstractRSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization.

A lightweight YOLOv11-based model for rice false smut detection under complex field conditions →

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.

Remote evaluation of rice nitrogen utilization efficiency using chlorophyll-related spectral indices derived from unmanned aerial vehicle imagery. →

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).

Fine classification of rice diseases under field conditions based on improved ConvNeXt network →

Abstract Rice disease identification is a critical technique for ensuring yield and quality in precision agriculture. However, complex field backgrounds, subtle lesion features, and similar symptomatic manifestations have led to low accuracy and poor robustness in traditional classification methods. To address these issues, this study proposes an improved ConvNeXt network model (Improve‑ConvNeXt) for the fine classification of rice diseases under field conditions. A high‑quality dataset containing six categories (healthy rice, rice blast, brown spot, bacterial leaf blight, bacterial leaf streak, and bacterial grain rot) was constructed from field images and public datasets, with a total of 5,663 samples. Using ConvNeXt‑Tiny as the backbone, the model integrates a Hybrid Attention Transformer (HAT) to enhance the perception of lesion regions and key channels, and introduces Spatial and Channel Reconstruction Convolution (SCConv) to reduce feature redundancy and strengthen effective information expression. Experiments show that the Improve‑ConvNeXt model achieves 96.27% accuracy on the test set, which is 4.85% higher than that of the original ConvNeXt and significantly outperforms ResNet and DenseNet. The precision, recall, and F1‑score reach 95.84%, 96.11%, and 95.95%, respectively. Confusion matrix and Grad‑CAM visualization prove that the model can accurately focus on lesion areas and effectively distinguish similar diseases. This method provides high precision and strong generalization for rice disease identification in complex field environments, and offers a reliable technical reference for intelligent monitoring and precise management of rice fields.

Why it matches plant phenotyping methodsイネの病徴画像から病害状態を分類する画像・深層学習手法を開発し、データセットと性能比較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す。

abstractthis study proposes an improved ConvNeXt network model (Improve‑ConvNeXt) for the fine classification of rice diseases under field conditions.

Multi-Context Validation of Global Fractional Vegetation Cover Products in Croplands Using Multi-Source Crop FVC References →

Fractional Vegetation Cover of Crops (CropFVC) is a critical canopy parameter for monitoring crop growth, yet the behavior of widely used global FVC products (GLASS, GEOV1, GEOV2, and GEOV3) over croplands remains insufficiently understood due to fragmented validation references and limited crop-specific assessments. This study compiled a multi-source global CropFVC reference dataset (2000–2024) by integrating five international validation networks, the literature-derived samples, and newly acquired UAV and Jilin-1 satellite-derived CropFVC samples from China in 2024. The references were organized into three complementary validation contexts (V1~V3) to examine product behavior under different temporal coverage, crop purity, and reference conditions, together with spatio-temporal observations at the KONZ site. Results show that (1) across validation contexts, the evaluated products showed consistent behavior patterns, including shared overestimation under dense canopy conditions and reduced differences at low FVC levels; (2) spatio-temporal analysis at the KONZ site confirmed that peak-season deviations reflect shared response behavior rather than site-specific reference uncertainties; (3) historical mixed references (V1~V2) showed similar bias structures, whereas crop-specific validation (V3) preliminary revealed clearer crop-dependent responses, with predictive difficulty following winter wheat > maize > rice > soybean and improved stability after integrating 2024 observations. The integration of recent high-resolution crop observations expands existing global CropFVC references and enables behavior-oriented interpretation of global FVC products beyond simple accuracy ranking, providing an updated validation perspective for future development and application of global CropFVC products in agricultural monitoring.

Why it matches plant phenotyping methods作物キャノピーのFVCという植物形質を対象に、複数の全球FVC推定プロダクトを多様な参照データで体系的に検証し、UAV・衛星観測を含むCropFVC参照データセットを構築している。形質取得・検証が研究の中心である。

titleMulti-Context Validation of Global Fractional Vegetation Cover Products in Croplands Using Multi-Source Crop FVC References

Towards precision agriculture for assessing germination rates and density of rice seedling using hierarchical convolutional neural network on drone imagery →

Rice is a significant food that plays a vital part in delivering nutrition to the world's population. Hence, approaches for assessing rice yield have received considerable study. The amount of rice seedlings (density) is a main agronomic module. It is related to harvest and also plays a significant part in the survival rate. Unmanned Aerial Vehicles (UAVs) are prepared with lightweight sensors, which creates a substantial effect in the field of crop phenotyping. The UAV was effectively used to measure germination rates and density in an accurate and effective method that would otherwise be laborious and expensive to obtain when compared to manual valuation. In image processing, mainly over the applications of deep learning (DL) models, there was a notable academic search for the value of UAV images for varied agricultural monitoring tasks. This work develops a Rice Seedlings for Assessing Germination Rates and Density using Aerial Images with Hierarchical Deep Network (RSAGRD-AIHDN) model. The goal of this paper is to assess germination rates and seedling density in rice fields using remote sensing (RS) or UAV-based imaging techniques for improved crop establishment monitoring. To accomplish that, the image pre-processing stage is initially applied with dual stages, such as image acquisition and pre-processing, to ensure high-quality and consistent inputs. Furthermore, the RSAGRD-AIHDN model employs the ConvNeXt method for the feature extraction process. For rice seed detection and classification, the RSAGRD-AIHDN model implements ensemble models, namely stacked autoencoder (SAE), bidirectional temporal convolution network (BiTCN), and Deep Q-Learning (DQL). The experimental assessment of the RSAGRD-AIHDN method is performed under the aerial dataset of rice seedlings. The experimentation of the RSAGRD-AIHDN method portrayed a superior accuracy value of 98.68% over existing approaches.

Why it matches plant phenotyping methodsUAV画像と深層学習モデルを用いて、イネの発芽率と苗密度という植物形質を推定する手法を開発・評価しており、表現型取得が研究の中心である。

abstractThe UAV was effectively used to measure germination rates and density in an accurate and effective method

AGROSENSE: Smart Farming and Rice Crop Disease Detection Using IoT and Machine Learning →

Rice cultivation is affected by water mismanagement and plant diseases, leading to reduced productivity. This paper presents AgroSense, an IoT- and Machine Learning-based smart farming system for real-time monitoring and disease detection in rice crops. IoT sensors measure soil moisture, temperature, humidity, and pH, while a Convolutional Neural Network (CNN) model classifies rice leaf diseases such as Blast, Sheath Blight, and Bacterial Blight. Automated irrigation is triggered based on soil moisture thresholds to optimize water usage. Experimental results show reliable sensor performance and a validation accuracy of approximately 89% for disease detection. Cloud integration enables real-time monitoring and alert notifications through a mobile/web interface. The system reduces manual intervention, improves early disease identification, and supports efficient and sustainable rice farming.

Why it matches plant phenotyping methodsイネ葉の病害状態をCNNで分類する手法とIoT計測システムが研究の中心であり、植物病害フェノタイプの取得・判定に該当する。

abstractThis paper presents AgroSense, an IoT- and Machine Learning-based smart farming system for real-time monitoring and disease detection in rice crops.

Image-based leaf SPAD value and chlorophyll measurement using a mobile phone: enabling accessible and sustainable crop management →

Abstract Purpose Leaf chlorophyll content is a critical indicator of plant health. On-farm monitoring is limited by the high cost and accessibility of specialised meters and laboratory assays. This study evaluates a smartphone-based imaging method (PhotoFolia) as a practical, low-cost alternative for accurately estimating leaf SPAD (Soil and Plant Analysis Development) values and chlorophyll content. Methods Image-based estimates from the PhotoFolia mobile application were validated against standard laboratory assays and SPAD-502+ meter readings. The validation was conducted across four commercial rice varieties cultivated in Thailand. Results The smartphone imaging method predicted SPAD values with a mean absolute error (MAE) of 1.2 units and chlorophyll concentrations with a mean absolute percentage error (MAPE) of 7.2% relative to laboratory benchmarks. Conclusion With SPAD error approaching the industry ±1 unit standard and chlorophyll estimation remaining below a 10% relative error threshold, this approach demonstrates that ordinary mobile phones can serve as highly accessible, cost-effective tools for routine on-farm crop monitoring, eliminating the need for dedicated hardware. Highlights Novel low-cost approach for chlorophyll assay and SPAD-value measurement using standard mobile phone. Achieves accuracy comparable to commercial tools. Eliminates need for specialised sensors or laboratory equipment. Impact This study demonstrates that mobile phone-based image analysis can accurately estimate leaf SPAD and chlorophyll levels in rice under ambient lighting conditions, offering a low-cost, accessible tool for monitoring plant health.

Why it matches plant phenotyping methodsスマートフォン画像から葉のSPAD値とクロロフィル量を推定する手法を開発・標準測定と検証しており、植物表現型取得が中心である。

abstractThis study evaluates a smartphone-based imaging method (PhotoFolia) as a practical, low-cost alternative for accurately estimating leaf SPAD (Soil and Plant Analysis Development) values and chlorophyll content.

Hyperspectral-Informed Sentinel-2-Based Monitoring of Paddy Residue Burning through Crop-State Discrimination →

Abstract Accurate mapping of agricultural residue burning using satellite data remains challenging due to the rapid temporal overlap and spectral similarity of mature crops, harvested fields, and burnt residues during peak harvest periods. This study presents a scalable, decision-rule–based methodology for the concurrent mapping of mature, harvested, and burnt paddy fields, integrating field-scale hyperspectral measurements with multi-temporal Sentinel-2 multispectral imagery. Hyperspectral observations captured systematic changes in crop reflectance associated with maturity, harvest intensity, and post-burn ash deposition, which were subsequently upscaled to Sentinel-2 spectral bands to evaluate a comprehensive set of vegetation and burn-sensitive indices. The analysis identified the Chlorophyll Absorption Ratio Index (CARI) as the most effective indicator for separating mature from harvested rice, while the delta Normalized Burn Ratio (dNBR) exhibited the highest sensitivity for distinguishing harvested fields from burnt residues. These indices were combined within a hierarchical decision-tree framework and applied to multi-date Sentinel-2 imagery to map rice burning dynamics across intensively cultivated districts in northern India. The approach achieved an overall classification accuracy of 92.57% with a kappa coefficient of 0.80, demonstrating strong spatial and temporal consistency with field observations. By explicitly addressing intra-seasonal spectral confusion in agricultural landscapes, the proposed framework advances burned-area mapping beyond single-index detection toward integrated crop-state discrimination. The methodology is computationally efficient, sensor-transferable, and suitable for operational implementation, offering significant potential for large-scale agricultural monitoring, emission assessment, and policy-driven residue management in rice-based cropping systems globally.

Why it matches plant phenotyping methods圃場規模のハイパースペクトル／Sentinel-2データからイネの成熟・収穫・焼却状態を識別する手法を開発・検証しており、植物の作物状態を抽出する方法が研究の中心である。

abstractThis study presents a scalable, decision-rule–based methodology for the concurrent mapping of mature, harvested, and burnt paddy fields

Characterization of Rhizosphere Oxidation Associated with Root Development in Rice Using Planar Oxygen Optodes →

Rhizosphere oxidation is a key adaptive mechanism in reductive soil environments, in which oxygen released from roots alters rhizosphere redox conditions and regulates biogeochemical processes. Rice plants possess an internal oxygen transport system, and radial oxygen loss (ROL) from roots is closely associated with root development. However, the spatial patterns of ROL in soil and their relationships with root traits remain poorly characterized. In this study, we developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice. Daily time-course tracking of individual crown roots revealed dynamic changes in the spatial distribution and magnitude of rhizosphere oxygen in relation to root elongation and aging. Root thickness was positively correlated with dissolved oxygen levels near root tips. Genotypic comparisons further identified a cultivar with reduced rhizosphere oxidation despite possessing thicker roots among the tested genotypes, thereby indicating the involvement of additional physiological processes. Overall, these findings demonstrate that rhizosphere oxidation is regulated by root growth stage and thickness and dynamically modulated during root development.

Why it matches plant phenotyping methods平面酸素オプトードとX線CTを統合したマルチモーダル画像システムを開発し、イネ根の発達と根圏酸化を時系列・空間的に測定しているため、植物フェノタイピング手法が研究の中心である。

abstractwe developed a multimodal imaging system that integrates planar oxygen optodes with X-ray computed tomography to simultaneously visualize rhizosphere oxidation and root development in rice.

Detection of Rice Diseases: Leaf Blast, Bacterial Leaf Light, and Brown Spot Using Image Enhancement and Faster Region-Based Convolutional Neural Network →

Rice diseases such as leaf blight, blast, and brown spot remain major constraints on food security and rural livelihoods across Southeast Asia, causing significant yield losses each year. In Indonesia, particularly in Lamongan, East Java, these pathogens threaten smallholder productivity and disrupt national rice supply chains. This study aims to enhance automated rice disease detection under real agricultural conditions by integrating image preprocessing techniques with a deep learning-based detection framework. The main contribution lies in developing a hybrid pipeline that combines RGB-to-grayscale conversion and contrast stretching prior to model training, effectively mitigating low-contrast conditions and noise commonly found in field-acquired image datasets. The enhanced images are subsequently processed using the Faster Region-Based Convolutional Neural Network (Faster R-CNN) with a ResNet-50 backbone to localize and classify disease symptoms. Experiments conducted on a dataset of 1,500 annotated rice leaf images achieved high detection performance, with accuracies of 97.37% for leaf blight, 94.12% for blast, and 95.24% for brown spot. Compared with the baseline Faster R-CNN model, the proposed approach improved classification accuracy from 0.8906 to 0.9297, reduced false negatives from 0.439 to 0.1998, increased foreground classification accuracy from 0.55 to 0.78, and descreased total loss from 0.839 to 0.6493. These results demonstrate that integrating RGB-to-grayscale conversion and contrast stretching significantly enhances feature representation, leading to improved detection accuracy, reduced error rates, and more stable training behavior. Overall, the proposed framework provides a robust and reliable approach for rice disease identification and offers strong potential for practical deployment in precision agriculture systems.

Why it matches plant phenotyping methodsイネ葉の病徴を画像から検出・分類する画像前処理とFaster R-CNNの統合手法を開発し、性能比較・検証しているため、植物表現型取得が中心である。

abstractThe main contribution lies in developing a hybrid pipeline that combines RGB-to-grayscale conversion and contrast stretching prior to model training

OPTIMIZING PRE-PROCESSING OF NEAR INFRARED SPECTRA FOR PHENOMIC PREDICTION USING SINGULAR VALUE DECOMPOSITION →

Phenomic prediction (PP) is a genetic value prediction method based on near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, the choice of pre-processing is usually done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose to implement a singular value decomposition (SVD) step in the pre-processing pipeline where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative, orthogonal and interpretable features of spectra instead of many correlated, uninformative wavelengths. We tested this pre-processing method on five datasets representing four plant species (maize, rice, sorghum and grapevine). Results show that estimating genetic values on components of raw spectra, that are not weighted by their eigenvalues, performs as well as doing it on spectra pre-processed with the best classical chemometrics methods in most cases, while requiring less parameter optimization. Moreover, this SVD step opens up possibilities for better understanding and selecting parts of the spectral information that are relevant for PP. Plain language summary Cultivated plants are the result of a breeding process during which their genetic values are used to select those to breed. Estimating these values requires heavy experimental means and is time consuming. Phenomic prediction is a low cost and high throughput method that is increasingly being used for this purpose. It often uses, as predictors, near infrared spectroscopy measurements that are easy to collect and thus routinely used in many species. However, near infrared spectra generally require pre-processing before being used in prediction. Currently used pre-processing methods arise from the chemometrics community, and still deserve a better in-depth appropriation by geneticists. In this study, we propose a pre-processing approach that performs as well as the best chemometrics pre-processing generally used, reduces computation time, and allows for a better understanding of what parts of spectral information are relevant for prediction. Core Ideas The SVD-based pre-processing performs as well as the best performing classical chemometrics pre-processing in most cases Using the SVD-based pre-processing reduces computing time of genetic value estimation and requires less parameter optimization than using classical chemometrics pre-processing Spectra are composed of chemical and physical information and classical pre-processing methods remove the physical part of the signal It is likely that chemical information is the most important for phenomic prediction even though physical information remains valuable Performance of the SVD-based pre-processing is likely due to a good estimation of the genetic part of spectra and the conservation of physical information of spectra

Why it matches plant phenotyping methods植物のNIRSスペクトルから遺伝的価値を推定するフェノミック予測について、SVDベースの前処理法を提案し、複数植物種のデータセットで既存法と比較検証しているため、フェノタイピング手法が中心である。

abstractIn this study, we propose to implement a singular value decomposition (SVD) step in the pre-processing pipeline where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths.

Image-based QTL mapping of grain size for establishment of predictive breeding in rice →

Rice is a staple food and a major source of calories for much of the global population. With the global population continuing to rise, breeding high-yielding rice cultivars is critical for future food security. Grain size is a key trait directly related to rice yield. In this study, QTL mapping was conducted using both phenotypic data collected with Vernier calipers and image-based phenotyping. All QTLs identified through caliper measurements were also detected using image data, which allowed for more precise localization with higher LOD scores. Grain size-related QTLs were identified on chromosomes 3, 5, 6, and 7, including major genes such as GS3, qSW5, and GW7. A novel QTL region between markers RM586 and RM1163 on chromosome 6 was identified, which has not been previously reported. Introgression of this region positively affected grain length, and an additive effect was observed when combined with qGL3. Within the RM586-RM1163 region, 16 open reading frames (ORFs) were annotated, and Gene Ontology (GO) analysis suggested their roles in regulating cellular structures and organelle functions during grain development. Among these, OsGSq6 showed a significant increase in expression from the panicle formation stage to the heading stage. Fifteen SNPs were identified within the gene, resulting in 11 distinct haplotypes, several of which were predominantly found in indica rice. OsGSq6 encodes a phosphotyrosyl phosphatase activator, suggesting its role in grain development. Image-based phenotyping also enabled the detection of varietal admixtures, contributing to improved genetic purity. This approach offers a promising strategy for enhancing rice breeding precision.

Why it matches plant phenotyping methods画像ベース表現型解析を用いてイネ粒サイズを定量し、ノギス測定との比較でQTL検出精度を評価しており、表現型取得法の実質的な適用が研究上重要です。

abstractQTL mapping was conducted using both phenotypic data collected with Vernier calipers and image-based phenotyping.

Metaheuristic optimization based improved neural network for the timely prediction of paddy leaf diseases. →

Rice is a fundamental dietary staple that embodies cultural identity, culinary diversity, food security and economic stability, providing a significant portion of daily caloric intake for considerable portion of world populace. However, vulnerability of rice crops to various leaf diseases poses a considerable concern to its productivity and quality, which in turnemphasizes the need for an effective disease prediction technique. This research focuses on prompt detection of pathogenic threats to paddy leaves through image analysis. The inputs are pre-processed using an Adaptive Gabor Filter (AGF), for improving the quality and reducing the noise from the image. Subsequently, using a Histogram of Oriented Gradient (HOG), the extraction of relevant disease-related information is assured. For the classification stage, an OptimizedCapsule Networks (CapsNet) is employed for identifying diseases affecting paddy foliage with high accuracy. The proposed CapsNet achieves improved generalization and robustness by integrating the Glow Worm Swarm Algorithm (GWSA) for parameter optimization. In addition to disease prediction, the proposed system integrates a Fertilizer-Based Disease Management (FBDM) approach, which suggests precise fertilization strategies based on the identified disease. By analysing disease symptoms, the system recommends optimal nutrient applicationssuited to enhance plant resistance and recovery. This integration of disease prediction with precision agriculture techniques enables site-specific fertilizer application, preventing overuse and reducing environmental impact.

Why it matches plant phenotyping methodsイネ葉の病徴を画像から抽出・分類する手法が研究の中心であり、植物の病害状態を直接推定する画像ベースの表現型解析に該当する。

abstractThis research focuses on prompt detection of pathogenic threats to paddy leaves through image analysis.

Digitization of Field Rice Leaf Greenness (LCC 3 and 4) Using Drone-Based Remote Sensing and Machine Learning →

Precision monitoring of crops using drone or unmanned aerial vehicle (UAV) technology is rapidly growing as a climate-smart agriculture practice in rice farming systems in Sri Lanka and globally. In rice fields, the Leaf Color Chart (LCC) is traditionally used for manual comparison of a leaf to the standard LCC categories in the field to determine the fertilizer condition of the plant. However, this lacks autonomous monitoring, rapid monitoring of larger fields, scalability, and the digital transformation of the scores with sprayer drones for targeted fertilizer application. Drones with multispectral cameras could pose a greater rapid and digitalized solution for delineation of leaf color instead of LCC, in the field. Thus, this paper presents a novel attempt of digitization of conventional LCC levels 3 and 4, rice plant leaf greenness levels in the field, with classification and production of a spatial map using drone multispectral images and machine learning algorithms. The experimental setup consisted of ground sampling of LCC levels 3 and 4 from farmer fields and acquisition of drone imagery data above the field with a DJI Phantom 4 Multispectral UAV, from which fifteen vegetation indices related to crop spectra were extracted. The vegetation indices were then employed for training (70%) and testing (30%) with machine learning algorithms: Random Forest (RF), as well as SVM-linear and SVM-RBF, focusing on LCC 3–4 class classification. The results showed good classification performance, with the RF algorithm reporting a test accuracy of 98.2%, outperforming SVM-linear (82.5%) and SVM-RBF (87.5%). The RF model outputs SR, EVI, MSR, NDVI, and TCARI as feature importance indices for the classification of LCC levels 3 and 4 in the rice field. The findings of this proposed method greatly encourage the adaptation of drone technology for real-time monitoring of rice leaf fertilizer levels linked to LCC levels three and four, and spatial identification of the zones across the field. This imposes greater advancement towards climate-smart rice cultivation, targeted fertilizer application and rice field landscape pattern change analysis, underpinning the importance of field digitization.

Why it matches plant phenotyping methodsドローン multispectral画像と機械学習により、圃場のイネ葉の緑色（LCC 3/4）を自動分類・空間化する手法が研究の中心であり、植物状態の測定・抽出に該当する。

abstractthis paper presents a novel attempt of digitization of conventional LCC levels 3 and 4, rice plant leaf greenness levels in the field, with classification and production of a spatial map using drone multispectral images and machine learning algorithms.

Identifying Nutrition Deficiency in Paddy Leaf using Neural Network →

Agriculture is the primary source of livelihood for majority of India’s population, with paddy serving as a staple food for a large segment of people. However, paddy cultivation is affected by several challenges that vary with climate, location, and farming practices. Among these, nutrient deficiencies in paddy leaves significantly impact crop yield and quality, making early detection crucial for effective farm management. The following study presents a novel approach to identifying nutrient deficiencies using neural networks and also provides a solution for their early detection in paddy leaves . A diverse dataset of paddy leaf images showing different types and severity levels of nutrient deficiencies is collected, and a Convolutional Neural Network (CNN) is used in order for image classification. The model is trained and tested on diverse dataset, demonstrating strong performance in accurately detecting nutrient deficiencies in paddy leaves.

Why it matches plant phenotyping methodsイネ葉画像から栄養欠乏という植物状態をCNNで分類する手法とデータセット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。

abstractThe following study presents a novel approach to identifying nutrient deficiencies using neural networks and also provides a solution for their early detection in paddy leaves .

Research on the Application of Agricultural Big Data in Plant Growth Prediction →

The intelligent transformation of agriculture places plant growth prediction as a critical component for ensuring food security, optimizing resource allocation, and enhancing sustainable productivity. Traditional methods reliant on empirical or simplified mechanistic models struggle with the nonlinearity, high dimensionality, and spatiotemporal heterogeneity inherent in agro-ecological systems. This study investigates the paradigm shift enabled by agricultural big data integrating multi-source, real-time streams from IoT sensors, satellites, UAVs, and farm management systems. We propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment. Empirical validation across major crops (rice, wheat, maize, tomato) in diverse eco-regions of China (2023--2025) demonstrates significant improvements: the MDA-HI model achieved average RMSE reductions of 42.7% for yield prediction and 38.1% for key phenological stage prediction relative to best-in-class standalone models. A large-scale case study on rice-wheat rotation systems showed that data-driven prescriptions reduced nitrogen fertilizer use by 22.5% and irrigation water by 18.3% while increasing yield by 5.1%. The study further establishes a five-dimensional evaluation system covering accuracy, robustness, interpretability, scalability, and economic benefit. Remaining challenges include edge computing for real-time inference, federated learning for privacy-preserving collaboration, and explainability of complex ``black-box'' models. This research concludes that agricultural big data constitutes a foundational catalyst for predictive, precise, and proactive cognitive agriculture, with profound implications for global food system resilience.

Why it matches plant phenotyping methods農業ビッグデータを用いて生育・収量・フェノロジーを推定するMDA-HI手法を提案し、複数作物・地域で性能検証しており、植物形質推定手法が研究の中心である。

abstractWe propose a ``Multi-source Data Assimilation and Hybrid Intelligence'' (MDA-HI) framework that synergistically couples process-based crop models with ensemble machine learning algorithms---including Transformer-based architectures and Physics-Informed Neural Networks---within a holistic pipeline encompassing multi-modal data fusion, hybrid modeling, and scalable deployment.

