Plant diseases greatly affect agricultural production, especially in developing countries, where prompt diagnosis can be quite challenging due to the limited availability of experts in real-time. Deep learning techniques for image analysis is gaining popularity and are increasingly considered an alternative to traditional manual inspection of plants. This research presents the evaluation of plant leaf disease detection system based on a convolutional neural network (CNN) optimized with different nature-inspired algorithms. The backbone model is based on the EfficientNet-B0 pretrained on ImageNet. Therefore, transfer learning is used to adapt the model to an updated PlantVillage dataset. Experiments have been conducted with multiple nature-inspired algorithms to improve generalisation and training efficiency of the prediction model. Different data preparation techniques have been carefully applied to the dataset, creating a unified approach to ensure consistency in the preprocessing pipeline for the training, validation, and testing phases. Our experiments indicate that application of the grey wolf optimizer (GWO) for tuning key hyperparameters of the model, including dropout, learning rates, and weight decay produced the best results, with an accuracy around 99.45%.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する深層学習手法を、複数の最適化アルゴリズムで評価・改良しており、病害表現型の取得・推定が研究の中心である。
abstractThis research presents the evaluation of plant leaf disease detection system based on a convolutional neural network (CNN) optimized with different nature-inspired algorithms.
Plant diseases are still posing a challenge to the productivity, quality of crops, and food security, especially in locations where field diagnosis is based on manual visual inspec-tion. This paper assesses deep learning network-based automated classification of plant leaf diseases on public RGB leaf-image datasets, such as the Kaggle New Plant Diseases Dataset (Augmented) and PlantVillage images. They investigated four archi-tectures: EfficientNetV2B0, ResNet152V2, DenseNet201, and one hybrid Vision Trans-former (ViT)-based model. The steps of the experiment involved loading the dataset, exploratory analysis, preprocessing, resizing, normalizing, augmentation, transfer learning, independent model training, and evaluation metrics such as accuracy, preci-sion, recall, F1-score, training curves, testing results, and confusion matrices. The hy-brid ViT-based model was reported to have the best accuracy of 99.5%. On the smaller seven class subset of PlantVillage, EfficientNetV2B0 scored 98.11%. On the 38-class dataset, DenseNet201 improved test accuracy (97.34) and validation classification ac-curacy (around 98). ResNet152V2 scored 97.01 on the 38-class test set. The results demonstrate that CNN and transformer-based models can help to recognize plant diseases accurately whereas hybrid attention-based structures provide a promising path to enhance fine-grained classification. Since the model notebooks had varying class settings and splits, the comparison is seen as a model-structured assessment as opposed to a precisely identical benchmark across all architectures.
Why it matches plant phenotyping methods植物葉画像から病害状態を分類する深層学習手法を複数モデルで比較・評価しており、病害表現型の取得・抽出と技術検証が研究の中心である。
abstractThis paper assesses deep learning network-based automated classification of plant leaf diseases on public RGB leaf-image datasets
Reproduction assets foundThe paper's phenotyping inputs are two public plant leaf-image datasets explicitly named in the Data Availability statement: the Kaggle New Plant Diseases Dataset (Augmented) and the PlantVillage dataset, both with public URLs. No author code, models, or supplementary materials are deposited (supplementary materials: 'Dataset · publicy available. Plant leaf images were
obtained from the Kaggle New Plant Diseases Dataset (Augmented) and PlantVillage
datasets. The datasets contain publicly accessible RGB images of healthy and diseased
plant leaves used for supervised image classification research.
DATASET SOURCES
Kaggle New Plant Diseases Dataset (Augmented):
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset
PlantVillage Dataset: https://plantvillage.psu.edu/All processed data, experimental configurations, and model implementation details are
described within the manuscript. Additional materials may be made available from the
corresponding author upon reasonable request.
ACKNOWLEDGMENTS
The author acknowOpen asset ↗Kaggle · new-plant-diseases-datasetpdf-raw-page:24 lines:1-23Dataset · publicgmented) and PlantVillage
datasets. The datasets contain publicly accessible RGB images of healthy and diseased
plant leaves used for supervised image classification research.
DATASET SOURCES
Kaggle New Plant Diseases Dataset (Augmented):
https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset
PlantVillage Dataset: https://plantvillage.psu.edu/All processed data, experimental configurations, and model implementation details are
described within the manuscript. Additional materials may be made available from the
corresponding author upon reasonable request.
ACKNOWLEDGMENTS
The author acknowledges Istanbul Aydin University for academic support and research
guidance duringOpen asset ↗PlantVillagepdf-raw-page:24 lines:1-23Code / dataset availability confirmedOpenAlex · arXiv · checked 5 Sept 2026
High-throughput phenotyping depends on accurate 3D reconstruction of plants across growth stages, yet the development and evaluation of temporal completion methods are limited by the lack of datasets with complete geometric ground truth. To address this challenge, we introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods. Using this dataset, we evaluate a two-stage pipeline that combines spatial denoising and temporal point cloud completion. First, a denoising module removes structural artifacts from raw laser-scanned point clouds. The resulting data are then processed by an Adaptive Temporal PoinTr model that reconstructs the current growth stage (t) using information from the previous stage (t-1), enabling recovery of regions missing due to self-occlusion. We evaluate the proposed framework on both SynthCrop4D and the real-world Pheno4D dataset (tomato and maize) under settings with and without denoising. Results show that denoising substantially improves reconstruction quality, with the best configuration achieving a Chamfer Distance of 0.0061 on SynthCrop4D (Temporal PoinTr + Mamba-DG) and an F-Score of 0.2080 on Pheno4D (Vanilla PoinTr + Mamba-DG). We further demonstrate the use of completed point clouds for phenotypic trait extraction, including plant height, canopy width, and convex hull volume, obtaining hull-volume MAEs of 0.021 on synthetic data and 0.343 on real data. Together, SynthCrop4D and the proposed pipeline provide a benchmark and methodology for temporal plant reconstruction and high-throughput crop phenotyping.
Why it matches plant phenotyping methods植物の3D点群再構成・時系列補完を開発し、合成データセットと実データで性能検証するとともに、草丈・群落幅・凸包体積を抽出する手法を中心に扱っている。
abstractwe introduce SynthCrop4D, a procedurally generated synthetic dataset of temporally evolving plant point clouds that provides controllable noise, occlusion, and complete plant geometry for benchmarking reconstruction methods.
Reproduction assets foundThe paper's authors explicitly state that source code, implementation details, and pre-trained model weights are publicly available on GitHub, and that the paper-specific SynthCrop4D synthetic dataset can be reproduced via scripts in that codebase. Pheno4D is a cited prior public dataset, not a paper-specific asset.Code · publicThe source code, implementation details, and pre-trained model weights for this study are publicly available on GitHub at https://github.com/Mrudul2006/3d_plant-reconstruction .Open asset ↗Mrudul2006/3d_plant-reconstructionlines:1724-1761Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Chlorophyll fluorescence provides sensitive information on plant photochemical responses, but its measurement requirements can limit high-throughput application. This study investigated whether RGB imagery could approximate chlorophyll-fluorescence-derived photochemical status across garden plant species during progressive soil drying. A Photochemical Status Index (PSI) was constructed by principal component analysis from five highly correlated JIP-test energy-flux variables (RC/CS, ABS/CS, TRo/CS, ET2o/CS, and RE1o/CS). The dataset comprised 50 aggregated species-by-soil-moisture-stage observations representing ten species and five sequential soil-moisture stages. Eleven RGB-derived variables were evaluated, and a partial least-squares regression model was assessed using nested leave-one-species-out validation, with all data-dependent procedures repeated within each outer training fold. PC1 explained 96.5% of the shared variation among the fluorescence-derived fluxes. The predictors g, GLI, ExG, ExGR, and CIVE were retained in all ten outer folds. The final model yielded a pooled out-of-fold R2 of 0.469, an RMSE of 1.585, and an MAE of 1.183. However, species-specific R2 ranged from −0.179 to 0.959, and a calibration slope of 0.509 indicated prediction-range compression. These findings provide proof-of-concept evidence of moderate RGB-based approximation of fluorescence-derived photochemical status, but inconsistent species transferability and the common soil-moisture/time gradient require external validation before practical deployment.
