The simultaneous diagnosis of diseases and evaluation of age quality grades in tea leaves are critical for precision agriculture and the economic valuation of tea products. Although deep learning has shown promise in agricultural vision tasks, current multi-task models often suffer from performance degradation due to feature conflicts: tea leaf disease recognition relies heavily on macro-structural lesions, whereas tea leaf-age quality grading depends on micro-textural features such as trichome density and color uniformity. To address this discrepancy, we propose a novel dual branch fusion network. Our architecture fundamentally decouples the feature extraction process by utilizing a dual branch mechanism. The first branch employs global average pooling to capture first-order spatial statistics; it can retain the global structural layout necessary for macro-lesion detection. The second branch introduces a dimensionality-reduced self-bilinear pooling module to compute second-order covariance matrices; it can effectively capture the fine-grained textural patterns essential for micro-grade classification. These decoupled features are subsequently fused and optimized through a weighted multi-task loss function. Experimental results on a comprehensive tea leaf dataset demonstrate that the proposed dual fusion framework significantly outperforms baseline models. The proposed network can rescue the disease classification accuracy drop observed in standard bilinear models while maintaining exceptional grading performance. Furthermore, the proposed network maintains a compact parameter footprint and low computational complexity. This balance renders it suitable for deployment on agricultural Internet of Things edge devices where inference speed is critical.
Why it matches plant phenotyping methods茶葉の病害状態と葉齢品質を画像から推定する深層学習手法を開発し、データセット上でベースラインと比較評価しており、表現型取得・推定法が中心である。
abstractwe propose a novel dual branch fusion network
Reproduction assets foundThe paper's Data Availability Statement publicly releases the two tea leaf image datasets used for its phenotyping tasks (disease recognition and leaf-age quality grading) via Mendeley Data. The authors' analysis code and trained models are only promised 'upon acceptance' with no public URL, so they do not qualify.Dataset · publicThe tea leaf disease recognition dataset analyzed in this study is available from https://data.mendeley.com/datasets/744vznw5k2/3 (accessed on 11 February 2026)Open asset ↗744vznw5k2/3lines:514-565Dataset · publicthe tea leaf grading dataset is available from https://data.mendeley.com/datasets/7t964jmmy3/1 (accessed on 11 February 2026)Open asset ↗7t964jmmy3/1lines:514-565Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Traditional manual grading of fresh chili peppers suffers from inconsistent quality control and low efficiency. To meet the demand for accurate fruit shape recognition during the post-harvest stage, this study proposes an intelligent recognition method based on an improved DenseNet-121 network. This approach facilitates the application of machine vision in agricultural sorting equipment. DenseNet-121 serves as the backbone network. The Convolutional Block Attention Module (CBAM) is introduced to enhance feature focus on fruit shapes. A regularization strategy (Dropout = 0.3, weight decay = 1 × 10 -4 ) and a cross-entropy loss function with label smoothing (LS = 0.1) are integrated to optimize decision boundaries. These configurations prevent the model from overfitting to hard training labels and yield a robust classification architecture. Experimental results demonstrate that the proposed model achieves a precision of 90.09%, a recall of 89.60%, an F1-score (the harmonic mean of precision and recall) of 89.53%, and an overall accuracy of 89.74%. The model contains 7.09 M parameters and requires a single-frame inference time of 7.35 ms. Comprehensive evaluations indicate that the proposed model achieves an optimal balance among environmental noise robustness, prediction accuracy, and computational efficiency. Consequently, by maintaining high fine-grained classification accuracy alongside a low memory footprint and rapid inference speed, the model demonstrates strong potential for real-time deployment on resource-constrained edge devices within actual agricultural optical sorting equipment.
Why it matches plant phenotyping methodsチリペッパー果実の形状という植物器官形質を画像から分類する深層学習手法の開発・評価が中心であり、単なる品質測定ではない。
abstractExperimental results demonstrate that the proposed model achieves a precision of 90.09%, a recall of 89.60%, an F1-score (the harmonic mean of precision and recall) of 89.53%, and an overall accuracy of 89.74%.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe original image dataset is provided as Supplementary Materials .Open asset ↗lines:30-40Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Root phenotypic analysis is closely related to crop yield and stress resistance. Although deep learning can improve the efficiency of root phenotype recognition, existing methods suffer from insufficient segmentation accuracy under complex soil backgrounds and focus on a single target. To address the issues of limited accuracy and operational complexity in existing root segmentation models, this paper proposes a novel wavelet-enhanced full-scale segmentation network. The WaveUNet+ model is based on U-Net3plus, replaces traditional downsampling with the Haar wavelet transform, and introduces the EMA module. The impact of the wavelet transform is validated using Grad-CAM, and HD95 is employed to evaluate the improvement in segmentation quality brought by the attention mechanism from the perspective of boundary accuracy. Transfer learning is used to improve model generalization, and the test results on diverse roots and various soils are compared. A Docker containerized root image segmentation method is designed to achieve convenient and practical operation, and the deployment feasibility of the model on edge devices is also verified. Our model effectively enhances the recognition of fine roots in soil backgrounds, leading to improvements across various metrics, achieving an Accuracy of 99.2%, while improving model accuracy with relatively low parameter count and model size. Compared with the original U-Net model, mIoU is increased by 1.52% and Recall by 2.93%. The results show that the model not only performs excellently on the original dataset but also maintains good generalization ability across different imaging modalities, crop species, and soil conditions. With Docker, users can achieve root image segmentation on their own computers without tedious program installation and environment configuration. In the future, we will attempt methods such as pruning and quantization to reduce model size, so as to better adapt to the deployment requirements of edge devices.
Why it matches plant phenotyping methods根系画像から根系形態を抽出するセグメンテーション手法を開発し、複数条件で精度・汎化性・境界性能を検証しているため、植物フェノタイピング手法が中心である。
abstractTo address the issues of limited accuracy and operational complexity in existing root segmentation models, this paper proposes a novel wavelet-enhanced full-scale segmentation network.
Reproduction assets foundThe paper's Data Availability Statement explicitly states the analysis code is publicly available at the authors' GitHub repository (WaveUNet-), which implements the WaveUNet+ root segmentation and phenotyping analysis. The supplementary materials only contain Grad-CAM figures and parameter tables, not datasets or codeCode · publicData Availability Statement
The data are available in a publicly accessible repository. The code can be obtained from https://github.com/WLL-cyber/WaveUNet-.git (accessed on 16 June 2026).Open asset ↗WLL-cyber/WaveUNet-lines:182-213Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Manually counting sunflower seeds on capitula is labor-intensive, requiring approximately one person-hour per head, and can be inconsistent for densely packed heads. Existing phenotyping approaches often depend on laboratory-based equipment, limiting their accessibility. In this study, we developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads. A dataset of 1093 sunflower capitula was imaged under fixed indoor lighting, and individual seeds were annotated as developed or aborted. A YOLOv8m one-stage object detector was trained and evaluated using a counting-focused protocol, in which a single confidence threshold was selected on the validation set and then applied unchanged to an independent test set of 109 images. The baseline model was compared with recent YOLO variants and different augmentation strategies. On the test set, the model achieved a mean absolute count error of 61.3 seeds per image, a mean relative error of 12.0%, and an mAP50 of 0.18 at the locked confidence threshold of 0.15. Only 13.8% of test images had relative errors below 2%. Larger YOLO models and augmentation variants did not improve performance. These findings show that the proposed system provides approximate, non-destructive seed-count estimation under controlled imaging conditions, while highlighting the need for improved localization in dense regions and domain adaptation for fresh heads or field conditions. The annotated dataset and trained model weights are made available to support reproducible research.
Why it matches plant phenotyping methodsヒマワリ頭花の発達・不稔種子数という植物形質を、画像取得とYOLOによる推定パイプラインで定量化する手法を開発・評価しており、方法が研究の中心である。
abstractwe developed a benchtop image-based pipeline for rapid, non-destructive estimation of developed and aborted seeds on intact dried sunflower heads.
Reproduction assets foundThe authors state the source code is available on GitHub and the CVAT-annotated dataset is available via a public share link; the GitHub repository URL is explicitly provided and matches an allowed URL. The dataset link itself is not given, so only the code/checkpoint repository qualifies as an actionable public asset.Code · publicThe developed system is available as a Telegram bot [ 19 ] and the source code is available on GitHub [ 20 ]. The CVAT annotated dataset is available via a public share link.Open asset ↗lines:84-103Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Field / plotMultimodalLeafSegmentationDisease symptoms / severity
Camellia oleifera is an economically vital woody oil crop. Its productivity and oil quality are severely compromised by various diseases. Implementing pixel-level lesion segmentation within complex field environments is crucial for advancing precision plant protection. Despite recent progress, existing segmentation methods struggle with three primary challenges: semantic ambiguity arising from evolving pathological stages, blurred boundaries due to overlapping lesions, and the high omission rate of micro-lesions. To address these issues, this paper presents TB-DLossNet (Text-Conditioned Boundary-Aware Network with Dynamic Loss Reweighting), a novel segmentation framework based on semantic-visual multi-modal fusion. Leveraging VMamba as the visual backbone, the proposed model innovatively integrates BERT-encoded structured text as an auxiliary modality to resolve visual ambiguities through cross-modal semantic guidance. Furthermore, a boundary enhancement branch is incorporated alongside a multi-scale deep supervision strategy to mitigate boundary displacement and ensure the topological continuity of lesion structures. To tackle the detection of small-scale targets, we designed a dynamic weight loss function conditioned on lesion area, significantly bolstering the model's sensitivity to minute pathological features. Additionally, to alleviate the scarcity of high-quality data, we curated a comprehensive multi-modal dataset encompassing seven typical diseases of Camellia oleifera . Experimental results demonstrate that TB-DLossNet achieves a Mean Intersection over Union (mIoU) of 87.02%, outperforming the state-of-the-art unimodal VMamba and multimodal Lvit by 4.9% and 2.59%, respectively. Qualitative evaluations confirm that our model exhibits lower false-negative rates and superior boundary-fitting precision in heterogeneous field scenarios. Finally, generalization tests on an apple disease dataset further validate the robustness and transferability of the proposed framework.
Why it matches plant phenotyping methods植物病害の病斑を画素レベルで抽出する新規セグメンテーション手法を開発し、データセット整備と性能比較・汎化検証も行っているため、病害状態の画像ベース表現型計測が中心である。
abstractImplementing pixel-level lesion segmentation within complex field environments is crucial for advancing precision plant protection.
Reproduction assets foundThe authors state their code and experimental dataset (the multimodal Camellia oleifera disease segmentation dataset) are publicly available on GitHub, matching an allowed URL.Code · publicOur code and experimental dataset are available at https://github.com/zzzsq239/TB-1.Open asset ↗zzzsq239/TB-1html-lines:820-841Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Reliable identification of maize leaf diseases is critical for mitigating crop losses, particularly in regions where farmers have limited access to experts. Although vision transformers (ViTs) have recently demonstrated strong performance in image recognition, their weak inductive bias and limited modeling of local texture patterns make them non-ideal for fine-grained maize leaf disease classification. To address these limitations, we propose ConvDeiT-Tiny, a lightweight hybrid ViT that improves DeiT-Ti by placing depthwise convolutions in parallel with multi-head self-attention modules in the first three transformer blocks. The local and global features captured by the convolution and attention modules are concatenated along the embedding dimension and fused using a multilayer perceptron. This results in richer token representations without significantly increasing model size. Across three datasets, ConvDeiT-Tiny (6.9 M parameters) consistently outperformed DeiT-Ti, DeiT-Ti-Distilled, and DeiT-S (21.7 M parameters) when trained from scratch. With transfer learning, ConvDeiT-Tiny achieved an accuracy of 99.15%, 99.35%, and 98.60% on the CD&S, primary, and Kaggle datasets, respectively, surpassing many previous studies with far fewer parameters. For explainability, we present gradient-weighted transformer attribution visualizations showing the disease lesions driving model predictions. These results indicate that injecting local inductive bias in early transformer blocks is beneficial for accurate maize leaf disease classification.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴画像を対象に、病害分類のための新規Vision Transformerモデルを開発・比較しており、植物病害状態の画像ベース表現型推定が中心である。
abstractwe propose ConvDeiT-Tiny, a lightweight hybrid ViT that improves DeiT-Ti by placing depthwise convolutions in parallel with multi-head self-attention modules in the first three transformer blocks.
Reproduction assets foundThe authors publicly release their analysis code and dataset splits (including their field-collected primary maize leaf image dataset) via a GitHub repository, and the paper's classification experiments use the public Kaggle Corn or Maize Leaf Disease Dataset (COMLDD). Both are paper-specific, public, and actionable.Code · publicThe program code and dataset splits for the three datasets used in this study, including our primary data, can be found at https://github.com/DamarisWaema/ConvDeiT-Tiny (accessed on 18 March 2026).Open asset ↗DamarisWaema/ConvDeiT-Tinylines:121-289Dataset · publicGhose S.
Corn or Maize Leaf Disease Dataset
Available online: https://www.kaggle.com/datasets/smaranjitghose/corn-or-maize-leaf-disease-dataset (accessed on 10 July 2025)Open asset ↗lines:474-623Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Plants defend against pathogens such as fungi by initiating coordinated structural and chemical responses. Pathogen perception triggers rapid cytosolic calcium influx and calcium oscillations that drive defense gene expression, yet the mechanisms by which these signals encode stressor intensity and propagate systematically remain unclear. Here, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens (Hedw.) upon precise and reversible exposure to fungal chitin oligosaccharides. Epifluorescent imaging of cells expressing the calcium indicator GCaMP6f revealed a rapid, coordinated calcium response to chitin addition, followed by stereotyped oscillations that subsided quickly upon stimulus removal. We implemented an unbiased image segmentation algorithm using pixel-based k -means clustering to automatically locate regions with specific oscillatory signatures. Calcium dynamics were distinct across adjacent cells, distinguishable by cell type, and significantly modulated by circadian rhythm, adaptation time within the device, and stimulus timing. Cytosolic calcium oscillations, which rose and fell symmetrically within about 60 s, occurred spontaneously during the subjective night and following short adaptation periods. Chitin elicited strong oscillations with increased frequency, amplitude, and duration, and repeated pulses entrained regular, colony-wide oscillations at the stimulation interval. This study complements prior investigations of whole plant and growth tip dynamics and provides a quantitative framework to study calcium signaling in plants, including mechanisms of signal propagation and the role of oscillation frequency on gene expression.