A multispectral imaging framework for early-stage modelling and precision estimation of rice plant density using MSAVI-derived fractional vegetation cover →

Accurate early-stage estimation of rice plant density is essential for precision crop management. However, current remote sensing methods face limitations in spatial resolution, revisit frequency, and sensitivity under sparse canopy conditions, highlighting the need for scalable, high-resolution UAV-based approaches. This study presents a UAV-based multispectral imaging framework for early-stage rice plant density estimation, proposing a scalable and cost-efficient solution for precision agriculture. Fractional vegetation cover derived from the Modified Soil Adjusted Vegetation Index (MSAVI) was used as the primary predictor variable in linear regression modelling. UAV imagery was acquired across varying flight altitudes (15–30 m) and crop growth stages (14–32 DAS). Five-fold cross-validation results shows that accuracy improved with crop development, with notable gains between 14 and 20 DAS. During the early vegetative stage, RMSE ranged from 39-41 plants/m2 and MAPE averaged ~30%, reflecting moderate predictive accuracy caused by sparse canopy cover and strong soil interference. As the crop progressed to early tillering, prediction error declined, with RMSE improving to approximately 30 plants/m2 and MAPE decreasing to about 29%. This improvement was attributed to denser canopy structure and stronger spectral separation between vegetation and background soil. Further analysis identified 18–25 DAS as the optimal developmental window for reliable plant density estimation, wherein models achieved high coefficients of determination (R² = 0.9139–0.9395) and the lowest RMSE (34 plants/m2). No significant differences were observed among flight altitudes, suggesting higher-altitude flights can maintain accuracy while improving operational efficiency and coverage.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からイネの植物密度を推定する枠組みを開発・検証しており、植物形質の取得・推定手法が研究の中心である。

abstractThis study presents a UAV-based multispectral imaging framework for early-stage rice plant density estimation

A Deep Learning-Based Method for Paddy Leaf Disease Detection and Growth Stage-Specific Treatment Recommendation. →

Paddy leaf diseases significantly affect rice yield and quality, making early detection and proper treatment essential for precision agriculture. This study proposes a deep learning-based decision support system for paddy leaf disease detection, growth-stage prediction, and stage-specific treatment recommendations. The dataset used in this study comprises paddy leaf images collected from multiple sources and categorized by growth stage and disease class. The dataset was divided into training (80%), validation (10%), and test (10%) sets to ensure proper model evaluation. For growth stage prediction, a lightweight Convolutional Neural Network (CNN) model was developed, while disease classification was performed using transfer learning models, including VGG16, ResNet50, InceptionV3, and MobileNetV2. An ensemble method based on average probability voting was used to improve classification performance. The models were evaluated using accuracy, precision, recall, and F1-score on an independent test set. The experimental results show that the ensemble model achieved higher accuracy compared to individual models, demonstrating improved robustness and generalization. The proposed system was implemented as a Streamlit web application that provides disease detection, growth-stage prediction, and treatment recommendations. The proposed integrated framework can support farmers and agricultural experts in making timely and accurate disease management decisions.

Why it matches plant phenotyping methods葉画像から病害状態と生育ステージを推定する深層学習手法を開発・評価しており、植物フェノタイピングが中心的です。治療推薦も含まれますが、画像による状態推定が中核です。

abstractThis study proposes a deep learning-based decision support system for paddy leaf disease detection, growth-stage prediction, and stage-specific treatment recommendations.

Rice LAI estimation using UAV-based multi-parameter fusion at the booting stage. →

The leaf area index (LAI) is a key parameter for characterizing crop growth and water use efficiency. Therefore, efficient and accurate monitoring of LAI is essential for precision rice management. To overcome the limitations of traditional LAI measurement methods, which are time consuming, labor intensive, and difficult to scale, this study proposes an inversion framework that integrates multi-source UAV remote sensing features with machine learning models. The framework incorporates color indices (CIs) derived from RGB imagery, vegetation indices (VIs) derived from multispectral data, texture features (TIs), and texture feature indices (TFIs), and employs six machine learning algorithms to develop optimized LAI estimation models for the rice booting stage. The results indicate that at a flight altitude of 30 m, the CNN model integrating CIs and TIs achieved an accuracy of R 2 = 0.815. At 60 m, the RF model combining VIs and TFIs showed superior performance, with an R 2 of 0.866. Further integration of CIs, VIs, and TFIs at 30 m produced the best results, increasing R 2 to 0.901, reducing RMSE to 0.273, and raising RPD to above 3.0. These findings demonstrate that TFIs significantly enhance the spectral-spatial representation capability of multispectral data, thereby improving model accuracy. The combined use of CIs and VIs across different sensors compensates for the inherent limitations between spectral and spatial information, while the integration of multi-resolution TIs and TFIs effectively overcomes the constraints of single-source data. Overall, the proposed approach provides a robust and efficient solution for high-precision LAI estimation during critical growth stages of rice, offering strong support for precision agricultural management.

Why it matches plant phenotyping methodsUAV画像・マルチスペクトル特徴量と機械学習によるイネLAI推定フレームワークの開発・性能評価が研究の中心であり、植物形質の取得手法に該当する。

abstractthis study proposes an inversion framework that integrates multi-source UAV remote sensing features with machine learning models.

Integration of drone imagery and artificial intelligence for high-throughput phenotypic selection of abiotic stress traits →

High-throughput phenotyping is a core prerequisite for breeding climate-resilient crops. To complete related breeding work, breeders must evaluate the performance of large-scale crop populations under seven types of field abiotic stresses including drought and high temperature, and the combined technology of unmanned aerial vehicle (UAV) imaging and artificial intelligence can provide core support to meet this demand. This review centers on three core sets of content: first, the integration of various UAV platforms, five types of imaging technologies, and machine learning and deep learning models to support phenotyping selection of abiotic stress-related traits; second, sorting out the biological significance of 12 categories of image-derived traits; third, breaking down the seven full workflow nodes ranging from flight planning to breeding decision support. Existing prior research on six crop types including wheat and rice has confirmed that this technology can improve the speed, scale and repeatability of field screening, and delivers outstanding effects when combined with multi-environment testing, genomic tools, and breeders’ expertise. This paper also sorts out six core limitations currently restricting the real-world deployment of this technology, and puts forward six future development directions to support its large-scale application.

Why it matches plant phenotyping methodsUAV画像とAIによる作物のストレス関連形質の取得・選抜ワークフローを中心に整理したレビューであり、植物フェノタイピング手法が中核です。

abstractThis review centers on three core sets of content: first, the integration of various UAV platforms, five types of imaging technologies, and machine learning and deep learning models to support phenotyping selection of abiotic stress-related traits;

Diversity of photosynthesis-related and high-throughput phenotyping traits in indica rice →

Manual phenotyping of photosynthesis-related traits in rice is labor-intensive and limits the scale and temporal resolution of genetic analysis under field conditions. Here, we integrated unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) with genome-wide association studies (GWAS) to dissect the diversity and genetic architecture of photosynthesis-related traits in a large indica rice diversity panel (>300 accessions) evaluated across three dry seasons. A total of 45 traits, including UAV-derived NDVI, canopy height, and canopy temperature, together with leaf gas-exchange, stomatal, anatomical, and agronomic traits, were quantified. UAV-derived traits captured temporal growth and senescence dynamics and showed strong and consistent correlations with leaf photosynthetic rate, stomatal conductance, flowering time, biomass, and grain yield. GWAS identified multiple QTLs for photosynthetic and HTP traits, including a cross-year stable transpiration-rate QTL (qTRMMOL-2-2) and a photosynthetic-rate QTL (qPHOTO-1-2). Haplotype analyses revealed that the wall-associated receptor-like kinase gene OsWAK6 and the potassium transporter gene OsHAK1 were strongly associated with variation in photosynthetic rate and transpiration, respectively. Several elite accessions with consistently high photosynthetic performance carried superior haplotypes at multiple qPHOTO loci, suggesting their potential value for breeding. Together, our results demonstrate that UAV-based HTP provides reliable field-scale proxies for physiological performance, and that integrating HTP with GWAS can enable the identification of genetic targets for improving photosynthesis, water use, and yield potential in rice. • Forty-five traits, including HTP, photosynthesis, and leaf morphology, were measured across three dry seasons in diverse Indica rice. • GWAS identified genes linked to photosynthesis and stomatal density, aiding in breeding resilient, high-yield rice. • UAV-based HTP data effectively tracked plant growth and senescence, correlating with photosynthetic rate. • GWAS co-localization revealed shared QTLs, suggesting multi-trait regulation by common genes.

Why it matches plant phenotyping methodsUAVベースのHTPによる植物形質取得と生理性能の推定が研究の中心であり、45形質を大規模・反復的に測定し、信頼性や他の生理形質との相関も評価している。

abstractwe integrated unmanned aerial vehicle (UAV)-based high-throughput phenotyping (HTP) with genome-wide association studies (GWAS)

Development of a Spatiotemporal Estimation Method for Rice Plant Height Using Pattern Matching Based on Time-Series Satellite-Derived Vegetation Indices and In Situ Measurements →

Rice plant height is a key indicator of crop growth and phenology, yet continuous daily estimation remains challenging under limited field observations. This study proposes an interpretable Bayesian LUT-based framework to estimate rice plant height from time-series, satellite-derived GCVI, and sparse in situ measurements. Daily plant height was estimated as a posterior-weighted ensemble of multiple LUT-derived heights, together with uncertainty reflecting ambiguity among plausible growth trajectories. Applied to rice paddies in Ryugasaki City, Japan, using Harmonized Landsat–Sentinel-2 data from the 2025 growing season, the method achieved R2=0.85 and RMSE = 7.08 cm on the validation dataset, outperforming simple baseline approaches. The estimated daily height time series also enabled evaluation of the timing at which plant height reached 70 cm, revealing clear spatial variability among fields and an associated uncertainty of approximately 10 days. Although this threshold was discussed with reference to previous studies on L-band SAR sensitivity, the present study relied solely on optical observations. Overall, the proposed framework provides a data-efficient and explainable approach for daily, spatially explicit rice growth monitoring, while current limitations include the single-region, single-year LUT construction and the simplified statistical assumptions used in the Bayesian weighting framework.

Why it matches plant phenotyping methods衛星由来時系列データからイネの草丈を推定する手法を開発し、検証データで性能評価しているため、植物フェノタイピング手法が中心です。

abstractThis study proposes an interpretable Bayesian LUT-based framework to estimate rice plant height from time-series, satellite-derived GCVI, and sparse in situ measurements.

Evaluation of SAR C-band radar vegetation indices for rice crop monitoring in Tamil Nadu, India →

Rice (Oryza sativa) cultivation plays a critical role in food security across Asia, where smallholder farmers depend heavily on timely information about crop development and field conditions. Monitoring these changes using optical remote sensing is constrained by persistent cloud cover during monsoon-driven growing seasons, underscoring the necessity of Synthetic Aperture Radar (SAR) for continuous observation. This study evaluates the capability of Sentinel-1 C-band SAR for tracking rice phenology in two smallholder fields in Mayiladuthurai District, Tamil Nadu, during the Late Samba season (September to January). Field-scale analysis of VV, VH, and NDVI time series for 2023–2024 captured key phenological transitions, with polarization showing a strong correlation with NDVI (Farm 1: r = 0.75; Farm 2: r = 0.73). Radar Vegetation Indices (RVI, mRVI, and RVI4S1) were computed from multi-year Sentinel-1 data (2018–2023) and compared with MODIS NDVI. Although the radar indices showed high inter-correlation (r > 0.90), their relationship with NDVI remained weak (0.15–0.30). Machine learning experiments over a 1.5 × 1.5 km region (2018–2022) demonstrated that a Linear Regression model (6.92 × 10−5) outperformed Random Forest Regression (0.000258) in predicting RVI4S1 from VV and VH, indicating linear relationship between radar channels and the index. The study highlights the suitability of Sentinel-1 SAR-particularly VH polarization – for phenology tracking in smallholder contexts, especially where optical data are limited by cloud cover.

Why it matches plant phenotyping methodsSARセンシングとレーダー植生指数を用いてイネの生育・フェノロジーを推定し、光学指標との比較および機械学習による技術評価を行っており、表現型取得法が中心である。

abstractThis study evaluates the capability of Sentinel-1 C-band SAR for tracking rice phenology in two smallholder fields in Mayiladuthurai District, Tamil Nadu, during the Late Samba season (September to January).

Development of Intelligent Pesticide Sprinkling System Determined by the Infection Level of a Plant (IOT based) →

This paper presents the development of an IoT-based intelligent pesticide sprinkling system for rice crops using image processing and machine learning techniques. The system aims to overcome the limitations of traditional pesticide spraying methods, which often result in excessive chemical usage, environmental pollution, and health risks to farmers. A camera module (ESP32-CAM) captures real-time images of rice leaves, which are processed using OpenCV and analyzed through a Convolutional Neural Network (CNN) model trained using TensorFlow. The model identifies common rice diseases such as bacterial leaf blight, brown spot, and leaf smut, and determines the infection severity. Based on the detection results, the ESP32 microcontroller activates a relay module that controls a DC pump to spray pesticides only on infected areas. The system also features an IoT-based dashboard for real-time monitoring, visualization, and remote operation. Experimental results demonstrate effective disease classification, with clear visualization using Grad-CAM and probability graphs. The proposed system reduces pesticide usage, minimizes human exposure to harmful chemicals, and enhances crop productivity. It provides a low-cost, efficient, and scalable solution for precision agriculture and smart farming applications.

Why it matches plant phenotyping methods葉画像から病害の種類と感染重症度を推定し、その結果で散布を制御する画像・機械学習システムが研究の中心であり、植物の病害状態を直接評価するため、農業制御用途を含んでも植物フェノタイピングに該当する。

abstractusing image processing and machine learning techniques

Dynamics and Crosstalks of H 2 S and H 2 O 2 Signaling in Plant Abiotic Stress Response Deciphered by a Disposable SERS Sensing Patch. →

Abiotic stresses caused by climate change pose a serious threat to global crop productivity, making the early detection of plant stress responses crucial. Hydrogen sulfide (H 2 S) and hydrogen peroxide (H 2 O 2 ), as key signaling molecules, their dynamic synergistic effects are central to understanding the mechanisms of plant stress adaptation. However, real-time tracking of the dynamic changes of these molecules remains challenging. This study developed a wearable plasmonic nanoarray sensor integrated with metal-organic frameworks (MOFs), which cleverly combines the high sensitivity of surface-enhanced Raman scattering (SERS) with the gas enrichment capacity of the MOF, incorporates 2D plasmonic membrane assembly technology and 4-mercaptophenylboronic acid (4-MPBA) conjugation strategy, and successfully achieves real-time and synchronous detection of H 2 S and H 2 O 2 in plants. The 24 h dynamic monitoring results showed that under different stress conditions, H 2 S and H 2 O 2 in tomatoes and rice both had specific dynamic change rules, and there was a complex cross-regulation mechanism between them. By combining sensor data with partial least squares discriminant analysis (PLS-DA), the classification accuracy of stress types exceeds 95%. This non-destructive and highly sensitive detection system can provide real-time dynamic data of stress signals, bringing a breakthrough to the in-situ monitoring of plant physiological states.

Why it matches plant phenotyping methods植物内のストレスシグナルをリアルタイム測定するウェアラブルSERSセンサーの開発が研究の中心であり、植物の生理状態の表現型取得に直接結び付いている。

abstractThis study developed a wearable plasmonic nanoarray sensor integrated with metal-organic frameworks (MOFs)

Improving the estimation accuracy of rice leaf protein nitrogen using data augmentation, explainable machine learning, and UAV hyperspectral imagery. →

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.

Additional file 1 of Geometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions →

Supplementary Material 1

Why it matches plant phenotyping methodsタイトルから、幾何学的画像ベース表現型解析と生理学的解析を用いたイネ耐塩性スクリーニングの検証が主題であり、植物表現型取得法の技術検証に該当する。

titleGeometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions

Geometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions. →

Salinity stress in coastal areas threatens the stability of rice production in Indonesia, necessitating innovative breeding strategies to adapt to this stress. In breeding, screening methods are crucial to improve selection effectiveness. One approach is pot selection on saline soil. However, this concept requires a precise approach, so integrating image-based phenotyping (IBP) screening and validation by physiological traits provides a rapid and effective approach to assessing salinity tolerance in rice genotypes. This study aimed to identify robust IBP traits for pot salinity screening and validate them through physiological response patterns among rice genotypes under salinity conditions. Six rice genotypes were evaluated under normal and saline environments using artificial pot trials. IBP traits related to plant geometry were quantified and complemented with physiological indicators, including Na⁺/K⁺ balance, chlorophyll pigments, and proline accumulation. Based on the result, perimeter and ferret were identified as effective IBP selection criteria. Both criteria captured differences in osmotic regulation and photosynthetic performance under salinity stress. Principal component analysis clearly separated tolerant, moderately tolerant, and sensitive genotypes, with geometric traits contributing most strongly to genotype discrimination. It supported a bit of physiological responses, which revealed distinct tolerance patterns. Tolerant genotypes (Pokkali, HS4.15.1.70, and HS4.15.2.4) maintained better Na⁺/K⁺ balance, lower chlorophyll loss, and adaptive proline responses, while sensitive genotypes (IR 29 and Ciherang) showed pronounced ionic imbalance and chlorophyll reduction; HS4.45.1.66 exhibited intermediate responses. The integration of IBP and physiological traits offers a practical framework for high-throughput salinity screening.

Why it matches plant phenotyping methods画像ベース表現型形質を定量化し、塩分耐性スクリーニングの選抜基準として検証することが中心である。生理形質による妥当性検証も含む。

abstractThis study aimed to identify robust IBP traits for pot salinity screening and validate them through physiological response patterns among rice genotypes under salinity conditions.

Additional file 1 of Geometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions →

Supplementary Material 1

Why it matches plant phenotyping methodsタイトルで画像ベース表現型解析と生理解析を用いた耐塩性スクリーニングの検証を明示しており、表現型取得・検証が中心的な方法論的役割を持つ。

titleGeometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions

Additional file 2 of Geometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions →

Supplementary Material 2

Why it matches plant phenotyping methods題名が米の塩耐性スクリーニングを検証する幾何学的画像ベース表現型解析を明示しており、表現型取得・検証が中心と判断できる。

titleGeometric image-based phenotyping and physiological analysis for validation of rice salinity tolerance screening under artificial pot conditions

Hybrid CNN-transformer architecture for multi-class crop disease detection and severity assessment: CropHybrid-Net with benchmark evaluation on CropDisease-12 →

Grain diseases lead to losses of 20-40% of the harvests each year, representing a threat to the food security of the world. Accurate and automated diagnosis of disease from remote picture taking would be key to prompt and directed interventions. Most current deep-learning approaches, however, are based on controlled lab images, on a single crop and ignore the assessment of disease severity. CropHybrid-Net is a three-branch ensemble architecture with ResNet-50, EfficientNet-B4 and Swin Transformer (Swin-T) that combines them using Efficient Channel Attention (ECA) fusion layer for simultaneous disease detection and severity estimation. CropDisease-12 is a benchmark dataset of 43200 images belonging to 12 classes representing four major crops (tomato, wheat, rice, and cotton) from PlantVillage and its own disease dataset collected in Yavatmal, Maharashtra, India. When evaluated on the CropDisease-12 test split, CropHybrid-Net outperforms all baselines tested such as standalone Swin-T (94.5%), ViT-B/16 (93.9%) and EfficientNet-B4 (93.7%), with the highest accuracy of 97.8%, and macro f1 score of 97.3%. The average value of AUC for the 12 classes is 0.995. In addition, a comprehensive literature review has been conducted, comprising of 62 papers (2015-2024), and grouped into five research streams: conventional machine learning, CNN-based methods, transfer learning, transformer-based methods, and multi-task severity approaches. The Grad-CAM visualizations are in line with the locations of biologically meaningful lesions. The framework proposed is deployed on common precision agriculture-edge of-use devices and achieves the goal of 39.3 ms per image, being relevant to smart precision agriculture applications.

Why it matches plant phenotyping methods植物病害の画像から病害状態と重症度を推定するCNN・Transformer手法を開発し、ベンチマークデータセットで評価しているため、植物フェノタイピング手法が中心である。

abstractCropHybrid-Net is a three-branch ensemble architecture with ResNet-50, EfficientNet-B4 and Swin Transformer (Swin-T) that combines them using Efficient Channel Attention (ECA) fusion layer for simultaneous disease detection and severity estimation.

Evaluating SAR-Derived Phenological Metrics for Monsoon (Kharif) Crop Monitoring in Diversified Agricultural Systems: Insights from Central India →

Effective crop monitoring during monsoon growing seasons in Central India faces challenges from persistent cloud cover that limits optical remote sensing during critical agricultural periods. This study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems. Five major monsoon crops (cotton, rice, maize, soybean, and urad) were analyzed across five different agroclimatic zones in Central India using Sentinel-1 data for the 2021 growing season. Phenological features were extracted from VV, VH polarizations, and their ratio, including seasonal extrema, threshold crossings, duration measures, curve shape descriptors, and area under the curve. Distinct crop-specific signatures were observed, with cotton showing extended phenology and cereal–legume crops displaying compressed, overlapping growth patterns. VV polarization achieved the highest statistical discrimination for intensity-based metrics, with 75% thresholds (VV_HP75V: F = 1287) providing higher separability than other thresholds by capturing near-peak biomass differences. VH performed best for duration and integration-based metrics, while VH/VV provided limited additional separability across metric types. For area-under-the-curve metrics, AUC25 outperformed AUC50 and AUC75 by capturing cumulative backscatter across the broader growing season while remaining robust to soil- and residue-dominated backscatter variability at sowing and harvest. Multiclass classification achieved 48.3% overall accuracy with systematic cereal–legume confusion, reflecting fundamental phenological convergence among monsoon-aligned crops. Cotton achieved the highest performance (F1: 0.79), with VH polarization dominating feature importance (65% of top 20 features). Binary classification revealed crop-specific discrimination patterns: cotton was best separated using VV intensity metrics, maize using the VH/VV ratio, and rice using timing-based features. Cross-district transferability showed the highest mean overall accuracy for rice (74%) and cotton (72%), while the remaining crops showed lower accuracy due to their phenological similarity. These findings highlight both the potential and limitations of SAR phenological metrics for monsoon crop discrimination, with effective results for structurally distinct crops but persistent cereal–legume confusion, requiring further investigation with multi-sensor approaches.

Why it matches plant phenotyping methodsSAR時系列から作物のフェノロジー指標を抽出・評価し、識別性能や転移性を検証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として含める。

abstractThis study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems.

A Simplified Heat-Tolerance Evaluation System at the Pollen Development Stage in Rice ( Oryza sativa L.). →

Heat stress, particularly during the reproductive stage, poses a major challenge to rice production, as pollen development is highly sensitive to elevated temperatures. Accurate assessment of heat tolerance during this period is crucial for improving rice heat-stress tolerance but is hindered by asynchronous panicle development and imprecise staging. In this study, we identified a pair of near-isogenic lines, ZP15 and ZP17, which exhibited contrasting seed-setting rates under heat stress. We demonstrated that this divergence arises from differential tolerance during the pollen developmental stage, corresponding to a critical window (9-16 days before heading). Taking these lines as references, we established a reliable system that synchronizes developmental staging and quantitatively assesses heat-induced fertility loss. Validated using heat-tolerant N22 and heat-sensitive Wushansimiao, this system was applied to assess four conventional varieties and eight hybrids. Huanghuazhan and self-bred hybrids (Yangxianyou 912, Yangxianyou 903, and Yangxian 9A/P119-8) displayed high tolerance comparable to control varieties, whereas Yangdao 6 and multiple hybrids showed pronounced sensitivity. Collectively, this work provides a precise and reproducible framework for evaluating heat tolerance during pollen development, offering a valuable tool for accelerating the breeding of heat-resilient rice varieties.

Why it matches plant phenotyping methodsイネの花粉発育期における高温耐性と受精率低下を定量評価する、再現性のある評価システムを開発・検証しており、植物表現型取得が研究の中心である。

abstractwe established a reliable system that synchronizes developmental staging and quantitatively assesses heat-induced fertility loss.

An Integrated Tunable-Focus Light Field Imaging System for 3D Seed Phenotyping: From Co-Optimized Optical Design to Computational Reconstruction →

Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 104 points/cm2 while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging.

Why it matches plant phenotyping methods種子の3D形態形質を取得する光学・計算イメージングシステムの開発と検証が研究の中心であり、フェノタイピング手法として明確に適格。

abstractThis paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation.

An End-to-End Precision Phenotyping Framework: Rice Panicle Detection and Counting in Complex Fields via Lightweight DETR →

Accurate, high-throughput quantification of rice panicles plays a vital role in advancing precision yield prediction. However, transitioning to real-time, edge-deployable unmanned aerial vehicle phenotyping is often impeded by extreme spatial scale variations from altitude fluctuations and complex unstructured backgrounds. To address this, we constructed a comprehensive composite dataset specifically capturing multi-altitude and varying illumination field conditions. We then propose Panicle-DETR, a highly optimized precision phenotyping framework incorporating a frequency-aware CSP backbone. By projecting visual perception into the frequency domain, the architecture inherently suppresses low-frequency environmental noise and minimizes computational redundancy. Furthermore, a Lossless Feature Encoder prevents the irreversible pixel decimation of micro-targets across varying operational altitudes, while a composite metric loss explicitly disentangles heavily adhered panicle clusters. Evaluated on our composite dataset, Panicle-DETR achieved an outstanding detection Precision of 90.97% alongside robust agronomic counting stability, demonstrated by a Mean Absolute Error of 4.28 and an \( R^2 \) of 0.957. With a compact footprint of only 13.78 M parameters, this framework fundamentally overcomes the computational and spatial limitations of traditional vision models, establishing a highly reliable paradigm for autonomous, onboard agricultural monitoring.

Why it matches plant phenotyping methods米の穂を画像から検出・計数する軽量なUAV表現型解析フレームワークを開発し、複合データセット上で性能評価しており、表現型取得・抽出手法が中心である。

abstractwe constructed a comprehensive composite dataset specifically capturing multi-altitude and varying illumination field conditions.