Why it matches plant phenotyping methodsRGB画像から蛍光由来の植物光化学状態を推定する手法を構築し、種間交差検証で性能評価しており、植物フェノタイピング手法が中心である。
abstractThis study investigated whether RGB imagery could approximate chlorophyll-fluorescence-derived photochemical status across garden plant species during progressive soil drying.
Abstract Living tissues contain dynamic biochemical information that is difficult to capture with conventional hyperspectral microscopes because sequential spectral acquisition is poorly matched to in vivo molecular processes that evolve during measurement. Here we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue. The system integrates a passive spectral encoder, implemented here as a low-angle scattering LDPE layer, into a 22-mm miniaturized probe and learns a supervised mapping from ultraviolet-excited autofluorescence measurements to biomolecular abundance maps. Unlike conventional pipelines that first reconstruct hyperspectral datacubes and then perform spectral unmixing, the deployed system directly estimates endogenous molecular contrast associated primarily with lignin and chlorophyll in poplar tissue, with additional suberin-associated contrast evaluated in suberin-rich tissue. This reframing makes the measurement task biomolecular inference rather than spectral reconstruction, enabling biochemical mapping under low-photon autofluorescence conditions while reducing data burden and computational latency. In living poplar stems, the platform captures autofluorescence-derived videos of embolism propagation and wound-induced biochemical remodeling, dynamic processes for which sequential spectral acquisition can introduce temporal mixing because the molecular contrast evolves during the scan itself. The system also resolves genotype-dependent reductions in lignin-associated autofluorescence in engineered poplar lines. Direct molecular inference improves biomolecular estimation relative to a reconstruction-based pipeline, while probabilistic decoding provides uncertainty estimates. These results show that compact passive spectral encoding, when optimized for biological inference rather than datacube recovery, enables deployable, label-free molecular videography of living plant tissue dynamics after task-specific calibration.
Why it matches plant phenotyping methods生体植物組織の生化学的状態を動画取得・推定する光学センシング手法を開発し、校正、比較評価、不確実性推定まで行っており、表現型取得法が中心である。
abstractHere we introduce a task-specific optical encoding framework for video-rate molecular inference in living plant tissue.
Plant diseases can reduce crop quality and productivity, making early detection an important aspect of modern agriculture. Recent advances in deep learning, particularly Convolutional Neural Networks (CNN), have shown promising performance in image-based plant disease classification. This study proposes an explainable deep learning approach for multi-class plant disease classification using ResNet50 and EfficientNetB0 combined with Grad-CAM visualization. The experiments were conducted using the PlantVillage dataset consisting of 15 classes of healthy and diseased plant leaves.The research process included image preprocessing, data augmentation, transfer learning, model training, performance evaluation, and explainability analysis. The dataset was divided into training and validation sets with a ratio of 80:20. Model performance was evaluated using accuracy, loss, confusion matrix, precision, recall, and f1-score metrics. Experimental results showed that ResNet50 achieved the best performance with an accuracy of 92% and a validation loss of 0.19, outperforming EfficientNetB0 which obtained 76% accuracy and 0.82 validation loss. The classification report demonstrated that ResNet50 provided more stable and consistent predictions across most disease classes. Furthermore, Grad-CAM visualization successfully highlighted disease-relevant regions such as lesions, discoloration, and damaged leaf areas, improving the interpretability of the CNN model. The findings indicate that the combination of ResNet50 and Grad-CAM is effective for plant disease classification and provides better explainability for deep learning-based agricultural applications.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類・可視化する深層学習手法が研究の中心であり、病徴領域の推定も評価しているため、植物フェノタイピング手法として採用。
abstractThis study proposes an explainable deep learning approach for multi-class plant disease classification using ResNet50 and EfficientNetB0 combined with Grad-CAM visualization.
Abstract Purpose : The system for diagnosing diseases in Solanaceae crops (SolanAPP), including tomatoes, potatoes, peppers, and eggplants, represents a promising tool for supporting decision-making in agricultural fields using AI. This system reconciles the computational intensity of multitasking models with the infrastructural limitations of rural environments, thereby increasing digital literacy. Its architecture is based on two fundamental pillars: (i) autonomous, offline operation for the detection and classification of diseases in Solanaceae crops; (ii) a georeferenced epidemiological surveillance network with agricultural recommendations for crop monitoring. Methods : The core diagnostic process combines crop-specific semantic segmentation and disease classification models exported to TensorFlow Lite, enabling on-device visual inference and pixel-level severity estimation. An optional online layer integrates Groq’s large language model (LLM)-based reasoning and Firebase services to generate structured agronomic explanations and facilitate the creation of georeferenced community reports when a connection is available. Preprocessing and management of the dataset were performed using the Roboflow platform. The mobile app was developed natively in Kotlin. Model performance was rigorously evaluated using accuracy, recall, F1 score, mean IoU, mPA, inference latency, model size, and decision matrix. The optimal model for the crop was selected using Simple Additive Weighting (SAW). Finally, the overall framework quality and usability were evaluated in Cuba through a user validation survey using a 5-point Likert scale and aligned with the ISO/IEC 25010 software quality model. Results : The model that yielded the best results for most crops was DeepLabV3+ with MobileNetV2, which achieved a classification accuracy of over 97\% while operating with lower inference latency. Beyond individual diagnoses, the system incorporates a collaborative georeferencing feature that allows users to share observations and precise geographic coordinates of detected pathologies to facilitate regional epidemiological monitoring. The user satisfaction survey yielded a satisfaction rating of 4.5/5, with users highlighting the importance of offline diagnosis. Conclusion : Plant disease diagnosis using computer vision can support earlier intervention in resource-constrained agricultural settings, but practical deployment requires models that are accurate, lightweight, interpretable, and usable under limited connectivity. SolanAPP, an offline-first Android framework for detecting foliar pathologies in Solanaceae crops, not only establishes a framework for disease identification in complex natural environments but also provides a theoretical and practical foundation for automated agronomic treatment recommendations and community-based crop surveillance. Impact SolanAPP is a free framework that supports the synergy between multitask deep learning for offline disease diagnosis and LLM-driven reasoning for decision-making in the field. Beyond the quantitative metrics obtained from the selected models, the deployment of SolanAPP in rural contexts serves a fundamental socio-technical purpose: it acts as a catalyst for open access, digital literacy, and agronomic decision-making under unfavorable development conditions. It also represents a strong effort to foster a collaborative epidemiological surveillance network in the agricultural sector. Although it faces challenges, such as the use of field images for model training, this framework marks a promising step in the deployment of edge AI, balancing technical accuracy with practical utility.
Why it matches plant phenotyping methods植物病害を画像からセグメンテーションし、病害のピクセル単位の重症度を推定する手法と、オフライン実装・性能評価を中心とした研究であり、植物状態の計測方法が中核です。
abstractThe core diagnostic process combines crop-specific semantic segmentation and disease classification models exported to TensorFlow Lite, enabling on-device visual inference and pixel-level severity estimation.