Why it matches plant phenotyping methods植物細胞のカルシウム動態を定量するマイクロ流体・蛍光イメージング系と自動画像セグメンテーションを開発し、植物の生理状態を抽出する方法が研究の中心である。
abstractHere, we present a microfluidic system to characterize intracellular calcium dynamics in protonemal colonies of the moss Physcomitrium patens
Reproduction assets foundThe paper's Data Availability Statement explicitly makes analysis scripts and sample data publicly available on the authors' GitHub repository (albrechtLab/moss_calcium), and the MDPI supplementary materials (plants-15-00582-s001.zip) contain the paper's timelapse calcium-imaging videos and supplementary figures. Raw/全Code · publicData are available upon request. Analysis scripts and sample data are publicly available at https://github.com/albrechtLab/moss_calcium (accessed on 1 January 2026).Open asset ↗albrechtLab/moss_calciumlines:188-251Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Multispectral / hyperspectralFlowerObject detectionStress response / tolerancePlant / canopy temperature
Hyperspectral imaging (HSI) is a noncontact camera-based technique that enables deep learning models to learn various plant conditions by detecting light reflectance under illumination. In this study, we investigated the effects of four light sources-halogen (HAL), incandescent (INC), fluorescent (FLU), and light-emitting diodes (LED)-on the quality of spectral images and the vase life (VL) of cut roses, which are vulnerable to abiotic stresses. Cut roses 'All For Love' and 'White Beauty' were used to compare cultivar-specific visible reflectance characteristics associated with contrasting petal pigmentation. HSI was performed at four time points, yielding 640 images per light source from 40 cut roses. The results revealed that the light source strongly affected both the image quality (mAP@0.5 60-80%) and VL (0-3 d) of cut roses. The HAL lamp produced high-quality spectral images across wavelengths (WL) ranging from 480 to 900 nm and yielded the highest object detection performance (ODP), reaching mAP@0.5 of 85% in 'All For Love' and 83% in 'White Beauty' with the YOLOv11x models. However, it increased petal temperature by 2.7-3 °C, thereby stimulating leaf transpiration and consequently shortening the VL of the flowers by 1-2.5 d. In contrast, INC produced unclear images with low spectral signals throughout the WL and consequently resulted in lower ODP, with mAP@0.5 of 74% and 69% in 'All For Love' and 'White Beauty', respectively. The INC only slightly increased petal temperature (1.2-1.3 °C) and shortened the VL by 1 d in the both cultivars. Although FLU and LED had only minor effects on petal temperature and VL, these illuminations generated transient spectral peaks in the WL range of 480-620 nm, resulting in decreased ODP (mAP@0.5 60-75%). Our results revealed that HAL provided reliable, high-quality spectral image data and high object detection accuracy, but simultaneously had negative effects on flower quality. Our findings suggest an alternative two-phase approach for illumination applications that uses HAL during the initial exploration of spectra corresponding to specific symptoms of interest, followed by LED for routine plant monitoring. Optimizing illumination in HSI will improve the accuracy of deep learning-based prediction and thereby contribute to the development of an automated quality sorting system that is urgently required in the cut flower industry.
Why it matches plant phenotyping methods切り花の状態評価に用いるHSIについて、照明条件が画像品質と検出精度に及ぼす影響を比較・検証し、実運用向けの照明戦略を提案しているため、植物フェノタイピング手法が中心である。
abstractwe investigated the effects of four light sources-halogen (HAL), incandescent (INC), fluorescent (FLU), and light-emitting diodes (LED)-on the quality of spectral images and the vase life (VL) of cut roses
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Table S1: SNR of hyperspectral images acquired under different illumination sources in two cut rose cultivars (‘All For Love’ and ‘White Beauty’); Figure S1: Effect of light sources on hyperspectral image (HSi) quality in cut roses ‘All For Love’ and ‘White Beauty’.Open asset ↗lines:64-174Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Grapevine water relations are increasingly influenced by drought under climate change, with significant implications for yield, fruit composition and wine quality. Stable isotopes of hydrogen, oxygen, carbon and nitrogen (δ 2 H, δ 18 O, δ 13 C and δ 15 N) provide sensitive tracers of plant water sources and physiological responses to stress. Here, we combined dual water isotopes (δ 2 H, δ 18 O), carbon and nitrogen isotopes (δ 13 C, δ 15 N), and high-resolution micrometeorological/soil observations to diagnose drought dynamics in Vitis vinifera cv. Sauvignon blanc (Orlești, Romania; 2023-2024). Dual-isotope relationships delineated progressive evaporative enrichment along the soil-plant-atmosphere continuum, with slopes LMWL ≈ 6.41 > stem ≈ 5.0 > leaf ≈ 2.2, consistent with kinetic fractionation during transpiration (leaf) superimposed on source-water signals (stem). Weekly leaf δ 18 O covaried strongly with relative humidity (RH; r = -0.69) and evapotranspiration (ET; r = +0.56), confirming atmospheric control of short-term enrichment, while stem isotopes showed buffered responses to soil water. We integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought (IDI 0-100 > 90; RH 40 mm wk -1 , Soil 60 > 100 cb). Carbon and nitrogen isotopes provided complementary, integrative diagnostics: δ 13 C increased (less negative) with drought (r = -0.52 with RH; +0.49 with IDI), reflecting higher intrinsic water-use efficiency, whereas δ 15 N rose with soil dryness and IDI (leaf: r ≈ +0.48 with Soil 60 ; +0.42 with IDI), indicating constraints on N acquisition and enhanced internal remobilization. Together, multi-isotope and environmental data yield a mechanistic, field-validated framework linking atmospheric demand and edaphic limitation to vine physiological and biogeochemical responses and demonstrate the operational value of an isotope-informed drought index for precision viticulture.
Why it matches plant phenotyping methods複数同位体と環境データからブドウの水分状態・干ばつ応答を推定するIsotopic Drought Indexを構築し、圃場で検証した研究であり、植物の生理状態取得手法が中心である。
abstractWe integrated Δ 18 O (leaf-stem), RH, ET, and soil matric potential at 60 cm (Soil 60 ) into an Isotopic Drought Index (IDI), which captured the onset, intensity, and persistence of the July-August 2024 drought
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicTable S1: Isotopic data of leaf and stem of Vitis vinifera cv. Sauvignon Blanc blanc from Orlești-Vâlcea (Romania), during 2023-2024 vintage; Table S2: Meteorological and soil measurements (Romania), during the sampling campaign (Orlești – Vâlcea, Romania; 2023-2024 vintage)Open asset ↗lines:149-204Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Accurate detection and counting of wheat spikes are crucial for yield estimation and variety selection in precision agriculture. However, challenges such as complex field environments, morphological variations, and small target sizes hinder the performance of existing models in real-world applications. This study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices. The architecture integrates four key modules: (1) FEMANet, a mixed aggregation feature enhancement network with Efficient Multi-scale Attention (EMA) for improved small-target representation; (2) BiAFA-FPN, a bidirectional asymmetric feature pyramid network for efficient multi-scale feature fusion; (3) ADown, an adaptive downsampling module that preserves structural details during resolution reduction; and (4) GSCDHead, a grouped shared convolution detection head for reduced parameters and computational cost. Evaluated on a hybrid dataset combining GWHD2021 and a self-collected field dataset, FEWheat-YOLO achieved a COCO-style AP of 51.11%, AP@50 of 89.8%, and AP scores of 18.1%, 50.5%, and 61.2% for small, medium, and large targets, respectively, with an average recall (AR) of 58.1%. In wheat spike counting tasks, the model achieved an R 2 of 0.941, MAE of 3.46, and RMSE of 6.25, demonstrating high counting accuracy and robustness. The proposed model requires only 0.67 M parameters, 5.3 GFLOPs, and 1.6 MB of storage, while achieving an inference speed of 54 FPS. Compared to YOLOv11n, FEWheat-YOLO improved AP@50, AP_s, AP_m, AP_l, and AR by 0.53%, 0.7%, 0.7%, 0.4%, and 0.3%, respectively, while reducing parameters by 74%, computation by 15.9%, and model size by 69.2%. These results indicate that FEWheat-YOLO provides an effective balance between detection accuracy, counting performance, and model efficiency, offering strong potential for real-time agricultural applications on resource-limited platforms.
Why it matches plant phenotyping methodsコムギ穂の検出・計数という植物器官形質の画像ベース推定手法を開発し、データセット上で精度・計算効率・堅牢性を評価しているため、植物フェノタイピング手法が中心である。
abstractThis study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices.
Reproduction assets foundThe paper uses the public GWHD2021 wheat spike detection dataset (available on Kaggle) as part of its hybrid dataset, with an explicit availability statement and URL. The self-collected Xinjiang field dataset is private and available only on request. No author analysis code, trained model checkpoints, or other paper-特定Dataset · publiciting, W.W., S.L. and Y.L.; supervision, J.C.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The public part of the dataset used in this study is available from the Global Wheat Head Detection (GWHD2021) dataset at https://www.kaggle.com/competitions/global-wheat-detection , accessed on 30 September 2025. The remaining part of the dataset is private and cannot be shared due to institutional or privacy restrictions. Requests for access to the private dataset may be directed to the corresponding author.
Conflicts of Interest
The authors declare no conflicts of interest. The funderOpen asset ↗GWHD2021lines:854-929Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
High-quality disease segmentation plays a crucial role in the precise identification of rice diseases. Although the existing deep learning methods can identify the disease on rice leaves to a certain extent, these methods often face challenges in dealing with multi-scale disease spots and irregularly growing disease spots. In order to solve the challenges of rice leaf disease segmentation, we propose KBNet, a novel multi-modal framework integrating language and visual features for rice disease segmentation, leveraging the complementary strengths of CNN and Transformer architectures. Firstly, we propose the Kalman Filter Enhanced Kolmogorov-Arnold Networks (KF-KAN) module, which combines the modeling ability of KANs for nonlinear features and the dynamic update mechanism of the Kalman filter to achieve accurate extraction and fusion of multi-scale lesion information. Secondly, we introduce the Boundary-Constrained Physical-Information Neural Network (BC-PINN) module, which embeds the physical priors, such as the growth law of the lesion, into the loss function to strengthen the modeling of irregular lesions. At the same time, through the boundary punishment mechanism, the accuracy of edge segmentation is further improved and the overall segmentation effect is optimized. The experimental results show that the KBNet framework demonstrates solid performance in handling complex and diverse rice disease segmentation tasks and provides key technical support for disease identification, prevention, and control in intelligent agriculture. This method has good popularization value and broad application potential in agricultural intelligent monitoring and management.
Why it matches plant phenotyping methodsイネ葉の病斑という植物の病態を画像からセグメンテーションする新規手法を開発しており、病害識別のための表現抽出・境界推定が研究の中心である。
abstractwe propose KBNet, a novel multi-modal framework integrating language and visual features for rice disease segmentation
Reproduction assets foundThe paper's rice disease segmentation images (1550 leaf images of Blast, Bacterial Blight, and Tungro) were sourced from the public PlantVillage dataset on Kaggle, which is publicly available at the allowed URL. However, the paper-specific derived assets — the authors' pixel-level Labelme segmentation masks, text描述, 4:Dataset · publici: 10.1007/s10462-024-10717-2.
26. Li A., Jiao L., Zhu H., Li L., Liu F. Multitask Semantic Boundary Awareness Network for Remote Sensing Image Segmentation. IEEE Trans. Geosci. Remote Sens. 2022;60:1–14. doi: 10.1109/TGRS.2021.3050885.
27. Kaggle, PlantVillage Dataset. 2019. [(accessed on 19 September 2022)]. Available online: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset .
28. Li Z., Li Y., Li Q., Wang P., Guo D., Lu L., Jin D., Zhang Y., Hong Q. LViT: Language Meets Vision Transformer in Medical Image Segmentation. IEEE Trans. Med. Imaging. 2024;43:96–107. doi: 10.1109/TMI.2023.3291719.
29. Liu Z., Wang Y., Vaidya S., Ruehle F., Halverson J., Soljacic M., Hou T.Y., TOpen asset ↗Kaggle · plantvillage-datasetlines:409-431Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
As a globally important cash crop, the optimization of tomato yield and quality is strategically significant for food security and sustainable agricultural development. In order to address the problem of missing point cloud data on fruits in a facility agriculture environment due to complex canopy structure, leaf shading and limited collection viewpoints, the traditional geometric fitting method makes it difficult to restore the real morphology of fruits due to the dependence on data integrity. This study proposes an adaptive symmetry self-matching (ASSM) algorithm. It dynamically adjusts symmetry planes by detecting defect region characteristics in real time, implements point cloud completion under multi-symmetry constraints and constructs a triple-orthogonal symmetry plane system to adapt to multi-directional heterogeneous structures under complex occlusion. Experiments conducted on 150 tomato fruits with 5-70% occlusion rates demonstrate that ASSM achieved coefficient of determination (R 2 ) values of 0.9914 (length), 0.9880 (width) and 0.9349 (height) under high occlusion, reducing the root mean square error (RMSE) by 23.51-56.10% compared with traditional ellipsoid fitting. Further validation on eggplant fruits confirmed the cross-crop adaptability of the method. The proposed ASSM method overcomes conventional techniques' data integrity dependency, providing high-precision three-dimensional (3D) data for monitoring plant growth and enabling accurate phenotyping in smart agricultural systems.
Why it matches plant phenotyping methodsトマト果実の遮蔽点群を補完し、果実の長さ・幅・高さを推定する新規アルゴリズムを開発・検証しており、植物形質取得が研究の中心である。
abstractThis study proposes an adaptive symmetry self-matching (ASSM) algorithm.
Reproduction assets foundThe paper's Data Availability Statement deposits the study's tomato/eggplant fruit point cloud data on ScienceDB, a public repository, making the paper-specific phenotyping data (3D point clouds of 150 tomato fruits used for completion and trait measurement) publicly actionable.Dataset · publicData Availability Statement
The data are available online at https://doi.org/10.57760/sciencedb.25084 .Open asset ↗sciencedb · 10.57760/sciencedb.25084lines:312-345Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Cotton is the most widely cultivated natural fiber crop worldwide, yet it is highly susceptible to various diseases and pests that significantly compromise both yield and quality. To enable rapid and accurate diagnosis of cotton diseases and pests-thus supporting the development of effective control strategies and facilitating genetic breeding research-we propose a lightweight model, the Resource-efficient Cotton Network (RF-Cott-Net), alongside an open-source image dataset, CCDPHD-11, encompassing 11 disease categories. Built upon the MobileViTv2 backbone, RF-Cott-Net integrates an early exit mechanism and quantization-aware training (QAT) to enhance deployment efficiency without sacrificing accuracy. Experimental results on CCDPHD-11 demonstrate that RF-Cott-Net achieves an accuracy of 98.4%, an F1-score of 98.4%, a precision of 98.5%, and a recall of 98.3%. With only 4.9 M parameters, 310 M FLOPs, an inference time of 3.8 ms, and a storage footprint of just 4.8 MB, RF-Cott-Net delivers outstanding accuracy and real-time performance, making it highly suitable for deployment on agricultural edge devices and providing robust support for in-field automated detection of cotton diseases and pests.
Why it matches plant phenotyping methods綿花の病害を画像から分類する軽量深層学習モデルと画像データセットを開発・評価しており、植物の病害状態を抽出するフェノタイピング手法が中心である。
abstractwe propose a lightweight model, the Resource-efficient Cotton Network (RF-Cott-Net), alongside an open-source image dataset, CCDPHD-11, encompassing 11 disease categories.