AI Crop Disease Detection Using Mobile Camera →

Abstract-The paddy farming industry suffers a lot due to a number of diseases that are capable of producing a crop yield of 20-70 per cent. Conventional disease surveillance systems are slow, costly and need a person who is skilled and hence not accessible to small-scale farmers. The proposed AI-based system in this paper will involve the detection of the paddy disease through the remote sensing data of the Bhuvan and Bhoomi systems of the Indian Space Research Organization and the mobile camera. The given system uses a deep convolutional neural network (CNN) model that is mobile-oriented with an accuracy of 96.8 percent to recognize the major paddy diseases such as bacterial leaf blight, blast disease, brown spot, and sheath blight. The system is based on MobileNetV2 structure to perform efficient on-device inference with a mean processing time of 85ms per image. Combination with satellite Bhuvan imagery and Bhoomi land records allows monitoring of disease on a multi- scale; focusing on individual plants down to the area level. The process of field validation on 250 farmers confirmed the user satisfaction and the high rate of early disease detection increased considerably. The suggested system is a viable, economical, and accessible system of precision agriculture and food security. Index TermsPaddy disease detection, Mobile AI, Deep learn- ing, CNN, MobileNetV2, Remote sensing, Bhuvan, Bhoomi, Precision agriculture. Keywords: Paddy Disease Detection, Mobile AI, Deep Learning, MobileNetV2, Precision Agriculture.

Why it matches plant phenotyping methodsモバイルカメラ画像とCNNによりイネの病害状態を直接推定する手法を開発し、精度・処理時間・圃場検証を報告しており、植物フェノタイピング手法が中心である。

abstractThe proposed AI-based system in this paper will involve the detection of the paddy disease through the remote sensing data of the Bhuvan and Bhoomi systems of the Indian Space Research Organization and the mobile camera.

Rice Leaf Disease Classification Using Convolutional Neural Network EfficientNetB4 with Gaussian Filter →

Rice (Oryza sativa) is a vital global food crop, despite its importance, rice production is often hindered by leaf diseases, such as Leaf Scald and Bacterial Leaf Blight, which can significantly reduce yields. Conventional disease diagnosis techniques are often error-prone and inefficient. To address this, A deep learning-based approach using EfficientNetB4 architecture was proposed and combined with a Gaussian filter for enhanced image preprocessing. The Gaussian filter reduces noise and enhancing image, while EfficientNetB4 leverages its optimized depth, width, and resolution scaling for accurate classification. The dataset consists of 2,627 rice leaf images categorized into six classes and divided into training, validation, and testing. Preprocessing includes resizing images to$224 \times 224$pixels, data augmentation, and Gaussian filtering with$5 \times 5$kernel and standard deviation value is 1. The model is evaluated using$\text{F 1}$-score, precision, recall, and accuracy. Results demonstrate that EfficientNetB4 with Gaussian filtering achieves 97.62 % accuracy, outperforming the unfiltered model 96.19 %. This highlights the efficacy of Gaussian filtering in improving feature extraction and classification performance for rice leaf diseases.

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が中心であり、画像前処理と分類性能を評価しているため、植物病害フェノタイピング手法として含める。

abstractResults demonstrate that EfficientNetB4 with Gaussian filtering achieves 97.62 % accuracy, outperforming the unfiltered model 96.19 %.

Research on Intelligent Recognition and Location Method of Crop Diseases Based on Multi-spectral Images of Unmanned Aerial →

This paper studies the intelligent identification and location method of crop diseases based on multispectral images of unmanned aerial vehicles. With the development of precision agriculture, traditional crop disease monitoring methods have become difficult to meet the demands of large-scale, high-efficiency and early warning. The article first constructs a multispectral image dataset including visible light, near-infrared and red-edge bands, covering common types of crop diseases. Subsequently, an improved deep learning network architecture was proposed. The attention mechanism was adopted to enhance the model's ability to extract disease features, and a multi-scale feature fusion strategy was introduced to handle disease spots of different sizes. The research designed a data augmentation method based on spectral-spatial joint optimization, which effectively solved the problem of unbalanced samples of crop diseases. To improve positioning accuracy, this paper proposes a disease area positioning algorithm combined with geographic information system, achieving centimeter-level positioning accuracy. The experimental results show that the proposed method improves the accuracy of disease identification by 15.3% compared with the traditional methods, reduces the positioning error to an average of 3.2 centimeters, and can maintain high stability in complex field environments. In addition, this paper has established a complete technical system covering data collection, disease identification and information visualization, and has conducted application verification on crops such as wheat and rice. It has been confirmed that this method can effectively support precise pesticide application decisions in agricultural production and has significant economic and ecological benefits

Why it matches plant phenotyping methodsマルチスペクトル画像から作物病害の特徴・病斑領域を抽出する認識および位置推定手法の開発、検証、実地適用が研究の中心であり、植物の病害状態を直接評価している。

abstractThis paper studies the intelligent identification and location method of crop diseases based on multispectral images of unmanned aerial vehicles.

Using Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early →

Abstract In sustainable agriculture, detecting pests and diseases early is critical. Recent technological advances in deep learning (DL) and multimodal imaging like multispectral and thermal data crop health monitoring is promising. Despite the progress, obtaining high accuracy across various crops with real-time performance is still a challenge. The hybrid convolutional neural network (CNN)-attention model integrating multispectral and thermal data for pest and disease detection has been introduced. A total of 1760 samples were collected from six crops (maize, rice, wheat, tomato and cassava), across different growth stages, labelled fungal, bacterial, viral and pest infections. The data was divided into 70% training, 15% validation, and 15% test sets. 3,500 samples were used for training. 750 samples were used for validation and test set. The hybrid CNN-attention model was contrasted with certain baseline models (SVM, Random Forest, CNN-RGB, CNN-Multispectral) and certain fusion methods (early, late, and hybrid fusion) based on accuracy, precision, recall, F1-score, and early detection sensitivity. The highest accuracy of 91.0% for rice at the vegetative stage was achieved by the hybrid model. It beats baseline and fusion models. The F1-score of the classification was reasonably high. Rice's sensitivity is 88.1%, and maize is 87.3%. The model fared well for all classes, getting 92.0 % for the healthy plant and 88.2 % for pest infestation. Future work can enhance the dataset with more crops and diseases and environmental factors and optimize detection time and early sensitivity for real-time deployment in agricultural decision support systems.

Why it matches plant phenotyping methodsマルチスペクトル・熱画像から植物の病害および害虫状態を推定するCNNモデルを開発し、複数モデルとの比較検証を行っており、表現型取得・判定手法が中心である。

titleUsing Multispectral Imaging and Artificial Intelligence to Detect Crop Diseases and Pests Early

Structural volume composition of internodes determines culm non-structural carbohydrates accumulation in rice →

Abstract Non-structural carbohydrates (NSC) stored in the stem play a crucial role in supporting yield formation in rice. However, internode morphological determinants of NSC accumulation are unclear. This study aimed to clarify the relationship between internode morphology and NSC accumulation and to identify a robust morphological indicator for evaluating NSC accumulation capacity. Two years of field experiments were conducted using multiple cultivars. The NSC content was quantified for individual internodes and at the whole-plant culm level, and its relationships with internode morphological traits were analyzed. Since the upper internodes (UIN; first and second internodes) and lower internodes (LIN; third and subsequent internodes) exhibited contrasting roles in NSC accumulation, a novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm. The VCR of UIN/LIN showed the strongest correlation with culm NSC and high reproducibility across years, outperforming simple morphological traits. Manipulation of internode development using plant growth regulators demonstrated that altering VCR effectively modified culm NSC accumulation. Accordingly, the VCR of UIN/LIN serves as a robust morphological indicator of culm NSC accumulation capacity, providing a practical framework for improving NSC accumulation to achieve high and stable yield performance in rice.

Why it matches plant phenotyping methodsイネ茎の形態からNSC蓄積能を評価する新規指標VCRを導入し、再現性と既存形態形質との比較まで行っており、表現型評価法が研究の中心である。

abstracta novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm.

Predicting Rice Quality in Indica Rice Using Multidimensional Data and Machine Learning Strategies →

Integrating agricultural remote sensing and phenomics for full-growth-period rice quality prediction is vital for early non-destructive screening and breeding; however, studies integrating genomic and multi-source phenotypic data across multiple environments remain limited. This study addressed this gap by integrating genomic SNP data, UAV-based spectral data, and individual multidimensional phenotypic data of 61 indica rice varieties (field and greenhouse environments). As a proof-of-concept study, feature selection methods (LASSO, MI, RFE, SPA) were used to mitigate overfitting and the “p >> n” problem, with further validation needed in larger populations. The results showed that amylose content is genetically dominated, protein content is genetically determined and influenced by gene-environment interactions, and chalkiness traits are determined by three combined factors. For amylose content, SNP data under the Random Forest model at the population level (phenomics data from field UAV remote sensing of variety populations) achieved optimal performance (R2 = 0.92; MAE = 1.1; RMSE = 1.5), while the Stacking Ensemble method enhanced accuracy at the individual level (phenomics data from greenhouse single-plant phenotyping per variety). Chalky grain rate and chalkiness degree showed SNP-comparable prediction accuracy, with Stacking significantly improving performance at the population level (R2 = 0.89 and 0.85, respectively). Protein content prediction remained relatively low (optimal R2 = 0.56) due to strong environmental sensitivity and complex interactions. This framework extends traditional single-environment/single-data-source approaches, providing an effective strategy for early, high-throughput, non-destructive rice quality screening. Further validation with larger datasets, more growing seasons, or independent populations is required for reliable application in breeding-related practices.

Why it matches plant phenotyping methodsUAVスペクトル計測と個体フェノタイピングを統合し、機械学習でイネの品質形質を非破壊・高 throughput に予測する枠組みが研究の中心である。

abstractIntegrating agricultural remote sensing and phenomics for full-growth-period rice quality prediction is vital for early non-destructive screening and breeding

Machine learning-based prediction of dynamic heterosis for plant height with pathway biomarkers in rice. →

Key message The metabolomic landscape of dynamic heterosis for plant height was displayed in rice. Heterosis-associated pathways across most developmental stages were developed into robust pathway biomarkers. The development of robust biomarkers enables accurate prediction of complex phenotypes, which contribute to the advancement of precision breeding. However, the dynamic nature of biomarkers is often underestimated, as their quantitative changes during development are directly connected to phenotypic transformations, influencing crop agronomic traits. Here, we performed comparative metabolomic analyses to investigate dynamic heterosis for plant height in rice, which is an agronomic trait varying throughout development and is a key determinant of yield heterosis. We found that the levels of pyruvaldehyde were predictive of height heterosis specifically at the seedling stage, while 4-hydroxycinnamic acid positively correlated with height heterosis across four developmental stages. We identified metabolic pathways associated with height heterosis and found that metabolomic changes during the elongation stage had a greater impact than those in other stages. Finally, 11 heterosis-associated pathways were developed into pathway biomarkers using random forest analysis, enabling the prediction of height heterosis in an independent population under different growth conditions. We elucidate the metabolomic landscape of dynamic height heterosis in rice through the identification of heterosis-associated analytes and pathways across stages. Our findings provide a strategy to develop robust biomarkers for heterosis of important agronomic traits by integrating metabolic pathways involved in heterosis across most developmental stages, contributing to the establishment of precision pairing in hybrid crop breeding.

Why it matches plant phenotyping methods植物高の異型接合優勢という明示的な植物形質を、代謝経路バイオマーカーとランダムフォレストで予測する手法を開発し、独立集団・異なる栽培条件で検証しており、単なる代謝測定ではなく形質予測法が中心である。

abstractFinally, 11 heterosis-associated pathways were developed into pathway biomarkers using random forest analysis, enabling the prediction of height heterosis in an independent population under different growth conditions.

HTPRootSlides: A high-throughput phenotyping platform for crop root germination dynamic screening →

Root phenotyping is crucial for advancing our understanding of plant development and adaptation. However, existing platforms often face challenges in balancing high-throughput capacity with long-term, high-frequency monitoring. To overcome this limitation, we present HTPRootSlides, an integrated root phenotyping platform designed for dynamic and scalable trait analysis. Its design features a circulating zone that accommodates 141 specialized root boxes for high-throughput operation synchronously. Root boxes follow a continuous S-shaped trajectory step by step, facilitating repetitive imaging for high-throughput, time-series data acquisition. To address challenges such as water vapor condensation and fine root entanglement, we developed a dedicated segmentation algorithm, achieving 89.56 % accuracy in root isolation. Combining morphological and skeleton-based feature extraction techniques, the platform ensures comprehensive and efficient phenotypic trait quantification. We validated HTPRootSlides by dynamically monitoring root development in four staple crops (soybean, maize, wheat, and rice) during early-stage germination (<14 d). The results demonstrate the capability of HTPRootSlides for high-frequency, high-precision and large-scale root phenotyping (< 1h with 141 root boxes per run), offering researchers a powerful tool to investigate root dynamics and optimize crop performance through trait selection.

Why it matches plant phenotyping methods根の動態を高スループットで撮像・分割・特徴抽出し、形態・骨格形質を定量するプラットフォームの開発と検証が中心である。

abstractwe present HTPRootSlides, an integrated root phenotyping platform designed for dynamic and scalable trait analysis

Improving rice leaf area index monitoring accuracy via robot-integrated multi-sensors and meteorological data fusion with explainable machine learning →

Accurate and non-destructive estimation of rice Leaf Area Index (LAI) is vital for crop growth assessment and yield prediction. Close-range, non-contact optical methods are commonly used for LAI monitoring. However, their accuracy is often limited by the platform, and most rely on single-source data prone to saturation effects and background interference. To overcome these limitations, this study develops a phenotyping robot-based multi-source, high-resolution data fusion approach for field-scale LAI monitoring. A phenotyping robot equipped with multispectral and high-resolution RGB cameras was used to collect vegetation indices, color indices, texture features, and canopy coverage extracted from high-resolution RGB imagery. These features were further combined with meteorological variables to build machine learning models. The Random Forest model achieved the best performance (R² = 0.92, RMSE = 0.302). SHAP (Shapley Additive Explanations) was applied to interpret the model and quantify the importance of multispectral features, RGB-derived texture information, canopy coverage and meteorological factors. Canopy coverage, NDVI and Clgreen were identified as the key factors for improving model performance, and the complementary mechanism between canopy coverage and other features can alleviate the saturation effect in the high LAI stage. The results show that combining high-resolution remote sensing data from robots with meteorological data can effectively mitigate the saturation effect and soil background interference in LAI estimation, and significantly improve the accuracy of LAI estimation. This study provides a practical and scalable framework for field phenotyping and offers technical support for precise rice cultivation and smart agriculture.

Why it matches plant phenotyping methodsロボット搭載マルチセンサーと画像特徴量融合によるイネLAI推定手法を開発・評価しており、表現型取得・推定が研究の中心である。

abstractthis study develops a phenotyping robot-based multi-source, high-resolution data fusion approach for field-scale LAI monitoring.

Exploring the potential role of multi-source remote sensing data during different growth stages in crop yield prediction. →

Accurate prediction of grain yield is essential for enhancing food security, particularly in the context of climate change. Although remote sensing indices have been extensively utilized to monitor vegetation growth and estimate crop yields, there has been limited research comparing their effectiveness for predicting grain yield, especially across different growth stages. This study examined the performance of multi-source indices, such as normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIR V ), and solar-induced chlorophyll fluorescence (SIF), in predicting grain yield at various growth stages at Shangshan Rice Research Station in Zhejiang Province, China. The results indicated that SIF exhibited the strongest and most consistent correlation with grain yield ( R 2 = 0.34 to 0.75), followed by NIR V ( R 2 = 0.34 to 0.71). SIF also demonstrated advantages in capturing the dynamic changes of GPP during the reproductive period. During both the vegetative and reproductive stages, leaf area index (LAI) showed significant correlations with NDVI, NIR V , and SIF, whereas leaf chlorophyll concentration exhibited comparatively weaker associations with these indicators. These findings provide valuable insights for improving crop yield forecasts using remote sensing, thereby contributing to enhanced agricultural management and food security strategies under climate change.

Why it matches plant phenotyping methods作物収量を推定するマルチソースリモートセンシング指標の成長段階別性能比較・検証が研究の中心であり、植物の収量やLAIなどの形質推定手法を評価している。

abstractThis study examined the performance of multi-source indices, such as normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIR V ), and solar-induced chlorophyll fluorescence (SIF), in predicting grain yield at various growth stages

Rice seedling age detection under field conditions using M-Lresnet50 with image and environmental data. →

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.

Automated Identification of Crop Diseases using Computer Vision →

Early and accurate identification of crop diseases is essential for ensuring agricultural productivity, food security, and sustainable farming practices. This study presents an automated computer vision based framework for multi-crop disease classification using a lightweight deep learning architecture. The proposed system employs the ReXNet-1.5 convolutional neural network as the core feature extractor, integrating efficient hierarchical feature learning with low computational complexity. A publicly available multi-crop dataset comprising 13,324 images across 17 disease and healthy classes covering corn, rice, potato, wheat, and sugarcane is used for model training and evaluation. Experimental results demonstrate strong performance, achieving 97.45% accuracy and a macro-F1 score of 96.26%, indicating reliable class-balanced prediction under dataset imbalance. Grad-CAM based visual explainability is incorporated to provide interpretable disease localization, enhancing transparency and trust in model predictions. Additionally, the model exhibits high computational efficiency, enabling real-time inference suitable for deployment on resource constrained platforms. The proposed framework offers an accurate, interpretable, and deployable solution for real world crop disease diagnosis, supporting intelligent decision-making and scalable agricultural monitoring systems.

Why it matches plant phenotyping methods植物画像から病害状態を分類・局在化するコンピュータビジョン手法が研究の中心であり、単なる病害測定ではなく、モデル開発と性能評価を実施している。

abstractThis study presents an automated computer vision based framework for multi-crop disease classification using a lightweight deep learning architecture.

High-performance prediction of protein content in brown rice via multi-spectral fusion and deep learning: a comparative study of visible, near infrared, mid infrared spectroscopy and hyperspectral imaging. →

This original work explored the potential of near-infrared (NIR), mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI) in visible-near-infrared (Vis-NIR-HSI) and short-wave infrared (SWIR-HSI) range for non-destructive prediction and visualization of protein content in brown rice from 138 rice varieties. Feature selection and predictive modeling were integrated to identify protein-related wavelengths and enable pixel-level protein mapping. Using full-spectrum data, the MIR-based convolutional neural network (CNN) model achieved the highest predictive accuracy (R p 2 = 0.96, RPD = 4.84), confirming the superior chemical sensitivity of MIR spectroscopy. Following feature band selection, the NIR-based uninformative variable elimination-support vector machine (UVE-SVM) model exhibited optimal performance among wavelength-reduced models (R p 2 = 0.90, RPD = 3.16), surpassing Vis-NIR-HSI and SWIR-HSI-based approaches. For multi-spectral feature fusion integrating selected wavelengths from all four spectral modes, the competitive adaptive reweighted sampling-CNN (CARS-CNN) model yielded the best overall performance, demonstrating synergistic advantages of combining complementary spectral information. Furthermore, protein distribution maps from SWIR hyperspectral images displayed superior spatial resolution compared to Vis-NIR-HSI images. Overall, this study establishes a high-performance and transferable protein prediction framework based on multi-spectral fusion and deep learning, and provides a systematic comparison of visible, near-infrared, mid-infrared spectroscopy, and hyperspectral imaging for brown rice quality assessment.

Why it matches plant phenotyping methods褐玄米のタンパク質含量という植物器官形質を対象に、分光・ハイパースペクトル画像、特徴選択、深層学習による予測を開発・比較・検証しており、表現型取得法が研究の中心である。

abstractThis original work explored the potential of near-infrared (NIR), mid-infrared (MIR) spectroscopy, and hyperspectral imaging (HSI) in visible-near-infrared (Vis-NIR-HSI) and short-wave infrared (SWIR-HSI) range for non-destructive prediction and visualization of protein content in brown rice from 138 rice varieties.

A Web-Based Rice Seedling Detection System Using UAV Imagery and YOLO Algorithm for Optimizing Crop Production →

Rice is a strategic commodity in supporting national food security. However, its productivity remains hindered by manual growth monitoring processes, climate change challenges, and limited human resources. This final project develops a seedling detection and counting system using the YOLO (You Only Look Once) algorithm, with aerial imagery input acquired from UAV (Unmanned Aerial Vehicle), presented through an interactive web-based dashboard. The dataset is enhanced with MIRV (mirror vertical) and MIRH (mirror horizontal) augmentation techniques to improve training data diversity. All experiments were conducted on three models: YOLO11n, YOLOv10n, and YO- LOv8n. Evaluation shows that the YOLO11n configuration using AdamW and a learning rate of 0.01 achieves mAP@50 of 0.592 and precision of 0.852. The system supports data-driven agronomic decision-making to anticipate crop failure risks, thus assisting large-scale rice field owners in monitoring seedling effectively and efficiently.

Why it matches plant phenotyping methodsUAV画像とYOLOによるイネ苗の検出・計数手法およびWebシステムの開発が中心で、苗数という植物状態を定量化しているため。

abstractThis final project develops a seedling detection and counting system using the YOLO (You Only Look Once) algorithm, with aerial imagery input acquired from UAV (Unmanned Aerial Vehicle), presented through an interactive web-based dashboard.

Comparative Study of Simple CNN and U-Net Architectures for NDVI-Based Rice Crop Health Assessment Using Multispectral Imagery →

This research offered a comparison of simple Convolutional Neural Network (CNN) and U-Net architectures for plant health evaluation of rice plants under pest infestation based on Normalized Difference Vegetation Index (NDVI) imagery. The paper examined the model's accuracy, efficiency, and robustness across different environmental conditions and plant growth stages. The research methodology was based on a quality audit of labeled data across three classes (damaged, slightly damaged, and healthy), mitigating data misalignment through image and mask augmentation, and standardizing model training and testing. The model was evaluated using classification and regression metrics and confusion matrices. The quality audit showed a predominance of damaged (72.8%) over other classes (slightly damaged 26.8%, healthy 0.4%), with moderate noise levels and a manageable boundary consistency. These were addressed through appropriate augmentation techniques. The model's accuracy was 0.916, Intersection over Union (IoU) 0.578, and F1-Score 0.915, with low regression errors of 0.2892 and 0.0837 for root-mean-square error and mean absolute error, respectively. The performance of the U-Net without skip connections was nearly identical, indicating that the skip connections did not significantly affect performance under homogeneous vegetation patterns. However, the simple CNN model performed worse than the other models, with an accuracy of 0.878, an IoU of 0.528, an F1 Score of 0.875, a root-mean-square error of 0.3489, and a mean absolute error of 0.1218. These results revealed the effectiveness of the U-Net model for rice health segmentation under pest-infested conditions, using NDVI images.

Why it matches plant phenotyping methodsNDVI画像からイネの病害・健全状態を推定するCNN/U-Net手法を比較・評価しており、植物状態の取得・抽出方法が研究の中心である。

abstractThis research offered a comparison of simple Convolutional Neural Network (CNN) and U-Net architectures for plant health evaluation of rice plants under pest infestation based on Normalized Difference Vegetation Index (NDVI) imagery.

Hi MagicRing, tell me where I am: Toward affordable, physically reliable 3D plant phenotyping with MobilePheno3D →

3D plant phenotyping has garnered significant interest for its ability to quantify key structural traits such as plant volume and canopy architecture. However, standard monocular 3D reconstruction techniques suffer from inherent scale ambiguity, requiring an additional step to recover the true metric scale of the plants. Existing scale recovery methods, whether based on precisely fabricated 3D objects or planar patterns such as checkerboards, have been successfully applied in controlled environments but face practical constraints in certain real-world scenarios: some require costly fabrication or pre-reconstruction calibration, which can limit throughput in dynamic field environments. Here, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach that addresses these specific constraints and provides a complementary solution for high-throughput, mobile, and field-based phenotyping. MagicRing features a simple red ring printed on A4 paper with a known diameter. By leveraging color-based segmentation and geometric curve fitting, our approach automatically detects the ring within 3D point clouds, recovers the metric scale, and establishes a standardized world coordinate system without the need for pre-calibration. Its planar, isotropic design ensures robustness even under significant occlusion. We demonstrate the utility of MagicRing through MobilePheno3D, an integrated smartphone-based pipeline that performs fully automated 3D reconstruction, scale recovery, and phenotypic extraction from video sequences. This system, which was validated across multiple plant species, including vegetables, wheat, rice, and maize in both indoor and field settings, reliably reconstructs aboveground and root structures and supports continuous growth monitoring. MagicRing decouples data collection from data analysis, enabling a workflow transition from conventional step-by-step, scene-specific calibration toward more scalable, high-throughput 3D plant phenotyping.

Why it matches plant phenotyping methods植物の3D形態形質を抽出するためのスケール復元法とスマートフォン型フェノタイピング・パイプラインを開発し、複数植物種・環境で検証しており、手法が研究の中心である。

abstractHere, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach

Integrating image-based phenotyping and QTL mapping to enhance genetic resistance and accelerate breeding for bacterial grain rot resistance in rice. →

Bacterial grain rot (BGR), caused by Burkholderia glumae, is a major disease that reduces the yield of rice (Oryza sativa L.), thereby threatening food security. Conventional phenotypic analysis methods face limitations in objectively evaluating disease resistance and understanding the genetic basis. In this study, we integrated image-based phenotypic analysis with QTL mapping to screen for QTLs and candidate genes associated with B. glumae resistance. B. glumae was inoculated into 189 recombinant inbred lines (RILs) derived from Kele (resistant) and IS592BB (susceptible), followed by visualization and quantitative analysis using DAB staining. Phenotypic parameters, including the field resistance score, ratio of diseased spikelets (%), DAB staining intensity, and ratio of diseased area (%), were measured and used for QTL mapping. On chromosome 1, within Chr01_24592710-Chr01_37274755, four QTLs-qFRS1 [LOD: 5.98, phenotype variation explained (PVE): 15.41%], qRDS1 (LOD: 5.29, PVE: 18.56%), qQDS1 (LOD: 9.58, PVE: 22.02%), and qRDA1 (LOD: 8.44, PVE: 31.51%)-were identified as overlapping. After fine-mapping we narrow down Chr01_33472174-Chr01_33838140 and a total of 16 candidate genes were screened this region. Among which OsBGq1 was found to encode a nucleotide-binding LRR receptor (NLR) domain. OsBGq1 expression increased significantly upon B. glumae infection. Additionally, RILs Kele type of Chr01_33472174-Chr01_33838140 presented increased ROS-scavenging enzyme activity and phytoalexin accumulation upon B. glumae infection, contributing to increased resistance. The integration of DAB-based quantitative phenotyping with QTL mapping is proposed to provide a more objective indicator for identifying genes associated with resistance to BGR.