Plant disease and plant stress early warning systems have advanced through deep learning, remote sensing, digital phenotyping, disease forecasting, and sensor networks. Detection accuracy, precision, recall, F1-score, and area under the curve remain indispensable, but they are insufficient for judging whether a warning can support timely and proportionate phytoprotection under field variability. This Mini Review argues that intelligent plant health warning systems should be evaluated not only as prediction models, but also as safety-relevant decision-support systems embedded in biological, agronomic, and operational contexts. We first relate AI-based detection to established plant disease forecasting and decision-support traditions, including weather-based models, epidemiological forecasting, and integrated disease management. We then adapt selected safety-assurance concepts, including risk assessment, failure mode and effects analysis, Bow-tie reasoning, warning-threshold governance, reliability analysis, resilience thinking, and response closure, to host-pathogen-environment warning chains. The proposed framework links AI or sensor outputs with pathogen biology, host susceptibility, environmental conduciveness, inoculum pressure, uncertainty assessment, risk classification, threshold decisions, human or automated verification, intervention, and feedback learning. Illustrative crop-pathogen scenarios, including wheat rust, rice blast, potato late blight, grapevine downy mildew, and citrus greening, show how safety assurance can complement existing forecasting and decision-support systems rather than replace them. The framework remains conceptual, and whether these added assurance functions improve existing warning systems requires comparative evaluation under field conditions. Future systems should be evaluated through detection performance and response-oriented indicators such as lead time, calibration, false-alert burden, missed-warning rate, response completion, disease suppression, economic value, and learning after field action.
Why it matches plant phenotyping methods植物病害・ストレスの検出を含む知的警戒システムについて、AI・リモートセンシング・デジタルフェノタイピング・センサーネットワークの評価枠組みを体系的に論じる方法論レビューであり、方法論が中心です。
abstractPlant disease and plant stress early warning systems have advanced through deep learning, remote sensing, digital phenotyping, disease forecasting, and sensor networks.
Why it matches plant phenotyping methodsトマト葉の病害を画像と深層学習で検出する手法が題名上の中心であり、植物の病害状態を観察的に推定するフェノタイピング研究に該当する。
titleDetection of Tomato Leaf Disease in Leaves with Deep Learning MobileNetV2 with Gaussian and Gabor Preprocessing
Reproduction assets foundThe paper's phenotyping analysis is based on the publicly available PlantVillage plant leaf disease image dataset hosted on Kaggle (54,303 labeled leaf images across 38 classes), which the authors explicitly state was sourced from a publicly available Kaggle dataset. No author-specific code, models, or derived datasetsDataset · publicThe research incorporated PlantVillage dataset(24)
accessible on Kaggle that contains 54,303 plant leaf
images showing both healthy and diseased
conditions spanning across 38 specific categories.Open asset ↗Kagglepdf-raw-page:2 lines:1-105Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
The uniformity of maize plant spacing serves as a critical indicator for assessing sowing quality, seed vigor, and field seedling emergence stability. However, manual measurement is inefficient, and complex field conditions make automatic seedling detection and plant spacing measurement challenging. Aiming at the challenges of missed detection, insufficient accuracy for small targets, and large errors in plant spacing calculation under complex field conditions, this study constructs a high-quality dataset containing 693 maize seedling images and implements preprocessing enhancement for images degraded by haze or dust. An intelligent maize seedling detection and plant spacing measurement method based on improved YOLOv8 is proposed. The Global Attention Mechanism (GAM) is embedded into the backbone network to strengthen cross-dimension information interaction between channels and spaces, suppress background interference, and reduce the missed detection rate. The Bi-directional Feature Pyramid Network (BiFPN) is adopted to replace the original PAFPN for enhanced multi-scale feature fusion and deep semantic representation. A new 160 × 160 high-resolution small-object detection layer is added to significantly improve the detection performance of weak and small seedlings. Experimental results demonstrate that the improved model achieves a precision, recall, mAP50, and mAP50-95 of 89.4%, 90.3%, 94.4%, and 49.4%, respectively, which are 2.6, 0.5, 1.5, and 2.7 percentage points higher than those of the original YOLOv8 model. These results indicate that the proposed model improved maize seedling detection performance under complex field conditions. Automatic plant spacing calculation is realized based on detection outputs; the average plant spacing of the dataset is 30.42 cm, with a relative error of only 4.93% compared with the preset sowing spacing of 32 cm. The proposed method can efficiently accomplish field seedling identification, plant spacing quantification, and sowing quality evaluation, providing reliable technical support for precision maize sowing, seeder parameter optimization, and intelligent field management, which is of great significance for promoting the intelligent upgrading of grain crop production.
Why it matches plant phenotyping methodsトウモロコシ幼苗の検出画像から株間という植物形態・配置形質を自動抽出するYOLOv8手法を開発し、データセット構築と性能検証まで行っており、フェノタイピング手法が中心である。
abstractAutomatic plant spacing calculation is realized based on detection outputs
Automated plant identification based on leaf morphology has gained significant attention in recent years due to its wide range of applications in precision agriculture, biodiversity conservation, environmental monitoring, and botanical informatics. Advances in digital image processing and machine learning have enabled the development of intelligent systems capable of identifying plant species from leaf characteristics with minimal human intervention. Despite these advancements, achieving reliable and accurate classification remains challenging because leaf images are often affected by variations in illumination, complex backgrounds, image noise, differences in orientation and scale, as well as natural leaf deformation. These factors can obscure important morphological features, reduce the effectiveness of feature extraction, and ultimately decrease the accuracy and robustness of automated plant classification systems. Consequently, there is a growing need for intelligent frameworks that can effectively handle these challenges while preserving critical leaf morphology and venation information for reliable plant identification. This study proposes an Intelligent Morphology-Driven Framework that integrates advanced digital image processing and machine learning for robust leaf venation analysis and plant classification. The proposed framework integrates multiple digital image processing and machine learning techniques to enable accurate and automated leaf venation analysis and plant classification. Initially, leaf images undergo preprocessing using grayscale conversion, histogram equalization, Contrast Limited Adaptive Histogram Equalization (CLAHE), Gaussian filtering, Laplacian sharpening, Gabor filtering, and homomorphic filtering to improve image quality and enhance venation and structural details. The enhanced images are then processed through threshold-based segmentation followed by morphological operations, including erosion, dilation, opening, closing, convex hull generation, and skeletonization, to accurately isolate leaf regions while preserving their geometric structure.To characterize leaf morphology, the framework extracts a comprehensive set of features, including geometric descriptors such as area, perimeter, circularity, aspect ratio, solidity, eccentricity, and vein density, together with Hu invariant moments that provide rotation-, translation-, and scale-invariant shape representation. In addition, the framework investigates the influence of image compression by comparing lossless PNG and lossy JPEG formats to evaluate their impact on preserving morphological features and venation details. The extracted feature vectors are subsequently classified using a Random Forest classifier to categorize leaf venation patterns into parallel, reticulate-pinnate, and reticulate-palmate classes.Experimental evaluation demonstrates that the proposed framework achieves an overall classification accuracy of 93.2%, while effectively preserving important morphological characteristics and maintaining computational efficiency. The combination of adaptive image enhancement, morphology-preserving segmentation, comprehensive feature extraction, and robust machine learning classification makes the proposed approach reliable, interpretable, and scalable. Consequently, the framework has significant potential for applications in digital herbarium systems, automated plant identification, biodiversity monitoring, botanical informatics, and precision agriculture.