Reproduction assets foundThe paper's authors publicly released their self-constructed cotton disease/pest image dataset CCDPHD-11 (18,953 images, 11 classes) on GitHub, as stated in the Data Availability Statement. No code or trained model deposit is mentioned.Dataset · publicew and editing, K.C., H.W., P.W.C. and R.-F.W.; visualization, H.-W.Z. and R.-F.W.; supervision, H.W., P.W.C. and R.-F.W.; project administration, H.W., P.W.C. and R.-F.W. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The proposed CCDPHD-11 dataset can be found online ( https://github.com/SweefongWong/CCDPHD-11-Dataset , accessed on 9 March 2025).
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research received no external funding.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributOpen asset ↗SweefongWong/CCDPHD-11-Datasetlines:384-405Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Mulberry ( Morus spp.), as an economically significant crop in sericulture and medicinal applications, faces severe threats to leaf yield and quality from pest and disease infestations. Traditional detection methods relying on chemical pesticides and manual observation prove inefficient and unsustainable. Although computer vision and deep learning technologies offer new solutions, existing models exhibit limitations in natural environments, including low recognition rates for small targets, insufficient computational efficiency, poor adaptability to occlusions, and inability to accurately identify structural features such as leaf veins. We propose Mamba-YOLO-ML, an optimized model addressing three key challenges in vision-based detection: Phase-Modular Design (PMSS) with dual blocks enhancing multi-scale feature representation and SSM selective mechanisms and Mamba Block, Haar wavelet downsampling preserving critical texture details, and Normalized Wasserstein Distance loss improving small-target robustness. Visualization analysis of the detection performance on the test set using GradCAM revealed that the enhanced Mamba-YOLO-ML model demonstrates earlier and more effective focus on characteristic regions of different diseases compared with its predecessor. The improved model achieved superior detection accuracy with 78.2% mAP50 and 59.9% mAP50:95, outperforming YOLO variants and comparable Transformer-based models, establishing new state-of-the-art performance. Its lightweight architecture (5.6 million parameters, 13.4 GFLOPS) maintains compatibility with embedded devices, enabling real-time field deployment. This study provides an extensible technical solution for precision agriculture, facilitating sustainable mulberry cultivation through efficient pest and disease management.
Why it matches plant phenotyping methods桑葉の病害状態を画像から検出する深層学習モデルを開発・評価しており、植物病害表現型の取得手法が研究の中心である。
abstractWe propose Mamba-YOLO-ML, an optimized model addressing three key challenges in vision-based detection
Reproduction assets foundThe paper's plant-phenotyping input is the publicly available 'Mulberry Leaf Diseases and Pests Dataset' from PaddlePaddle AI Studio, which the authors re-annotated (1788 images) and used for all detection experiments. The dataset URL is explicitly given in the text and matches an allowed URL. No author analysis code,训Dataset · publicThe dataset used in this study was sourced from the publicly available PaddlePaddle dataset. For detailed information, please visit https://aistudio.baidu.com/datasetdetail/265143/0 (accessed on 3 April 2024) to learn more about theOpen asset ↗PaddlePaddle · 265143lines:33-39Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Tea ( Camellia sinensis L.) disease detection in complex field conditions faces significant challenges due to the scarcity of labeled data. While current mainstream visual deep learning algorithms depend on large-scale curated datasets. To address this, we propose a novel few-shot end-to-end detection network called MAF-MixNet that achieves robust detection with minimal annotation data. The network effectively overcomes the bottleneck of insufficient feature extraction under limited samples of existing methods, through the design of a mixed attention branch (MA-Branch) and a multi-path feature fusion module (MAFM). The former extracts contextual features, while the latter combines and enhances the local and global features. The entire model uses a two-stage paradigm to pretrain on public datasets and fine-tune on balanced subset datasets, including novel tea disease classes, anthracnose, and brown blight. Comparative experiments with six models on four evaluation metrics verified the advancement of our model. At 5-shot, MAF-MixNet achieves scores of 62.0%, 60.1%, and 65.9% in precision, nAP50, and F1 score, respectively, significantly outperforming other models. Similar superiority is achieved in the 10-shot scenario, where nAP50 is 73.8%. Our model maintains a certain computational efficiency and achieves the second fastest inference speed at 11.63 FPS, making it viable for real-world deployment. The results confirm MAF-MixNet's potential to enable cost-effective, intelligent disease monitoring in precision agriculture.
Why it matches plant phenotyping methods植物病害の症状を画像から検出する新規深層学習手法を開発し、複数モデルとの比較検証を行っているため、植物フェノタイピング手法が中心である。
abstractwe propose a novel few-shot end-to-end detection network called MAF-MixNet
Reproduction assets foundThe authors openly released the annotated leaf disease detection dataset (718 JPEG images with XML annotations of tea and cotton diseases) used in this study via Hugging Face Datasets with a DOI, making it a public, paper-specific, actionable asset.Dataset · publicThe leaf disease detection dataset supporting the findings of this
study is openly available in Hugging Face Datasets. This dataset contains 718 annotated images
of tea and cotton leaves across four disease categories (Tea Anthracnose Disease, Tea Brown Blight
Disease, Cotton Fusarium Wilt Disease, and Cotton Powdery Mildew), formatted as JPEG with
accompanying XML metadata.Open asset ↗Hugging Face Datasetspdf-page:26 lines:1-58Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The automated recognition of disease in tomato leaves can greatly enhance yield and allow farmers to manage challenges more efficiently. This study investigates the performance of YOLOv11 for tomato leaf disease recognition. All accessible versions of YOLOv11 were first fine-tuned on an improved tomato leaf disease dataset consisting of a healthy class and 10 disease classes. YOLOv11m was selected for further hyperparameter optimization based on its evaluation metrics. It achieved a fitness score of 0.98885, with a precision of 0.99104, a recall of 0.98597, and a mAP@.5 of 0.99197. This model underwent rigorous hyperparameter optimization using the one-factor-at-a-time (OFAT) algorithm, with a focus on essential parameters such as batch size, learning rate, optimizer, weight decay, momentum, dropout, and epochs. Subsequently, random search (RS) with 100 configurations was performed based on the results of OFAT. Among them, the C47 model demonstrated a fitness score of 0.99268 (a 0.39% improvement), with a precision of 0.99190 (0.09%), a recall of 0.99348 (0.76%), and a mAP@.5 of 0.99262 (0.07%). The results suggest that the final model works efficiently and is capable of accurately detecting and identifying tomato leaf diseases, making it suitable for practical farming applications.
Why it matches plant phenotyping methodsトマト葉の病徴を画像から認識するYOLOモデルを対象に、モデル比較とハイパーパラメータ最適化を行っており、植物の病害状態を推定する画像ベース手法が研究の中心です。
abstractThis study investigates the performance of YOLOv11 for tomato leaf disease recognition.
Reproduction assets foundThe paper's tomato leaf disease images derive from the public Kaggle 'Tomato Disease Multiple Sources' dataset, explicitly cited as openly available in the Data Availability Statement. The supplement (Data S1) contains the random-search performance configurations from the authors' hyperparameter optimization analysis.Dataset · publicand D.-H.A.; visualization, Y.-S.L.; supervision, Y.B.S.; project administration, D.-H.A. and G.-D.K.; funding acquisition, Y.-S.L. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The data presented in this study are openly available in Tomato Disease Multiple Sources at https://www.kaggle.com/datasets/cookiefinder/tomato-disease-multiple-sources/data , accessed on 30 December 2023.
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2021R1I1A1A0Open asset ↗Kaggle · Tomato Disease Multiple Sourceslines:2202-2219Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Computer vision technology is widely used in smart agriculture, primarily because of its non-invasive nature, which avoids causing damage to delicate crops. Nevertheless, the deployment of computer vision algorithms on agricultural machinery with limited computing resources represents a significant challenge. Algorithm optimization with the aim of achieving an equilibrium between accuracy and computational power represents a pivotal research topic and is the core focus of our work. In this paper, we put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberry flowers. Firstly, a multi-branch architecture-based fast convolutional sampling module, designated as Light C2f, is proposed to replace the C2f module in the backbone of YOLOv8, in order to enhance the network's capacity to perceive multi-scale features. Secondly, a state space model-based lightweight neck with a global sensitivity field, designated as VMambaNeck, is proposed to replace the original neck of YOLOv8. After the training and testing of the improved algorithm on a self-constructed strawberry flower dataset, a series of experiments is conducted to evaluate the performance of the model, including ablation experiments, multi-dataset comparative experiments, and comparative experiments against state-of-the-art algorithms. The results show that the VM-YOLO network exhibits superior performance in object detection tasks across diverse datasets compared to the baseline. Furthermore, the results also demonstrate that VM-YOLO has better performances in the mAP, inference speed, and the number of parameters compared to the YOLOv6, Faster R-CNN, FCOS, and RetinaNet.
Why it matches plant phenotyping methodsイチゴ花の検出を目的とする画像解析モデルを開発し、データセット上でアブレーション、比較、性能評価を行っており、植物器官の取得・抽出法が研究の中心である。
abstractwe put forward a lightweight hybrid network, named VM-YOLO, for the purpose of detecting strawberry flowers.
Reproduction assets foundThe authors' self-constructed strawberry flower dataset (3388 labeled images used for all VM-YOLO experiments) is explicitly made publicly available via a Google Drive link in the Data Availability statement. The other listed datasets (Global Wheat Head 2020, CropAndWeed, Strawberry Disease) are cited prior public setsDataset · publicThe datasets can be found at the following: The strawberry flower dataset (Accessed on 2 February 2024): https://drive.google.com/drive/folders/1aT6ur3cLPp0xD0urIH6ex_mrFYkIAtm8 ; The Global Wheat Head Detection Dataset 2020 (Accessed on 10 February 2024): http://www.global-wheat.com/gwhd.html ; The CropAndWeed dataset (Accessed on 15 February 2024): https://github.com/cropandweed/cropandweed-dataset ; and The Strawberry Disease dataset (Accessed on 20 February 2024): www.kaggle.com/usmanafzaal/strawbeOpen asset ↗lines:145-326Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Recruitment poses significant challenges for narrow endemic plant species inhabiting extreme environments like vertical cliffs. Investigating seed traits in these plants is crucial for understanding the adaptive properties of chasmophytes. Focusing on the Iberian endemic genus Petrocoptis A. Braun ex Endl., a strophiole-bearing Caryophyllaceae, this study explored the relationships between seed traits and climatic variables, aiming to shed light on the strophiole's biological role and assess its classificatory power. We analysed 2773 seeds (557 individuals) from 84 populations spanning the genus' entire distribution range. Employing cluster and machine learning algorithms, we delineated well-defined morphogroups based on seed traits and evaluated their recognizability. Linear mixed-effects models were utilized to investigate the relationship between climate predictors and strophiole area, seed area and the ratio between both. The combination of seed morphometric traits allows the division of the genus into three well-defined morphogroups. The subsequent validation of the algorithm allowed 87% of the seeds to be correctly classified. Part of the intra- and interpopulation variability found in strophiole raw and relative size could be explained by average annual rainfall and average annual maximum temperature. Strophiole size in Petrocoptis could have been potentially driven by adaptation to local climates through the investment of more resources in the production of bigger strophioles to increase the hydration ability of the seed in dry and warm climates. This reinforces the idea of the strophiole being involved in seed water uptake and germination regulation in Petrocoptis . Similar relationships have not been previously reported for strophioles or other analogous structures in Angiosperms.
Why it matches plant phenotyping methods種子形態計測とクラスタリング・機械学習による形態群分類を中心に扱い、分類アルゴリズムの検証も行っているため、植物形質の計測・抽出手法として収録対象。
abstractEmploying cluster and machine learning algorithms, we delineated well-defined morphogroups based on seed traits and evaluated their recognizability.
Reproduction assets foundThe authors openly deposited the study's seed morphometric phenotype data (2773 seeds, 557 individuals, 84 populations) in Zenodo, as stated in the Data Availability Statement. The MDPI supplementary materials contain population tables and model statistics but the Zenodo deposit is the paper-specific public dataset.Dataset · publicThe data presented in this study are contained within the article and Supplementary Materials and openly available in Zenodo at https://doi.org/10.5281/zenodo.13972509 .Open asset ↗Zenodo · 10.5281/zenodo.13972509lines:397-411Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The epidermal leaf patterns of plants exhibit remarkable diversity in cell shapes, sizes, and arrangements, driven by environmental interactions that lead to significant adaptive changes even among closely related species. The Solanaceae family, known for its high diversity of adaptive epidermal structures, has traditionally been studied using qualitative phenotypic descriptions. To advance this, we developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology. Applied to nine wild potato species, this workflow quantified key morphological parameters, identifying descriptors for trichomes, stomata, and pavement cells, and revealing interdependencies among these traits. Principal component analysis (PCA) highlighted two main axes, accounting for 45% and 21% of variance, corresponding to features such as guard cell shape, trichome length, stomatal density, and trichome density. These axes aligned well with the historical and geographical origins of the species, separating southern from Central American species, and forming distinct clusters for monophyletic groups. This workflow thus establishes a quantitative foundation for investigating leaf epidermal cell morphology within phylogenetic and geographic contexts.
Why it matches plant phenotyping methods葉表皮細胞の形態形質を画像から抽出・定量するコンピュータビジョン／画像処理ワークフローの開発と適用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収載する。
abstractwe developed a workflow combining multi-scale computer vision, image processing, and data analysis to extract digital descriptors for leaf epidermal cell morphology.
Reproduction assets foundThe paper's quantitative phenotyping measurements (trichome types and morphometric parameters of leaf epidermal cells for nine wild potato species) are publicly available as Supplementary Tables S1 and S2 at the MDPI supplementary URL. Microscopy images are not publicly deposited and are available only upon request; noSupplement · publicObjects of the Institute of Cytology and Genetics SB RAS.
Abbreviations
The following abbreviations are used in this manuscript:
LSM
Laser scanning microscopy
PI
Propidium iodide
DAPI
4′,6-diamidino-2-phenylindole
PCA
Principal component analysis
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants13213084/s1 , Table S1: Trichome types for the studied wild potato species; Table S2: Morphometric parameters for leaf epidermal cells of the studied wild potato species, including Area, Length, Width, Elongation, Circularity, Rectangularity, Perimeter, Convex Hull Area, Convex Hull Perimeter, and Convex Hull Coverage.