Why it matches plant phenotyping methodsイネ病害抵抗性の遺伝解析が主目的だが、DAB染色を用いた画像ベースの病徴定量化を主要な手法として統合し、客観的な抵抗性指標として提案しているため、表現型取得法の実質的応用に該当する。

abstractwe integrated image-based phenotypic analysis with QTL mapping to screen for QTLs and candidate genes associated with B. glumae resistance.

A Lightweight Residual Dilated CNN–Transformer Framework for Efficient Rice Leaf Disease Classification →

Abstract Although rice is a staple food for more than half of the world's population, several illnesses represent a severe threat to rice farming, lowering yields by as much as 70%. Human visual examination, the basis of traditional disease diagnosis, has several limitations, including subjectivity, inconsistent results, and the inability to cover large agricultural areas. So far, using deep learning to automate plant disease diagnosis has been effective, but there are still numerous problems to address. Some of these include that they are difficult to understand, require a lot of computer power, and are not well-suited for field use. This study proposes an innovative hybrid deep learning framework called RiceLeafCNN-Transformer, which integrates transformer architectures with convolutional neural networks to address existing limitations. A proposed method for representing features at multiple scales enhances computing speed by combining dilated convolutions, depth wise separable convolutions, and squeeze-excitation blocks. We can accomplish global contextual modelling and hierarchical feature extraction by combining a transformer encoder with a lightweight convolutional neural network (CNN) backbone. Proposed model shows state-of-the-art performance in its rigorous experimental validation using three datasets of rice leaf disease. Dataset A has 18,445 photos, and Dataset B has 11,790 photos. It does this by keeping inference times between 8.3 and 9.9 ms and getting 99.2% accuracy. The proposed strategy can help farmers and agricultural specialists save time and effort by linking theoretical lab performance to real-world use. This way, they get information that is both useful and relevant.

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開発と複数データセットでの技術検証が中心であり、植物病害フェノタイプの取得・推定に該当する。

abstractThis study proposes an innovative hybrid deep learning framework called RiceLeafCNN-Transformer, which integrates transformer architectures with convolutional neural networks to address existing limitations.

A Comprehensive Survey of Machine Learning and Deep Learning Methods for Rice Crop Disease Detection →

Rice is one of the simple food crops that has been cultivated in the majority of countries. Rice leaf diseases (RLDs) are a major problem in crop production since they may result in low productivity and economic losses. Traditional ways of detecting an illness may be time-consuming and even labor-intensive, and at times may need specialized skills. The popularity of preceding works on detecting RLDs has relied on machine learning (ML) and image processing approaches. On the other hand, deep learning (DL) methodologies are more applicable in disease detection problems because they can learn stipulated patterns on big data without using feature extraction methods. This systematic review explores various ML and DL methods used in the literature for RLD detection, which includes survey articles based on convolutional neural network (CNN), transfer learning, and advanced AI approaches. The review of existing open-source datasets is also discussed in this survey. In addition, it examines limitations of current models related to practical implementation, data diversity, domain adaptation, and hardware limitations. Lastly, this survey identifies future research directions to improve the strength and usage of DL models in real-world agriculture settings. This survey comprehensively reviews more than 70 peer-reviewed publications (2019–2025) sourced from IEEE, Elsevier, Springer, ACM, and MDPI digital libraries.

Why it matches plant phenotyping methodsイネ葉の病害を画像から検出する機械学習・深層学習手法を体系的にレビューしており、植物の病害状態を推定するフェノタイピング手法が中心である。

abstractThis systematic review explores various ML and DL methods used in the literature for RLD detection

LMRNet: a lightweight convolutional neural network for real-time mountain rice leaf disease recognition on edge devices. →

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

Starch fine structure predicts glycemic index variation in whole-grain rice. →

Here, we profiled a diverse panel of whole-grain rice accessions (n = 384 covering wide range of pigmentation) for in vitro glycemic index (GI), resistant starch (RS), digestible carbohydrate (DC), and debranched starch chain-length distributions (CLD) resolved into fine degree-of-polymerization (DP) intervals. Across the panel, low-GI phenotypes were rare, and GI distributions overlapped substantially across pigmented and non-pigmented groups, indicating starch architecture as the dominant determinant of digestibility. Regression and classification models using DP-resolved predictors achieved robust GI prediction (R 2 = 0.70 for whole grain), and model simplification identified a reduced set of informative DP windows. Notably, DP33-36 emerged as a negative predictor of GI, showing an opposing effect relative to adjacent mid-chain intervals. To provide structural context for interval-specific effects, representative lines with contrasting DP architectures were examined by X-ray diffraction (XRD) and solid-state 13 C NMR. Biophysical analyses supported that glycemic variation is not explained by crystalline polymorph type alone, but by localized microstructural organization within an A-type framework. For polished rice, incorporating RS content further improved the model's explanatory power (R 2 = 0.78). These results establish a DP-resolved structure-function framework for GI variation in rice to accelerate screening and selection of low-GI donors for breeding.

Why it matches plant phenotyping methodsイネ系統のGIという植物由来形質を、DP分解データに基づく回帰・分類モデルで予測し、スクリーニングと育種選抜に用いる構造機能フレームワークを提示しており、形質推定ワークフローが中心的です。

abstractRegression and classification models using DP-resolved predictors achieved robust GI prediction (R 2 = 0.70 for whole grain)

SPECGAN: Extracting sensitive bands from plant disease spectra based on generative adversarial network. →

Hyperspectral imaging provides detailed spectral information for non-destructive plant disease diagnosis, yet its use is limited by high dimensionality of the original spectra, as well as insufficient and imbalanced data records. These issues hinder the extraction of weak pathological signals and ultimately reduce model applicability. To overcome these challenges, this study proposes SPECGAN, a Generative Adversarial Network with a Temporal-Domain Feature Pyramid Fusion (TD-FPNF) and a residual attention mechanism. SPECGAN forms a general framework for both sensitive band extraction and data augmentation. A multi-scale convolutional module captures local narrow-band features related to biochemical changes, as well as global broadband trends linked to physiological structure. The residual attention mechanism further enhances subtle disease cues by adaptively reweighting multi-level fused features and suppressing background noise. SPECGAN accurately identifies key discriminatory bands based on gradient saliency analysis of the discriminator, while generating high-quality synthetic samples to alleviate data scarcity. Experiment results demonstrate that the sensitive bands concentrate in the green peak (520-550 nm) and red-edge (680-720 nm) regions, consistent with disease-induced physiological changes. By only inputting the top 20 bands (8% of the spectrum), the MLP classifier achieves 96.22% accuracy. Under a 14.6:1 imbalance scenario, generating 1500 synthetic samples boosts performance by 6%-13%. Overall, SPECGAN provides an efficient and interpretable approach for early diagnosis of rice bacterial leaf blight.

Why it matches plant phenotyping methods植物病害の症状・生理状態を対象に、ハイパースペクトル画像から感受性バンドを抽出し、データ拡張も行うSPECGAN手法を開発しており、病害表現型の取得・解析が中心である。

abstractthis study proposes SPECGAN, a Generative Adversarial Network with a Temporal-Domain Feature Pyramid Fusion (TD-FPNF) and a residual attention mechanism.

Development of a Multi-Scale Spectrum Phenotyping Framework for High-Throughput Screening of Salt-Tolerant Rice Varieties →

Soil salinization severely threatens agricultural sustainability in saline–alkali regions, and high-throughput, efficient screening of salt-tolerant rice varieties is critical to mitigating this threat. Traditional evaluation methods are constrained by low throughput, limited spatiotemporal resolution, and the lack of standardized indicators. To address these gaps, this study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice. Field experiments were conducted with 12 rice lines at five key growth stages in Ningxia, China, with synchronous ground spectral measurements and UAV image acquisition on the same day for each stage. Five feature selection methods were employed to screen salt stress-sensitive hyperspectral bands, with classification accuracy validated via a Support Vector Machine (SVM) model. The results showed that: (1) rice spectral characteristics varied dynamically across growth stages, and first-order differential transformation effectively amplified subtle spectral variations in stress-sensitive regions; (2) the Minimum Redundancy–Maximum Relevance (mRMR) method outperformed other methods, achieving 100% classification accuracy at key growth stages, with sensitive bands dominated by red edge bands (58.33%); (3) the constructed Salt Stress Index (SIR) showed strong correlations with classical vegetation indices and rice yield, and could clearly distinguish salt-tolerant and salt-sensitive rice varieties, with stable performance against field environmental noise; and (4) band matching between UAV and Sentinel-2 data enabled multi-scale data fusion and regional-scale salt stress monitoring. This framework realizes the transformation from qualitative spectral description to quantitative salt tolerance evaluation, providing standardized technical support for salt-tolerant rice breeding and precision management of saline–alkali lands.

Why it matches plant phenotyping methods植物の塩ストレス耐性をスペクトルデータから定量評価するマルチスケール表現型解析フレームワークの開発であり、特徴選択、分類検証、指標構築、センサー間融合が中心的な方法論的貢献である。

abstractthis study established a multi-scale spectral phenotyping framework integrating ground-based hyperspectral, UAV-borne multispectral, and Sentinel-2 satellite remote sensing data for high-throughput screening of salt-tolerant rice.

Integrating UAV, environmental, and management data to improve rice nitrogen nutrition index prediction using an ensemble learning algorithm. →

BACKGROUND: Accurate and timely estimation of nitrogen nutrition index (NNI) is critical for assessing crop nitrogen (N) status and implementing precision N management. While machine learning (ML) techniques combined with unmanned aerial vehicle (UAV) remote sensing have been increasingly utilized, their performance across different agricultural conditions is often influenced by weather, soil properties, and field practices. Effectively integrating these variables within an ensemble ML model is therefore essential for reliable cross-stage N diagnosis. In this study, a stacking ensemble learning framework was developed to enhance the estimation accuracy of rice NNI across multiple growth stages by integrating multi-source data, including UAV-derived vegetation indices (VIs), meteorological data, soil properties, and fertilization rates. These data were acquired from two field experiments involving different N treatments over two growing seasons and covering four key growth stages. Ten ML models were employed as base learners and their performance was systematically evaluated. RESULTS: Results showed that models relying solely on VIs exhibited limited accuracy and stability, whereas the inclusion of meteorological, soil, and fertilization data substantially improved NNI prediction. The performance of individual base ML models varied considerably across growth stages and input data combinations. The stacking ensemble model effectively integrated multi-source information and leveraged the strengths of base learners, consistently achieved superior prediction accuracy and robustness. It improved R² by 0.52–3.24% compared to the best base models across different growth stages, thereby strengthening the reliability of cross-stage NNI estimation. SHAP (SHapley Additive exPlanations) analysis further revealed the dynamic contributions of input features throughout the growing season, with VIs, soil properties, and fertilization rates played a dominant role in early to mid-stages, while climatic factors became more influential later. CONCLUSION: This study confirms the significant potential of integrating multi-source data with ensemble learning for reliable NNI monitoring, providing a practical tool for supporting in-season N status diagnosis and precision fertilization management in rice production systems.

Why it matches plant phenotyping methodsUAV由来の植生指数と環境・管理データを統合し、イネの窒素栄養指数（NNI）を推定するアンサンブル手法を開発・評価しており、植物状態の取得・推定方法が研究の中心である。

abstracta stacking ensemble learning framework was developed to enhance the estimation accuracy of rice NNI across multiple growth stages by integrating multi-source data, including UAV-derived vegetation indices (VIs), meteorological data, soil properties, and fertilization rates.

Comparative Evaluation of Ground-based, Manual, and Remote Sensing Approaches for Crop Stress Detection: A Review between Malaysia and China →

This study presents a comparative evaluation of manual inspection, ground-based sensors, and UAV-based remote sensing for detecting crop stress in paddy, maize, and coconut fields across Malaysia and China. Although sensing technologies have advanced considerably, cross-country comparisons between regions with differing levels of technological maturity remain limited. China, recognised for its advanced adoption of UAV and sensor-based agriculture, provides a benchmark against Malaysia’s developing digital agriculture landscape. Each method was assessed based on accuracy, responsiveness, scalability, and cost-effectiveness under field conditions. UAV-based remote sensing achieved the highest overall accuracy (mean 92%) and demonstrated superior scalability, enabling rapid large-area monitoring using vegetation indices such as NDVI and NDRE. Ground-based sensors, including soil moisture probes and chlorophyll meters, showed moderate accuracy (mean 81%) and were suitable for plot-level monitoring with real-time feedback. Manual inspection recorded the lowest accuracy (mean 68%) and limited scalability due to labour dependency and subjective assessment. UAV methods were particularly effective in early stress detection, with thermal imaging identifying canopy temperature anomalies 3–5 days before visible symptoms, especially in maize and coconut fields. Integrating UAV and ground-based sensing provided more comprehensive and timely assessments than individual approaches. These findings support the development of scalable precision agriculture frameworks tailored to tropical and subtropical systems.

Why it matches plant phenotyping methods作物ストレスという植物状態を対象に、手動観察・地上センサー・UAVリモートセンシングを精度、応答性、拡張性、費用で比較評価しており、センシング手法の技術評価が中心である。

abstractThis study presents a comparative evaluation of manual inspection, ground-based sensors, and UAV-based remote sensing for detecting crop stress in paddy, maize, and coconut fields across Malaysia and China.

A three-tier deep learning framework with mobile application integration for multi-crop disease diagnosis →

Abstract Crop diseases remain a critical threat to global food security, contributing to substantial yield losses and reduced farmer incomes. Timely and accurate identification of these diseases is essential to mitigate their impact. Traditional diagnostic methods, dependent on expert visual inspection, are labour-intensive, time-consuming, and prone to judgment errors. Accurate and timely detection of crop diseases supports sustainable agricultural management and contributes to achieving global objectives under the United Nations Sustainable Development Goal 2 on Zero Hunger. This study proposes a three step framework that relies on pattern recognition and classification of visual disease symptoms to deliver reliable, field-applicable diagnostics. The approach combines image acquisition through smartphone camera with a structured processing pipeline that includes feature extraction, classification, and result delivery via a mobile application built on a three-tier architecture. Convolutional Neural Networks and an optimized VGG-16 model form the core classification engine, trained to recognize 19 leaf based diseases across wheat, rice, fodder, maize, and sugarcane. The models were trained and evaluated on a dataset comprising both field-collected and publicly available images using repeated stratified k-fold cross-validation. The framework achieves accuracies of 84.61% for wheat, 44.15% for rice, 85.71% for fodder, 95.23% for maize, and 64.28% for sugarcane (testing accuracy of the best-performing model per crop, where VGG-16 demonstrated superior generalization). The framework is able to support farmers, by integrating a technically robust backend with a simple and oriented interface, with diagnosis of multiple crops from a single platform, offering a scalable solution for precision agriculture and sustainable crop protection.

Why it matches plant phenotyping methods植物葉の病徴画像を対象に、画像取得・特徴抽出・分類・モバイルアプリ提供を一体化した診断手法を開発・評価しており、植物病害状態の表現型推定が中心です。

abstractThis study proposes a three step framework that relies on pattern recognition and classification of visual disease symptoms to deliver reliable, field-applicable diagnostics.

Integrating Plant Height into Hyperspectral Inversion Models for Estimating Chlorophyll and Total Nitrogen in Rice Canopies →

Rice undergoes rapid growth and exhibits a high demand for nutrients during the tillering and booting stages. SPAD readings, which reflect relative leaf chlorophyll status, and leaf nitrogen content (LNC) are key indicators of plant nutritional status, directly influencing photosynthetic efficiency and biomass accumulation, while plant height (PH) reflects canopy structure and nutrient availability. Establishing quantitative relationships among these traits at key growth stages is essential for stage-specific precision rice management. In this study, Unmanned Aerial Vehicle (UAV) hyperspectral imagery and ground-truth measurements of SPAD, LNC, and PH were collected from rice fields in Qingbaijiang District, Chengdu, China. Twelve vegetation indices (VIs) were calculated, and three machine learning algorithms—partial least squares regression (PLSR), support vector regression (SVR), and random forest regression (RFR)—were employed to develop stage-specific retrieval models. A stage-specific modeling framework integrating PH with hyperspectral data was developed to statistically enhance estimation accuracy at the tillering and booting stages. The optimal models for SPAD readings and LNC achieved R2 values of 0.916 and 0.936, respectively. The results indicate that integrating canopy structural information with hyperspectral features can improve the estimation accuracy of SPAD-related chlorophyll indicators and nitrogen status in rice. Under the controlled field conditions of this study, the proposed framework provides a plot-scale proof-of-concept demonstration for UAV-based stage-specific nitrogen monitoring.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像と機械学習を統合し、イネのクロロフィル指標・窒素状態を推定する段階別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ある。

abstractA stage-specific modeling framework integrating PH with hyperspectral data was developed to statistically enhance estimation accuracy at the tillering and booting stages.

Feature Selection using intersection of SVM-RFE and ARFA with ABi-LSTM Classification for Paddy Plant Leaf Disease →

Abstract The major aim of the study is to determine some of the diseases affecting rice plants which lead to crop loss. The suggested model is designed into four primary phases, i.e., pre-processing, feature extraction, feature selection, and classification. The proposed models have been applied to two sets of data that include Rice Disease and the Rice Leaf Disease Image that constitute a total of four types of classes of the paddy leaves: healthy, blast, bacterial blight, and tungro. The dataset images are initially upgraded in the first stage of pre-processing in order to enhance the quality of the images. A Gaussian filter is used to eliminate noise on the green spectral band and to convert the input images to RGB color space. The step second involves deriving color and texture information out of each of the pre-processed images. The third step Features selection SVM-RFE has been used to select features with the help of the intersection with the ARFA technique. The fourth step involves a final step where the features chosen are applied to classify the kind of disease existing in each picture. ABi-LSTM model is used in the classification process and the accuracy is 97.05.

Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定する画像ベースの表現型解析手法を、前処理・特徴抽出・特徴選択・分類のワークフローとして開発しており、方法が中心である。

abstractThe suggested model is designed into four primary phases, i.e., pre-processing, feature extraction, feature selection, and classification.

Mobile-Optimised Deep Learning Architecture for Multi-Crop Disease Detection Using CNN and SVM →

In this paper, we propose a deep learning pipeline for real-time crop disease classification on mobile devices. Our system employs a custom Convolutional Neural Network (CNN) trained on publicly available crop disease datasets (Maize, Tomato, Potato, Rice). In addition, two transfer-learning models; ResNet-50 and MobileNet are used as fixed feature extractors, with their output features classified by a multi-class Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel. We compare the models’ performance across all crop datasets and evaluate inference latency and model size. Experimental results show that the ResNet50-SVM hybrid attains near-perfect accuracy (≈100% for Maize, Tomato, Potato; 99.96% for Rice) on plant disease classification, far exceeding both the custom CNN and MobileNet-SVM approaches. The MobileNet-SVM pipeline is notably faster (≈23–66 ms per image) and compact (~8.7 MB) than ResNet50+SVM (≈108–192 ms, ~90 MB), making it well-suited for on-device deployment. The final model is converted to TensorFlow Lite for mobile inference; on a typical smartphone CPU it processes an input image in ~0.15–0.19s on average, enabling practical field use. These results demonstrate an efficient mobile AI solution for crop disease detection that balances accuracy with resource constraints. The proposed system can empower farmers with timely, in-field disease diagnosis, helping to mitigate yield losses and improve crop management through accessible AI-driven tools.

Why it matches plant phenotyping methods植物画像から病害状態を分類する深層学習パイプラインを開発・比較し、精度、推論遅延、モデルサイズ、モバイル実装を評価しており、植物病害表現型の取得・抽出が中心です。

abstractwe propose a deep learning pipeline for real-time crop disease classification on mobile devices

Comparative Assessment of UAV-Based TSEB and Field-Calibrated AquaCrop for Evapotranspiration on the Arid Coast of Peru →

Precise estimation of evapotranspiration (ET) is essential for sustainable water management in arid agroecosystems, particularly for high-water-demand crops such as rice. This study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season. A network of 24 drainage lysimeters provided an independent observational benchmark (ETlys); to represent the treatment-level response, lysimeter observations were aggregated as the mean across the 24 units for each UAV campaign. Thirteen UAV surveys supplied radiometric surface temperature and biophysical inputs (e.g., NDVI and fractional cover) to derive spatially explicit ET, while AquaCrop provided continuous daily simulations between flight dates. Direct lysimeter-based validation indicated high agreement for AquaCrop (R2 = 0.85; RMSE = 0.26 mm d−1; MBE = 0.01 mm d−1) and moderate agreement for UAV–TSEB (R2 = 0.66; RMSE = 0.81 mm d−1; MBE = 1.01 mm d−1). Model intercomparison further showed consistent temporal dynamics of ET (R2 = 0.70; RMSE = 1.35 mm d−1) and robust partitioning of crop transpiration (R2 = 0.79; RMSE = 0.99 mm d−1) and soil evaporation (R2 = 0.76; RMSE = 1.03 mm d−1) while revealing a systematic divergence under near-complete canopy cover: AquaCrop tended to suppress evaporation, whereas UAV–TSEB detected residual evaporation from the flooded surface. Overall, the results highlight the complementarity of both approaches—UAV–TSEB as a spatial diagnostic tool and AquaCrop as a temporally continuous simulator—providing a robust framework for ET monitoring, flux partitioning, and water-use-efficiency assessment in water-scarce rice systems.

Why it matches plant phenotyping methodsUAV熱・マルチスペクトル画像とTSEB/AquaCropによる作物キャノピーの蒸発散・蒸散・蒸発推定を、ライシメータで独立検証・比較しており、植物の生理状態計測手法が中心である。

abstractThis study integrated very-high-resolution UAV thermal–multispectral imagery with a Two-Source Energy Balance model (UAV–TSEB) and a field-calibrated AquaCrop model to quantify daily ET and its components under continuous flooding on the arid Peruvian coast during the 2024–2025 season.

Early screening of salt-tolerant and high-yielding rice varieties via cost-effective UAV data. →

Breeding rice varieties that are both salt-tolerant and high-yielding is essential for utilizing saline-alkaline lands and ensuring food security. However, However, high-throughput and accurate phenotyping at early growth stages remains a major bottleneck in breeding programs. In this study, unmanned aerial vehicle (UAV) imaging was employed to screen salt-tolerant and high-yielding varieties among 60 rice varieties under saline-alkaline field conditions. Red-green-blue (RGB), multispectral, and thermal canopy images were acquired throughout the growing season by UAV, from which 41 phenotypic traits were extracted at each growth stage. These traits were categorized into early-stage (tillering and jointing), late-stage (booting, flowering, and maturity), and whole-growth-stage (from tillering to maturity) and subsequently used to screen salt-tolerant and high-yielding rice varieties. Results showed that: (1) An early high-throughput screening method for salt-tolerant rice varieties was developed based on the membership function and UAV phenotypes (MFuav), achieving high performance (Precision >0.8, OA > 0.7). MFuav demonstrated the highest accuracy at the early-stage, with Precision increasing by 0.29 and 0.43 compared to the late- and whole-stage models, respectively. (2) A machine learning based UAV phenotypes framework (MLuav) was developed to further improve salt-tolerance screening performance. Within this framework, the partial least squares regression (PLSR) was employed for early-stage salt-tolerance screening, which achieved a Precision of 0.97 and an OA of 0.78, outperforming the MFuav by 0.11 and 0.08, respectively. In addition, within the same MLuav framework, early-stage UAV phenotypes were further used for actual yield prediction using a Random Forest (RF) model. The model achieved a high Recall for high-yielding varieties (Recall = 1.00), ensuring that no potentially high-yielding germplasm was missed, although this was accompanied by a moderate Precision (0.51) and an overall accuracy of 0.70. (3) The MLuav consistently outperformed the MFuav in screening salt-tolerant and high-yielding varieties across all 60 rice varieties. Among the five referenced salt-tolerant and high-yielding rice varieties, the MLuav correctly screened four using early-stage phenotypes, whereas the MFuav only screened three. Overall, the proposed method enables early screening of salt-tolerant and high-yielding rice varieties, offering an efficient tool for the screening and utilization of elite stress-resilient germplasm.

Why it matches plant phenotyping methodsUAV画像から多数のイネ表現型形質を抽出し、塩耐性・収量性を早期スクリーニングする方法と機械学習フレームワークを開発・評価しており、表現型取得・解析手法が研究の中心である。

abstracthigh-throughput and accurate phenotyping at early growth stages remains a major bottleneck in breeding programs

Rice tiller number estimation based on an improved Swin-UNet model and multi-feature fusion. →

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.

An explainable deep learning framework for few shot crop disease detection in rice and sugarcane using CNN based feature extraction →

Abstract Where crop health is essential to global food security. Our focus is on early crop disease detection in the field of agriculture, especially Rice and Sugar cane leaf disease. This prompts researchers to consider quick, automated, cost-effective, precise, and efficient methods of identifying the kinds of diseases utilizing contemporary technologies like image processing, artificial intelligence (AI), and Explainable Artificial Intelligence (XAI). This paper proposes an framework to detect pest infestation for rice and Sugar cane cultivation and suggests an effective framework for rice and Sugar cane disease detection and forecasting that uses image processing to standard, resizing, and normalization rice and Sugar cane images then, using feature extractor using CNN after that we using few-shot learning (FSL) techniques such as like Prototypical Networks and Model-Agnostic Meta-Learning (MAML) learning techniques for superior decision-making in smart farming systems. The experimental findings demonstrated the Accuracy and specificity of the suggested framework in identifying and effectively predicting the kind of disease. According to the results, the suggested framework outperformed the state-of-the-art benchmark algorithms in disease prediction while producing results that were plausible. With Prototypical Networks and MAML for rice leaf disease datasets, it increased by up to 97.6% and 95.27%, respectively. For effective rice disease identification, Prototypical Networks and MAML for Sugar cane leaf disease datasets increased by up to 91.68% and 90.27%, respectively. Interpretable AI-driven insights were further made possible by the combination of proposed system with Grad-CAM Explanation, which improved decision-making transparency.