Why it matches plant phenotyping methods葉画像から形態・葉脈形質を抽出し分類する画像処理・機械学習フレームワーク自体が研究の中心であり、植物表現型の取得・解析手法として適格。
abstractThis study proposes an Intelligent Morphology-Driven Framework that integrates advanced digital image processing and machine learning for robust leaf venation analysis and plant classification.
White lupin ( Lupinus albus L.) is a cool-season grain legume with seed crude protein of 33-47%, competitive with soybean ( Glycine max L.) meal. It also fixes nitrogen and mobilizes soil phosphorus. Because soybean is a summer crop, white lupin can occupy Southeastern winter fields as a complementary protein source. Breeding for seed protein is limited by the cost and throughput of reference phenotyping. To determine how each is best deployed, we compared the utility of near-infrared spectroscopy (NIRS)-based phenomic selection with genomic selection based on 246,847 SNPs from low-pass, whole genome sequencing in a panel of Auburn University breeding lines and USDA National Plant Germplasm System germplasm. A handheld NIR calibration against Dumas reference protein reached screening-grade accuracy (R 2 = 0.81). Under common cross-validation, phenomic predictive ability was 0.93 and genomic was 0.12. The low genomic value was consistent with moderate heritability (H 2 = 0.33) and strong genotype-by-year interaction. Beyond predictive ability, NIRS recovered superior accessions the strictest selection intensity, and 40 to 60 reference assays sufficed to calibrate the model. Handheld NIRS is a low-cost tool for protein calibration and early-generation screening, while genomic prediction remains suited to parental selection, together supporting a complementary strategy for legume breeding Plain Language Summary Soybean meal is the main protein source for livestock and fish farms in the United States. Because soybean is a summer crop, many Southeastern fields sit idle or grow low-value cover crops in winter. White lupin, a cool-season legume whose seeds are as protein-rich as soybean meal, makes a good complementary winter crop: it yields high-protein grain while serving as a cover crop that fixes nitrogen and frees up soil phosphorus for later crops. In our early-stage lupin breeding program, measuring seed protein by standard lab methods is slow and costly. We built a calibration that lets a handheld scanner estimate protein from light, and compared it with predicting protein from the plant’s DNA. The scanner gave accurate, low-cost protein screening from only about 40-60 lab tests, while DNA-based prediction remains suited to guiding parent selection. Used together, these tools offer breeders a practical path to develop high-protein white lupin. Core ideas Handheld NIRS provides screening-grade prediction of white lupin seed crude protein. Spectra carried more usable protein signal than markers by measuring seed chemistry directly. NIRS and genomic prediction serve different stages of a white lupin breeding program. About 40 to 60 reference assays sufficed to calibrate NIRS to near-full accuracy.
Why it matches plant phenotyping methods携帯型NIRSによる種子タンパク質形質の推定・校正・精度検証が研究の中心であり、育種スクリーニングへの実質的応用も評価している。
abstractA handheld NIR calibration against Dumas reference protein reached screening-grade accuracy (R 2 = 0.81).
Purpose Conventional plant disease detection is time-consuming and prone to human error. The purpose of this study is to propose an edge artificial intelligence (AI)-based deep learning framework for plant disease detection under real-field conditions. The model integrates convolutional neural networks (CNNs) with a Sliding Window Mean Absolute Deviation (SWMAD) preprocessing technique to address illumination variability and complex background conditions. Design/methodology/approach A dual-layer CNN model is developed for plant disease classification using HSV segmentation, flood-fill segmentation and SWMAD preprocessing to handle real-field variations. The model is trained on PlantVillage and real farm images and deployed via TensorFlow Lite for real-time offline detection. TensorFlow Lite is used to enable efficient on-device inference for deployment in resource-constrained environments. Findings Benchmark experiments conducted on the PlantVillage dataset achieved a classification accuracy of 99.91% under controlled conditions. On the hybrid dataset comprising PlantVillage and real-field images, the optimized CNN framework achieved a validation accuracy of 95.01% following extensive evaluation of optimizers, layer architectures and worker configurations. The integration of the proposed SWMAD preprocessing technique into the finalized architecture further improved the validation accuracy to 97.38%, demonstrating enhanced robustness and classification performance under practical agricultural conditions in the final deployed model. Originality/value The originality of this study lies in several novel contributions. First, we introduce an SWMAD-based preprocessing technique, which enhances local statistical variations in leaf images by capturing pixel-level deviations from neighborhood intensity means. Unlike, conventional preprocessing methods, SWMAD is specifically designed to handle real-field challenges such as illumination variation, noise and complex backgrounds. The improvement in validation accuracy demonstrates the effectiveness of the proposed approach in capturing more discriminative features compared to existing techniques, thereby improving overall model robustness and reliability in practical agricultural environments.
Why it matches plant phenotyping methods植物病害という植物状態を画像から推定する深層学習・前処理・エッジ展開手法が研究の中心であり、実環境での検証も行っているため。
abstractThe purpose of this study is to propose an edge artificial intelligence (AI)-based deep learning framework for plant disease detection under real-field conditions.
Crop stress develops through a sequence that begins with molecular and biophysical perturbation, progresses through physiological dysfunction, and only later becomes visually apparent. Precision agriculture therefore requires sensors that can shorten the interval between stress onset and actionable diagnosis while preserving spatial context. This critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection. Literature published from 2000 to 1 June 2026 was selected through live searches of accessible scholarly indexes, DOI registries, publisher records, institutional repositories, and citation networks, with foundational studies retained where necessary. The evidence shows that nano-enabled interfaces can measure early biochemical, ionic, volatile, electrical, and microclimatic signals at high temporal resolution, whereas geospatial technologies reveal the distribution, persistence, and management relevance of stress across canopies and fields. Optical nanotube sensors, surface-enhanced Raman probes, electrochemical microneedles, ion-selective wearables, and flexible leaf sensors have demonstrated biologically meaningful signals before visible symptoms in controlled or pilot field settings. Yet most remain constrained by sparse sampling, crop-specific calibration, bio-interface effects, power and communication burdens, uncertain durability, and limited agronomic validation. Geospatial methods are operationally more mature, particularly thermal and multispectral imaging for water stress and hyperspectral imaging for disease and nutrient-related changes, but they often infer stress through non-specific proxies that are confounded by canopy structure, atmosphere, soil background, phenology, and co-occurring stresses. The strongest future architecture is therefore not a contest between nanoscale and landscape-scale sensing. It is a multiscale system in which physiologically specific plant sensors anchor and interpret spatial imagery, while remote sensing directs where high-specificity measurements and interventions are most valuable. Progress depends on prospective field trials, reference measurements, uncertainty-aware data fusion, interoperability, lifecycle safety assessment, and decision thresholds linked to economic and agronomic outcomes.
Why it matches plant phenotyping methods植物ストレス状態の検出に用いるナノセンサー、ウェアラブルセンサー、熱・マルチスペクトル・ハイパースペクトル画像などを中心に批判的に統合した方法レビューであり、単なる農業応用紹介ではなく、センサー性能、校正、検証、データ融合を論じている。
abstractThis critical narrative review examines the complementary roles of nanosensors, plant-wearable and implantable electronics, proximal sensing, unmanned aerial vehicles, satellite remote sensing, and geographic information systems in early crop-stress detection.