Author Open asset ↗lines:139-176Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
The spike shape and morphometric characteristics are among the key characteristics of cultivated cereals, being associated with their productivity. These traits are often used for the plant taxonomy and authenticity of hexaploid wheat species. Manual measurement of spike characteristics is tedious and not precise. Recently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis. Here, this method is applied to study variations in spike size and shape for 190 plants of seven hexaploid (2 n = 6 x = 42) species and one artificial amphidiploid of wheat. Five manually estimated spike traits and 26 traits obtained from digital image analysis were analyzed. Image-based traits describe the characteristics of the base, center and apex of the spike and common parameters (circularity, roundness, perimeter, etc.). Estimates of similar traits by manual measurement and image analysis were shown to be highly correlated, suggesting the practical importance of digital spike phenotyping. The utility of spike traits for classification into types (spelt, normal and compact) and species or amphidiploid is shown. It is also demonstrated that the estimates obtained made it possible to identify the spike characteristics differing significantly between species or between accessions within the same species. The present work suggests the usefulness of wheat spike shape analysis using an approach based on characteristics obtained by digital image analysis.
Why it matches plant phenotyping methods小麦穂の2D画像解析による形態計測法を実際に適用し、手動測定との相関検証とデジタル形質の有用性評価を行っており、植物フェノタイピング手法が中心である。
abstractRecently, the authors of this study developed a method for wheat spike morphometry utilizing 2D image analysis.
Reproduction assets foundThe paper's spike image dataset (the 2D images used for quadrangle-model phenotyping of 190 wheat plants) is publicly deposited on Zenodo, explicitly linked in the Data Availability Statement. The supplementary files contain statistical results (normality tests, ANOVA tables, confusion matrices, specimen descriptions)衍Dataset · publicThe spike image dataset is available at https://zenodo.org/records/13837454 , accessed on 27 September 2024.Open asset ↗Zenodo · 13837454lines:895-912Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Pollen is becoming an increasingly important subject for molecular researchers in genetic engineering, plant breeding, and environmental monitoring. To broaden the scope of these studies, it is essential to develop accessible methods for scientists who are not specialized in palynology. The article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM). The protocol is convenient for any molecular laboratory due to its small set of reagents, ease of execution, low cost, does not require special equipment, and takes only one hour to complete. The high penetrating ability of formaldehyde and the final delicate dehydration using hexamethyldisilazane (HMDS) instead of critical point drying allow for sufficient preservation of the architecture of the aperture, which is considered a gateway for the passage of biomolecules. The method was successfully applied to pollen grains of representatives of dicotyledons (beetroot, petunia, radish, tomato and tobacco) and monocotyledons (lily, onion, corn, rye and wheat). Species studied included insect-pollinated (entomophilous) and wind-pollinated (anemophilous) species. A comparative analysis of the sizes of fresh living pollen grains under a light microscope and those prepared for SEM showed some shrinkage. Quantitative analysis of the degree of pollen grain shrinkage showed that this process depends on the initial shape of dry pollen grains, and the number and structure of apertures. The results support the theoretical model of the folding/unfolding pathways of pollen grains.
Why it matches plant phenotyping methods植物花粉のSEM観察用試料調製法そのものを開発し、複数植物で適用・比較検証しているため、形態計測に関する中心的な方法論研究である。
abstractThe article presents a simplified technical procedure for preparing pollen grains for scanning electron microscopy (SEM).
Reproduction assets foundThe paper's quantitative pollen shrinkage measurements (Table S1) and light microscopy images (Figures S3–S4) are contained in the publicly downloadable MDPI Supplementary Materials, which directly reproduce this paper's phenotyping measurements. No author analysis code or trained models are mentioned.Supplement · publicoly Bogdanov—at the department of electron microscopy, Lomonosov Moscow State University.
Abbreviations
The following abbreviations are used in this manuscript:
SEM
Scanning Electron Microscopy
HMDS
Hexamethyldisilazane
SA
Short axis
LA
Long axis
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants13152140/s1 , Figure S1: The order of steps for pollen preparation according to the developed protocol; Figure S2: Scheme of measured pollen grain diameters; Figure S3: Light microscopy of pollen grains of insect-pollinated species; Figure S4: Light microscopy of pollen grains of wind-pollinated species; Table S1: Comparison Open asset ↗lines:98-127Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In this study, our aim is to find an effective method to solve the problem of disease similarity caused by multiple diseases occurring on the same leaf. This study proposes the use of an optimized RegNet model to identify seven common apple leaf diseases. We conducted comparisons and analyses on the impact of various factors, such as training methods, data expansion methods, optimizer selection, image background, and other factors, on model performance. The findings suggest that utilizing offline expansion and transfer learning to fine-tune all layer parameters can enhance the model's classification performance, while complex image backgrounds significantly influence model performance. Additionally, the optimized RegNet network model demonstrates good generalization ability for both datasets, achieving testing accuracies of 93.85% and 99.23%, respectively. These results highlight the potential of the optimized RegNet network model to achieve high-precision identification of different diseases on the same apple leaf under complex field backgrounds. This will be of great significance for intelligent disease identification in apple orchards in the future.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から識別する最適化CNNを提案し、学習条件・背景・データセットで性能比較と精度評価を行っており、植物表現型取得法が中心である。
abstractThis study proposes the use of an optimized RegNet model to identify seven common apple leaf diseases.
Reproduction assets foundThe paper's apple leaf disease dataset fuses authors' field-collected images (not publicly deposited) with the public Kaggle Plant Pathology 2020 (FGVC7) dataset, which is directly used in this paper's phenotyping/classification measurements. No author analysis code, trained model checkpoints, or data deposit is statedDataset · publich period from apple tree germination to harvesting, and each disease image sample contained different stages of onset.
Considering the insufficient number of collected apple leaf disease images and the fact that the background and category of the apple disease leaf images in the “Plant Pathology Challenge” for CVPR 2020-FGVC7 ( https://www.kaggle.com/c/plantpathology-2020 fgvc7, accessed on 20 December 2023) [ 17 ] are consistent with the collected image data, the two were fused to form the dataset used in this study. Additionally, we collaborated with professional scholars to screen, classify, and organize the above two image types one by one, establishing a relatively reliable dataset of aOpen asset ↗Kaggle · plantpathology-2020lines:34-43Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Deep learning plays a vital role in precise grapevine disease detection, yet practical applications for farmer assistance are scarce despite promising results. The objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare (Version 1, created by Osama Elsherbiny). This approach utilizes RGB imagery and hybrid deep networks for the detection and prevention of grapevine diseases. Exploring the optimal deep learning architecture involved combining convolutional neural networks (CNNs), long short-term memory (LSTM), deep neural networks (DNNs), and transfer learning networks (including VGG16, VGG19, ResNet50, and ResNet101V2). A gray level co-occurrence matrix (GLCM) was employed to measure the textural characteristics. The plant disease detection platform (PDD) created a dataset of real-life grape leaf images from vineyards to improve plant disease identification. A data augmentation technique was applied to address the issue of limited images. Subsequently, the augmented dataset was used to train the models and enhance their capability to accurately identify and classify plant diseases in real-world scenarios. The analyzed outcomes indicated that the combined CNN RGB -LSTM GLCM deep network, based on the VGG16 pretrained network and data augmentation, outperformed the separate deep network and nonaugmented version features. Its validation accuracy, classification precision, recall, and F-measure are all 96.6%, with a 93.4% intersection over union and a loss of 0.123. Furthermore, the software developed through the proposed approach holds great promise as a rapid tool for diagnosing grapevine diseases in less than one minute. The framework of the study shows potential for future expansion to include various types of trees. This capability can assist farmers in early detection of tree diseases, enabling them to implement preventive measures.
Why it matches plant phenotyping methodsブドウ葉のRGB画像から病害状態を推定する深層学習手法とデータセット、診断ソフトウェアを開発・評価しており、植物表現型取得が中心である。
abstractThe objective of this research is to develop an intelligent approach, supported by user-friendly, open-source software named AI GrapeCare
Reproduction assets foundThe authors publicly deposited the Python script, the trained hybrid deep network model, real-world grape disease sample images, and the standalone AI GrapeCare software on Google Drive. The PDD grape leaf image dataset (295 images) is also public at pdd.jinr.ru, but that URL is not in the allowed list, so only the verCode · publicface [ 32 ]. To ensure cross-platform compatibility, including Windows, Linux, and Mac OS, PyInstaller [ 33 ] was applied. The Python script, the hybrid deep network that was generated, grape disease samples from real-world conditions, and the stand-alone version of this software are all available for download on Google Drive ( https://drive.google.com/file/d/1uOVAMiFDWBZsm8U9alzSdSk2c-A8zWBN , accessed on 10 December 2023), packaged in a RAR file with a size of 1.03 GB. As depicted in the overarching flowchart ( Figure 7 ), the pseudo-code explains the establishment of the AI GrapeCare software and its associated functions. The software workflow is organized into five primary stages: (1) loOpen asset ↗Google Drivelines:148-246Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
WheatRGB / grayscaleSeed / grainClassificationGrowth / development / phenology
Grain filling is essential for wheat yield formation, but is very susceptible to environmental stresses, such as high temperatures, especially in the context of global climate change. Grain RGB images include rich color, shape, and texture information, which can explicitly reveal the dynamics of grain filling. However, it is still challenging to further quantitatively predict the days after anthesis (DAA) from grain RGB images to monitor grain development. Results The WheatGrain dataset revealed dynamic changes in color, shape, and texture traits during grain development. To predict the DAA from RGB images of wheat grains, we tested the performance of traditional machine learning, deep learning, and few-shot learning on this dataset. The results showed that Random Forest (RF) had the best accuracy of the traditional machine learning algorithms, but it was far less accurate than all deep learning algorithms. The precision and recall of the deep learning classification model using Vision Transformer (ViT) were the highest, 99.03% and 99.00%, respectively. In addition, few-shot learning could realize fine-grained image recognition for wheat grains, and it had a higher accuracy and recall rate in the case of 5-shot, which were 96.86% and 96.67%, respectively. Materials and methods In this work, we proposed a complete wheat grain dataset, WheatGrain, which covers thousands of wheat grain images from 6 DAA to 39 DAA, which can characterize the complete dynamics of grain development. At the same time, we built different algorithms to predict the DAA, including traditional machine learning, deep learning, and few-shot learning, in this dataset, and evaluated the performance of all models. Conclusions To obtain wheat grain filling dynamics promptly, this study proposed an RGB dataset for the whole growth period of grain development. In addition, detailed comparisons were conducted between traditional machine learning, deep learning, and few-shot learning, which provided the possibility of recognizing the DAA of the grain timely. These results revealed that the ViT could improve the performance of deep learning in predicting the DAA, while few-shot learning could reduce the need for a number of datasets. This work provides a new approach to monitoring wheat grain filling dynamics, and it is beneficial for disaster prevention and improvement of wheat production.
Why it matches plant phenotyping methodsコムギ粒のRGB画像から登熟段階（日数）を推定する画像解析手法を開発・比較し、データセットとモデル性能を評価しており、表現型取得・推定が研究の中心である。
abstractTo predict the DAA from RGB images of wheat grains, we tested the performance of traditional machine learning, deep learning, and few-shot learning on this dataset.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産2件を確認しました。Code · publicThese traits were extracted via Python and OpenCV (a Python library), and the codes are available online at https://github.com/shem123456/wheat-grain-traits (accessed on 21 September 2023).Open asset ↗shem123456/wheat-grain-traitslines:61-116Code · publicFinally, the Siamese network with contrastive loss was built using PyTorch, and the configuration of its training was consistent with that of the deep learning model described above. The codes are available online at https://github.com/shem123456/grain-filling-classification (accessed on 21 September 2023).Open asset ↗shem123456/grain-filling-classificationlines:117-128Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
In greenhouse production, grey mould caused by Botrytis cinerea Pers. is one of the most widespread and damaging diseases affecting medicinal cannabis (MC). Fungicide options to control this disease are extremely limited due to the regulations surrounding fungicides and chemical residues as the product end users are medical patients, often with compromised immune systems. Screening for alternative disease control options, such as biological and organic products, can be time-consuming and costly. Here, we optimise and validate a detached leaf assay as a quick and non-destructive method to evaluate interactions between plants and pathogens, allowing the assessment of potential pathogens' infectivity and product efficacy. We tested eight industrial hemp varieties for susceptibility to B. cinerea infection. Using detached leaves from a susceptible variety, we screened a variety of chemical or organic products for efficacy in controlling the lesion development caused by B. cinerea . A consistent reduction in lesion growth was observed using treatments containing Tau-fluvalinate and Myclobutanil, as well as the softer chemical alternatives containing potassium salts. The performance of treatments was pH-dependent, emphasizing the importance of applying them at optimal pH levels to maximise their effectiveness. The detached leaf assay differentiated varietal susceptibility and was an effective method for screening treatment options for diseases caused by Botrytis. The results from the detached leaf assays gave comparable results to responses tested on whole plants.
Why it matches plant phenotyping methodsボトリティス感染による葉の病斑進展を測定するデタッチドリーフアッセイを最適化・検証し、品種感受性と製品効果のスクリーニングに用いているため、植物病害状態の取得手法が中心です。
abstractHere, we optimise and validate a detached leaf assay as a quick and non-destructive method to evaluate interactions between plants and pathogens, allowing the assessment of potential pathogens' infectivity and product efficacy.
Reproduction assets foundThe paper's detached leaf assay lesion measurements are contained in the manuscript and its Supplementary Materials (Tables S1 and S2 with average daily lesion length data), which are publicly downloadable from the MDPI article's supplementary file URL. No author analysis code or trained models are mentioned.Supplement · publicdual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12183278/s1 , Table S1: Average daily lesion length results from four repeated experiments screening the efficacy of products against B. cinerea .; Table S2: Detached leaf assay results for four repeated experiments screening the efficacy of products with amended pH against B. cinerea .
Click here for additional data file.
AuOpen asset ↗lines:67-140Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Measuring lycopene in tomatoes is fundamental to the agrifood industry because of its health benefits. It is one of the leading quality criteria for consuming this fruit. Traditionally, the amount determination of this carotenoid is performed using the high-performance liquid chromatography (HPLC) technique. This is a very reliable and accurate method, but it has several disadvantages, such as long analysis time, high cost, and destruction of the sample. In this sense, this work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp. A Raspberry Pi 4 programmed with Python language was used to develop the lycopene prediction model. Various regression models were evaluated using neural networks, fuzzy logic, and linear regression. The best model was the fuzzy nonlinear regression as the RGB input, with a correlation of R 2 = 0.99 and a mean error of 1.9 × 10 -5 . This work was able to demonstrate that it is possible to determine the lycopene content using a digital camera and a low-cost integrated system in a non-invasive way.
Why it matches plant phenotyping methodsトマト果皮画像の色からリコペン含量を非破壊推定する低コストセンサーと予測モデルの開発が中心であり、植物器官の形質測定法に該当する。
abstractthis work proposes a low-cost sensor that correlates the lycopene content in tomato with the color present in its epicarp.