Why it matches plant phenotyping methodsイネとサトウキビの葉画像から病害状態を推定する画像解析・深層学習フレームワークが研究の中心であり、植物病害フェノタイピング手法の開発・評価に該当する。

abstractThis paper proposes an framework to detect pest infestation for rice and Sugar cane cultivation and suggests an effective framework for rice and Sugar cane disease detection and forecasting that uses image processing

Quantitative Approach for Simultaneous In Situ Profiling of Lignin, Cellulose, and Hemicellulose Using Confocal Raman Microscopy. →

Label-free confocal Raman microscopy (CRM) is characterized by its high chemical specificity, making it a promising tool for the in situ quantitative analysis of plant cell walls. However, the simultaneous quantification of components in Gramineous species remains challenging. This is due to the complex "lignin-ferulate-carbohydrate" cross-linked network, as well as the amorphous property of hemicellulose, specifically its weak Raman signal and severe spectral overlap with cellulose. To address these issues, this study developed a quantitative strategy that combines CRM with cosine similarity (CRM-CS). We acquired CRM mapping images of rice stems pretreated with acidified sodium chlorite (ASC) for varying durations. The CS values between the preprocessed cell wall spectra and reference spectra (milled wood lignin, microcrystalline cellulose, and xylan) were then calculated and used as quantitative indicators. The results showed that CS values allow for accurate profiling, exhibiting significant positive correlations with the contents of lignin, cellulose, and hemicellulose. These correlations follow piecewise linear relationships with high determination coefficients ( R 2 ) of 0.9728 and 0.9809 for lignin, 0.9592 and 0.9810 for cellulose, and 0.9004 and 0.9901 for hemicellulose. The CS-based method consistently outperforms the conventional characteristic peak intensity approach. In particular, it resolves the difficulty of accurately quantifying hemicellulose, a task where single-band methods typically underperform ( R 2 in situ simultaneous quantification of lignin, cellulose, and hemicellulose contents in rice stem cell walls during ASC pretreatment. Thus, the CRM-CS algorithm enables simultaneous in situ quantification in Gramineous cell walls, offering a valuable approach for crop breeding and the high-value utilization of lignocellulosic biomass.

Why it matches plant phenotyping methods植物細胞壁中のリグニン、セルロース、ヘミセルロース含量を定量するCRM-CS手法を開発・検証しており、植物形質の取得方法が研究の中心である。

abstractTo address these issues, this study developed a quantitative strategy that combines CRM with cosine similarity (CRM-CS).

Spectral characterization and severity assessment of rice brown planthopper damage using multivariate models. →

Brown planthopper (BPH) is a serious rice pest that threatens global food security by causing yield losses of up to 80%. Conventional methods for assessing BPH infestation are labour-intensive and lack real-time precision. This study evaluates hyperspectral remote sensing as a rapid, non-invasive approach for quantifying BPH population severity in three rice varieties: Pusa Basmati-1509, Pusa Basmati-1121, and TN-1. Leaf-level spectral measurements (350–2500 nm) acquired using a portable spectroradiometer effectively differentiated BPH population severity levels. Among 28 spectral indices evaluated, Structural Insensitive Pigment Index (SIPI), Pigment Specific Normalized Difference Index (PSND) for chlorophyll b, Pigment Specific Simple Ratio (PSSR a) for chlorophyll a, and (PSSR b) for chlorophyll b, showed high sensitivity to BPH infestation. Multivariate Regression models, including Partial Least Squares Regression (PLSR), Support Vector Machine (SVM), and Random Forest (RF), were developed for severity prediction. Among the tested models, RF achieved the highest accuracy for vegetation indices-based estimation (R2 = 0.99), while PLSR showed strong relationships between hyperspectral data and BPH population severity (R2 = 0.62) and key biochemical parameters, including chlorophyll (R2 = 0.84), carotenoids (R2 = 0.77), and protein (R2 = 0.84). In contrast, flavonoids exhibited weak predictability (R2 = 0.34). Field validation confirmed model robustness, with vegetation index-based predictions achieving R2 values ranging from 0.72 to 0.86. Overall, the results demonstrate the potential of hyperspectral sensing combined with machine learning for early, non-destructive detection and monitoring of BPH stress, supporting precision pest management in rice.

Why it matches plant phenotyping methodsイネ葉のハイパースペクトル計測と機械学習により、害虫被害の重症度や関連する植物生理形質を推定する手法を開発・検証しており、フェノタイピング手法が中心である。

abstractThis study evaluates hyperspectral remote sensing as a rapid, non-invasive approach for quantifying BPH population severity in three rice varieties

A Hybrid RTM-Informed Machine Learning Framework with Crop-Specific Canopy Structural Parameterization for Crop Fractional Vegetation Cover Estimation →

Fractional vegetation cover of crops (CropFVC) is a critical indicator for remote sensing-based crop monitoring. However, existing inversion models are largely developed for general vegetation types, limiting their effectiveness for crop-specific applications. Here, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model. The model was validated with 43343 CropFVC samples of four major crops (winter wheat, rice, maize, and soybean) across China during March to August 2024, spanning key phenological stages, and further compared against SNAP (10 m) and GEOV3 (300 m) products. Results showed that (1) the proposed model achieved stable performance across diverse canopy structures, with average RMSE

Why it matches plant phenotyping methods作物の葉面積被覆率という明示的な植物キャノピー形質を推定するハイブリッドモデルを開発し、多数のサンプルと既存プロダクトで検証しており、測定・推定手法が研究の中心である。

abstractHere, we developed a gap-fraction-refined hybrid CropFVC model that integrates crop-specific PROSAIL calibration, an ALA (averages of leaf angle) -based dynamic projection function, and a Random Forest model.

TraitDiscover: An automated high-throughput platform for multimodal plant phenotyping with real-time trait analysis →

Plant phenotyping is essential for elucidating genotype–environment interactions, yet conventional methods remain labor-intensive and low-throughput. TraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform. Aligned with the ”Plant Phenotyping Trinity” framework, the system comprises a millimetre-accurate triaxial automation unit, a modular sensor array–RGB imaging, three-dimension laser scanner or LiDAR (3D), infrad (IR) thermal imaging, hyperspectral imaging (HSI), and photosynthesis (PS) imaging–and the dedicated software TraitNavigator suite into one cohesive system. A unified spatiotemporal synchronization mechanism enables robust time-series analysis and fusion of multisource phenotypic data across the entire crop growth period, while the DepthCropSeg algorithm and a night-time imaging module enhance trait extraction under complex conditions, providing G × E × P-ready, multimodal phenotypic datasets. Validation across soybean, maize, and rice trials demonstrated high sensitivity—detecting drought stress four days before visible symptoms, identifying glyphosate injury 24 hours ahead of manual scoring, and quantifying local adaption patterns across ecological gradients. While challenges remain in scaling to complex open-field conditions, TraitDiscover offers a scalable, data-driven approach to accelerate stress phenotyping and breeding decisions and is readily poised for deeper integration with AI to advance sustainable agriculture.

Why it matches plant phenotyping methodsマルチモーダルセンシング、画像解析、同期機構、形質抽出アルゴリズムを統合した植物フェノタイピング基盤の開発と検証が中心であり、ストレス検出や形質定量も実証している。

abstractTraitDiscover transcends these constraints by uniting multimodal sensing with tightly coupled hardware-software orchestration in a single, end-to-end phenotyping platform.

Quantitative assessment and predictive modelling of stem damage during seedling separation in mechanical rice transplanting →

Rice seedling stems are particularly vulnerable to structural damage during the seedling separation phase of mechanical transplanting, especially under non-ideal plant-machine interactions. Owing to its internal and transient nature, such damage is inherently difficult to quantify or predict. This study presents a novel modelling framework for stem damage assessment, which establishes a quantitative relationship between the maximum impact load (Fₘₐₓ) during seedling separation and internal damage severity, quantified by the damaged area ratio (Dₐᵣ). High-speed imaging and triaxial force sensors were employed to measure Fₘₐₓ across seedlings aged 20, 30 and 40 d under varying transplanting speeds. Microscopic cross-sections of stems were analysed to calculate Dₐᵣ. A composite impact force model, incorporating stem bending rigidity, lateral needle–stem offset and contact duration, was developed to support experimental design. A strong positive correlation was observed between Fₘₐₓ and Dₐᵣ across all seedling age groups (ρ > 0.93, p 8 %. Age-specific linear regression models achieved high predictive accuracy and good calibration (cross-validated R² of 0.86–0.91; RMSE of 0.33–0.73 percentage points in Dₐᵣ), while extending these models with a restricted cubic spline further reduced errors in the upper damage tail. This framework offers theoretical insights into age- and speed-dependent stem damage and practical tools for optimising transplanting parameters and supporting real-time, damage-aware control strategies to mitigate mechanical damage risk and improve seedling survival and post-transplant performance.

Why it matches plant phenotyping methods稲苗の茎損傷を画像・力センサー・断面解析で定量化し、予測モデルを開発・検証しており、植物状態の取得・推定方法が研究の中心である。

abstractThis study presents a novel modelling framework for stem damage assessment

Improving crop biophysical parameter estimation using high-resolution multispectral UAV imagery and PROSAIL model →

Timely, field-scale retrieval of crop biophysical variables is widely regarded as central to data-driven agronomy. In this study, a practical workflow was evaluated in which high-resolution unmanned aerial system (UAS) multispectral imagery was coupled with PROSAIL inversion to map rice canopy traits across three phenological stages. Multispectral and RGB acquisitions were processed, and indices sensitive to chlorophyll, water, and pigment dynamics (e.g., Normalized Difference Red-Edge Index (NDRE), Leaf Chlorophyll Index (LCI), Modified Chlorophyll Absorption Ratio Index (MCARI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Structure-Insensitive Pigment Index 2 (SIPI2), Triangular Greenness Index (TGI), and Visible Atmospherically Resistant Index (VARI)) were derived. Leaf and canopy parameters, leaf chlorophyll content (Cab), carotenoids (Car), leaf water content (Cw), dry matter (Cm), mesophyll structure (N), and leaf area index (LAI)—were retrieved via lookup-table (LUT) inversion of PROSAIL. Independent ground measurements were used for validation, and a same-date Sentinel-2 benchmark was performed (subject to cloud constraints). Consistent phenological trajectories were observed: NDRE/LCI and Cab/LAI were found to peak at maximum greenness, while SIPI2 was observed to rise during senescence alongside declining Cab and Cw. Stage-dependent errors were identified in PROSAIL RMSE maps, with the lowest and most homogeneous errors detected at peak canopy. Strong agreement with field data was obtained (R² > 0.98 for most variables at the first date). For Cab, R²/RMSE values of 0.996/1.555, 0.978/2.104, and 0.972/0.2 were recorded across the three dates, respectively. Lower accuracy was produced by Sentinel-2 at field scale (e.g., LAI R²/RMSE ≈ 0.81/0.7; Cab ≈ 0.78/6.5), although useful cross-sensor complementarity was indicated. An operational pathway to within-field mapping of rice biophysics is thereby offered by the “UAS multispectral + PROSAIL” pipeline. The results demonstrate high accuracy at field scale, with phenology-dependent retrievals outperforming Sentinel-2-based estimates, highlighting the potential of UAV-based approaches for precise crop monitoring. Enhanced robustness to phenological change and cloud-related gaps is achieved when red-edge and pigment-ratio indices are fused with physical inversion, and straightforward extensibility to other cereals and management contexts is suggested.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像とPROSAIL逆解析を統合し、イネの生理・構造形質を推定して地上測定で検証するワークフローが研究の中心であり、実質的な植物フェノタイピング手法の適用・評価である。

abstracta practical workflow was evaluated in which high-resolution unmanned aerial system (UAS) multispectral imagery was coupled with PROSAIL inversion to map rice canopy traits across three phenological stages.

Real‐time crop leaf disease detection and classification using a hybrid Morlet wavelet interactive attention neural network optimized by the Red‐Billed Blue Magpie algorithm →

Abstract Traditional disease classification is slow and lab‐dependent. Machine learning aids faster image‐based detection but faces challenges like lighting variations, complex leaf shapes, background noise, and limited labeled data. This research develops a robust image‐based method to automatically classify corn, rice, and wheat leaf diseases under diverse environmental and imaging conditions. A Hybrid Morlet Wavelet Interactive Attention Neural Network optimized by the red‐billed blue magpie optimizer (HMWIANN‐RBBMO) is proposed in this study for accurate classification corn ( Zea mays ), rice ( Oryza sativa ), and wheat ( Triticum aestivum ) leaf diseases. First, a modified square‐root SageHusa adaptive Kalman filter is used to remove noise and improve image quality by image preprocessing. The DeepLabV3+ is used to accurately segment disease‐prone areas, and then the Sharpbelly Fish Optimization is used to identify the most discriminative features in the images. The HMWIANN will combine Morlet wavelet transformation with interactive attention to exhaust the capabilities of the classifier to recognize Healthy (No pathogen), Common Rust ( Puccinia sorghi ), Blight ( Xanthomonas oryzae ), Gray Leaf Spot ( Cercospora zeae‐maydis ), BrownSpot ( Bipolaris oryzae ), Hispa ( Dicladispa armigera ), LeafBlast ( Magnaporthe oryzae ), Stripe rust ( Puccinia striiformis ), and septoria ( Zymoseptoria tritici ). Furthermore, the RBBMO will be used to improve convergence speed, generalization, and classification accuracy. A graph‐based hybrid recommendation system is also incorporated to assist disease management decisions. Experimental evaluation on corn, rice, and wheat leaf disease dataset demonstrates superior performance, achieving 99.70% accuracy, 99.80% precision, 99.50% recall, 99.40% F1‐score, and a low false positive rate of 0.8%, outperforming existing state‐of‐the‐art methods.

Why it matches plant phenotyping methods植物葉の病害症状を画像から分割・特徴抽出・分類する手法を開発し、病害状態という植物表現型を直接推定して性能評価しているため、方法が中心的である。

abstractThis research develops a robust image‐based method to automatically classify corn, rice, and wheat leaf diseases under diverse environmental and imaging conditions.

A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments →

Abstract Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language Summary Temporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.

Why it matches plant phenotyping methods植物画像から形態・スペクトル・テクスチャ形質を抽出する統合パイプラインの開発と評価が中心であり、植物フェノタイピング手法として明確に該当する。

abstractWe developed an integrated multispectral phenotyping framework

Integrating SEM-based phenotyping with GWAS reveals the genetic architecture of rice straw secondary cell wall and internode cell features →

Rice stem performs assimilate transport and promises sturdiness due to cell wall structure and composition. However, less is known about the genetic basis of its structural characteristics. In this study, for the first time, the scanning electron microscope (SEM) imaging technique was developed to capture digital phenotypes to assess 18 straw traits collected from the cross-sections of 147 rice accessions. Genome-wide association studies (GWAS) identified 54 significant single-nucleotide polymorphisms (SNPs; integrated into 28 quantitative trait loci) residing in the genic sequences of rice (promoter and coding DNA sequence), and classified into three groups: 1) cell wall-defining genes, 2) cell size-defining genes, and 3) transcription factors. DUF246 and DUF1218 , galactose oxidase , mitochondrial Rho GTPase , WUSCHEL-related homeobox 5 and scarecrow-like 9 are the novel genes identified among the 21 candidate genes. These genes may play roles in stem development traits, specifically the distance from the vascular bundle to the end of the parenchymal cells (DVBEPC) and the thickness of the straw cell wall in the protruding part (TSCWP). Post-GWAS analyses showed one significant haplotype on chromosome 4 and 25 significant epistatic interactions. Most notably, nine TF families were repeatedly detected among the significant QTL. Os07g0644300 (XPA-binding protein 2), located in the q7-1 genomic segment and associated with DVBEPC, was found to have a missense mutation. Phenotyping via SEM imaging provides precise genome-phenome association in understanding rice stem cell size and cell wall architecture, which ultimately can define biomass and lodging resistance. systematic scheme of the current study • This study pioneers the use of SEM imaging to digitally phenotype rice stem traits, revealing the genetic basis of cell size and wall structure using GWAS. • The GWAS studies identified 28 QTLs and 21 candidate genes, including novel ones linked to stem strength and architecture. • The findings from the study enhance our understanding of rice stem development and provide a foundation for improving biomass and lodging resistance through precise genome-phenome associations.

Why it matches plant phenotyping methodsSEM画像を用いたイネ茎のデジタル表現型取得法の開発が研究の中心で、18形質を定量化しGWASに適用しているため。

abstractthe scanning electron microscope (SEM) imaging technique was developed to capture digital phenotypes to assess 18 straw traits

Rice Plant Disease Detection System based on Leaf Image using Web-based CNN Algorithm →

Rice (Oryza sativa) plays a crucial role as a major staple food commodity. However, diseases such as Bacterial Blight, Brown Spot, and Leaf Blast can cause significant crop losses. Current manual identification methods have limitations due to high subjectivity and long diagnosis time. This study proposes a web-based automatic detection system using a Convolutional Neural Network (CNN). The dataset was obtained from Kaggle and consisted of 2,800 images evenly distributed across four classes (700 images per class). The data were split using an 80:20 ratio for training and validation sets, followed by preprocessing steps including resizing to 224×224 pixels and data augmentation. The CNN architecture was designed with four convolutional blocks and optimized using the Adam optimizer. Training for 50 epochs achieved an accuracy of 77.50%, precision of 82.98%, recall of 77.50%, and an F1-score of 72.84%. Based on the confusion matrix analysis, the model performed very well in detecting Bacterial Blight and Brown Spot but still faced difficulties in identifying the Leaf Blast class. Overall, the developed system has the potential to serve as a decision-support tool for farmers, although further performance improvements are required, particularly for detecting specific disease variants.

Why it matches plant phenotyping methodsイネ葉画像から病害状態をCNNで自動推定する手法の開発・評価が研究の中心であり、植物の病害表現型を直接測定しているため採用。

abstractThis study proposes a web-based automatic detection system using a Convolutional Neural Network (CNN).

Deep Learning-Based Classification of Paddy Crop Diseases Using a Custom Image Dataset →

Plant diseases pose significant threats globally due to the high economic losses and effects on food security. Traditional disease identification methods usually have limitations regarding their accuracy and efficiency. This study discusses six advanced deep learning models: VGG19, DenseNet201, Xception, InceptionResNetV2, MobileNetV2, and EfficientNetV2B3. A dataset is used that is rich in diversity and contains high-quality images of diseased sections or parts of plants. These deep models are discussed and compared for studying their efficiencies in recognizing plant diseases accurately. EfficientNetV2B3 and Xception outperformed the rest of the models due to the ability of the model to capture major features from the image of the infected region. MobileNetV2 was also useful which provided a good trade-off between accuracy and computational efficiency. The study further applied transfer learning and image augmentation in boosting model performance and addressing the issue of class imbalance in the dataset. Results showed that the proposed approach proved much more reliable and efficient compared to conventional approaches to plant disease detection. Future efforts will be geared towards early detection of diseases to further assist farmers and researchers in order to upgrade the practices related to crop management. Additional data will be integrated, including hyperspectral images and environmental factors, for developing a robust and efficient system for plant disease detection. These models will be deployed in intelligent farming systems.

Why it matches plant phenotyping methods植物の感染部位画像から病害状態を分類する深層学習手法の比較・評価が中心であり、植物病害表現型の画像ベース推定に該当する。

abstractThis study discusses six advanced deep learning models: VGG19, DenseNet201, Xception, InceptionResNetV2, MobileNetV2, and EfficientNetV2B3.

An Efficient Depthwise Multiscale Feature Learning Convolutional Network for Plant Leaf Disease Classification in Agriculture →

Abstract Plant disease detection and early disease treatment are essential for sustainable crop production. Computer vision for crop science is growing with the advancement in deep learning. The proposed work systematically addresses these issues through three datasets as Plant Village Maize Dataset (D1), Paddy Doctor Dataset (D2), and Sugarcane Leaf Image Dataset (D3) with different classes. The dataset contains 4188, 16225, and 6748 images from dataset sets D1, D2, and D3, respectively. This work has used a Generative Adversarial Network (GAN) to generate a synthetic dataset. Further use data preprocessing, and the data has been resized to 224×224×3. The proposed model use Depth-wise Multiscale Feature Learning ConvoNet (DMFL-ConvoNet) model, which includes the Depth-Wise Convolutional Block (DCB ) block of DMFL-ConvoNet with 3 × 3 and 5 × 5, facilitates the extraction of multiscale plant disease characteristics. Furthermore, it has added 2.5 million parameters. The proposed DMFL-ConvoNet model offers state-of-the-art performance and decreases computational complexity at 33 frames per second, making it ideal for real-time applications. The proposed DMFL-ConvoNet model has been compared with several transfer learning models, including ResNet50V2, InceptionResNetV2, NASNetMobile, EfficientNetV2L, and EfficientNetV2B0 models, and the proposed model has achieved 99.52% data accuracy in the multiple datasets.

Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習手法の開発・比較が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用する。

abstractThe proposed work systematically addresses these issues through three datasets as Plant Village Maize Dataset (D1), Paddy Doctor Dataset (D2), and Sugarcane Leaf Image Dataset (D3) with different classes.

XooNet: a high-throughput UAV-based approach for field screening of bacterial blight-resistant germplasm in wild rice. →

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

RiceDetect-Net: a lightweight real-time detection framework for rice diseases. →

Rice disease detection is vital for food security, prevention efficiency, pesticide reduction, and sustainable agriculture. Challenges like poor model applicability, low accuracy, and limited datasets make this research essential. Existing models face issues with large parameters, complex computations, and insufficient semantic information capture. This paper introduces a large rice disease dataset and proposes the RiceDetect-Net model to address these challenges. The model integrates a brand-new lightweight detection head LE-Head to reduce the parameter quantity and computational complexity. To boost accuracy, the model integrates the newest FCA attention mechanism into its high-level semantic processing component, strengthening its capacity to interpret complex semantic data. Testing on a custom rice disease dataset comprising 54,240 images, the model attained 94.3% accuracy with a parameter count of 2.32 M. The enhanced model achieves a 0.4% increase in accuracy while reducing parameters by 10% relative to the baseline YOLOv11. The detection model is more lightweight, can adapt to the computing power of field detection equipment, is more suitable for practical scenario applications, and provides technical support for the development of smart agriculture.

Why it matches plant phenotyping methodsイネ病害を画像から検出するモデルと大規模データセットの開発・評価が中心で、植物の病害状態を推定する画像ベース表現型手法に該当する。

abstractThis paper introduces a large rice disease dataset and proposes the RiceDetect-Net model to address these challenges.

IFSA-Inception-CBAM: An Early Detection Model for Rice Blast Disease Based on Integrated Feature Selection and a Deep Convolutional Neural Network →

Rice blast disease is one of the most contagious and destructive diseases affecting rice, posing a serious threat to global rice production and the agricultural economy. To enable accurate early detection under field conditions, this study proposes an integrated feature sorting algorithm (IFSA). The algorithm integrates five spectral feature selection methods—partial least squares, successive projections algorithm (SPA), principal component analysis loading (PCA-Loading), genetic algorithm (GA), and random forest (RF)—and employs the Borda count method for comprehensive feature ranking and selection. Field experiments were conducted in Haicheng, Anshan, Liaoning Province, China, using the rice cultivar Yanfeng 47. A total of 4893 hyperspectral samples were collected under natural field conditions. The results demonstrate that IFSA effectively identifies key spectral wavelengths for the early diagnosis of rice blast disease, achieving significantly higher detection accuracy than conventional single-method dimensionality reduction approaches. Based on the IFSA-selected wavelengths, an early detection model (Inception-CBAM) was further developed by integrating a multi-channel convolutional neural network with a convolutional block attention module, thereby enhancing the extraction and recognition of early disease-related features. Compared with six baseline models (InceptionV4, ResNet, BiGRU, RF, support vector machine, and extreme learning machine), Inception-CBAM achieved an overall accuracy of 95.44 ± 0.50% and a Kappa coefficient of 93.92 ± 0.67% for early rice blast disease detection, outperforming all competing methods. This study confirms the effectiveness of IFSA for hyperspectral feature selection and demonstrates that the proposed Inception-CBAM model provides strong capability for early disease detection. Nevertheless, the data were collected from a single cultivar and a single region; therefore, the model’s generalization performance across broader environments requires further improvement. Future work will extend the evaluation to multi-cultivar and multi-region scenarios to facilitate practical deployment for real-time field diagnosis.

Why it matches plant phenotyping methodsイネ葉のハイパースペクトル情報から病害状態を推定する特徴選択法と深層学習モデルを開発・比較検証しており、植物病害表現型の取得・抽出が研究の中心である。

abstractTo enable accurate early detection under field conditions, this study proposes an integrated feature sorting algorithm (IFSA).

Improving rice leaf area index monitoring accuracy via robot-integrated multi-sensors and meteorological data fusion with explainable machine learning →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsロボット統合型マルチセンサーと機械学習によるイネ葉面積指数（LAI）の推定精度向上が主題であり、植物形質の取得・推定手法が中心である。

titleImproving rice leaf area index monitoring accuracy via robot-integrated multi-sensors and meteorological data fusion with explainable machine learning

AI-Driven Identification of Rice Crop Disorders Using Multi-Model Classification Framework →

Cultivated in many nations across the globe, rice is a staple food of great importance. Rice leaf diseases can severely affect crop cultivation, resulting in low crop yields and financial losses. In an older method of identifying leaf diseases, they are classified based on their color, morphology, texture, and shape. Fully automated instructional systems can quickly identify diseased leaves with minimal human assistance. Most of the earlier research on identifying leaf diseases in rice crops used machine learning and feature extraction methods. Characteristics like its shade, surface, patterns of veins, and lesion extent were retrieved from photos of sick leaves. Stated differently, machine learning identifies the illness by extracting characteristics. Instead, machine learning-based feature vector extraction is not totally superior because it involves retraining and missing one dimension. The proposed hybrid model predicts the diseased leaves of rice crops with 97% accuracy and minimum training and validation losses of 0.80 and 1.25, respectively.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から自動推定する分類手法が研究の中心であり、植物の病徴・病害状態を直接評価しているため。

abstractFully automated instructional systems can quickly identify diseased leaves with minimal human assistance.