To address the challenges of insufficient sensor stability and poor consistency among multi-source data during crop phenotyping in unstructured field environments, this study develops a hardware-software co-optimization framework for wheat canopy sensing based on a four-wheel-drive, high-clearance phenotyping platform. A multi-sensor stabilization device integrated with an ESO-LQR control strategy suppresses pitch disturbances during motion. A ROS-based hierarchical framework coordinates LiDAR, cameras, and inertial sensors, while spatial calibration and timestamp-based software synchronization ensure spatiotemporal consistency. A tightly coupled LiDAR-IMU SLAM algorithm enables centimeter-level 3D reconstruction of farmland. To mitigate terrain effects, a two-stage ground point extraction method integrating verticality and spatial distribution features improves canopy height estimation. Field experiments demonstrate stable platform operation at 1.5 m s⁻¹ while maintain high efficiency and data quality, with a coverage efficiency of 95.2%, retained-point ratio of 85.3%, and an MTF of 0.35 for image clarity. Phenotypic evaluation shows strong agreement between predicted wheat plant height and manual measurements, with R² values of 0.898 and 0.729 and RMSE values of 1.30 cm and 1.64 cm at the jointing and grain-filling stages, respectively. Moreover, at the jointing stage, both the 2D green area index (GAI) and the 3D point-cloud-based canopy coverage exhibit strong consistency with ImageJ-derived results (R² = 0.873 and 0.910). These findings demonstrate that the proposed approach enables stable and efficient acquisition of crop phenotypic information in complex field environments, providing a solid technical foundation for digital field monitoring, data-driven crop management, and intelligent agricultural systems.
Why it matches plant phenotyping methods小麦キャノピーの表現型取得を目的としたマルチセンサープラットフォーム、データ同期・3D再構成・キャノピー高さ推定を開発し、手動測定やImageJとの一致性を検証しているため、方法が研究の中心である。
abstractthis study develops a hardware-software co-optimization framework for wheat canopy sensing based on a four-wheel-drive, high-clearance phenotyping platform.
Low-cost uncooled snapshot hyperspectral sensors mounted on UAV platforms offer new opportunities for field-scale remote sensing high-throughput phenotyping, but their reliability is constrained by sensor-intrinsic artefacts, particularly dark current. In this study, we present the first spatio-spectro-temporal characterisation and correction of dark current in the Senop HSC-2 dual-CMOS Fabry-Perot snapshot hyperspectral camera. Six controlled dark experiments (∼6,100 image cubes) revealed that dark current in the sensor is highly structured and reproducible, exhibiting CMOS-specific baseline offsets, monotonic temporal drift, pixel-wise dark signal non-uniformity (DSNU) with wavelength-dependent structure, and persistent hot pixels, and exposure-dependent baseline shifts that do not scale linearly with integration time. These results confirm that conventional single-frame dark subtraction is insufficient for quantitative analysis in uncooled snapshot hyperspectral sensors. Building on this characterisation, a modular correction framework was developed to stabilise the dark signal across spatial, spectral, and temporal domains. Across laboratory datasets, the framework reduced temporal drift by 70-85%, DSNU variance by approximately 37.5%, and suppressed >99.9% of persistent hot pixels, substantially improving radiometric stability. Corrected data exhibited simultaneous spatial uniformity, temporal stability, and spectral integrity, enabling downstream radiometric processing without introducing spectral distortion. Application of Senop HSC-2 to UAV-acquired wheat canopy imagery demonstrated effective transfer to field conditions, improving spectral continuity and robustness of vegetation indices after dark current correction. These results establish a transferable calibration approach for affordable snapshot hyperspectral sensors and demonstrate that rigorous dark current correction is essential for achieving quantitative radiometric performance in UAV-based phenotyping and precision agriculture applications without active thermal control, extending calibration principles traditionally applied in satellite hyperspectral systems to low-cost UAV snapshot sensors.
Why it matches plant phenotyping methodsUAVハイパースペクトルセンサの暗電流を空間・スペクトル・時間的に補正する手法の開発と検証が中心であり、圃場のコムギ群落画像への適用も行っているため、植物フェノタイピング手法として採用する。
abstractIn this study, we present the first spatio-spectro-temporal characterisation and correction of dark current in the Senop HSC-2 dual-CMOS Fabry-Perot snapshot hyperspectral camera.
The precise identification of unsound soybean seeds is a critical step in deep soybean processing and seed selection. The accuracy of this identification directly influences the quality of subsequent processed products, as well as the germination rate and yield of soybean crops. This study proposes a nondestructive identification method for unsound soybean seeds based on hyperspectral imaging (HSI), Gramian Angular Field (GAF), and a Dual-Channel Residual-Squeeze-and-Excitation Network with GAF Fusion (DC-RSEN-GF). According to common damage types, soybeans were categorized into six classes: sound seeds, thermal-damaged seeds, insect-damaged seeds, broken seeds, spotted seeds, and moldy seeds. Spectral data from these six soybean categories were acquired using a hyperspectral camera and transformed into two-dimensional GAF images. The DC-RSEN-GF network integrates one-dimensional spectral data with two-dimensional GAF images. After preprocessing with Savitzky-Golay (SG) smoothing, high-precision classification was achieved through residual blocks, an attention mechanism (using SENet), and feature fusion. Compared to five benchmark models-Extremely Randomized Trees (ERT), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), VGG19, and ResNet18-the DC-RSEN-GF model achieved superior performance, with accuracy, precision, specificity, and F1-scores of 96.36%, 96.43%, 97.92%, and 96.36%, respectively. The accuracy, precision, and F1-scores are all superior to traditional machine learning and existing deep learning models, demonstrating better classification capabilities. In addition, t-distributed Stochastic Neighbor Embedding (t-SNE) was employed for visual analysis of soybean spectra, further validating the reliability of the DC-RSEN-GF model. The proposed detection method, based on HSI and DC-RSEN-GF, enables accurate and nondestructive identification of unsound soybean seeds and holds significant potential for practical application.
Why it matches plant phenotyping methodsハイパースペクトル画像と深層学習を用いて、種子の損傷・病変状態を非破壊的に分類する取得・解析手法が研究の中心であり、植物状態の表現型測定に該当する。
abstractThis study proposes a nondestructive identification method for unsound soybean seeds based on hyperspectral imaging (HSI), Gramian Angular Field (GAF), and a Dual-Channel Residual-Squeeze-and-Excitation Network with GAF Fusion (DC-RSEN-GF).
Proper diagnosis of plant disease and nutrient deficiency is crucial in enhancing crop productivity, reducing yield losses and early agricultural interventions. This paper introduces the machine learning approach for automated identification of plant diseases and their severity by analyzing the images of plant leaves. The proposed framework involves image preprocessing, feature extraction, classification and severity estimation, which would enable the accurate identification of various types of disease and estimation of their severity levels while detecting nutrient deficiencies. An extensive image dataset of healthy, diseased and nutrient deficient leaves was used to train and test the models. As illustrated by the experimental results, the proposed framework outperforms the existing machine learning and deep learning methods for plant disease identification and nutrient deficiency detection with the classification accuracy of 98.76% and 98.14% respectively. In addition, the severity assessment module estimates well to enable accurate pesticide and nutrient application, which minimizes chemical use. The proposed framework provides a scalable, efficient, and precise approach for smart crop health monitoring and precision agriculture applications.
Why it matches plant phenotyping methods葉画像から植物病害・栄養欠乏の同定と重症度推定を行う機械学習フレームワークが研究の中心であり、植物状態の画像ベース表現型計測に該当する。
abstractThis paper introduces the machine learning approach for automated identification of plant diseases and their severity by analyzing the images of plant leaves.