Reproduction assets foundThe paper's Data Availability Statement links to a public Google Drive folder containing the data supporting the reported lycopene measurement results (tomato RGB/L*a*b* image-derived measurements and HPLC-calibrated model data). No separate code deposit is described; the models were built in MATLAB toolboxes without aDataset · publicl analysis, M.-G.B.-S.; investigation, J.-A.P.-M.; writing—original draft preparation, J.P.-O. and M.-J.V.-A.; writing—review and editing, A.-I.B.-G.; supervision, A.-I.B.-G. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
Data supporting reported results can be found at: https://drive.google.com/drive/folders/1d1Q_RtEWmo2lbpipMCNG4x53s09-pB-C?usp=sharing .
Conflicts of Interest
The authors declare no conflict of interest.
Appendix A
Listed below are the 18 inference rules and weights for each of the two fuzzy systems red, green, and blue:
If (L is Low_L) and (a is Low_a) and (b is Low_b) then (Lycopene is Lycopenemf1)
If (L is Low_L) Open asset ↗lines:75-128Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Rice is a crucial food crop, but it is frequently affected by diseases during its growth process. Some of the most common diseases include rice blast, flax leaf spot, and bacterial blight. These diseases are widespread, highly infectious, and cause significant damage, posing a major challenge to agricultural development. The main problems in rice disease classification are as follows: (1) The images of rice diseases that were collected contain noise and blurred edges, which can hinder the network's ability to accurately extract features of the diseases. (2) The classification of disease images is a challenging task due to the high intra-class diversity and inter-class similarity of rice leaf diseases. This paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images. Additionally, a new neural network (ICAI-V4) is designed based on the Inception-V4 backbone structure, with a coordinate attention mechanism added to enhance feature capture and overall model performance. The INCV backbone structure incorporates Inception-iv and Reduction-iv structures, with the addition of involution to enhance the network's feature extraction capabilities from a channel perspective. This enables the network to better classify similar images of rice diseases. To address the issue of neuron death caused by the ReLU activation function and improve model robustness, Leaky ReLU is utilized. Our experiments, conducted using the 10-fold cross-validation method and 10,241 images, show that ICAI-V4 has an average classification accuracy of 95.57%. These results indicate the method's strong performance and feasibility for rice disease classification in real-life scenarios.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する画像処理・ニューラルネットワーク手法の開発と10分割交差検証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 paper proposes the Candy algorithm, an image enhancement technique that utilizes improved Canny operator filtering (the gravitational edge detection algorithm) to emphasize the edge features of rice images and minimize the noise present in the images.
Reproduction assets foundThe paper's rice disease images come from Kaggle/Mendeley sources whose URLs are not in the allowed list, and no author analysis code or trained model is publicly deposited ('All data are presented in the article'). The only qualifying paper-specific public asset is the PlantVillage dataset, which the authors used to验证Dataset · publicthe results of the tests are presented in Table 12 . Our proposed model demonstrated strong performance on three publicly available datasets, suggesting that it possesses a high degree of generalizability to other datasets.
Table 11
Detailed information about the dataset.
Dataset
Category
Total
Available
PlantVillage
38
55,400
https://www.kaggle.com/datasets/hiyash99/plantvillage (accessed on 24 March 2023)
Stanford cars
196
16,185
https://www.kaggle.com/datasets/jutrera/stanford-car-dataset-by-classes-folder (accessed on 24 March 2023)
ImageNetDogs
120
20,580
http://vision.stanford.edu/aditya86/ImageNetDogs/ (accessed on 24 March 2023)
Table 12
Public dataset test results.
Dataset
Accuracy Open asset ↗Kaggle · hiyash99/plantvillagelines:417-573Dataset · publicthree publicly available datasets, suggesting that it possesses a high degree of generalizability to other datasets.
Table 11
Detailed information about the dataset.
Dataset
Category
Total
Available
PlantVillage
38
55,400
https://www.kaggle.com/datasets/hiyash99/plantvillage (accessed on 24 March 2023)
Stanford cars
196
16,185
https://www.kaggle.com/datasets/jutrera/stanford-car-dataset-by-classes-folder (accessed on 24 March 2023)
ImageNetDogs
120
20,580
http://vision.stanford.edu/aditya86/ImageNetDogs/ (accessed on 24 March 2023)
Table 12
Public dataset test results.
Dataset
Accuracy (%)
PlantVillage
96.25
Stanford cars
94.67
ImageNetDogs
97.24
4. DiscussionOpen asset ↗Kaggle · jutrera/stanford-car-dataset-by-classes-folderlines:417-573Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Cultivation of wild rocket [ Diplotaxis tenuifolia (L.) D.C.] as a baby-leaf vegetable for the high-convenience food chain is constantly growing due to its nutritional and taste qualities. As is well known, these crops are particularly exposed to soil-borne fungal diseases and need to be effectively protected. At present, wild rocket disease management is performed by using permitted synthetic fungicides or through the application of agro-ecological and biological methods that must be optimized. In this regard, the implementation of innovative digital-based technologies, such as infrared thermography (IT), as supporting systems to decision-making processes is welcome. In this work, leaves belonging to wild rocket plants inoculated with the soil-borne pathogens Rhizoctonia solani Kühn and Sclerotinia sclerotiorum (Lib.) de Bary were analyzed and monitored by both active and passive thermographic methods and compared with visual detection. A comparison between the thermal analysis carried out in both medium (MWIR)- and long (LWIR)-wave infrared was made and discussed. The results achieved highlight how the monitoring based on the use of IT is promising for carrying out an early detection of the rot diseases induced by the investigated pathogens, allowing their detection in 3-6 days before the canopy is completely wilted. Active thermal imaging has the potential to detect early soil-borne rotting diseases.
Why it matches plant phenotyping methods野生ルッコラの感染植物を対象に、能動・受動熱画像法を比較し、熱画像による病害症状の早期検出性能を評価しており、植物状態の取得手法が中心である。
abstractleaves belonging to wild rocket plants inoculated with the soil-borne pathogens Rhizoctonia solani Kühn and Sclerotinia sclerotiorum (Lib.) de Bary were analyzed and monitored by both active and passive thermographic methods and compared with visual detection.
Reproduction assets foundThe paper's supplementary materials (hosted publicly by MDPI) contain the paper-specific thermographic images of inoculated vs. control wild rocket leaves (Figures S1–S5) and the Right Prevision (RP%) calculation underlying the AT analysis. No separate phenotype dataset deposit or author analysis code repository is披露; Supplement · public–6 days before they are detectable by visual analysis. Our results confirm the potential of these techniques in monitoring wild rocket for the preventive detection of soil diseases with the aim of avoiding their extension and preserving the crops.
Supplementary Materials
The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/plants12081615/s1 , Figure S1: Examples of active thermography analysis for uninoculated control leaves of rocket plants; Figures S2–S5: Examples of active thermography analysis for leaves of rocket plants inoculated with treatments T1, T2, T3, and T4. Right Prevision (RP%) calculation.
Click here for additional data file.
Author COpen asset ↗lines:153-168Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Rice lodging seriously affects rice quality and production. Traditional manual methods of detecting rice lodging are labour-intensive and can result in delayed action, leading to production loss. With the development of the Internet of Things (IoT), unmanned aerial vehicles (UAVs) provide imminent assistance for crop stress monitoring. In this paper, we proposed a novel lightweight detection system with UAVs for rice lodging. We leverage UAVs to acquire the distribution of rice growth, and then our proposed global attention network (GloAN) utilizes the acquisition to detect the lodging areas efficiently and accurately. Our methods aim to accelerate the processing of diagnosis and reduce production loss caused by lodging. The experimental results show that our GloAN can lead to a significant increase in accuracy with negligible computational costs. We further tested the generalization ability of our GloAN and the results show that the GloAN generalizes well in peers' models (Xception, VGG, ResNet, and MobileNetV2) with knowledge distillation and obtains the optimal mean intersection over union (mIoU) of 92.85%. The experimental results show the flexibility of GloAN in rice lodging detection.
Why it matches plant phenotyping methodsUAV画像からイネの倒伏領域（植物状態）を抽出する軽量な検出システムとGloANを提案・評価しており、表現型取得・推定手法が中心である。
abstractwe proposed a novel lightweight detection system with UAVs for rice lodging.
Reproduction assets foundThe authors explicitly state that the rice lodging dataset (UAV images with annotations) and the source code for the GloAN analysis are open sourced and publicly available on GitHub.Dataset · publicThe dataset and source code used in this study have been open sourced and are publicly available at https://github.com/Stephenkgb/GloAN-and-rice-lodging-dataset .Open asset ↗Stephenkgb/GloAN-and-rice-lodging-datasetlines:214-228Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R 2 and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.
Why it matches plant phenotyping methodsVIS-NIR-SWIRハイパースペクトロスコピーとAIによるレタスの色素表現型推定・分類が研究の中心であり、植物形質取得手法の開発・評価に該当する。
abstractthe use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants12061333/s1 . Table S1. Descriptive analysis parameters of lettuce varieties. Pigment of leaves expressed by leaf area (mg m −2 ), mass (mg g −1 ), and volume (mL L −1 ) ( n = 132); Table S2. STEPW and VIPs by wavelengths selected according to classified algorithm-based ANOVA and information gain ratio ( p < 0.001) by band range spectroscopy from reflectance leaves.Open asset ↗lines:80-115Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 14 Sept 2026
Wheat ( Triticum aestivum L.) is one of the most important crops as it provides 20% of calories and proteins to the human population. To overcome the increasing demand in wheat grain production, there is a need for a higher grain yield, and this can be achieved in particular through an increase in the grain weight. Moreover, grain shape is an important trait regarding the milling performance. Both the final grain weight and shape would benefit from a comprehensive knowledge of the morphological and anatomical determinism of wheat grain growth. Synchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages. Coupled with 3D reconstruction, this method revealed changes in the grain shape and new cellular features. The study focused on a particular tissue, the pericarp, which has been hypothesized to be involved in the control of grain development. We showed considerable spatio-temporal diversity in cell shape and orientations, and in tissue porosity associated with stomata detection. These results highlight the growth-related features rarely studied in cereal grains, which may contribute significantly to the final grain weight and shape.
Why it matches plant phenotyping methodsシンクロトロンX線マイクロCTと3D再構成を中核に、発達中コムギ粒の3D形状・細胞形態・組織空隙を抽出しており、植物器官の形態表現型取得が中心である。
abstractSynchrotron-based phase-contrast X-ray microtomography (X-ray µCT) was used to study the 3D anatomy of the growing wheat grain during the first developmental stages.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe development was integrated into the Imago software, which is
freely available at https://github.com/SciCompJ/Imago (accessed on 21 February 2023).Open asset ↗SciCompJ/Imagopdf-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Effective identification of apple leaf diseases can reduce pesticide spraying and improve apple fruit yield, which is significant to agriculture. However, the existing apple leaf disease detection models lack consideration of disease diversity and accuracy, which hinders the application of intelligent agriculture in the apple industry. In this paper, we explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods. The combination of the feature pyramid and pan in YOLOv5 can obtain richer semantic information and enhance the semantic information of low-level feature maps but lacks the output of multi-scale information. Thus, the FEM was adopted to improve the output of multi-scale information, and the CA was used to improve the detection efficiency. The experimental results show that Apple-Net achieves a higher mAP@0.5 (95.9%) and precision (93.1%) than four classic target detection models, thus proving that Apple-Net achieves more competitive results on apple leaf disease identification.
Why it matches plant phenotyping methodsリンゴ葉の病害状態を画像から検出するYOLOv5改良モデルを開発し、複数モデルとの性能比較で検証しているため、植物病害フェノタイピング手法が中心です。
abstractwe explore an accurate and robust detection model for apple leaf disease called Apple-Net, improving the conventional YOLOv5 network by adding the Feature Enhancement Module (FEM) and Coordinate Attention (CA) methods.
Reproduction assets foundThe paper's apple leaf disease detection dataset (12,500 labeled images of five apple leaf diseases) is explicitly stated to be publicly available online at a Baidu AI Studio dataset URL in the Data Availability Statement. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicThe apple leaf dataset is publicly available online at https://aistudio.baidu.com/aistudio/datasetdetail/11591 (accessed on 26 December 2022).Open asset ↗datasetdetail/11591lines:86-193Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
The accurate segmentation of significant rice diseases and assessment of the degree of disease damage are the keys to their early diagnosis and intelligent monitoring and are the core of accurate pest control and information management. Deep learning applied to rice disease detection and segmentation can significantly improve the accuracy of disease detection and identification but requires a large number of training samples to determine the optimal parameters of the model. This study proposed a lightweight network based on copy paste and semantic segmentation for accurate disease region segmentation and severity assessment. First, a dataset for rice significant disease segmentation was selected and collated based on 3 open-source datasets, containing 450 sample images belonging to 3 categories of rice leaf bacterial blight, blast and brown spot. Then, to increase the diversity of samples, a data augmentation method, rice leaf disease copy paste (RLDCP), was proposed that expanded the collected disease samples with the concept of copy and paste. The new RSegformer model was then trained by replacing the new backbone network with the lightweight semantic segmentation network Segformer, combining the attention mechanism and changing the upsampling operator, so that the model could better balance local and global information, speed up the training process and reduce the degree of overfitting of the network. The results show that RLDCP could effectively improve the accuracy and generalisation performance of the semantic segmentation model compared with traditional data augmentation methods and could improve the MIoU of the semantic segmentation model by about 5% with a dataset only twice the size. RSegformer can achieve an 85.38% MIoU at a model size of 14.36 M. The method proposed in this paper can quickly, easily and accurately identify disease occurrence areas, their species and the degree of disease damage, providing a reference for timely and effective rice disease control.
Why it matches plant phenotyping methodsイネ葉の病斑領域を画像から分割し、病害の種類と被害程度を推定する手法の開発・評価が中心であり、植物病害状態の表現型取得に該当する。
abstractThis study proposed a lightweight network based on copy paste and semantic segmentation for accurate disease region segmentation and severity assessment.
Reproduction assets foundThe paper's curated rice disease image dataset with semantic segmentation masks is publicly deposited on Kaggle by the authors, and one of the three source image datasets (Dataset 3) is also a public Kaggle dataset used directly in this study. No author analysis code or trained model checkpoint is explicitly deposited;Dataset · publicThe three varieties of diseased rice leaf images and masks used in this study are available at https://www.kaggle.com/datasets/slygirl/rice-leaf-disease-with-segmentation-labels (accessed on 3 November 2022) and can be shared on request.Open asset ↗Kaggle · slygirl/rice-leaf-disease-with-segmentation-labelslines:314-332Dataset · public26. Leaf Rice Disease | Kaggle. [(accessed on 7 August 2022)]. Available online: https://www.kaggle.com/datasets/tedisetiady/leaf-rice-disease-indonesia .Open asset ↗Kaggle · tedisetiady/leaf-rice-disease-indonesialines:333-333Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Humans depend heavily on agriculture, which is the main source of prosperity. The various plant diseases that farmers must contend with have constituted a lot of challenges in crop production. The main issues that should be taken into account for maximizing productivity are the recognition and prevention of plant diseases. Early diagnosis of plant disease is essential for maximizing the level of agricultural yield as well as saving costs and reducing crop loss. In addition, the computerization of the whole process makes it simple for implementation. In this paper, an intelligent method based on deep learning is presented to recognize nine common tomato diseases. To this end, a residual neural network algorithm is presented to recognize tomato diseases. This research is carried out on four levels of diversity including depth size, discriminative learning rates, training and validation data split ratios, and batch sizes. For the experimental analysis, five network depths are used to measure the accuracy of the network. Based on the experimental results, the proposed method achieved the highest F1 score of 99.5%, which outperformed most previous competing methods in tomato leaf disease recognition. Further testing of our method on the Flavia leaf image dataset resulted in a 99.23% F1 score. However, the method had a drawback that some of the false predictions were of tomato early light and tomato late blight, which are two classes of fine-grained distinction.