Deep learning-based methods for phenotypic trait extraction in rice panicles. →

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.

Organ-level 3D phenotyping of saffron using a low-cost dual-camera workflow. →

BACKGROUND: Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet systematic 3D measurements have remained elusive due to extreme morphological challenges: ultra-narrow leaves, severe mutual occlusion, and prostrate growth architecture. Traditional single-view imaging systems fail to resolve individual leaves under such conditions, limiting phenotypic analysis to whole-canopy descriptors. Here, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction. RESULTS: > 0.94, MAPE < 6%), achieving accuracy benchmarks established for broad-leaved crops using commercial-grade hardware at 100 × lower cost. Systematic voxel sensitivity analysis across nine scales identified optimal preprocessing parameters (2 cm voxel size) balancing measurement precision with computational efficiency, addressing a critical reproducibility gap in plant phenotyping. Exploratory longitudinal tracking revealed that above-ground biomass was correlated with final corm yield (r = 0.68, P < 0.001), with mid-vegetative canopy volume also showing strong correlation (r = 0.52, P < 0.01), suggesting potential resource allocation trade-offs between vegetative expansion and storage organ development. CONCLUSIONS: This work demonstrates that organ-level 3D phenotyping of narrow, overlapping leaves is achievable using low-cost imaging hardware and transparent methodological workflows. Complete documentation of algorithmic parameters and hardware specifications enables direct replication and adaptation to other narrow-leaved crops (wheat, rice, onion, leek), democratizing access to high-throughput phenotyping in resource-limited settings. The workflow advances plant phenomics by demonstrating that methodological transparency and cost-effectiveness need not compromise measurement precision, opening new avenues for phenotype-to-genotype mapping and predictive breeding in underutilized crops.

Why it matches plant phenotyping methods低コストの双眼カメラとSfM-MVSによるサフラン葉の器官レベル3D形質取得ワークフローを開発し、精度検証、再現性、パラメータ最適化まで扱っており、植物フェノタイピング手法が研究の中心である。

abstractHere, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction.

Context-Aware Explanation Drift Detection (CA-EDD): For Plant Disease Severity Estimation →

Deep learning models have shown high accuracy in automated plant disease classification; however, their black-box nature limits adoption in precision agriculture, where biological validity and interpretability are critical. Conventional Explainable Artificial Intelligence (XAI) methods, such as Grad-CAM, generate visual saliency maps that often lack alignment with true pathological symptoms, leading to predictions that are accurate yet biologically inconsistent. This paper proposes SymptomConsistency Guided Explainable AI (SCG-XAI), a novel ContextAware Explanation Drift Detection (CA-EDD) framework that validates model reasoning against established plant pathology principles. The framework integrates an explanation generator, an explanation embedding module that encodes attribution maps into structured symptom descriptors capturing lesion color, texture, and spatial distribution, a temporal drift analyzer to detect shifts in model reasoning across disease severity stages, and a context integration layer that constrains explanation validity using agronomic criteria, specifically the Relative Lesion Height (RLH) and the Standard Evaluation System (SES) for rice sheath blight caused by Rhizoctonia solani. By evaluating the semantic consistency between model explanations and physiological disease symptoms, SCG-XAI enables the detection of logic drift that is not reflected in conventional performance measures. Experimental results on a multi-severity rice sheath blight dataset demonstrate that SCG-XAI maintains competitive classification accuracy while ensuring that model explanations are biologically consistent and trustworthy for real-world field deployment.

Why it matches plant phenotyping methods植物病害の症状・重症度を対象に、説明生成、症状記述、重症度段階のドリフト検出を統合した手法を開発・評価しており、病害表現型の推定方法が中心である。

abstractThis paper proposes SymptomConsistency Guided Explainable AI (SCG-XAI), a novel ContextAware Explanation Drift Detection (CA-EDD) framework that validates model reasoning against established plant pathology principles.

Transformer-Based Phenotyping of Rice Root Aerenchyma Across Environments Enables Climate-Smart Rice Selection →

ABSTRACT Quantification of root anatomical traits such as cortical aerenchyma is key to understanding rice adaptation to diverse water regimes. Recently, the role of aerenchyma in regulating methane emissions has been demonstrated, making it a target for climate change mitigation. Despite its importance, breeding for root anatomical traits remains limited because manual analysis of root cross-sections is labor-intensive, inconsistent, and poorly scalable, and analysis pipelines do not generalize across heterogeneous imaging conditions. We present a deep learning pipeline based on a recent vision transformer architecture to automatically segment rice root anatomical structures and quantify aerenchyma. The model was trained on a multi-environment dataset of 1,760 annotated rice root cross-sections acquired across growth stages, cultivation systems, and countries, using a collaboratively defined annotation protocol. The model achieved high segmentation performance (mean Intersection-over-Union > 0.92) and near-perfect aerenchyma ratio quantification (R 2 = 0.98), and was evaluated by two experts as performing on par with, and in some cases better than, expert annotators. Delivered as open-source software with an online interactive demonstrator, the pipeline revealed differences in aerenchyma across genotypes, water regimes, environments, and developmental stages. Overall, this work demonstrates that transformer-based segmentation enables high-throughput anatomical phenotyping, supporting scalable and climate-smart rice breeding. HIGHLIGHTS Transformer-based segmentation enables robust aerenchyma phenotyping across environments A SegFormer model achieves expert-level accuracy on diverse rice root cross-sections Automated analysis delivers near-perfect lacuna-to-cortex ratio quantification (R 2 ≈ 0.98) Our online demonstrator supports scalable, climate-smart rice breeding applications

Why it matches plant phenotyping methodsイネ根の画像から通気組織を自動分割・定量する深層学習パイプラインを開発し、異なる環境で性能検証した、中心的な植物フェノタイピング研究である。

abstractWe present a deep learning pipeline based on a recent vision transformer architecture to automatically segment rice root anatomical structures and quantify aerenchyma.

Development and Field Validation of a Smartphone-Based Web Application for Diagnosing Optimal Timing of Mid-Season Drainage in Rice Cultivation via Canopy Image-Derived Tiller Estimation →

In recent years, excessive tillering caused by high temperatures during early growth has contributed to rice quality deterioration in warm regions of Japan. Accurate determination of midseason drainage timing is essential but remains difficult due to year- and cultivar-dependent variability. In this study, we developed a smartphone-based web application that estimates rice tiller number from canopy images and diagnoses the optimal timing of midseason drainage by comparing estimated tiller numbers with cultivar-specific target values. The system operates entirely on a smartphone using HTML5 canvas-based pixel extraction, JavaScript computation, and Google Apps Script-based backend processing. Field experiments conducted in Chiba Prefecture using three rice cultivars showed a strong linear relationship between estimated and observed tiller numbers (R 2 = 0.9439). The root mean square error (RMSE) was 42.6 tillers m -2 , with a consistent negative bias (-34.6 tillers m -2 ), indicating systematic underestimation. Considering typical tiller increase rates near midseason drainage (12.0-24.3 tillers m -2 day -1 ), these errors correspond to approximately 1-3 days of growth progression, which is acceptable for timing-based decision-making. Although the system does not aim to provide precise absolute tiller counts, it reliably captures relative growth-stage dynamics and supports threshold-based diagnosis. The proposed approach enables rapid, on-site decision support using only a smartphone, contributing to labor-saving and improved water management in rice production.

Why it matches plant phenotyping methodsスマートフォン画像からイネの分げつ数を推定する手法とWebアプリを開発し、圃場で精度検証しているため、植物表現型取得が研究の中心である。

abstractwe developed a smartphone-based web application that estimates rice tiller number from canopy images

Detection study of early-stage classification of rice diseases using a hyperspectral multi-feature fusion model (PMA-VRNet) driven by the vegetation index RBDI →

Rice Blast (RB) is a highly destructive fungal disease that causes millions of tons of rice yield loss worldwide every year. Therefore, using precise indicators to quantify diseases and achieve early detection is crucial for establishing a preventive plant protection system. However, there is currently a technological gap in quantitative research on early grading and detection of RB. This study employs Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing technology to acquire hyperspectral images of rice canopies, and then preprocesses them to extract spectral, textural, and structural features. To address the limitation of traditional vegetation indices (VI) in reflecting the spatial structural characteristics of vegetation, the rice blast indices RBDI1 and RBDI2 are constructed based on vegetation cover (FVC), effectively quantifying rice disease indices (DI) and improving the accuracy and spatial resolution of disease identification. Furthermore, the Parallel Multi-Head Attention VGG-ResNet (PMA-VRNet) model was proposed, which integrates multiple feature data such as RBDI, Texture Features (TF), and Canopy Coverage (CC) through a parallel Multi-Head Attention mechanism. This model deeply integrates the high semantic feature extraction capabilities of VGG with the deep residual learning advantages of ResNet, achieving high-precision grading detection of rice diseases in the early stage. The study has shown that the PMA-VRNet model demonstrates excellent feature learning capabilities in data fusion, particularly on the sensitive wavelength dataset selected by the BS-CARS algorithm, achieving a detection accuracy of OA = 93.5 % and Kappa = 91.86 %. Compared with the comparative model, OA improved by 1.17 %-3.00 % and Kappa improved by 0.62 %-3.76 %. Additionally, SMOTE data augmentation is better than the original data, and the performance of combined feature modeling is better than single-feature modeling. Among them, the model combining VI_BS-CARS, TF, and CC achieves the highest accuracy (OA = 94.5 %, Kappa = 93.12 %). This study provides an efficient and feasible technical solution for accurately monitoring of RB using UAV hyperspectral images.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像からイネの病害指数・病害重症度を定量化し、特徴抽出とモデル性能を検証する方法開発・技術評価が研究の中心である。

abstractThis study employs Unmanned Aerial Vehicle (UAV) hyperspectral remote sensing technology to acquire hyperspectral images of rice canopies, and then preprocesses them to extract spectral, textural, and structural features.

Automated detection of rice plant diseases using dual stage thresholding and twin support vector machine →

Rice plants are susceptible to various diseases such as brown spot, BLB, and blast, caused by viral, bacterial, or fungal infections, which significantly affect both the quantity and quality of rice production. This study introduces an automated method for detecting these diseases using dual thresholding (DT) in segmentation combined with twin support vector machine (TW-SVM) classification. Early detection and accurate identification of rice leaf diseases are crucial for effective management and optimization of production. The proposed method leverages the strengths of TW-SVM, including its ability to handle high-dimensional data efficiently. The approach is compared with three SVM-based techniques: basic SVM, least-square SVM, and proximal SVM. Simulations are performed using images from both a public dataset and a real-time drone image dataset. Thirteen features, including color, texture, and shape, are extracted for classification. Results show that the proposed dual stage thresholding (DST) TW-SVM achieves superior performance in terms of time complexity and accuracy, with 95% accuracy on the public dataset and 99.3% accuracy on the drone image dataset.

Why it matches plant phenotyping methodsイネ葉の病徴を画像から検出・分類する画像解析手法を開発し、複数データセットと既存手法で性能比較しており、植物病害状態の表現型取得が中心である。

abstractThis study introduces an automated method for detecting these diseases using dual thresholding (DT) in segmentation combined with twin support vector machine (TW-SVM) classification.

Inversion modeling of rice chlorophyll content based on optimized UAV hyperspectral remote sensing image data →

Chlorophyll content is an important indicator of rice growth status. Accurately estimating the nutritional status of rice canopies using hyperspectral data and inversion models is of great significance for precision farming. Ground-based spectrometers can obtain precise spectral data, but they are limited by spatial resolution and cannot be used for large-scale observations. Unmanned aerial vehicle (UAV) imaging spectrometers can be used to observe large areas of farmland, but due to sensor limitations, the data accuracy is poor, which in turn leads to poor inversion accuracy. This study optimizes UAV hyperspectral data based on ground-based spectrometer data. A wavelength random combination traversal algorithm is used to select wavelengths. Inversion models for rice chlorophyll content are constructed using ELM, CPO-ELM, and FLA-ELM. The results indicated that the optimized hyperspectral reflectance was highly consistent with ASD reflectance. The root mean square error of reflectance (RMSEReflectance) across all bands decreased from 0.108 to 0.012 (88.89 % reduction), and the average RMSEReflectance across all samples decreased by 88.64 %. Compared to the original UAV data, the optimized data achieved the best inversion performance with the FLA-ELM model: the coefficient of determination (R²) of the test set increased from 0.608 to 0.755 (24.2 % relative improvement), and the RMSE of chlorophyll content (RMSEChl) decreased from 6.518 μg/cm2 to 5.371 μg/cm2. These results validate the effectiveness of the proposed spectral optimization scheme. In conclusion, modeling based on optimized UAV hyperspectral imagery improves the accuracy of rice chlorophyll content inversion, providing a novel approach for rice nutritional monitoring.

Why it matches plant phenotyping methodsUAVハイパースペクトル画像からイネのクロロフィル含量を推定する手法を最適化し、精度を定量的に検証しており、植物フェノタイピング手法が中心である。

abstractThis study optimizes UAV hyperspectral data based on ground-based spectrometer data.

Nutrient Deficiency Detection and Yield Loss Prediction in Black Pepper Using U2Net and Ensemble of Shallow CNN and MobileNetV2 →

BACKGROUND: Digital image analysis combined with deep learning offers powerful tools for detecting plant nutrient deficiency (ND), a critical challenge in precision agriculture. AIMS: This study aims to develop an ensemble transfer learning approach for ND detection in black pepper (BP) and evaluate its effectiveness for classification and yield‐loss (YL) prediction. METHODS: An ensemble of MobileNetV2 and a custom‐made shallow convolutional neural network was implemented, with U2Net‐based background removal to improve feature extraction. The model was validated on the BP Dataset (DS)—BPNutriDef03 (4469 images)—and tested on a rice DS (4399 images). The framework included (1) ND classification using leaf imagery analysis and (2) YL forecasting based on nutrient deficiency severity (NDS). RESULTS: The ensemble achieved classification accuracies of 99.22% for BP and 95.14% for rice. The yield prediction based on the NDS model estimated the YL of 27.83% for BP and 33.42% for rice. CONCLUSIONS: The proposed approach demonstrates robust performance and generalizability, offering a scalable, automated decision‐support system for ND monitoring and yield prediction in precision crop management.

Why it matches plant phenotyping methods植物の葉画像から栄養欠乏状態を推定する画像解析・深層学習手法を開発し、複数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。

abstractDigital image analysis combined with deep learning offers powerful tools for detecting plant nutrient deficiency (ND)

Modeling and validation of coupled air–water–mud structure interactions on rice seedlings under mechanical weeding disturbance →

Flow disturbances induced by mechanical weeding significantly affect the mechanical behavior of rice seedlings, posing a critical challenge to the mechanization of paddy field operations. To elucidate the mechanisms by which weeding blades affect the mechanical properties of rice seedlings, we developed a coupled CFD model integrating air–water–mud three-phase flow with the flexible structure of rice seedlings. The model accounts for both hydrodynamic forces and the biomechanical properties of seedlings, enabling accurate prediction of deflection, displacement, and stress responses under blade-induced disturbance. Validation experiments, including seedling deflection under steady flow and field measurements of flow fields around operating blades, demonstrated that the model’s predictions deviate from measured data by less than 10 %, confirming its accuracy and robustness. The results indicate that, during operation, maximum seedling stress reached 8.37 MPa, and maximum intra-row displacement was 53.34 mm (≈ 20 % of seedling height) at 10 days after transplanting, decreasing by 29.9 % by 30 days as stiffness increased. Stress concentration occurred primarily at the seedling base and near the water–air interface, indicating critical regions for structural failure. These findings provide new mechanistic insight into the coupled dynamics of seedlings, fluid, and weeding blades, establishing a quantitative foundation for optimizing blade spacing, rotational speed, and working depth to minimize seedling damage during mechanical weeding.

Why it matches plant phenotyping methodsイネ苗の変位・たわみ・応力という植物状態を推定する連成CFDモデルを開発し、実測値で検証しており、植物表現型の取得・推定手法が研究の中心である。

abstractwe developed a coupled CFD model integrating air–water–mud three-phase flow with the flexible structure of rice seedlings

Preparation and performance study of hydrogel microneedle sensors for in situ monitoring of potassium ions in rice plants. →

To achieve real-time in situ monitoring of potassioum ions (K⁺) concentrations within plants and overcome the limitations of traditional destructive sampling methods, this study developed a microneedle (MN) biosensor featuring an ion-selective hydrogel at its tip. The sensor’s calibration curve and sensitivity were evaluated through in vitro electrochemical testing. Microforce testing and scanning electron microscopy were used to analyze the mechanical strength and microstructure of the MNs. Practical performance was validated through agarose gel recovery experiments and in vivo salt stress monitoring in rice via ion chromatography. The results indicate that the sensor exhibits near-Nernstian sensitivity toward K⁺ (59.3 ± 0.35 mV/decade), with a linear range from 0.1 mM to 100 mM and a detection limit of 3.5 × 10⁻³ mM. The sensor exhibited a rapid response (T95% = 15 ± 3 s), excellent stability, and high batch-to-batch reproducibility (RSD = 0.038%). The average breaking force of the MNs was 25.3 ± 2.1 mN, significantly exceeding the puncture threshold of the rice leaf epidermis (approximately 5–15 mN). In vivo experiments demonstrated that the sensor successfully monitored rapid K⁺ efflux dynamics in rice leaves under salt stress, and the results were highly consistent with those of ion chromatography (R² = 0.999). The hydrogel microneedle sensor developed in this study demonstrates reliable performance, providing a robust in situ analytical tool for investigating plant ion physiology and mechanisms of response to environmental stresses.

Why it matches plant phenotyping methodsイネ葉内のK⁺動態という植物生理状態をin situ測定するマイクロニードルセンサーを開発し、校正・感度・再現性・実植物での妥当性を検証しており、植物フェノタイピング手法が中心である。

abstractthis study developed a microneedle (MN) biosensor featuring an ion-selective hydrogel at its tip

Application of Firefly Algorithm for Optimizing Backpropagation Method in Identifying Types of Rice Plant Diseases →

Rice is the main food source for Indonesians, with consumption continuing to increase in line with population growth. To meet this growing demand, the use of modern technology is important to increase rice production. However, rice plants are highly susceptible to various diseases that can reduce yields and lower the quality of rice crops. Diseases such as leaf blight, blast, leaf rot, brown spot, stripe spot, and tungro are threats to rice productivity, requiring rapid and accurate prevention. This study applies a classification system to detect diseases in rice plants using an artificial neural network (ANN) with the Backpropagation method. Backpropagation, although effective, has weaknesses, such as long convergence time and sensitivity to initial weight values, which often cause the model to get stuck at local minimum values, thereby reducing its overall performance. To overcome these weaknesses, the Firefly Algorithm (FA) is used as an optimization technique to improve the performance of Backpropagation. The results show that the use of the Backpropagation method produces an accuracy of 43%. However, when combined with the Firefly Algorithm (BPP-FA), the accuracy increases significantly, producing a value of 90%. This increase shows that BPP-FA improves accuracy in detecting diseases in rice plants. This combination of methods is expected to provide a reliable and efficient solution for detecting diseases in rice plants, thereby improving the quality and productivity of rice cultivation.

Why it matches plant phenotyping methodsイネ病害を対象に、BackpropagationとFirefly Algorithmを組み合わせた分類手法を開発・比較し、病害検出精度を評価している。植物の病害状態を観測から推定する計算手法が研究の中心である。

titleApplication of Firefly Algorithm for Optimizing Backpropagation Method in Identifying Types of Rice Plant Diseases

Self-Supervised Vision Transformer with Swarm Intelligence for Pattern-Aware Crop Stress Detection in Smart Farming Environments →

The following limitations exist in the traditional crop stress detection techniques in the background of smart farming applications: low detection accuracy, inefficient feature extraction, poor adaptability to the environment, and high computational complexity. Stress detection using traditional machine learning and convolution-based methods is inefficient in capturing complex stress patterns of drought, pests, diseases, and nutrient deficiency, affecting productivity and precision agriculture systems. To overcome these challenges, a novel self-supervised vision Transformer with swarm intelligence (SSVT-SI) based efficient and pattern-aware crop stress detection model is introduced. The proposed method leverages self-supervised learning for meaningful representation learning from unlabeled agricultural images and applies a Vision Transformer for long-range spatial relationships and hidden stress patterns in crops. Furthermore, to optimize feature selection, and to enhance the classification performance in low computational cost, Swarm Intelligence optimization is embedded. The model was tested on two sets of rice leaf disease and PlantVillage, and the pictures of healthy plants and stressed plants were taken under different farm conditions. Our experimental results achieve 98.42% accuracy, 97.86% precision, 97.54% recall, 97.70% F1-score and 0.052 loss value when compared with the existing CNN and hybrid deep learning methods. The framework provides for accurate early detection of stress, minimizes manual stress monitoring, supports the smart farming vision of agriculture and enables intelligent farming of crops to ensure sustainable agricultural production.

Why it matches plant phenotyping methods植物画像からストレス・病害状態を推定する自己教師ありVision Transformer手法の開発と比較評価が中心であり、植物表現型取得に該当する。

abstracta novel self-supervised vision Transformer with swarm intelligence (SSVT-SI) based efficient and pattern-aware crop stress detection model is introduced.

Prediction of Diseases in Paddy Crop Using Machine Learning and Deep Learning →

Abstract Paddy crop diseases have a significant impact on the production of rice across the globe with immense losses in crop production as well as food security. Conventional disease diagnosis systems depend on visual inspection where the visual system is time-consuming, subjective and in many cases inaccurate when in the field. The use of artificial intelligence or, specifically, machine learning, and deep learning offers potent plant disease detection instruments in recent developments. The paper provides a detailed disease prediction model of paddy crop based on machine learning and deep learning. An acquired dataset in the form of field was used with images of the leaves of healthy and diseased paddy, which included rice blast, bacterial leaf blight, brown spot, and sheath blight. In the case of handcrafted machine learning models, color, texture, and shape attributes were obtained and categorized with the help of Support Vector Machine, Random Forest, k-Nearest Neighbors, and Logistic Regression algorithms. Custom Convolutional Neural Network and transfer-based architectures (ResNet50 and EfficientNet-B0) were both trained to serve as deep learning models. Experiment scores prove that deep learning models are much better than traditional machine learning classifiers where EfficientNet-B0 model with highest classification accuracy of 97.4% was made. The confusion matrix and learning curve results indicate good generalization that has been achieved by the models in realistic field conditions. The results indicate deep learning as a powerful tool to diagnose paddy disease automatically and as being applicable to smart farming to predict disease early and avoid losses caused by disease.

Why it matches plant phenotyping methodsイネ葉画像から病害状態を自動分類する機械学習・深層学習モデルが研究の中心であり、植物病害という状態の推定手法を開発・評価している。

abstractThe paper provides a detailed disease prediction model of paddy crop based on machine learning and deep learning.

A Low-Cost RGB-Based Image Processing Method for High-Throughput Assessment of Rice Grain Chalkiness →

Although numerous rice genotypes have been developed worldwide, post-harvest evaluation of chalkiness, a key grain trait, remains a significant challenge in breeding programs. Conventional phenotyping methods rely on manual grain separation and analysis, which limits the speed and performance of decision-making. This study aimed to assess the efficiency of a low-cost, image-based phenotyping method for characterizing rice grain chalkiness and morphology traits (grain length and width) in comparison with traditional evaluation methods. Grains from 270 rice samples were imaged using a hyperspectral camera (VNIR, 400–1000 nm) and a Nikon digital single-lens reflex (DSLR) camera. Only RGB information was used for analysis, including RGB channels extracted from hyperspectral imagery to simulate low-cost setups. Python scripts were used to segment grains, estimate morphological parameters, and calculate chalkiness degree. Results from both imaging systems were compared with reference data obtained from the SeedCount platform. Strong correlations were observed with SeedCount data, reaching 93% for hyperspectral-RGB extraction and 76% for the RGB system. Binary classification metrics showed high discriminative performance, with area under the curve (AUC) values above 0.90 for most traits. The proposed method enabled image acquisition and processing in approximately 21 s per sample, compared to 1.5 min required by the conventional platform. The findings demonstrate the feasibility of a rapid and low-cost image-based phenotyping strategy to support rice breeding programs, particularly for chalkiness quantification and grain morphology assessment. The complete image-processing pipeline is provided as supplementary material, reinforcing the transparency and reproducibility of the method.

Why it matches plant phenotyping methods低コストRGB画像によるイネ粒の白未熟粒率・形態形質の抽出法を開発し、従来法およびSeedCountと比較検証しており、表現型取得・解析手法が研究の中心である。

abstractThis study aimed to assess the efficiency of a low-cost, image-based phenotyping method for characterizing rice grain chalkiness and morphology traits (grain length and width) in comparison with traditional evaluation methods.

Structural volume composition of internodes determines culm non-structural carbohydrates accumulation in rice →

Non-structural carbohydrates (NSC) stored in the stem play a crucial role in supporting yield formation in rice. However, internode morphological determinants of NSC accumulation are unclear. This study aimed to clarify the relationship between internode morphology and NSC accumulation and to identify a robust morphological indicator for evaluating NSC accumulation capacity. Two years of field experiments were conducted using multiple cultivars. The NSC content was quantified for individual internodes and at the whole-plant culm level, and its relationships with internode morphological traits were analyzed. Since the upper internodes (UIN; first and second internodes) and lower internodes (LIN; third and subsequent internodes) exhibited contrasting roles in NSC accumulation, a novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm. The VCR of UIN/LIN showed the strongest correlation with culm NSC and high reproducibility across years, outperforming simple morphological traits. Manipulation of internode development using plant growth regulators demonstrated that altering VCR effectively modified culm NSC accumulation. Accordingly, the VCR of UIN/LIN serves as a robust morphological indicator of culm NSC accumulation capacity, providing a practical framework for improving NSC accumulation to achieve high and stable yield performance in rice. Highlight This novel internode structural index robustly predicts the culm non-structural carbohydrate accumulation capacity, providing a practical morphological indicator for improving yield stability in rice.