The increasing demand for agricultural production requires reliable and non-destructive methods for monitoring plant physiological conditions in real time, particularly in controlled environments. Spectral sensing in the visible to near-infrared (VIS–NIR) region offers a promising approach; however, the performance of low-cost sensors is often limited by calibration accuracy, wavelength-dependent sensitivity, and insufficient validation against plant physiological indicators. This study aims to evaluate the performance of a VIS–NIR plant monitoring system based on the AS7265x multispectral sensor in a controlled hydroponic environment. Lettuce (Lactuca sativa L.) was grown under two nutrient concentrations (600 ppm and 1200 ppm), and spectral reflectance data were collected across the 410–760 nm range. Sensor measurements were calibrated and validated against a LI-COR LI-180 reference spectrometer using linear regression, with performance assessed using the coefficient of determination (R²), root mean square error (RMSE), and spectral response consistency. The results show strong calibration performance, with wavelength-specific R² values ranging from 0.9682 to 0.9870 and RMSE values between 0.26965 and 5.19772. Although a systematic offset was observed, the AS7265x sensor preserved key spectral patterns, particularly in the green (510–560 nm) and red-edge (705–730 nm) regions. Differences in nutrient concentration were consistently reflected in both spectral responses and SPAD measurements, indicating sensitivity to plant physiological variations. These findings demonstrate that the AS7265x sensor provides reliable spectral information for relative plant monitoring and has strong potential as a cost-effective tool for plant quality assessment in controlled environments.
Why it matches plant phenotyping methodsVIS–NIR植物モニタリングシステムの校正・検証が研究の中心であり、植物の生理状態を推定するセンサー手法を評価している。
abstractThis study aims to evaluate the performance of a VIS–NIR plant monitoring system based on the AS7265x multispectral sensor in a controlled hydroponic environment.
Penyakit Basal Stem Rot (BSR) merupakan penyakit penting pada tanaman kelapa sawit yang dapat menurunkan produktivitas. Analisis berbasis citra dapat mendukung deteksi penyakit, tetapi karakteristik distribusi warna perlu dipahami sebelum tahap klasifikasi. Penelitian ini bertujuan menganalisis distribusi warna citra daun kelapa sawit kelas Healthy dan BSR menggunakan Histogram pada ruang warna RGB dan HSV serta mengevaluasi kemampuan diskriminatif fitur warna. Dataset terdiri atas 2.438 citra dari repositori terbuka Roboflow. Setiap citra diproses melalui resize 224×224 piksel, Gaussian Blur, konversi RGB ke HSV, serta ekstraksi 12 parameter statistik berupa mean dan standar deviasi. Perbedaan distribusi antar kelas diuji menggunakan Mann–Whitney U dan besarnya perbedaan dihitung menggunakan Cohen's d. Kemampuan diskriminatif G_Mean, S_Mean, dan V_Mean dievaluasi menggunakan Receiver Operating Characteristic (ROC) dan Area Under the Curve (AUC). Hasil menunjukkan ketiga fitur memiliki perbedaan signifikan (p-value = 0,0000) dengan ukuran efek besar. Nilai AUC G_Mean, S_Mean, dan V_Mean masing-masing sebesar 0,9794, 0,7362, dan 0,9790. Hasil menunjukkan bahwa analisis distribusi warna dapat mengidentifikasi fitur diskriminatif sebagai dasar pemilihan fitur dan perancangan pra-pemrosesan citra BSR.
Why it matches plant phenotyping methods葉の画像からBSR感染状態を色特徴として抽出・評価する画像解析手法が研究の中心であり、単なる病害実験の routine measurement ではない。
abstractAnalisis berbasis citra dapat mendukung deteksi penyakit, tetapi karakteristik distribusi warna perlu dipahami sebelum tahap klasifikasi.
Agriculture plays a pivotal role in ensuring global food security, economic stability, and sustainable development. Plant diseases significantly reduce agricultural productivity, resulting in substantial economic losses and threatening food supply worldwide. Early and accurate identification of plant leaf diseases enables timely intervention, minimizes crop damage, and enhances agricultural yield. Traditional disease diagnosis relies heavily on visual inspection by agricultural experts, making the process labor-intensive, subjective, and unsuitable for large-scale deployment. Recent advances in artificial intelligence, particularly deep learning, have transformed plant disease diagnosis by enabling automatic feature extraction and highly accurate image-based classification. This review presents a comprehensive analysis of recent developments in deep learning techniques for plant leaf disease identification. Various convolutional neural network (CNN) architectures, including AlexNet, VGGNet, ResNet, DenseNet, EfficientNet, MobileNet, Inception, and Xception, are critically reviewed along with modern transformer-based models such as Vision Transformer (ViT), Swin Transformer, and hybrid CNN–Transformer frameworks. The paper also examines transfer learning strategies, object detection methods including YOLO and Faster R-CNN, and semantic segmentation approaches such as U-Net and DeepLabV3+. Publicly available benchmark datasets, including PlantVillage, PlantDoc, AI Challenger, Cassava Leaf Disease, and Rice Leaf Disease datasets, are discussed in terms of dataset diversity, annotation quality, and practical applicability. Furthermore, image preprocessing techniques, data augmentation methods, evaluation metrics, and deployment considerations for mobile and edge devices are comprehensively reviewed. The paper identifies current research challenges, including dataset imbalance, environmental variability, model interpretability, computational complexity, and limited real-world generalization. Finally, emerging research directions such as explainable artificial intelligence, federated learning, multimodal learning, self-supervised learning, lightweight architectures, and edge AI are discussed to provide future research opportunities. This review serves as a valuable resource for researchers, practitioners, and agricultural technologists interested in developing robust, scalable, and intelligent plant disease identification systems.
Why it matches plant phenotyping methods植物葉の病害状態を画像から識別する深層学習手法を中心に、モデル、データセット、評価、展開を体系的にレビューしており、植物表現型計測手法のレビューに該当する。
abstractThis review presents a comprehensive analysis of recent developments in deep learning techniques for plant leaf disease identification.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Climate extremes increasingly threaten agricultural production, yet many artificial intelligence systems in agriculture remain local, reactive and narrowly trained for one crop, region or sensing modality. Methods We present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions. AgriFM was pretrained using self-supervised objectives on 2.4 million field-season sequences and evaluated on a curated benchmark spanning maize, wheat, soybean, rice and sorghum across five agroclimatic regions. Results In held-out geography and time-split evaluations, AgriFM improved early stress detection and yield-failure prediction over statistical, crop-model and deep-learning baselines. The largest gains occurred during compound drought and heat events, for which AgriFM produced alerts 18 to 24 days earlier than the satellite-only baseline while maintaining improved calibration. Phenology-conditioned fusion improved transfer across planting calendars, and uncertainty calibration reduced false alerts at fixed recall. Discussion Because the study is based on retrospective datasets, these findings establish cross-region retrospective performance rather than prospective field efficacy. The results support further field-based evaluation of multimodal foundation models for climate-resilient crop monitoring.
Why it matches plant phenotyping methods作物ストレス状態と収量失敗リスクを衛星・レーダー・熱画像等から推定する基盤モデルを開発し、複数作物・地域のベンチマークで評価しており、植物状態の取得・推定手法が中心である。
abstractWe present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions.