Why it matches plant phenotyping methodsトマト葉画像から病害状態を推定する深層学習手法を開発・評価しており、植物の病徴認識が研究の中心です。
abstractan intelligent method based on deep learning is presented to recognize nine common tomato diseases.
Reproduction assets foundThe paper's phenotyping inputs are public leaf image datasets: the tomato leaf disease images from PlantVillage and the Flavia leaf dataset, both explicitly cited with public download URLs in the Data Availability Statement and Methods. No author code, models, or checkpoints are reported.Dataset · publicThe PlantVillage dataset is publicly available online at https://github.com/spMohanty/PlantVillage-Dataset (accessed on 13 November 2021).Open asset ↗PlantVillage-Datasetlines:818-820Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Dormancy is a physiological state that confers winter hardiness to and orchestrates phenological phase progression in temperate perennial plants. Weather fluctuations caused by climate change increasingly disturb dormancy onset and release in plants including tree crops, causing aberrant growth, flowering and fruiting. Research in this field suffers from the lack of affordable non-invasive methods for online dormancy monitoring. We propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants. It is based on continuous sensing of the photosynthetic activity of shoots via pulse-amplitude-modulated chlorophyll fluorescence sensors connected remotely to a data processing system. The resulting high-resolution time series of JIP-test parameters indicative of the responsiveness of the photosynthetic apparatus to environmental stimuli were subjected to frequency-domain analysis. The proposed approach overcomes the variance coming from diurnal changes of insolation and provides hints on the depth of dormancy. Our approach was validated over three seasons in an apple ( Malus × domestica Borkh.) orchard by collating the non-invasive estimations with the results of traditional methods (growing of the cuttings obtained from the trees at different phases of dormancy) and the output of chilling requirement models. We discuss the advantages of the proposed monitoring framework such as prompt detection of frost damage along with its potential limitations.
Why it matches plant phenotyping methods植物の休眠状態をクロロフィル蛍光センサーと周波数解析で非侵襲的に推定する監視手法を開発し、複数季節・従来法との比較で検証しており、表現型取得が研究の中心です。
abstractWe propose an automatic framework for low-cost, long-term, scalable dormancy studies in deciduous plants.
Reproduction assets foundThe authors explicitly state that the analysis code, accompanied by a subset of the data, is publicly available on GitHub (Lodinn/PAM-timeseries). The MDPI supplementary materials (S1) also contain paper-specific CF transient plots, correlation matrices, regression fits, and JIP-test parameter tables. Raw data and fullCode · publicCode used in the analysis, accompanied with a subset of the data, is available on GitHub ( https://github.com/Lodinn/PAM-timeseries , accessed on 20 September 2022).Open asset ↗Lodinn/PAM-timeserieslines:128-144Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11212811/s1 . A description of the PAM fluorimeter used in the work, including: Figure S1. The scheme of the experimental orchard plot.Open asset ↗lines:128-144Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Few-shot learning (FSL) is suitable for plant-disease recognition due to the shortage of data. However, the limitations of feature representation and the demanding generalization requirements are still pressing issues that need to be addressed. The recent studies reveal that the frequency representation contains rich patterns for image understanding. Given that most existing studies based on image classification have been conducted in the spatial domain, we introduce frequency representation into the FSL paradigm for plant-disease recognition. A discrete cosine transform module is designed for converting RGB color images to the frequency domain, and a learning-based frequency selection method is proposed to select informative frequencies. As a post-processing of feature vectors, a Gaussian-like calibration module is proposed to improve the generalization by aligning a skewed distribution with a Gaussian-like distribution. The two modules can be independent components ported to other networks. Extensive experiments are carried out to explore the configurations of the two modules. Our results show that the performance is much better in the frequency domain than in the spatial domain, and the Gaussian-like calibrator further improves the performance. The disease identification of the same plant and the cross-domain problem, which are critical to bring FSL to agricultural industry, are the research directions in the future.
Why it matches plant phenotyping methods植物病害状態を画像から認識するための周波数領域表現、周波数選択、分布較正手法を開発しており、病害表現型の抽出が研究の中心である。
abstractwe introduce frequency representation into the FSL paradigm for plant-disease recognition.
Reproduction assets foundThe paper's plant-disease recognition experiments are built on the PlantVillage dataset, which the authors explicitly state is publicly available at a Mendeley Data URL. No author analysis code, models, or checkpoints are disclosed.Dataset · publicThe PlantVillage dataset is available at https://data.mendeley.com/datasets/tywbtsjrjv/1 (accessed on 20 July 2022).Open asset ↗tywbtsjrjvlines:830-838Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
Current chemical methods used to control plant diseases cause a negative impact on the environment and increase production costs. Accurate and early detection is vital for designing effective protection strategies for crops. We evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging. We discuss the learning features of the techniques used, which will help researchers improve their understanding of the required data conditions and identify a method suitable for their research needs. A set of hyperspectral images of banana leaves inoculated with a conidial suspension of black sigatoka fungus ( Pseudocercospora fijiensis ) was used to train and validate machine learning models. Support vector machine (SVM), multilayer perceptron (MLP), neural networks, N-way partial least square-discriminant analysis (NPLS-DA), and partial least square-penalized logistic regression (PLS-PLR) were selected due to their high predictive power. The metrics of AUC, precision, sensitivity, prediction, and F1 were used for the models' evaluation. The experimental results show that the PLS-PLR, SVM, and MLP models allow for the successful detection of black sigatoka disease with high accuracy, which positions them as robust and highly reliable HSI classification methods for the early detection of plant disease and can be used to assess chemical and biological control of phytopathogens.
Why it matches plant phenotyping methodsバナナ葉の病徴をハイパースペクトル画像と複数の機械学習モデルで早期検出し、モデル性能を評価する手法研究であり、植物病害状態の取得・推定が中心です。
abstractWe evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging.
Reproduction assets foundThe paper's hyperspectral banana-leaf training/validation datasets and the authors' analysis source code (PLS-PLR, NPLS-DA, SVM, MLP) are explicitly stated as publicly available on the authors' GitHub repository.Code · publicThe source programs are available at the following link: https://github.com/JUG2019/Sigatoka-detect (accessed on 21 August 2022).Open asset ↗JUG2019/Sigatoka-detectlines:34-65Dataset · publicThe two datasets used in this study (i.e., the training dataset and validation dataset are available at: https://github.com/JUG2019/Sigatoka-detect (accessed on 21 August 2022).Open asset ↗JUG2019/Sigatoka-detectlines:234-247Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
WheatField / plotMicroscopyRootMorphology / geometry measurementRoot system architecture
Root hairs play an important role in absorbing water and nutrients in crop plants. Here we optimized high-throughput root hair length (RHL) and root hair density (RHD) phenotyping in wheat using a portable Dinolite™ microscope. A collection of 24 century wide spring wheat cultivars released between 1911 and 2016 were phenotyped for RHL and RHD. The results revealed significant variations for both traits with five and six-fold variation for RHL and RHD, respectively. RHL ranged from 1.01 mm to 1.77 mm with an average of 1.39 mm, and RHD ranged from 17.08 mm -2 to 20.8 mm -2 with an average of 19.6 mm -2 . Agronomic and physiological traits collected from five different environments and their best linear unbiased predictions (BLUPs) were correlated with RHL and RHD, and results revealed that relative-water contents (RWC), biomass and grain per spike (GpS) were positively correlated with RHL in both water-limited and well-watered conditions. While RHD was negatively correlated with grain yield (GY) in four environments and their BLUPs. Both RHL and RHD had positive correlation indicating the possibility of simultaneous selection of both phenotypes during wheat breeding. The expression pattern of TaRSL4 gene involved in regulation of root hair length was determined in all 24 wheat cultivars based on RNA-seq data, which indicated the differentially higher expression of the A- and D- homeologues of the gene in roots, while B-homeologue was consistently expressed in both leaf and roots. The results were validated by qRT-PCR and the expression of TaRSL4 was consistently high in rainfed cultivars such as Chakwal-50, Rawal-87, and Margallah-99. Overall, the new phenotyping method for RHL and RHD along with correlations with morphological and physiological traits in spring wheat cultivars improved our understanding for selection of these phenotypes in wheat breeding.
Why it matches plant phenotyping methods携帯型顕微鏡を用いたコムギ根毛長・密度のハイスループット表現型測定法を最適化し、品種で実証しているため、根形態フェノタイピング手法が中心である。
abstractHere we optimized high-throughput root hair length (RHL) and root hair density (RHD) phenotyping in wheat using a portable Dinolite™ microscope.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicTable S1: Name of the cultivars, pedigree, year of release and raw phenotypic data used in this study.Open asset ↗lines:71-196Code / dataset availability confirmedEurope PMC · Crossref · checked 8 Sept 2026
Maize yields worldwide are limited by foliar diseases that could be fungal, oomycete, bacterial, or viral in origin. Correct disease identification is critical for farmers to apply the correct control measures, such as fungicide sprays. Deep learning has the potential for automated disease classification from images of leaf symptoms. We aimed to develop a classifier to identify gray leaf spot (GLS) disease of maize in field images where mixed diseases were present (18,656 images after augmentation). In this study, we compare deep learning models trained on mixed disease field images with and without background subtraction. Performance was compared with models trained on PlantVillage images with single diseases and uniform backgrounds. First, we developed a modified VGG16 network referred to as "GLS_net" to perform binary classification of GLS, which achieved a 73.4% accuracy. Second, we used MaskRCNN to dynamically segment leaves from backgrounds in combination with GLS_net to identify GLS, resulting in a 72.6% accuracy. Models trained on PlantVillage images were 94.1% accurate at GLS classification with the PlantVillage testing set but performed poorly with the field image dataset (55.1% accuracy). In contrast, the GLS_net model was 78% accurate on the PlantVillage testing set. We conclude that deep learning models trained with realistic mixed disease field data obtain superior degrees of generalizability and external validity when compared to models trained using idealized datasets.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴画像から灰色斑点病を識別する深層学習手法を開発・比較し、背景分離や外部妥当性も評価しているため、植物病害フェノタイピング手法が中心です。
abstractDeep learning has the potential for automated disease classification from images of leaf symptoms.
Reproduction assets foundThe paper publicly releases its two paper-specific phenotyping image assets: the In-field (IF) dataset of 2332 field-captured, expert-labelled maize leaf images on Kaggle, and the IFL leaf-segmentation annotation dataset on segments.ai. The supplementary materials contain only performance figures/tables, not datasets, Dataset · publicThe IF dataset is available on Kaggle [ 39 ].Open asset ↗Kagglelines:142-207Code / dataset availability confirmedEurope PMC · checked 8 Sept 2026
RiceRaman / spectroscopyClassificationGrowth / development / phenology
Rice cultivation in cold regions of China is mainly distributed in Heilongjiang Province, where the growing season of rice is susceptible to low temperature and cold damage. Choosing and planting rice varieties with suitable GD according to the accumulated temperate zone is an important measure to prevent low temperature and cold damage. However, the traditional identification method of rice GD requires lots of field investigations, which are time consuming and susceptible to environmental interference. Therefore, an efficient, accurate, and intelligent identification method is urgently needed. In response to this problem, we took seven rice varieties suitable for three accumulated temperature zones in Heilongjiang Province as the research objects, and we carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets). The data preprocessing stage used a variety of methods (signal.filtfilt, difference, segmentation, and superposition) to process Raman spectral data to complete the fusion of local features and global features and data dimension transformation. A CapsNets containing three neuron layers (one convolutional layer and two capsule layers) and a dynamic routing protocol was constructed and implemented in Python. After training 160 epochs on the CapsNets, the model achieved 89% and 93% accuracy on the training and test datasets, respectively. The results showed that Raman spectroscopy combined with CapsNets can provide an efficient and accurate intelligent identification method for the classification and identification of rice GD in Heilongjiang Province.
Why it matches plant phenotyping methodsラマン分光とCapsNetを組み合わせ、イネの生育期間という植物形質を推定・分類する手法を開発し、学習・テスト精度で評価しているため、表現型取得・抽出法が中心です。
abstractwe carried out research on the identification of japonica rice GD based on Raman spectroscopy and capsule neural networks (CapsNets).
Reproduction assets foundThe paper's Data Availability Statement explicitly states that the study's data (Raman spectral data of 245 japonica rice grain samples) and code (spectral preprocessing and CapsNets classification) are openly available in the authors' public GitHub repository, which is listed in the allowed URLs.Code · publicData Availability Statement: The data and code presented in this study are openly available at:
https://github.com/zxxsnh/Rice_GD_classification_CapsNets (accessed on 14 June 2022).Open asset ↗zxxsnh/Rice_GD_classification_CapsNetspdf-page:12 lines:1-60Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
ArabidopsisLaboratory / benchtopRootMorphology / geometry measurementTrackingGrowth / development / phenologyRoot system architecture
Directional root growth control is crucial for plant fitness. The degree of root growth deviation depends on several factors, whereby exogenous growth conditions have a profound impact. The perception of mechanical impedance by wild-type roots results in the modulation of root growth traits, and it is known that gravitropic stimulus influences distinct root movement patterns in concert with mechanoadaptation. Mutants with reduced shootward auxin transport are described as being numb towards mechanostimulus and gravistimulus, whereby different growth conditions on agar-supplemented medium have a profound effect on how much directional root growth and root movement patterns differ between wild types and mutants. To reduce the impact of unilateral mechanostimulus on roots grown along agar-supplemented medium, we compared the root movement of Col-0 and auxin resistant 1-7 in a root penetration assay to test how both lines adjust the growth patterns of evenly mechanostimulated roots. We combined the assay with the D-root system to reduce light-induced growth deviation. Moreover, the impact of sucrose supplementation in the growth medium was investigated because exogenous sugar enhances root growth deviation in the vertical direction. Overall, we observed a more regular growth pattern for Col-0 but evaluated a higher level of skewing of aux1-7 compared to the wild type than known from published data. Finally, the tracking of the growth rate of the gravistimulated roots revealed that Col-0 has a throttling elongation rate during the bending process, but aux1-7 does not.