Why it matches plant phenotyping methods稲の節間形態から茎のNSC蓄積能力を評価する新規指標VCRを導入し、複数年・品種で再現性と予測性能を検証しているため、形態表現型の測定・評価法が研究の中心である。

abstracta novel index was introduced, the volume composition ratio (VCR) of UIN/LIN, which represents their relative volumetric contributions within a culm.

A Multi-Scale Perception-Enhanced Lightweight Network with Knowledge Distillation for Rice Leaf Disease Detection. →

Rice is a critical crop for global food security and economic stability. However, various diseases, including rice blast and bacterial leaf blight, pose significant threats to rice cultivation. Existing methods for detecting rice leaf diseases suffer from low efficiency and limited generalization capability. These methods are incapable of capturing variations of disease characteristics across different growth cycles. Therefore, a lightweight detection model named lightweight knowledge distillation YOLO (LWKD-YOLO) is proposed. The convolutional layers in the YOLOv8 network are replaced with the ADown module. This change significantly reduces computational load while improving detection accuracy. A lightweight detection head, termed the lightweight shared re-parameterizable convolutional detection head (LSRP-Head), was designed. It incorporates group normalization RepConv, further reducing computational complexity while enhancing multi-scale perception capabilities. Furthermore, based on the improved ADown module and LSRP-Head, the YOLOv8x model is employed as a teacher model for inter-channel correlation knowledge distillation. This effectively enhances the ability to learn complex rice leaf disease features. The effectiveness of the proposed method was verified through ablation and comparative experiments on the constructed rice leaf disease dataset. Compared with the baseline model, LWKD-YOLO increases mAP@50 by 1.4%, reduces the number of parameters by 1.3M, and lowers FLOPs by 3.1G. As a result, the proposed model enables efficient rice leaf disease detection in complex environments, demonstrating notable economic and practical significance.

Why it matches plant phenotyping methodsイネ葉の病害状態を画像から検出する軽量な深層学習手法を開発し、データセット上で比較・アブレーション検証しており、植物表現型取得が中心である。

abstracta lightweight detection model named lightweight knowledge distillation YOLO (LWKD-YOLO) is proposed.

Intelligent Evaluation of Rice Resistance to White-Backed Planthopper (Sogatella furcifera) Based on 3D Point Clouds and Deep Learning →

Accurate assessment of rice resistance to Sogatella furcifera (Horváth) is essential for breeding insect-resistant cultivars. Traditional assessment methods rely on manual scoring of damage severity, which is subjective and inefficient. To overcome these limitations, this study proposes an automated resistance evaluation approach based on multi-view 3D reconstruction and deep learning–based point cloud segmentation. Multi-view videos of rice materials with different resistance levels were collected over time and processed using Structure from Motion (SfM) and Multi-View Stereo (MVS) to reconstruct high-quality 3D point clouds. A well-annotated “3D Rice WBPH Damage” dataset comprising 174 samples (15 rice materials, three replicates each, 45 pots) was established, where each sample corresponds to a reconstructed 3D point cloud from a video sequence. A comparative study of various point cloud semantic segmentation models, including PointNet, PointNet++, ShellNet, and PointCNN, revealed that the PointNet++ (MSG) model, which employs a Multi-Scale Grouping strategy, demonstrated the best performance in segmenting complex damage symptoms. To further accurately quantify the severity of damage, an adaptive point cloud dimensionality reduction method was proposed, which effectively mitigates the interference of leaf shrinkage on damage assessment. Experimental results demonstrated a strong correlation (R2 = 0.95) between automated and manual evaluations, achieving accuracies of 86.67% and 93.33% at the sample and material levels, respectively. This work provides an objective, efficient, and scalable solution for evaluating rice resistance to S. furcifera, offering promising applications in crop resistance breeding.

Why it matches plant phenotyping methods3D画像再構成と深層学習によってイネの害虫被害症状・被害重症度を定量化する手法を開発・検証しており、植物表現型取得が研究の中心である。

abstractthis study proposes an automated resistance evaluation approach based on multi-view 3D reconstruction and deep learning–based point cloud segmentation.

Genetic Variation in Drought Resistance of Rice (Oryza sativa L.) at the Germination Stage →

Abstract Drought stress is a major abiotic factor limiting rice growth and productivity. Establishing scientifically screening methods at the germination stage is critical for identifying superior drought-adapted genotypes. This study aimed to develop a novel discriminant equation for evaluating drought resistance in rice at the germination stage, with field yield data used as a benchmark to calibrate laboratory-based assessments. Seventy-six rice accessions from southern China were used as experimental materials. Drought resistance during germination was assessed using nine key physiological indicators, including vigor index and root length. Three statistical methods—Membership Function Comprehensive Evaluation Value (MFSV), Principal Component Analysis Comprehensive Evaluation Value (PCASV), and Grading Coefficient (GC)—were applied to classify drought resistance levels from different analytical perspectives. Field drought treatment was conducted throughout the growth period, and final grain yield was used to represent the comprehensive drought resistance (CDR) of each accession. Results showed that the nine indicators responded variably to drought stress. Drought resistance from the three statistical methods were inconsistent, with only 45% of accessions showing consistent classification across all methods, indicating that the choice of analytical approach influences the outcome. Correlation analysis revealed that MFSV, PCASV, and GC were all positively correlated with CDR, supporting the use of CDR as a reliable reference factor for integrating laboratory-based evaluations. Finally, a Fisher discriminant function was established using MFSV, PCASV, and GC as independent variables (X) and CDR as the dependent variable (Y), providing a more comprehensive and scientifically grounded method for assessing drought resistance during rice germination.

Why it matches plant phenotyping methodsイネの発芽期の乾燥抵抗性を評価するスクリーニング手法と識別関数の開発が研究の中心であり、圃場収量を基準に検証している。

abstractEstablishing scientifically screening methods at the germination stage is critical for identifying superior drought-adapted genotypes.

A Comparative Study of Different CNN Architectures for Real-World Image Classification in Bangladesh →

Abstract Convolutional Neural Networks (CNNs) are widely used for image classification, yet their performance strongly depends on dataset complexity and deployment constraints. This study presents a comparative evaluation of custom-designed CNN architectures and popular pre-trained models on five real-world image datasets from Bangladesh, spanning agricultural and infrastructural applications. The tasks include mango variety classification (15 classes), paddy disease clas-sification (35 classes), and three binary classification problems: road damage, footpath encroachment, and auto-rickshaw detection. In addition to task-specific CNNs, VGG16 and ResNet50 are evaluated using fixed feature extraction and transfer learning strategies. The results show that transfer learning, particularly with ResNet50, achieves the highest accuracy on complex multi-class datasets, while custom CNNs deliver competitive performance on binary tasks with sub-stantially lower computational cost. These findings emphasize the trade-off between accuracy and efficiency and highlight the importance of selecting model architectures based on dataset characteristics and deployment requirements.

Why it matches plant phenotyping methods複数CNNアーキテクチャを実データセットで比較評価することが研究の中心であり、イネ病害画像分類は植物の病態を画像から推定するフェノタイピングに該当する。

abstractThis study presents a comparative evaluation of custom-designed CNN architectures and popular pre-trained models on five real-world image datasets from Bangladesh, spanning agricultural and infrastructural applications.

Enhancing multiclass plant disease classification using GAN-boosted vision transformer with XAI insights →

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.

StomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment →

ABSTRACT Stomata are microscopic pores that play a vital role in transpiration and gaseous exchange from leaf surfaces in plants. The stomatal density and size directly influence photosynthesis and hydrodynamics capacity. Conventional approaches for counting and determining stomatal density is labour-intensive and lack scalability. Although there are several AI-based stomata finder tools that were published in the last decade, existing models were trained on model plants like wheat, barley and Arabidopsis . Stomata in such model plants are generally elliptical, but applying a universal model to all plant species is not feasible due to their diverse morphological characteristics. Previous studies have suggested using the stomatal index to quantify the ratio between epidermal cells and total stomatal count. However, this approach can be difficult to apply consistently, as epidermal cell shape and size vary across plant species. Instead, we propose measuring stomatal density based on the number of stomata per total imaged pixel area in the captured images. In this study, a comparison between YOLOv12 and RF-DETR models were made for real-time stomata detection in normal and difficult-to-image and out-of-focus occluded images. The in-house training dataset consisted of images of 300 rice,100 barley and 50 sugarcane leaves that were captured against a dark background. YOLOv12 outperformed RF-DETR with higher mAP50:95 score. The models were trained with image augmentation for 300 epochs and YOLOv12 achieved a peak mean average precision of 98.5% and exceled at detecting stomata across abaxial and adaxial surfaces of leaves of both monocot and dicot plants. StomaQuant has also been shown to be effective for both epidermal peel and ethanol decolorised samples. Thus, StomaQuant can be used to effectively and efficiently estimate the stomatal density and size in a wide range of host plant species.

Why it matches plant phenotyping methods気孔の検出・密度・サイズ推定を目的とする深層学習画像解析手法を開発し、複数モデルおよび困難画像で性能比較・検証しており、植物表現型取得が研究の中心である。

titleStomaQuant: Deep Learning-Based Quantification for Stomatal Trait Assessment

Development and Performance Evaluation of a Hydrogel Microneedle Sensor for In Situ Monitoring of Potassium Ions in Rice Plants →

Abstract The dynamic balance of potassium ions (K + ) in rice plants is critical to their growth, development, and stress resistance. To achieve in-situ, real-time monitoring of K + levels in rice plants and overcome the limitations of traditional destructive sampling methods, this study developed a biosensor based on ion-selective hydrogel microneedles. Key performance parameters of the sensor, including its calibration curve and sensitivity, were systematically evaluated via in vitro electrochemical tests. Meanwhile, the mechanical strength and microstructure of the microneedles were characterized using micro-force testing. The practical applicability of the sensor was validated through agarose gel recovery experiments and in vivo K + monitoring in rice plants under salt stress, with results cross-validated against ion chromatography as a reference method. The sensor exhibited a sensitivity close to the Nernstian response (59.0 ± 0.11 mV/decade), a linear detection range of 10 − 4 to 10 − 1 M, and a detection limit of 3.0×10 − 5 M. It also demonstrated a fast response time (T 95 + loss in rice leaves under salt stress, showing a strong correlation with the standard method (R 2 = 0.985). In conclusion, the developed hydrogel microneedle sensor is a stable, reliable, and effective tool for in-situ K + analysis in rice plants, providing valuable insights into plant ion physiology and the mechanisms underlying responses to environmental stress.

Why it matches plant phenotyping methodsイネ体内のK⁺を非破壊・リアルタイム測定するセンサーの開発と性能検証が研究の中心であり、植物の生理状態を定量化するフェノタイピング手法に該当する。

abstractthis study developed a biosensor based on ion-selective hydrogel microneedles

Predicting Multiple Traits of Rice and Cotton Across Varieties and Regions Using Multi-Source Data and a Meta-Hybrid Regression Ensemble. →

Timely and accurate prediction of crop traits is critical for precision breeding and regional agricultural production. Previous studies have primarily focused on single crop yield traits, neglecting other crop traits and variety-specific analyses. To address this issue, we employed a Meta-Hybrid Regression Ensemble (MHRE) approach by using multiple machine learning (ML) approaches as base learners, integrating regional multi-year, multi-variety crop field trials with satellite remote sensing indices, meteorological and phenological data to predict major crop traits. Results demonstrated MHRE's optimal performance for rice and cotton, significantly outperforming individual models (RF, XGBoost, CatBoost, and LightGBM). Specifically, for rice crop, MHRE achieved highest accuracy for yield trait (R 2 = 0.78, RMSE = 0.59 t ha -1 ) compared to the best individual model (XGBoost: R 2 = 0.76, RMSE = 0.61 t ha -1 ); traits like effective spike also showed strong predictability (R 2 = 0.64, RMSE = 27.81 10,000·spike ha -1 ). Similarly, for cotton, MHRE substantially improved yield trait prediction (R 2 = 0.82, RMSE = 0.33 t ha -1 ) compared to the best individual model (RF: R 2 = 0.77, RMSE = 0.36 t ha -1 ); bolls per plant accuracy was highest (R 2 = 0.93, RMSE = 2.27 bolls plant -1 ). Moreover, rigorous validation confirmed that crop-specific MHRE models are robust across five rice and three cotton varietal groups and are applicable across six distinct regions in China. Furthermore, we applied the SHAP (SHapley Additive exPlanations) method to analyze the growth stages and key environmental factors affecting major traits. Our study illustrates a practical framework for regional-scale crop traits prediction by fusing multi-source data and ensemble machine learning, offering new insights for precision agriculture and crop management.

Why it matches plant phenotyping methods複数ソースデータとメタ・ハイブリッド回帰アンサンブルにより、イネ・ワタの収量や形態関連形質を推定し、モデル比較と品種群・地域横断検証を行っているため、植物形質推定手法が中心である。

abstractwe employed a Meta-Hybrid Regression Ensemble (MHRE) approach by using multiple machine learning (ML) approaches as base learners, integrating regional multi-year, multi-variety crop field trials with satellite remote sensing indices, meteorological and phenological data to predict major crop traits.

Plant-to-camera enabled 3D morphological reconstruction: A high-fidelity approach for plant phenotyping →

Three-dimensional (3D) plant modeling is fundamental for precise phenotyping analysis. In this study, a high-throughput, multi-stage 3D reconstruction pipeline is proposed to efficiently generate point clouds with real-world physical scales. The pipeline comprises five core components: data acquisition, semantic segmentation, sparse reconstruction, dense reconstruction, and phenotypic trait extraction. To enhance the accuracy of plant structure identification, the SegFormer semantic segmentation model is employed for pixel-level segmentation, thereby guiding the subsequent reconstruction stages to focus specifically on plant regions. In feature-based sparse reconstruction, the scarcity of texture information often results in an insufficient number of matching point pairs, leading to failures in camera parameter estimation. Furthermore, the reconstructed point clouds frequently lack consistency with real-world scale. To address these challenges, a calibration-constrained sparse reconstruction method, Sparse Reconstruction from Calibrated Images (SRCI) was proposed. By integrating precise calibration results computed in a custom world coordinate system, SRCI circumvents the limitations of traditional feature matching in scenarios with scarce features, thereby resolving camera pose estimation failures caused by insufficient matching pairs and generating sparse point clouds with true physical scale. Subsequently, CL-MVSNet was employed to generate dense point clouds. The validation experiments are conducted from three perspectives: visual comparison, phenotypic accuracy assessment, and reconstruction accuracy evaluation. First, five groups of rapeseed plants are selected to perform visual comparisons between the proposed reconstruction pipeline and other advanced reconstruction software. The results demonstrate that the proposed reconstruction pipeline achieves superior performance in terms of visual quality. Additionally, three phenotypic parameters of rapeseed plants—plant height, leaf width, and chord length are manually measured and compared with the corresponding phenotypic parameters extracted from the reconstructed point clouds. The analysis revealed mean absolute errors of 4.93 mm, 3.16 mm, and 6.02 mm; root mean square errors of 6.38 mm, 4.56 mm, and 8.35 mm; and coefficients of determination of 0.98, 0.94, and 0.93, respectively. To further validate the generalization performance and accuracy of the proposed method, four additional plant categories with progressively increasing complexity were selected for accuracy evaluation. For the first three plant categories, the Chamfer distances between the reconstructed point clouds and ground truth point clouds were all within 2.4 mm. In the most complex rice reconstruction experiments, the Chamfer distances between the reconstructed point clouds and ground truth point clouds were all within 9 mm, while other methods failed to achieve effective reconstruction. The proposed high-throughput, automated, and high-quality 3D reconstruction framework provides reliable technical support and data resources for genetic research applications, including gene localization, quantitative trait locus analysis, and genome-wide association studies. • We propose a Plant-to-Camera system for high-quality 3D plant reconstruction within 6 minutes, showing strong generalizability. • Our Sparse Reconstruction from Calibrated Images (SRCI) method prevents failures in feature-scarce scenes. • We develop SFNet, a feature descriptor module that fuses multi-frequency to enhance plant feature representation.

Why it matches plant phenotyping methods植物の3D再構成と形質抽出パイプラインを開発し、植物形質および再構成精度を検証しており、フェノタイピング手法が中心である。

abstracta high-throughput, multi-stage 3D reconstruction pipeline is proposed to efficiently generate point clouds with real-world physical scales

Characterization of Viruses in Phloem by Correlative X-Ray Microtomography (μCT)-Volume Electron Microscopy (vEM) Imaging. →

Studying virus-infected phloem is of significant importance, as it not only enhances our understanding of viral pathogenesis but also leverages viruses as tools to expand knowledge about plant phloem physiology. The uneven distribution pattern of phloem-infecting viruses poses methodological challenges for such studies-requiring both large field of view (FOV) and high-resolution imaging. A comprehensive anatomical analysis of the phloem necessitates global visualization, while resolving viral structures demands local high-resolution observation. This chapter describes a method, the X-ray microtomography (μCT)-volume electron microscopy (vEM) correlative imaging technique, which effectively addresses these methodological requirements, where μCT provides the large FOV for identification of regions of interest, followed by vEM acquisition of high-resolution images. It is a six-step protocol, including: (1) sample preparation, (2) flaw detection, (3) overview imaging by μCT, (4) identifying viral infection regions, (5) high-resolution imaging by vEM, and (6) image processing and analysis. In this workflow, the steps of sample preparation and identification of viral infection regions are critical. This protocol was originally established for investigating Southern rice black-streaked dwarf virus (SRBSDV) infection in rice phloem, with parameters optimized for plant reoviruses. We provide advice on how to adapt the approach for studying other viral infections.

Why it matches plant phenotyping methods植物のウイルス感染部位と師部構造をμCT・vEM相関イメージングで取得・解析する6段階プロトコルが中心であり、植物状態の画像ベース計測法に該当する。

abstractThis chapter describes a method, the X-ray microtomography (μCT)-volume electron microscopy (vEM) correlative imaging technique

A Non‐Destructive Method for Detecting Magnaporthe grisea Infection in Rice Plants at an Early Presymptomatic Stage Using Volatile Biomarkers →

Rice yields are severely affected by blast disease caused by Magnaporthe grisea (MGR), an ascomycete fungus. Plants and pathogens often interact through reprogramming of phytohormone‐mediated signalling pathways, which alters the pattern of volatile organic compounds (VOCs) produced. Many of these VOCs can be used to predict specific diseases and are unique to specific pathogen invasions. A high‐throughput technique that can detect new pathogen incursions at an early asymptomatic stage can increase our readiness to take mitigation action. In this study, we sought to develop a disease detection method that relies on signature volatile organic compounds (S‐VOCs) emissions to detect MGR infection in rice at its earliest and presymptomatic stage. As S‐VOCs in rice‐MRG interactions have not yet been identified, rice leaves were artificially inoculated and their volatile profiles monitored at three stages: healthy (mock inoculated), MGR challenged (asymptomatic), and MGR challenged (symptomatic). In headspace solid‐phase microextraction (HS‐SPME), VOCs are collected for analysis by GC-MS. Among the 34 annotated VOCs, two compounds (octadecanal and 1‐nonanol) were found only in MGR‐inoculated plants at the asymptomatic stage. In addition, compared with healthy control plants, MGR‐inoculated plants produced more methyl‐salicylate (MeSA) and reactive oxygen species (ROS), indicating that MeSA and ROS play a role in short‐ and long‐range signalling. In the early stages of MGR infection, when symptoms are barely noticeable, octadecanal and 1‐nonanol were both able to distinguish between healthy and MGR‐infected headspaces. This study further substantiates the potential for non‐invasive early disease detection using VOCs.

Why it matches plant phenotyping methodsイネの感染状態をVOCsで非破壊・早期検出する方法の開発が研究の中心であり、単なる病理実験の routine measurement ではない。

abstractwe sought to develop a disease detection method that relies on signature volatile organic compounds (S‐VOCs) emissions to detect MGR infection in rice at its earliest and presymptomatic stage.

RGB-TIR Fusion and Dual-Stream YOLO for Non-threshing Rice Panicle Phenotyping and Yield Trait Estimation →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsRGB-TIR融合とYOLOによるイネ穂の表現型解析および収量形質推定が題名上の中心であり、植物形質の取得・推定手法に該当する。

titleRGB-TIR Fusion and Dual-Stream YOLO for Non-threshing Rice Panicle Phenotyping and Yield Trait Estimation

MPRT-DETR: Multi-Platform Real-Time Rice Panicle Detection and Counting Based on the Transformer Method and Complex Field Imagery →

Abstract has not been obtained from indexed metadata or an accessible article page.

Why it matches plant phenotyping methodsイネ穂の検出・計数を目的とする画像解析手法の開発であり、植物器官数という表現型の抽出が研究の中心です。

titleMPRT-DETR: Multi-Platform Real-Time Rice Panicle Detection and Counting Based on the Transformer Method and Complex Field Imagery

Uncovering the importance of spatiotemporal resolution in satellite-based rice yield estimation using a simple but effective proxy →

Accurate crop yield mapping is essential for assessing climate change impacts on agriculture and identifying yield gaps. While high spatiotemporal resolution satellite products such as Planet Fusion (PF) with daily 3 m resolution imagery, offer new opportunities for detailed crop monitoring, the impact of the spatiotemporal resolution of satellite data on crop yield estimation remains underexplored. In this study, we create a benchmark dataset consisting of a 3 m resolution rice yield map for a heterogeneous paddy landscape with different cultivars, using PF-based accumulated near-infrared radiation from vegetation (NIRvPₐccᵤₘ) between heading and harvest stages. Comparisons against plot-level rice yield measurements yield an R² of 0.76. We cross-compare yield estimates from other satellite products—MODIS, Sentinel-2, Landsat 8, and a spatial-temporal Savitzky-Golay product—against the PF-based benchmark yield data resampled to relevant coarser spatiotemporal scales. We find that, compared to PF-based yield estimation, lower spatiotemporal resolution leads to higher yield underestimation. Additionally, the downsampled PF data exhibit patterns similar to those observed in the coarser-resolution products. High-spatiotemporal resolution PF data captures peak growth stages more accurately, alleviating the mixed-pixel problem and mitigating nonlinear effects where reflectance-yield relationships deviate from linear scaling. In contrast, coarser-spatiotemporal resolution products, such as Landsat 8 has longer revisit intervals, often miss critical phenological phase transitions (e.g., peak growing season), resulting in substantial yield underestimations compared to PF. Notably, we find that yield underestimations caused by lower spatiotemporal resolutions can surpass inter-annual yield variations. These findings underscore the importance of using satellite imagery with both high spatial resolution and frequent revisits to achieve sufficiently accurate yield estimates in smallholder-dominated, heterogeneous landscapes. By highlighting the trade-offs associated with different satellite-based spatiotemporal resolutions, the study underscores the importance of considering resolution impacts on yield estimation, offering insights for optimizing Earth observation-based agricultural management, particularly in smallholder farming settings.

Why it matches plant phenotyping methods衛星データによるイネ収量推定を中心に、ベンチマークデータセットの作成、異なる衛星時空間解像度の比較、圃場収量との検証を行っており、植物形質取得法が中核である。

abstractwe create a benchmark dataset consisting of a 3 m resolution rice yield map

A Non-Destructive Method for Detecting Magnaporthe grisea Infection in Rice Plants at an Early Presymptomatic Stage Using Volatile Biomarkers. →

Rice yields are severely affected by blast disease caused by Magnaporthe grisea (MGR), an ascomycete fungus. Plants and pathogens often interact through reprogramming of phytohormone-mediated signalling pathways, which alters the pattern of volatile organic compounds (VOCs) produced. Many of these VOCs can be used to predict specific diseases and are unique to specific pathogen invasions. A high-throughput technique that can detect new pathogen incursions at an early asymptomatic stage can increase our readiness to take mitigation action. In this study, we sought to develop a disease detection method that relies on signature volatile organic compounds (S-VOCs) emissions to detect MGR infection in rice at its earliest and presymptomatic stage. As S-VOCs in rice-MRG interactions have not yet been identified, rice leaves were artificially inoculated and their volatile profiles monitored at three stages: healthy (mock inoculated), MGR challenged (asymptomatic), and MGR challenged (symptomatic). In headspace solid-phase microextraction (HS-SPME), VOCs are collected for analysis by GC-MS. Among the 34 annotated VOCs, two compounds (octadecanal and 1-nonanol) were found only in MGR-inoculated plants at the asymptomatic stage. In addition, compared with healthy control plants, MGR-inoculated plants produced more methyl-salicylate (MeSA) and reactive oxygen species (ROS), indicating that MeSA and ROS play a role in short- and long-range signalling. In the early stages of MGR infection, when symptoms are barely noticeable, octadecanal and 1-nonanol were both able to distinguish between healthy and MGR-infected headspaces. This study further substantiates the potential for non-invasive early disease detection using VOCs.

Why it matches plant phenotyping methodsイネ感染個体の揮発性物質から無症状段階の病害状態を検出する非破壊法の開発が中心であり、単なる病理実験の routine 測定ではない。

abstractwe sought to develop a disease detection method that relies on signature volatile organic compounds (S-VOCs) emissions to detect MGR infection in rice at its earliest and presymptomatic stage.

Visualizing the distribution of various inorganic metals in brown rice by radiotracer experiments →

The distribution of inorganic elements in brown rice has been vigorously investigated for many years using the most advanced instruments of each era. The present study was a challenge to gain new insights into the distribution of various inorganic elements in brown rice by autoradiography using radioisotopes: 22Na, 45Ca, 54Mn, 55Fe, 60Co, 63Ni, 65 Zn, 90Sr, 203 Hg, and 210 Pb. Autoradiography of tissue sections using the Imaging Plate (IP) fully exploited its advantage of high-throughput imaging, enabling three-dimensional reconstruction that encompassed the entire brown rice grain. Consequently, characteristic distribution patterns of individual elements in the peripheral layer, endosperm, and embryo were identified following radiotracer supplementation to the culture solution. For instance, 63Ni was uniformly distributed within the endosperm during the early stages of development but progressively accumulated in the outer layers and embryo as growth advanced; such a pattern was not observed for 54Mn or 55Fe. To minimize the cost of the experiment, a direct injection method into the node was developed. This approach successfully visualized 203 Hg, demonstrating that its entry into the embryonic tissue is severely restricted irrespective of the developmental stage of the rice grain.