Abstract The diseases on sesame leaves have a huge implication on the production and earnings of farmers particularly in the developing areas. It is important to ensure that the disease is properly managed by identifying it early and correctly. The purpose of this study is to create a deep learning-based framework that could be used to categorize four of the most common scenarios involving sesame leaves, i.e. Healthy Leaf, Leaf Spot Disease, Yellowing Leaf Syndrome, and Leaf Damage by Insects using high-resolution images acquired in Pabna, Bangladesh. The preprocessing, augmentation and split of a set of 3,540 images were performed into training and validation sets. The pre-trained convolutional neural networks models were trained and tested on five models inceptionV3, EfficientNetB3, ResNet50, MobileNet, and DenseNet121 by measuring the metrics such as accuracy, precision, recall, and F1-score. MobileNet achieved the highest accuracy of 96.33%, precision of 96%, recall of 96%, and the F1-score of 96%, which is the best amongst them. The findings indicate that deep learning architectures are capable of classifying the sesame leaf diseases in a reliable and precision-oriented way that is not affected by different environmental circumstances. The study facilitates the creation of the automated, efficient methods of detecting the disease at an early stage, cutting down the number of pesticides used and enhancing crop control. Future direction will be to enlarge the dataset, add temporal data and to implement lightweight models so that it can be deployed to real-time field projects
Why it matches plant phenotyping methodsセサマ葉画像から病害状態をCNNで分類する手法の開発・比較評価が研究の中心であり、植物の病害表現型を直接推定している。
abstractThe purpose of this study is to create a deep learning-based framework that could be used to categorize four of the most common scenarios involving sesame leaves
Accurate color representation is critical for UAV-based crop phenotyping, yet UAV images are often distorted by variable illumination and camera exposure settings. Here, we propose Lite U-net FiLM (LUF-net), a lightweight U-net framework integrated with feature-wise linear modulation (FiLM) layers, which incorporates both multispectral-derived irradiance and camera exposure parameters as auxiliary inputs. This metadata-aware design enables dynamic modulation of intermediate image features, allowing the network to disentangle unified canopy color from environmental artifacts. Experiments conducted across diverse crop types (soybean, rice, maize), flight altitudes (6m, 12m, 25m, 40m), and illumination conditions (overcast skies, cloudy, sunny, early morning) demonstrate that the LUF-net model substantially reduces the mean absolute percentage error to below 5.5%, outperforming conventional Gray-world, U-net, AlexNet-MLP, and SIDBlock-MLP methods. Ablation experiments further show that both irradiance and exposure metadata provide complementary information, while FiLM-based conditioning effectively integrates these acquisition parameters into feature learning, jointly contributing to improved color reconstruction performance. Moreover, correlation analysis indicates that the model's color reconstruction errors are weakly dependent on irradiance and exposure settings, confirming that LUF-net reduces sensitivity to external imaging conditions while maintaining physically meaningful and robust corrections.
Why it matches plant phenotyping methodsUAV画像の色校正手法を開発し、複数作物・撮影条件で性能検証しており、作物フェノタイピングの画像取得・補正が中心である。
abstractAccurate color representation is critical for UAV-based crop phenotyping, yet UAV images are often distorted by variable illumination and camera exposure settings.
Reproduction assets foundThe paper's data and code availability statement explicitly points to a public GitHub repository containing training/evaluation/inference code, pretrained model weights, and example data for the LUF-net color calibration method.Code · publicThe data and source code for model training, evaluation, and inference, together with pretrained model weights, example data, and detailed usage instructions, is publicly available at: https://github.com/wangchufeng3652/color-correction.Open asset ↗wangchufeng3652/color-correctionhtml-lines:278-299Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
Accurate and reliable diagnosis of grape leaf diseases is essential for sustainable viticulture, enabling timely intervention, reducing economic losses, and supporting precision crop management. Although deep learning (DL) models have demonstrated remarkable classification performance, their reliability under real-world field conditions remains insufficiently explored. In particular, confidence estimates often fail to reflect true predictive correctness when models are exposed to distributional shifts, limiting their practical applicability. To address this challenge, this study proposes a confidence- and uncertainty-aware DL framework for grape leaf disease diagnosis that extends evaluation beyond conventional accuracy-based metrics. Two publicly available grape leaf datasets were employed for stratified five-fold cross-validation in binary and multiclass classification tasks, while a third independently collected dataset was reserved exclusively for leakage-free external validation. EfficientNet-B0 and MobileNetV3-Large were evaluated under four inference configurations: raw inference, temperature scaling (TempScaling), Monte Carlo dropout, and an ensemble strategy combining uncertainty estimation with calibration. External validation demonstrated that both architectures maintained discriminative capability under domain shift. Under ensemble inference, EfficientNet-B0 achieved an accuracy of 73.8%, a macro-F1 score of 73.3%, a Matthews correlation coefficient (MCC) of 0.468, and a receiver operating characteristic area under the curve (ROC-AUC) of 0.804, while MobileNetV3-Large achieved 71.8% accuracy, 71.2% macro-F1, an MCC of 0.429, and a ROC-AUC of 0.787. Despite these promising results, raw predictions exhibited substantial overconfidence, with Expected Calibration Error (ECE) values of 0.287 and 0.312 for EfficientNet-B0 and MobileNetV3-Large, respectively. TempScaling markedly improved calibration quality, reducing ECE to 0.038 and 0.042 without affecting classification performance. Ensemble inference further enhanced the balance between predictive discrimination and reliability. The results demonstrate that strong classification performance alone is insufficient for trustworthy deployment in agricultural environments. Confidence calibration and uncertainty quantification provide complementary information for identifying overconfident predictions and improving decision reliability under field variability. The proposed framework offers a reliability-oriented approach for developing trustworthy artificial intelligence systems for grape leaf disease diagnosis in precision agriculture.
Why it matches plant phenotyping methodsブドウ葉の病徴を画像から診断する深層学習手法を開発し、信頼度校正・不確実性推定と外部検証を中心に評価しているため、植物フェノタイピング手法として適格。
abstractthis study proposes a confidence- and uncertainty-aware DL framework for grape leaf disease diagnosis
Orthoimagery derived from unmanned aerial vehicles (UAVs) has become a valuable data source for crop-growth monitoring. Individual plant-level (IPL) information enables high-throughput analyses by capturing plant-to-plant variability within fields. However, reliable IPL-based analysis requires accurate extraction of plant-specific regions, which remains challenging in soybean cultivation due to weed interference and canopy overlap. This study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery. A segmentation model was developed using combinations of RGB and multispectral orthoimagery, and a furrow line detection algorithm was designed to generate IPL ROIs aligned with crop rows. The ensemble model combining U-Net, DeepLabV3+, and SegFormer achieved the most stable performance (F1-score up to 0.94 and IoU up to 0.89). The furrow-guided ROI generation algorithm also accurately estimated crop counts, showing strong agreement with manual observations (R² = 0.90 and RMSE = 6.35). The generated IPL ROIs enabled accurate quantification of growth-related features, with strong agreement between automatically generated and manually delineated ROIs (R² > 0.90). Overall, the proposed preprocessing framework provides a practical and scalable solution for UAV-based high-throughput phenotyping in soybean and other ridge-based cropping systems.
Why it matches plant phenotyping methodsUAV画像から個体単位の植物領域を抽出し、成長形質を定量化する前処理・セグメンテーション手法が研究の中心であるため。
abstractThis study proposed an automatic preprocessing framework for IPL soybean growth monitoring that integrates deep-learning-based semantic segmentation with a furrow-guided region of interest (ROI) generation strategy using UAV imagery.