Why it matches plant phenotyping methodsD-rootシステムと根貫通アッセイを組み合わせ、根の成長パターン・伸長速度を追跡して評価する測定ワークフローが研究の中心であるため。
abstractWe combined the assay with the D-root system to reduce light-induced growth deviation.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/plants11050650/s1 , Table S1: raw data.Open asset ↗lines:48-103Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Field / plotFruitGrowth / time-series analysisGrowth / development / phenologyYield / yield components
Cacao production systems in Colombia are of high importance due to their direct impact in the social and economic development of smallholder farmers. Although Colombian cacao has the potential to be in the high value markets for fine flavour, the lack of expert support as well as the use of traditional, and often times sub-optimal technologies makes cacao production negligible. Traditionally, cacao harvest takes place at exactly the same time regardless of the geographic and climatic region where it is grown, the problem with this strategy is that cacao beans are often unripe or over matured and a combination of both will negatively affect the quality of the final cacao product. Since cacao fruit development can be considered as the result of a number of physiological and morphological processes that can be described by mathematical relationships even under uncontrolled environments. Environmental parameters that have more association with pod maturation speed should be taken into account to decide the appropriate time to harvest. In this context, crop models are useful tools to simulate and predict crop development over time and under multiple environmental conditions. Since harvesting at the right time can yield high quality cacao, we parameterised a crop model to predict the best time for harvest cacao fruits in Colombia. The cacao model uses weather variables such as temperature and solar radiation to simulate the growth rate of cocoa fruits from flowering to maturity. The model uses thermal time as an indicator of optimal maturity. This model can be used as a practical tool that supports cacao farmers in the production of high quality cacao which is usually paid at a higher price. When comparing simulated and observed data, our results showed an RRMSE of 7.2% for the yield prediction, while the simulated harvest date varied between +/-2 to 20 days depending on the temperature variations of the year between regions. This crop model contributed to understanding and predicting the phenology of cacao fruits for two key cultivars ICS95 y CCN51.
Why it matches plant phenotyping methodsカカオ果実の成熟・生育（フェノロジー）を気象変数と熱時間から推定する作物モデルを構築・パラメータ化し、観測値と比較検証しており、植物状態の推定手法が研究の中心である。
abstractwe parameterised a crop model to predict the best time for harvest cacao fruits in Colombia
Reproduction assets foundThe authors' cacao crop model (kocolatl), the modified SIMPLE model code used for the paper's phenology/yield simulations, is stated to be available in a public repository with an authors' GitHub URL. The Fedecacao field phenotype data are not independently deposited; the handle.net URL is a cited reference, not a dataCode · publicSoftware will be provided upon user request. The data used in this study can be found as follows: kocolatl is available in the research data repository. https://github.com/anyelacamargo/kocolatl.git accessed on 15 December 2021, UK.
Conflicts of Interest
The authors declare no conflict of interest.
Footnotes
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References
1. Zuidema P.A., Leffelaar P.A., Gerritsma W., Mommer L., Anten N.P. A physiologicaOpen asset ↗github.com/anyelacamargo/kocolatl.git · kocolatllines:278-301Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.
Why it matches plant phenotyping methods低コストUGV・LiDARによる3D植物計測システムを開発し、樹冠形態指標を抽出・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ある。
abstractAn unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth.
Reproduction assets foundThe paper's Data Availability Statement provides an authors' public GitHub repository containing their code implementation for the UGV-LiDAR citrus crop mapping/phenotyping system. No separate phenotype dataset or point cloud deposit is stated.Code · publicOur code implementation is available online at https://github.com/HaroldMurcia/miniRover_LiDAR_citrush_crop.git , accessed on 25 November 2021.Open asset ↗HaroldMurcia/miniRover_LiDAR_citrush_croplines:356-358Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Stomatal observation and automatic stomatal detection are useful analyses of stomata for taxonomic, biological, physiological, and eco-physiological studies. We present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning. We tested eight clearing agent formulations based upon different ethanol and sodium hypochlorite (NaOCl) concentrations in order to improve the transparency in leaves. An optimal formulation-a 1:1 ( v / v ) mixture of 95% ethanol and NaOCl (6-14%)-produced better quality images of soybean stomata. Additionally, we evaluated fixatives and dehydrating agents and selected absolute ethanol for both fixation and dehydration. This is a good substitute for formaldehyde, which is more toxic to handle. Using imaging data from this clearing method, we developed an automatic stomatal detector using deep learning and improved a deep-learning algorithm that automatically analyzes stomata through an object detection model using YOLO. The YOLO deep-learning model successfully recognized stomata with high mAP (~0.99). A web-based interface is provided to apply the model of stomatal detection for any soybean data that makes use of the new clearing protocol.
Why it matches plant phenotyping methodsダイズ葉の気孔画像取得法と、気孔を自動検出・解析する深層学習手法を開発・評価しており、植物形質取得が研究の中心である。
abstractWe present a new clearing method for improved microscopic imaging of stomata in soybean followed by automated stomatal detection by deep learning.
Reproduction assets foundThe paper's soybean stomatal phenotype dataset (Table S2, manual vs. automatic stomatal density for 386 accessions) is publicly available via the MDPI supplement, and the trained YOLOv5 stomata-detection model is publicly served through the authors' web application. The 183-image training dataset and analysis code haveSupplement · publicnowledge the personnel from the Plant Genetics and Breeding lab at the Kyungpook National University for their time and work at the greenhouse.
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Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10122714/s1 , Table S1: The representative methods for detecting stomata in different species, Table S2: Comparison stomata density between counting by manual and develped program.
Click here for additional data file.
Author Contributions
Conceptualization, J.-D.L. and Y.J.K.; methodology, S.N.S.; formal analysis, H.P., S.H.COpen asset ↗lines:69-162Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Bacterial leaf blight (BLB) is caused by Xanthomonas oryzae pv. oryzae and is a major cause of rice yield reductions around the world. When diseased, plants produce a variety of metabolites to resist pathogens. In this study, the various defense metabolites were quantified using high-performance liquid chromatography (HPLC) after Xoo inoculation in a 120 Cheongcheong/Nagdong double haploid (CNDH) population. Quantitative trait locus (QTL) mapping was conducted using the concentration of the plant defense metabolites. HPLC analyzes the concentration of substances according to the severity of disease symptoms. Searching for BLB resistance candidate genes by applying this analysis method is very effective when mapping related genes. These resistance genes can be mapped directly to the causative pathogens. A total of 17 metabolites were detected by means of HPLC analysis after Xoo inoculation in the 120 CNDH population. QTL mapping of the metabolite concentrations resulted in the detection of the BLB resistance candidate gene, OsWRKYq6, in RM3343 of chromosome 6. OsWRKYq6 has a very high homology sequence with WRKY transcription factor 39, and when inoculated with Xoo, the relative expression level of the resistant population was higher than that of the susceptible population. Resistance genes have previously been detected using only phenotypic change data. In this study, resistance candidate genes were detected using the concentration of metabolites produced in plants after inoculation with pathogens. This newly developed analysis method can be used to effectively detect and identify genes directly involved in disease resistance for future studies.
Why it matches plant phenotyping methodsHPLCによる防御代謝物濃度の定量を、イネの病害抵抗性状態の表現型取得・QTL検出法として明示的に開発・適用しており、単なるルーチン測定ではない。
abstractIn this study, the various defense metabolites were quantified using high-performance liquid chromatography (HPLC) after Xoo inoculation in a 120 Cheongcheong/Nagdong double haploid (CNDH) population.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicThe following are available online at https://www.mdpi.com/article/10.3390/plants10102145/s1 , Figure S1: PCA (Principal Component Analysis) statistical analysis between the peak area in the HPLC analysis results and the infection length data of the corresponding leaf samples. Table S1: The plant traits and peak no. of 120 CNDH (Cheongcheong/Nagdong double haploid) populations by HPLC (high-performance liquid chromatography). Table S2: Details of QTL mapping using HPLC analysis results of 120 CNDH population after Xanthomonas oryzae pv. oryzae inoculation.Open asset ↗MDPIlines:71-91Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Recently, deep-learning techniques have become the foundations for many breakthroughs in the automated identification of plant diseases. In the agricultural sector, many recent visual-computer approaches use deep-learning models. In this approach, a novel predictive analytics methodology to identify Tobacco Mosaic Virus (TMV) and Pepper Huasteco Yellow Vein Virus (PHYVV) visual symptoms on Jalapeño pepper ( Capsicum annuum L.) leaves by using image-processing and deep-learning classification models is presented. The proposed image-processing approach is based on the utilization of Normalized Red-Blue Vegetation Index (NRBVI) and Normalized Green-Blue Vegetation Index (NGBVI) as new RGB-based vegetation indices, and its subsequent Jet pallet colored version NRBVI-Jet NGBVI-Jet as pre-processing algorithms. Furthermore, four standard pre-trained deep-learning architectures, Visual Geometry Group-16 (VGG-16), Xception, Inception v3, and MobileNet v2, were implemented for classification purposes. The objective of this methodology was to find the most accurate combination of vegetation index pre-processing algorithms and pre-trained deep- learning classification models. Transfer learning was applied to fine tune the pre-trained deep- learning models and data augmentation was also applied to prevent the models from overfitting. The performance of the models was evaluated using Top-1 accuracy, precision , recall , and F1-score using test data. The results showed that the best model was an Xception-based model that uses the NGBVI dataset. This model reached an average Top-1 test accuracy of 98.3%. A complete analysis of the different vegetation index representations using models based on deep-learning architectures is presented along with the study of the learning curves of these deep-learning models during the training phase.
Why it matches plant phenotyping methods葉の可視症状をRGB画像処理と深層学習で識別する手法を開発・比較しており、植物病害状態の表現型推定が中心である。
abstracta novel predictive analytics methodology to identify Tobacco Mosaic Virus (TMV) and Pepper Huasteco Yellow Vein Virus (PHYVV) visual symptoms on Jalapeño pepper ( Capsicum annuum L.) leaves by using image-processing and deep-learning classification models is presented.
Reproduction assets foundThe paper publicly releases its authors' analysis code on GitHub and its generated leaf image datasets (RGB plus vegetation-index versions) on Zenodo, both with explicit availability statements.Code · publicThe source code of this article is publicly released and can be downloaded from https://github.com/jrmillan1983/PHYVV_TMV_CNN .Open asset ↗jrmillan1983/PHYVV_TMV_CNNlines:371-472Dataset · publicThe datasets generated during and/or analyzed during the current study are available from https://doi.org/10.5281/zenodo.5500727 (accessed on 19 September 2021).Open asset ↗zenodo · 10.5281/zenodo.5500727lines:474-476Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Environmental factors might influence the carbon balance and sugar content in grapevine. In this two-year research, the STELLA software was employed to predict dry matter accumulation in Sangiovese vines, comparing the traditional vertical shoot positioning (VSP) and the single high wire (SHW) trellis systems. Every week, vegetative, eco-physiological and grape quality parameters were collected for 15 tagged vines per trellis system to set up the software. Significant differences in photosynthesis were recorded in 2014, with higher values in VSP (23-25% more). Shoot growth was significantly higher in VSP (20-25% more), whereas higher dry matter (30%) and yield (9-11% more) were detected for SHW. At harvest, berry composition suggested a slower ripening in SHW compared to VSP, which was linked to the shading of clusters in SHW. Finally, for the first time, linear regressions were found between measured berry sugar content and STELLA-estimated dry matter (R 2 = 0.96 in VSP; R 2 = 0.95 in SHW). This latter evidence allowed the estimation of berry sugar content, showing this software to be a practical tool to support winegrowers in decision making. Other studies are already underway to calibrate and validate the model for other varieties, training systems and environments.
Why it matches plant phenotyping methodsSTELLAモデルによるブドウの乾物蓄積・果実糖含量の推定と、実測値との回帰による検証が研究の中心であり、植物形質の計算推定手法として扱える。
abstractthe STELLA software was employed to predict dry matter accumulation in Sangiovese vines
Reproduction assets foundThe paper's phenotyping measurements (gas exchange, dry matter, berry composition) are reported only within the article itself ('Data is contained within the article'), with no public dataset deposit. However, the authors provide a public supplement containing paper-specific assets: Figure S1 (experimental site images)Supplement · publicbut, above all, herself for the tenacity in being able to finally publish the results of her master’s thesis. Another chapter is closed or not?
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Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10081675/s1 , Figure S1: Experimental site pictures, Figure S2: simplified model structure of STELLA software.
Click here for additional data file.
Author Contributions
Conceptualization, G.B.M. and L.S.; methodology and software validation, L.S. and E.C.; formal analysis, investigation and data curation, L.S., E.C., S.S., F.Open asset ↗lines:74-114Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
CoffeeLeafClassificationVisualization / data managementDisease symptoms / severity
Deep learning architectures are widely used in state-of-the-art image classification tasks. Deep learning has enhanced the ability to automatically detect and classify plant diseases. However, in practice, disease classification problems are treated as black-box methods. Thus, it is difficult to trust the model that it truly identifies the region of the disease in the image; it may simply use unrelated surroundings for classification. Visualization techniques can help determine important areas for the model by highlighting the region responsible for the classification. In this study, we present a methodology for visualizing coffee diseases using different visualization approaches. Our goal is to visualize aspects of a coffee disease to obtain insight into what the model "sees" as it learns to classify healthy and non-healthy images. In addition, visualization helped us identify misclassifications and led us to propose a guided approach for coffee disease classification. The guided approach achieved a classification accuracy of 98% compared to the 77% of naïve approach on the Robusta coffee leaf image dataset. The visualization methods considered in this study were Grad-CAM, Grad-CAM++, and Score-CAM. We also provided a visual comparison of the visualization methods.
Why it matches plant phenotyping methodsコーヒー葉の病害領域を画像から可視化・分類する手法が研究の中心であり、Grad-CAM系手法の比較と分類精度の評価を行っているため、植物病害状態の画像ベース表現型解析に該当する。
abstractIn this study, we present a methodology for visualizing coffee diseases using different visualization approaches.
Reproduction assets foundThe paper's plant-phenotyping input data (the Robusta coffee leaf image dataset, RoCoLe, used for disease classification and visualization experiments) is explicitly declared openly available in Mendeley Data. No author analysis code or trained model checkpoints are stated as publicly available. The only allowed URL isDataset · publicThe data presented in this study are openly available in Mendeley Data at doi:10.17632/c5yvn32dzg.2, reference number 36.Mendeley Data · doi:10.17632/c5yvn32dzg.2lines:198-232Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Japanese cedar ( Cryptomeria japonica ) is the most important timber species in Japan; however, its pollen is the primary cause of pollinosis in Japan. The total number of pollen grains produced by a single tree is determined by the number of male strobili (male flowers) and the number of pollen grains per male strobilus. While the number of male strobili is a visible and well-investigated trait, little is known about the number of pollen grains per male strobilus. We hypothesized that genetic and environmental factors affect the pollen number per male strobilus and explored the factors that affect pollen production and genetic variation among clones. We counted pollen numbers of 523 male strobili from 26 clones using a cell counter method that we recently developed. Piecewise Structural Equation Modeling (pSEM) revealed that the pollen number is mostly affected by genetic variation, male strobilus weight, and pollen size. Although we collected samples from locations with different environmental conditions, statistical modeling succeeded in predicting pollen numbers for different clones sampled from branches facing different directions. Comparison of predicted pollen numbers revealed that they varied >3-fold among the 26 clones. The determination of the factors affecting pollen number and a precise evaluation of genetic variation will contribute to breeding strategies to counter pollinosis. Furthermore, the combination of our efficient counting method and statistical modeling will provide a powerful tool not only for Japanese cedar but also for other plant species.