Why it matches plant phenotyping methods褐色米粒を対象に、オートラジオグラフィーとイメージングプレートで元素分布を高スループットに可視化し、三次元再構成する測定手法を中心に扱っているため、植物器官の状態を抽出するフェノタイピング手法として含める。

abstractThe present study was a challenge to gain new insights into the distribution of various inorganic elements in brown rice by autoradiography using radioisotopes

3D imaging reveals robustness and plasticity of cell division in rice early embryogenesis →

Abstract Embryogenesis is an essential process involving a series of formative cell divisions that contribute to establishing the plant’s body axis. In many dicotyledons, the asymmetric cell division of the zygote gives rise to two daughter cells, which develop into two distinct cell lineages. In contrast, the fate of the two daughter cells and their contribution to the body axis formation remain poorly understood in the monocots. To address this question, we developed a method for three-dimensional imaging of early rice embryos. Our observations demonstrated that both an egg cell and two synergids are polarized prior to fertilization and are anchored to the micropylar end of the ovule via a cell wall-like structure stained with SR2200. Upon fertilization, the zygote undergoes an asymmetric cell division with a ventrally tilted division plane. The following cell divisions are not strictly synchronized between the apical and basal lineages, exhibiting non-stereotypic patterns up to the globular stage of embryogenesis. Furthermore, we examined the role of auxin signaling in rice embryogenesis using the auxin response sensor DR5rev::NLS-3xVENUS. The reporter activity was first detected at the center of the globular embryos and subsequently extended along the apical–basal axis as embryogenesis progressed. Our results highlight the importance of the progressive establishment of the body axes within cell populations during early embryogenesis.

Why it matches plant phenotyping methodsイネ胚の三次元画像化法を開発し、細胞配置・分裂パターンという植物形態状態を取得しているため、表現型取得法が研究の中心である。

abstractwe developed a method for three-dimensional imaging of early rice embryos

Specificity of Arsenic Stress Detection by Raman Spectroscopy During Co-Occurrences of Nitrogen Deficiency and Narrow Brown Leaf Spot. →

Arsenic contamination in rice poses a potential health risk to populations dependent on their daily consumption. Previous work has shown that Raman spectroscopy is capable of nondestructively diagnosing arsenic uptake in rice; however, its diagnostic specificity in cases of concurrent abiotic and biotic stress remains unclear. As Raman spectroscopy relies on the detection of arsenic-induced stress patterns for diagnosis, the presence of additional stressors could potentially compromise diagnostic reliability. To address this gap, we evaluated the ability of Raman spectroscopy to detect arsenic uptake in the presence of both nitrogen deficiency (abiotic stress) and narrow brown leaf spot (biotic stress) across two Experiments. We found that nitrogen deficiency, while more severe than arsenic stress, did not affect arsenic detection. We also found that the diagnostic accuracy for both abiotic stressors (arsenic and nitrogen deficiency) depended on the plant growth stage, with arsenic detection being most reliable immediately after transplantation and nitrogen deficiency becoming more distinguishable as stress severity increased. Narrow brown leaf spot, though exhibiting minimal symptoms, remained sufficiently detectable. Altogether, these findings demonstrate that Raman spectroscopy remains a reliable method for diagnosing arsenic uptake and assessing overall rice health, even in the presence of additional stressors.

Why it matches plant phenotyping methodsラマン分光法によるイネのヒ素取り込み・ストレス状態の非破壊診断を、窒素欠乏や病害との併発条件で評価しており、植物表現型取得法の診断精度と頑健性が中心課題である。

abstractPrevious work has shown that Raman spectroscopy is capable of nondestructively diagnosing arsenic uptake in rice; however, its diagnostic specificity in cases of concurrent abiotic and biotic stress remains unclear.

High-Resolution Spectral Reflectance-based Crop Classification and Chlorophyll Content Estimation Using Machine Learning →

Precision agriculture progressively relies on remote sensing (RS) technologies to enhance crop classification and monitoring. Among various RS platforms, spectroradiometer offers the highest spectral precision, making them essential for validating the accuracy and performance of other RS methods. Each crop exhibits a unique spectral signature that corresponds to its biophysical characteristics. This spectral information plays a crucial role in accurately classifying crop types and assessing their health status, including water and nutrient availability. Specifically, evaluating crop chlorophyll content enables effective nitrogen management and yield optimization. This study focuses on collecting spectral data using a spectroradiometer (350-1050 nm) at a height of 30 cm above the crop canopy from eight crops, i.e., rice, finger millet, cotton, sunflower, sweet corn, broccoli, cauliflower, and brinjal, classifying the collected data, and measuring chlorophyll content using a Soil Plant Analysis Development (SPAD) meter and predicting the same using key spectral bands and machine learning (ML) techniques. Six supervised ML algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP) were employed for crop classification. The feature selection process revealed that the spectral range of 710-750 nm is the most significant for crop classification. The MLP model achieved the highest accuracy of 97% during training, 93% in testing, and 85% during validation stage, outperforming other ML classifiers. For chlorophyll content prediction, the RF demonstrated the best performance, with coefficient of determination values of 0.92 for training and 0.72 for testing stage. The ML-based framework, developed in this study, can be applied to various RS platforms, including satellites and unmanned aerial vehicles (UAVs), for crop classification and prediction of chlorophyll content. The developed modelling framework would assist government agencies and policymakers in identifying crop types accurately, enhancing agricultural planning, and optimizing resource allocation to support sustainable on-farm practices.

Why it matches plant phenotyping methods分光反射センシングと機械学習により作物のクロロフィル含量という植物形質を推定する枠組みが研究の中心であり、モデル性能の検証も行っているため。

abstractpredicting the same using key spectral bands and machine learning (ML) techniques

Improving nitrogen use efficiency in rice by estimating leaf nitrogen content with near-infrared spectroscopy and chemometric modeling. →

Accurate nitrogen management in rice (Oryza sativa L.) is essential for optimizing both crop productivity and environmental sustainability. This study evaluated the potential of Near-Infrared Spectroscopy (NIRS) combined with chemometric modeling to classify leaf nitrogen content (LNC) in five rice genotypes (Nerica, Rufipogon, IR64, Ciherang, and Curinga) subjected to five nitrogen fertilization levels (0%, 25%, 50%, 75%, 100%). Spectral data (350-2500 nm) were processed using Principal Component Analysis followed by Linear Discriminant Analysis (PCA-LDA) to distinguish nitrogen treatments and explore genotype-specific spectral responses. The 1700-2200 nm spectral region yielded the highest classification accuracy, consistently exceeding 94%, indicating strong sensitivity to nitrogen-related biochemical variation. Compared to conventional destructive methods, NIRS provides a non-invasive, rapid, and scalable alternative for nitrogen assessment in field conditions. This is the first study to demonstrate NIRS-based discrimination of nitrogen levels across multiple rice genotypes, offering new avenues for genotype-informed fertilization strategies and improved nitrogen use efficiency (NUE). The results support the objectives of the Green Campus Initiative at the Alliance Bioversity International & CIAT and contribute to broader Sustainable Development Goals (SDGs 2, 12, 13, and 15), promoting data-driven, environmentally responsible nutrient management in rice production.

Why it matches plant phenotyping methodsNIRSとケモメトリクスによりイネ葉窒素含量を非破壊推定・分類する手法が研究の中心であり、植物形質の取得と技術性能を評価している。

abstractThis study evaluated the potential of Near-Infrared Spectroscopy (NIRS) combined with chemometric modeling to classify leaf nitrogen content (LNC) in five rice genotypes

Deep Learning–Driven Image Classification Framework for Accurate Detection of Rice Plant Diseases →

Rice production is increasingly under threat by a serious fungal disease in the Chidambaram region of Cuddalore district, especially false smut, sheath blight, and brown spot, which are becoming more severe under global climate change. Usually, farmer do their inspections at a later stage, which causes critical damage to the rice crops. This manual inspection is error-prone, time-consuming, and subjective. In these situations, AI-enabled tools and methods are essential for accurate and timely rice disease prediction. This research introduces a novel approach using deep learning–driven image classification framework for accurate detection of rice plant diseases (DLDICF-ADRPD). The DLDICF-ADRPD undergoes three different stages, namely data collection, data preprocessing, feature extraction, detection and classification of diseases. This combination leads to an efficient and robust disease classification system. The series of experiments was conducted to assess the proposed DLDICF-ADRPD performance using large dataset of rice leaf images from different disease types and growth phases, obtained from the publicly accessible Kaggle datasets. When compared to other existing disease prediction models, our DLDICF-ADRPD model performs better. Overall, the suggested DLDICF-ADRPD design greatly increases the reliability and accuracy of disease recognition, supporting global food security and sustainable agriculture.

Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定する深層学習による画像分類手法の開発・性能評価が研究の中心であり、植物病害フェノタイピングに該当する。

abstractThis research introduces a novel approach using deep learning–driven image classification framework for accurate detection of rice plant diseases (DLDICF-ADRPD).

Phenotype-driven leaf deep metabolomics framework depicts key metabolisms and metabolites associated with yield traits in rice. →

Main conclusion This study links rice leaf metabolome to yield traits, identifying 13 key metabolites through computational metabolomics. These enable early prediction of high-yield varieties, enhancing screening strategies in crop breeding. Metabolites serve as dynamic indicators of plant phenotype, linking genotype and environment through metabolomics profiling. Here, we used a computational metabolomics approach to correlate leaf metabolites with yield traits in four indica rice varieties. Dani Gora, with the highest yield, showed distinct phenotypic and metabolic profiles compared to Njavera N96. Analysis of robust non-redundant mass features revealed maximal 'metabotype' and trait differences between these two varieties. Dani Gora displayed higher central metabolism diversity, while Njavera N96 showed elevated specialization in secondary metabolism. Comparative pathway impact analysis identified 14 central metabolites, especially involved in six metabolic pathways, significantly enriched and positively correlated with the yield parameters. Machine learning (Random Forest) and fold change analysis finally validated 13 key metabolites predictive of yield traits. This framework demonstrates how leaf metabolite classifiers can enable early, high-throughput screening for high-yield rice varieties, offering a tool for accelerating rice breeding strategies.

Why it matches plant phenotyping methods収量形質を予測する葉メタボローム解析・機械学習フレームワークを開発し、予測代謝物を検証しているため、単なる生物学的測定ではなく表現型推定法が中心である。

titlePhenotype-driven leaf deep metabolomics framework depicts key metabolisms and metabolites associated with yield traits in rice.

Epidermal Cell Dynamics Regulates Rice Lamina Joint Morphogenesis and Leaf Angle Formation through OsZHD1 and OsZHD2 Regulation. →

The lamina joint is a critical determinant of leaf angle and crop architecture. While epidermal cells play a fundamental role in organ morphogenesis, influencing the overall shape and function of plants, their impact on lamina joint morphology has been largely overlooked. A live-imaging system for the rice lamina joint epidermis is established in this study, enabling precise tracking of cellular dynamics during leaf angle formation. It is found that asymmetric elongation between the lateral and medial edges, determined by spatial differences in the longitudinal elongation and number of epidermal cells, is a key factor in leaf angle formation. Mutations in the homeobox genes OsZHD1 and OsZHD2 disrupt the growth patterns of lamina joint epidermal cells, resulting in a decreased leaf angle. Epidermis-specific restoration of OsZHD1 expression rescues the reduced leaf angle phenotype of oszhd1 oszhd2, confirming the pivotal role of epidermal development in lamina joint morphogenesis. Transcriptomic analysis indicates that OsZHD1 and OsZHD2 regulate auxin activity, which modulates leaf angle by restricting lamina joint epidermal growth. This study underscores the significance of epidermal cells in shaping the lamina joint and elucidates the critical role of OsZHD1 and OsZHD2 in regulating epidermal cell behavior and leaf angle formation.

Why it matches plant phenotyping methodsイネ葉舌関節表皮の細胞動態を追跡するライブイメージング系を確立し、葉角形成に関わる形態・成長を定量的に解析しており、表現型取得法が研究の中核に含まれる。

abstractA live-imaging system for the rice lamina joint epidermis is established in this study, enabling precise tracking of cellular dynamics during leaf angle formation.

Non-destructive detection of microplastics stress in rice seedling: an interpretable deep learning approach using excitation emission matrix fluorescence spectra of root exudates. →

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

EU-GAN: A root inpainting network for improving 2D soil-cultivated root phenotyping →

Beyond its fundamental roles in nutrient uptake and plant anchorage, the root system critically influences crop development and stress tolerance. Rhizobox enables in situ and nondestructive phenotypic detection of roots in soil, serving as a cost-effective root imaging method. However, the opacity of the soil often results in intermittent gaps in the root images, which reduces the accuracy of the root phenotype calculations. We present a root inpainting method built upon Generative Adversarial Networks (GANs) architecture In addition, we built a hybrid root inpainting dataset (HRID) that contains 1206 cotton root images with real gaps and 7716 rice root images with generated gaps. Compared with computer simulation root images, our dataset provides real root system architecture (RSA) and root texture information. Our method avoids cropping during training by instead utilizing downsampled images to provide the overall root morphology. The model is trained using binary cross-entropy loss to distinguish between root and non-root pixels. Additionally, Dice loss is employed to mitigate the challenge of imbalanced data distribution Additionally, we remove the skip connections in U-Net and introduce an edge attention module (EAM) to capture more detailed information. Compared with other methods, our approach significantly improves the recall rate from 17.35 % to 35.75 % on the test dataset of 122 cotton root images, revealing improved inpainting capabilities. The trait error reduction rates (TERRs) for the root area, root length, convex hull area, and root depth are 76.07 %, 68.63 %, 48.64 %, and 88.28 %, respectively, enabling a substantial improvement in the accuracy of root phenotyping. The codes for the EU-GAN and the 8922 labeled images are open-access, which could be reused by researchers in other AI-related work. This method establishes a robust solution for root phenotyping, thereby increasing breeding program efficiency and advancing our understanding of root system dynamics.

Why it matches plant phenotyping methods根画像の欠損を補完するGAN手法と再利用可能なデータセットを開発・評価し、根形態形質の推定誤差改善を実証しており、植物フェノタイピング手法が中心である。

abstractWe present a root inpainting method built upon Generative Adversarial Networks (GANs) architecture

Flexible spatial-frequency feature fusion for UAV-based semantic segmentation in rice phenotyping →

• FSFF improves UAV-based semantic segmentation for rice phenotyping. • LFFE builds the frequency awareness and mitigates the panicle-leaf similarity. • ASCE restores phase-aware local context and addresses the mutual occlusion. • The proposed method was evaluated in practical applications of rice breeding. UAV-based semantic segmentation offers new insights to accelerate breeding better varieties in rice breeding applications. However, the morphological similarity and mutual occlusion between the panicles and leaves still pose severe challenges for efficient rice phenotyping. To address these problems, this paper proposed a flexible spatial-frequency feature fusion (FSFF) method for high-throughput UAV-based semantic segmentation. The FSFF method consists of three key components: Learnable frequency feature extraction (LFFE), Adaptive spatial context enhancement (ASCE), and hierarchical feature fusion (HFF). LFFE is employed to build the foundation of frequency awareness, addressing the challenges from the morphological similarity between the panicles and leaves; ASCE is introduced to enhance boundary information and mitigate the negative effects of mutual occlusion. After that, the LFFE and ASCE modules are integrated in the HFF mode through a series of transformations. Ablation study was conducted to confirm the effectiveness of the proposed modules, and visualized explanation for performance improvement was explored by transforming the learned kernels to frequency spectrums. Later, the FSFF method was compared with the mainstream semantic segmentation approaches. Experimental results demonstrate that the FSFF method outperformed other counterparts in mIoU (+2.12%), pixel accuracy (+0.8%), and SSIM (+1.09%) with the best inference speed (0.7311 ms/image). Finally, the FSFF method was evaluated on the public dataset and practical rice breeding applications. The experimental results prove the generalization and potential of FSFF method in rice phenotyping, which may build a foundation to accelerate breeding cycles and ensure food security. Relevant codes will be available at https://github.com/ZZZ-bbb/FSFF/tree/master .

Why it matches plant phenotyping methodsUAV画像によるイネの穂・葉の形態を対象としたセマンティックセグメンテーション手法を開発し、アブレーション、比較評価、公開データセットおよび育種実環境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。

abstractFSFF improves UAV-based semantic segmentation for rice phenotyping.

Transformer-based detection of abnormal rice growth using drone-based multispectral imaging →

Rice is a vital staple food for global food security and a primary income source for millions of farmers worldwide. However, abnormal rice growth poses a serious threat to both yield stability and grain quality, undermining agricultural productivity. Early detection of such anomalies is therefore essential to mitigate yield losses. However, existing methods either targeted only one symptom at a time, or failed to generalize under various field conditions. Moreover, lightweight real-time inference is needed for on-board UAV deployment, yet most high-accuracy models incur prohibitive computational cost. In this study, we propose ARG-TR model, a lightweight transformer-based semantic segmentation framework built on the SegFormer architecture, which utilizes long-range dependencies to identify complex growth anomalies. The model is trained and validated on a large-scale, drone-captured multi-spectral dataset. By integrating a hierarchical transformer encoder with a lightweight decoder, ARG-TR achieves rapid convergence during training and demonstrates strong generalization to unseen data. The experimental results on a challenging dataset of abnormal rice growth patterns show that ARG-TR achieves a robust Intersection over Union (IoU) of 64.8, which outperforms state-of-the-art baselines such as MaskFormer and KNet in both accuracy and computational efficiency.

Why it matches plant phenotyping methodsドローンマルチスペクトル画像からイネの異常生育状態を抽出するセマンティックセグメンテーション手法を開発・検証しており、植物状態の取得方法が中心である。

abstractwe propose ARG-TR model, a lightweight transformer-based semantic segmentation framework built on the SegFormer architecture, which utilizes long-range dependencies to identify complex growth anomalies.

Spectral Kolmogorov-Arnold Transformer for few-shot rice germplasm viability detection using hyperspectral imaging →

Rice is a fundamental staple crop germplasm and a vital resource for germplasm innovation, playing a critical role in global food security. Viability is a key indicator for evaluating the conservation and utilization of germplasm resources, ensuring high and stable grain yields. Viability loss during the germplasm conservation process is a natural-aging process. Given the large number of varieties and the rarity of certain samples, excessive destructive tests for viability assessment should be minimized and ultimately replaced by intelligent non-destructive detection methods. Therefore, it is imperative to explore intelligent non-destructive, few-shot, cross-variety/germplasm, and viability detection of rice germplasm based on natural-aging. Current algorithms for rice germplasm viability detection encounter significant challenges in achieving optimal performance under few-shot conditions. We propose a spectral Kolmogorov-Arnold Transformer algorithm, specifically designed for viability detection of rice germplasm under few-shot conditions. A feature enhancement module is implemented to improve the spectral feature representation capabilities of germplasm hyperspectral image (GHSI). A multi-scale spectral feature extraction module is designed to extract spectral features across multiple scales. A fusion of convolutional neural network and Transformer module is introduced to capture both global and local features of GHSI. Finally, a learnable activation function (Kolmogorov-Arnold networks, KAN) and global average pooling are employed for viability classification. Under the condition of 15 samples per class, the SKA-T achieved overall accuracies of 92.87%, 92.30%, and 92.57% for the three rice lines, respectively. These results demonstrate the effectiveness of SKA-T in intelligent non-destructive viability detection of rice germplasm under few-shot conditions.

Why it matches plant phenotyping methodsイネ種子の生存性という植物状態を、ハイパースペクトル画像から非破壊推定する新規アルゴリズムを開発し、複数系統・少数サンプル条件で性能評価しているため、フェノタイピング手法が中心である。

abstractWe propose a spectral Kolmogorov-Arnold Transformer algorithm, specifically designed for viability detection of rice germplasm under few-shot conditions.

A lightweight rotating target detection method for rice leaf blast based on improved YOLOv8n →

Rice leaf blast significantly threatens rice quality and yield, necessitating efficient and precise identification methods for effective field management. Current methods face challenges in accurately detecting leaf blast and distinguishing dense targets due to their small size, scale variation, and dense distribution. This paper proposes a lightweight rotational rice leaf blast detection algorithm named Ro-YOLOv8-PKI. The algorithm adopts Oriented Bounding Boxes (OBB) over traditional Horizontal Bounding Boxes (HBB), uses Gaussian transform for target localization, and replaces ProbIoU with CIoU loss function to improve the accuracy of detecting rotated targets. To achieve model lightweight and improve detection performance to small targets, we replace the 32-fold downsampling-based feature fusion network with a 16-fold downsampling multi-scale feature fusion network. An improved C2f-PKI module is introduced to enhance multi-scale feature extraction and increase the model’s perception of critical regions and attention to central features. Experimental results show that Ro-YOLOv8-PKI outperforms the YOLOv8n baseline, improving F1 score and mean Average Precision (mAP) by 5.8 % and 9.6 %, respectively, while reducing parameters and model size by 69.1 % and 62.7 %. Additionally, the model achieves mAP gains of 2.3 %, 2.2 %, and 3.1 % over other rotated target detection algorithms, including ROI-Transformer, ReDet, and S2-Anet. This approach offers a practical reference for lightweight rice disease detection in natural environments and presents a new perspective on traditional parallel bounding box-based detection methods. An application has also been developed to demonstrate the real-world applicability of Ro-YOLOv8-PKI in field conditions. Part of the rice blast test dataset used in this study and the sheath blight dataset for future research are available at: https://github.com/qingyun259/RiceLeafBlastDataset.

Why it matches plant phenotyping methodsイネ葉いもち病の症状を画像から検出・定位する軽量アルゴリズムを開発し、精度比較と実環境アプリケーションまで評価しており、植物病害状態の画像ベース表現型取得が中心である。

abstractThis paper proposes a lightweight rotational rice leaf blast detection algorithm named Ro-YOLOv8-PKI.

Evaluating the potential of very high-resolution satellite data for the enhanced estimation of rice aboveground biomass by combining spectral and spatial information →

Monitoring aboveground biomass (AGB) using high spatial and temporal resolution remote sensing data is important for smart agriculture. Significant technological advances have been made in developing satellites with very high spatial resolution, delivering a promising avenue for vegetation observations. However, the high costs and limited revisit periods of high-resolution satellites hinder their widespread use, leaving the feasibility of combining vegetation indices (VIs) and textures derived from satellite images for AGB estimation uncertain and the quantitative improvements achieved by incorporating textures into estimation unclear. Airborne hyperspectral imaging with high spectral and spatial resolution offers a fresh opportunity to simulate the satellite imaging process objectively and realistically across both spectral and spatial dimensions. The study first evaluated the potential benefits of combining textures and VIs derived from different high-resolution satellites to enhance AGB retrieval. Rice samples and UAV hyperspectral data were collected throughout the rice growth cycle over three consecutive years. Each hyperspectral image was resampled in spectral and spatial dimensions to simulate nine multispectral satellites with sub-meter spatial resolution (WorldView-3, WorldView-2, GeoEye-1, SuperView-1C, GaoFen-2, Beijing-2, Jilin-1, GeoSat-2, KomPast-2). VIs, textures, and their combinations were employed to establish AGB models for the pre-heading, post-heading, and the entire growth stage, respectively. The results showed that combining VIs and textures always achieved the greatest rice AGB estimations, with the integration of multiple satellite data always yielding the best outcomes (overall validation rRMSE ≤ 0.35). For the texture-based monitoring, the impact of satellite spatial resolution was more pronounced on influencing the estimation effectiveness than spectral bands. The monitoring accuracy of rice AGB demonstrated a nonlinear decreasing trend as the spatial resolution dropped, and combining VIs and textures mitigated the negative impact of reduced spatial resolution on the monitoring accuracy of rice AGB. The combination of VIs and textures showed a compensatory effect and combining VIs and textures derived from red-edge band could offset the impact of the reduced spatial resolution on AGB estimation. The involvement of textures in modelling exerted an overall bigger impact on rice AGB estimation than the inclusion of red-edge variables. Satellites with higher spatial resolution and a red-edge band always performed the best in AGB estimation. This study facilitates the optimization of sensor design and farmland management.

Why it matches plant phenotyping methods高解像度リモートセンシング画像からイネの地上部バイオマスを推定する手法を、スペクトル情報・テクスチャ・空間解像度の組合せとして評価・検証しており、植物形質取得が研究の中心である。

abstractThe study first evaluated the potential benefits of combining textures and VIs derived from different high-resolution satellites to enhance AGB retrieval.

Rice Straw Tissue Preparation for Reproducible Electron Microscopy Imaging and Analysis. →

Common problems in biological sample processing for scanning electron microscopy (SEM) include cell collapse and destruction. To overcome the challenges surrounding SEM micrograph preparation, dried rice stems were used to develop a specific set of protocols for processing dried plant samples. Dried stems are rehydrated with a glycerol solution and fixed in formalin-acetic-alcohol to avoid cell wall collapse or organ distortion. The protocols detailed here comprise the first published method for preparing SEM images of dried plant tissue. The protocols offer a cost-effective approach to obtaining high-quality micrographs, facilitating the reconstruction of growth processes and the study of plant cell wall features. © 2025 The Author(s). Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Pretreatment and preparation of rice straw samples at the heading stage Basic Protocol 2: Paraffin infiltration and embedding Basic Protocol 3: Preparation of microscopic sections Basic Protocol 4: Transferring, adhering, and expanding sections on slides Support Protocol: Preparation of gelatin slides before sectioning to affix samples Basic Protocol 5: Preparation of samples for SEM imaging Basic Protocol 6: SEM analysis Basic Protocol 7: Processing and analysis of SEM images using ImageJ software.