Why it matches plant phenotyping methods花粉数という植物形質を測定する新開発のセルカウンター法を適用し、統計モデルと組み合わせた再利用可能な計測手法として位置づけているため、方法論的役割が中心的です。
abstractWe counted pollen numbers of 523 male strobili from 26 clones using a cell counter method that we recently developed.
Reproduction assets foundThe paper's phenotype measurements (pollen number, pollen size, male strobilus weight, direction/height for all 523 samples from 26 clones) are published as Supplementary Table S1, publicly available at the MDPI supplementary URL. No author analysis code or trained models are deposited; the Data Availability Statement仅Supplement · publicions will also be useful for examining other traits in the field research.
Acknowledgments
We thank Yukiko Ito (Niigata Prefectural Forest Research Institute) for providing breeding materials, and Naoto-Benjamin Hamaya (University of Zurich) for valuable suggestions.
Supplementary Materials
The following are available online at https://www.mdpi.com/article/10.3390/plants10050856/s1 . Supplementary Figure S1. Scatterplot of male strobilus weight and area. All samples are shown in this figure. Weight and area are strongly correlated (r = 0.892). Different clones are represented by different symbols (see Table 1 ). Supplementary Table S1. Data for all samples.
Click here for additional data filOpen asset ↗lines:258-292Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Sphagnum peatmosses play an important part in water table management of many peatland ecosystems. Keeping the ecosystem saturated, they slow the breakdown of organic matter and release of greenhouse gases, facilitating peatland's function as a carbon sink rather than a carbon source. Although peatland monitoring and restoration programs have increased recently, there are few tools to quantify traits that Sphagnum species display in their ecosystems. Colony density is often described as an important determinant in the establishment and performance in Sphagnum but detailed evidence for this is limited. In this study, we describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages. The pipeline was validated using images of different Sphagnum species growing in different habitats, taken on different days and with different smartphones. The developed pipeline achieves high accuracy scores, and we demonstrate its utility by estimating colony densities in the field and detecting intra and inter-specific colony densities and their relationship with habitat. This tool will enable ecologists and conservationists to rapidly acquire accurate estimates of Sphagnum density in the field without the need of specialised equipment.
Why it matches plant phenotyping methodsSphagnumの植物密度という明示的な形態・生態形質を、画像解析パイプラインで抽出する手法を開発し、異なる種・生育地・撮影条件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す。
abstractwe describe an image analysis pipeline that accurately annotates Sphagnum capitula and estimates plant density using open access computer vision packages.
Reproduction assets foundThe paper's field Sphagnum images and annotation data are deposited publicly (DOI 10.20391/9ba4df8f-2d28-4bce-b9b8-ec6368b636e0), and the authors' Capitula-Counter image analysis pipeline is freely available on the NPPC GitHub. Both are paper-specific, public, and actionable.Dataset · publicAll images used are available at https://doi.org/10.20391/9ba4df8f-2d28-4bce-b9b8-ec6368b636e0 (accessed on 21 April 2021).Open asset ↗10.20391/9ba4df8f-2d28-4bce-b9b8-ec6368b636e0lines:69-74Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Roots are at the core of plant water dynamics. Nonetheless, root morphology and functioning are not easily assessable without destructive approaches. Nuclear Magnetic Resonance (NMR), and particularly low-field NMR (LF-NMR), is an interesting noninvasive method to study water in plants, as measurements can be performed outdoors and independent of sample size. However, as far as we know, there are no reported studies dealing with the water dynamics in plant roots using LF-NMR. Thus, the aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively. To achieve this goal, a proof-of-concept study was designed using well-controlled environmental conditions. NMR and ecophysiological measurements were performed continuously over one week on three herbaceous species grown in rhizotrons. The NMR parameters measured were either the total signal or the transverse relaxation time T 2 . We observed circadian variations of the total NMR signal in roots and in soil and of the root slow relaxing T 2 value. These results were consistent with ecophysiological measurements, especially with the variation of fluxes between daytime and nighttime. This study assessed the feasibility of using LF-NMR to evaluate root water status in herbaceous species.
Why it matches plant phenotyping methodsLF-NMRによる根の水分状態・水動態の非破壊的測定可能性を中心に検証した proof-of-concept 研究であり、植物生理状態の取得手法が主要な貢献である。
abstractthe aim of this study is to assess the feasibility of using LF-NMR to characterize root water status and water dynamics non-invasively.
Reproduction assets foundThe authors deposited the paper's NMR and ecophysiological measurement data openly in Data INRAE (DOI 10.15454/NWRHDA), and supplementary materials at MDPI contain the CPMG decay curves and NNLS processing methods used for the T2 analysis.Dataset · publicThe data presented in this study are openly available in Data INRAE ( https://data.inrae.fr/ , accessed on 12 March 2021) repository at https://doi.org/10.15454/NWRHDA (accessed on 12 March 2021).Open asset ↗Data INRAE · 10.15454/NWRHDAlines:115-137Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Reflected light carries ample information about the biochemical composition, tissue architecture, and physiological condition of plants. Recent technical progress has paved the way for affordable imaging hyperspectrometers (IH) providing spatially resolved spectral information on plants on different levels, from individual plant organs to communities. The extraction of sensible information from hyperspectral images is difficult due to inherent complexity of plant tissue and canopy optics, especially when recorded under ambient sunlight. We report on the changes in hyperspectral reflectance accompanying the accumulation of anthocyanins in healthy apple (cultivars Ligol, Gala, Golden Delicious) fruits as well as in fruits affected by pigment breakdown during sunscald development and phytopathogen attacks. The measurements made outdoors with a snapshot IH were compared with traditional "point-type" reflectance measured with a spectrophotometer under controlled illumination conditions. The spectra captured by the IH were suitable for processing using the approaches previously developed for "point-type" apple fruit and leaf reflectance spectra. The validity of this approach was tested by constructing a novel index mBRI (modified browning reflectance index) for detection of tissue damages on the background of the anthocyanin absorption. The index was suggested in the form of mBRI = ( R 640 -1 + R 800 -1 ) - R 678 -1 . Difficulties of the interpretation of fruit hyperspectral reflectance images recorded in situ are discussed with possible implications for plant physiology and precision horticulture practices.
Why it matches plant phenotyping methodsリンゴ果実のハイパースペクトル画像から組織損傷を検出する指標を開発・検証しており、植物状態の取得手法が研究の中心である。
abstractThe validity of this approach was tested by constructing a novel index mBRI (modified browning reflectance index) for detection of tissue damages on the background of the anthocyanin absorption.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicThe sampling of the spectral data and rendering of the index images have been carried out using Gelion, the original software for processing hyperspectral images ( https://github.com/AlexanderMipt/Gelion ).Open asset ↗github.com/AlexanderMipt/Gelionlines:105-152Code / dataset availability confirmedEurope PMC · Crossref · checked 15 Sept 2026
Tissue clearing methods combined with confocal microscopy have been widely used for studying developmental biology. In plants, ClearSee is a reliable clearing method that is applicable to a wide range of tissues and is suitable for gene expression analysis using fluorescent reporters, but its application to the Arabidopsis thaliana embryo, a model system to study morphogenesis and pattern formation, has not been described in the original literature. Here, we describe a ClearSee-based clearing protocol which is suitable for obtaining 3D images of Arabidopsis thaliana embryos. The method consists of embryo dissection, fixation, washing, clearing, and cell wall staining and enables high-quality 3D imaging of embryo morphology and expression of fluorescent reporters with the cellular resolution. Our protocol provides a reliable method that is applicable to the analysis of morphogenesis and gene expression patterns in Arabidopsis thaliana embryos.
Why it matches plant phenotyping methodsArabidopsis胚の形態を細胞解像度で3D取得するClearSeeベースのクリアリング・画像化プロトコルが研究の中心であり、植物表現型の取得方法を開発している。
abstractHere, we describe a ClearSee-based clearing protocol which is suitable for obtaining 3D images of Arabidopsis thaliana embryos.
Reproduction assets foundThe paper's supplementary materials include Movie S1, the Z-stack confocal image data (157 serial optical sections) used for the paper's 3D embryo visualization analysis, publicly available at the MDPI supplementary URL. No author analysis code or trained models are reported.Supplement · publicThe following are available online at https://www.mdpi.com/2223-7747/10/2/190/s1 , Movie S1: Z-stack images of 157 serial optical sections used for Figure 2 ; Table S1: Primers used in this study.
Click here for additional data file.
Author Contributions
Conceptualization, M.A.; methodology, M.A.; validation, A.I. and M.A.; formal analysis, A.I.; investigation, A.I., M.Y., T.S., A.O., and M.A.; resources, TOpen asset ↗lines:58-85Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Image-based symptom scoring of plant diseases is a powerful tool for associating disease resistance with plant genotypes. Advancements in technology have enabled new imaging and image processing strategies for statistical analysis of time-course experiments. There are several tools available for analyzing symptoms on leaves and fruits of crop plants, but only a few are available for the model plant Arabidopsis thaliana (Arabidopsis). Arabidopsis and the model fungus Botrytis cinerea (Botrytis) comprise a potent model pathosystem for the identification of signaling pathways conferring immunity against this broad host-range necrotrophic fungus. Here, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves. Thus, a pixel classification strategy using color hue values from red-green-blue (RGB) images and a random forest algorithm was used to establish necrotic, chlorotic, and healthy leaf areas. Secondly, using chlorophyll fluorescence (ChlFl) imaging, the maximum quantum yield of photosystem II (F v /F m ) was determined to define diseased areas and their proportion per total leaf area. Both RGB and ChlFl imaging strategies were employed to track disease progression over time. This has provided a robust and sensitive method for detecting sensitive or resistant genetic backgrounds. A full methodological workflow, from plant culture to data analysis, is described.
Why it matches plant phenotyping methods植物病害の症状・重症度・進展を画像から定量化する方法の開発とワークフロー提示が中心であり、植物状態の表現型取得に該当する。
abstractHere, we present two strategies to assess severity and symptom progression of Botrytis infection over time in Arabidopsis leaves.
Reproduction assets foundThe authors explicitly state that all R and ImageJ scripts and the study data are openly available in their public GitHub repository, which directly reproduces this paper's Botrytis symptom phenotyping analysis.Code · publicAll R and ImageJ script generated to process are available at https://github.com/mipavici/MDPI_leaf_infection .Open asset ↗mipavici/MDPI_leaf_infectionlines:69-123Dataset · publicThe data presented in this study are openly available at https://github.com/mipavici/MDPI_leaf_infection .Open asset ↗mipavici/MDPI_leaf_infectionlines:69-123Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
The CO 2 and water vapor exchange between leaf and atmosphere are relevant for plant physiology. This process is done through the stomata. These structures are fundamental in the study of plants since their properties are linked to the evolutionary process of the plant, as well as its environmental and phytohormonal conditions. Stomatal detection is a complex task due to the noise and morphology of the microscopic images. Although in recent years segmentation algorithms have been developed that automate this process, they all use techniques that explore chromatic characteristics. This research explores a unique feature in plants, which corresponds to the stomatal spatial distribution within the leaf structure. Unlike segmentation techniques based on deep learning tools, we emphasize the search for an optimal threshold level, so that a high percentage of stomata can be detected, independent of the size and shape of the stomata. This last feature has not been reported in the literature, except for those results of geometric structure formation in the salt formation and other biological formations.
Why it matches plant phenotyping methods葉の顕微鏡画像から気孔を自動セグメンテーションする手法の開発が中心であり、植物の形態的形質取得に直接関わる。
titleAutomatic Stomatal Segmentation Based on Delaunay-Rayleigh Frequency Distance.
Reproduction assets foundThe paper's Supplementary Materials section explicitly states that the DRTB solution (authors' analysis code) is available online at https://github.com/mlacarrasco/drtb and that the images database (stomatal microscopy images used for phenotyping) is available at https://github.com/mlacarrasco/drtb/tree/main/database. Dataset · publicOur solution can be accessed online at https://github.com/mlacarrasco/drtb , and images database are available online at https://github.com/mlacarrasco/drtb/tree/main/database .Open asset ↗mlacarrasco/drtblines:61-134Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Plant simulation models are abstractions of plant physiological processes that are useful for investigating the responses of plants to changes in the environment. Because photosynthesis and transpiration are fundamental processes that drive plant growth and water relations, a leaf gas-exchange model that couples their interdependent relationship through stomatal control is a prerequisite for explanatory plant simulation models. Here, we present a coupled gas-exchange model for C4 leaves incorporating two widely used stomatal conductance submodels: Ball-Berry and Medlyn models. The output variables of the model includes steady-state values of CO2 assimilation rate, transpiration rate, stomatal conductance, leaf temperature, internal CO2 concentrations, and other leaf gas-exchange attributes in response to light, temperature, CO2, humidity, leaf nitrogen, and leaf water status. We test the model behavior and sensitivity, and discuss its applications and limitations. The model was implemented in Julia programming language using a novel modeling framework. Our testing and analyses indicate that the model behavior is reasonably sensitive and reliable in a wide range of environmental conditions. The behavior of the two model variants differing in stomatal conductance submodels deviated substantially from each other in low humidity conditions. The model was capable of replicating the behavior of transgenic C4 leaves under moderate temperatures as found in the literature. The coupled model, however, underestimated stomatal conductance in very high temperatures. This is likely an inherent limitation of the coupling approaches using Ball-Berry type models in which photosynthesis and stomatal conductance are recursively linked as an input of the other.
Why it matches plant phenotyping methodsC4葉のガス交換特性を推定する結合モデルを開発し、感度・信頼性・文献データ再現性・限界を検証しており、植物表現型の取得・推定手法が中心である。
abstractHere, we present a coupled gas-exchange model for C4 leaves incorporating two widely used stomatal conductance submodels: Ball-Berry and Medlyn models.
Reproduction assets foundThe authors explicitly state that a Jupyter notebook containing the model source code, calibration datasets (maize gas-exchange/SPAD measurements), and figure-generation scripts is publicly available on GitHub. The authors' Julia modeling framework (Cropbox.jl) used for the analysis is also publicly available.Code · publicA Jupyter notebook containing source code of the model with calibration datasets and scripts for producing figures presented in this paper is available at https://github.com/cropbox/plants2020 .Open asset ↗cropbox/plants2020lines:500-598