Reproduction assets foundThe authors state that the complete implementation of their IPL soybean preprocessing pipeline (semantic segmentation + furrow line detection) is publicly available on Zenodo. The annotated sample dataset, however, is only available upon request from the corresponding author, so it is not a public asset.Code · publicThe complete implementation of
this pipeline is publicly available at https://doi.org/10.5281/zenodo.21095307.Open asset ↗zenodo · 10.5281/zenodo.21095307pdf-page:7 lines:1-62Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Reliable ecological indicators of mangrove structure and carbon storage are essential for monitoring coastal ecosystem conditions, yet their accuracy remains uncertain in tall, structurally heterogeneous forests, where Earth observation products differ in sensor physics, spatial resolution, and acquisition dates. Here, we present a multi-scale framework to evaluate, calibrate, and improve two widely used ecological indicators of mangrove condition—canopy height and aboveground biomass (AGB)—across approximately 7000 ha of mangroves in the Marapanim estuary, northern Brazil. The framework integrates UAV photogrammetry, radar-derived digital elevation models (TanDEM-X and SRTM), and field measurements to quantify cross-scale discrepancies and identify the main sources of uncertainty affecting indicator retrieval. High-resolution UAV canopy-height models revealed exceptionally tall Avicennia forests reaching up to 53 m, among the tallest mangroves reported globally. At the local scale, mean AGB reached approximately 648 Mg ha −1 in the southern Avicennia -dominated sector and 430 Mg ha −1 in the northern mixed Rhizophora–Avicennia sector, with local maxima of ∼800 Mg ha −1 . In contrast, radar-derived products yielded substantially lower estimates of canopy height and biomass, with height differences of 8–10 m in tall and structurally heterogeneous stands. These discrepancies reflect the combined effects of sensor-dependent canopy representation, spatial averaging, and temporal mismatch between historical radar acquisitions and recent UAV observations. To improve the ecological interpretation of these products, we implemented a calibration strategy linking field and UAV measurements to satellite observations and complemented it with UAV-based three-dimensional volumetric reconstruction of individual trees as an independent structural check on allometric biomass estimates. Our results show that canopy height and AGB derived from coarse-resolution radar products can systematically underestimate mangrove structural condition and carbon storage in tall forests unless locally calibrated. Beyond documenting exceptionally tall and carbon-dense Amazonian mangroves, this study provides a transferable framework for evaluating and improving ecological indicators of forest structure and biomass in complex coastal ecosystems.
Why it matches plant phenotyping methodsUAV photogrammetry・レーダー・現地測定を統合し、マングローブの樹冠高と地上部バイオマスという植物形質の推定を評価・較正・改善する方法論が研究の中心である。
abstractHere, we present a multi-scale framework to evaluate, calibrate, and improve two widely used ecological indicators of mangrove condition—canopy height and aboveground biomass (AGB)—across approximately 7000 ha of mangroves in the Marapanim estuary, northern Brazil.
To address this challenge, we propose StyleGAN3-T, the translation-equivariant alias-free variant of StyleGAN3, as a generative framework for producing high-fidelity synthetic plant disease images, integrated with a hybrid Swin Transformer-ResNet50 classifier for precise recognition. Accurate detection of plant leaf diseases is essential for sustainable agriculture and early intervention. However, deep learning models often struggle with small, imbalanced datasets that limit generalization and robustness. To address this challenge, we propose StyleGAN3-T, a novel alias-free generative framework for producing high-fidelity synthetic plant disease images, integrated with a hybrid Swin Transformer-ResNet50 classifier for precise recognition. The proposed approach ensures translation-equivariant, artifact-free image synthesis and enhanced feature diversity. A balanced dataset of 18,000 images was developed by combining real and StyleGAN3-T-generated samples. In pooled GAN benchmarking, StyleGAN2-ADA achieved the strongest generative-quality metrics, whereas StyleGAN3-T was selected as the preferred augmentation model because its alias-free synthesis and spatial consistency yielded superior downstream classification performance in the proposed pipeline.
Why it matches plant phenotyping methods植物病害画像を合成・認識する画像解析手法が研究の中心であり、植物の病害状態を画像から推定するフェノタイピング手法に該当する。
abstractwe propose StyleGAN3-T, a novel alias-free generative framework for producing high-fidelity synthetic plant disease images, integrated with a hybrid Swin Transformer-ResNet50 classifier for precise recognition.
Reproduction assets foundThe paper's grape leaf disease image inputs are two publicly available Kaggle datasets explicitly named in the Data Availability statement. No author code, models, or synthetic dataset deposit is provided; other processed data is request-only.Dataset · publictechnical guidance. Y.L. and A.W. supervised the study, provided critical revisions, and contributed to the interpretation of results. All authors reviewed and approved the final manuscript.
Data availability
The datasets analyzed during the current study are publicly available from Kaggle: Grapevine Disease Dataset (Original) (https://www.kaggle.com/datasets/rm1000/grape-disease-dataset-original; accessed 13 March 2026; license: MIT) and Grape Leaf Disease 4 Class (https://www.kaggle.com/datasets/jawadulkarim117/grape-leaf-disease-4-class; accessed 13 March 2026; license: CC0: Public Domain). Additional processed metadata, label-harmonization records, dataset split definitions, and other daOpen asset ↗Kaggle · rm1000/grape-disease-dataset-originallines:549-576Dataset · publicthors reviewed and approved the final manuscript.
Data availability
The datasets analyzed during the current study are publicly available from Kaggle: Grapevine Disease Dataset (Original) (https://www.kaggle.com/datasets/rm1000/grape-disease-dataset-original; accessed 13 March 2026; license: MIT) and Grape Leaf Disease 4 Class (https://www.kaggle.com/datasets/jawadulkarim117/grape-leaf-disease-4-class; accessed 13 March 2026; license: CC0: Public Domain). Additional processed metadata, label-harmonization records, dataset split definitions, and other data used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Competing inOpen asset ↗Kaggle · jawadulkarim117/grape-leaf-disease-4-classlines:549-576Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Quantitative imaging of plant tissues by laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) is hindered by the lack of matrix-matched calibration standards. Established approaches, such as gelatine or homogenized tissue blocks, do not replicate plant matrices accurately. Here, we introduce a nano-dispenser-based calibration strategy that deposits nanolitre volumes of elemental standards directly onto paraffin-embedded grain sections, exploiting the low endogenous metal content of the endosperm to generate in situ calibration curves. Calibration performance for Mg, Mn, Cu, Zn and Mo was assessed using LA-ICP-MS imaging and Iolite data processing. The method demonstrated excellent linearity (R2 > 0.98), reproducibility across multiple grains, and sub-ppm limits of detection. Comparative analysis with in-house homogenized blocks and NIST wheat reference material confirmed superior accuracy and reproducibility of the nano-dispenser approach. As a proof-of-concept we have applied the method for the quantitative imaging of metals in a whole barley grain section and the results show excellent agreement with published data obtained by conventional liquid-mode ICP MS. The technique reported here provides a robust, scalable solution for quantitative metallomics studies of plants tissues, enabling improved assessment of nutrient distribution and supporting the development of standardised protocols for LA-ICP-MS Imaging
Why it matches plant phenotyping methods植物組織中の元素分布を定量画像化するための校正手法を開発し、直線性・再現性・精度を検証している。栄養元素という植物形質の取得法が研究の中心である。
abstractHere, we introduce a nano-dispenser-based calibration strategy that deposits nanolitre volumes of elemental standards directly onto paraffin-embedded grain sections
Mango (Mangifera indica L.) is among the most important commercial fruits grown throughout the world in the tropical and subtropical areas. Even though mangoes are economically important, their cultivation is continuously threatened by a wide variety of leaf and fungi diseases, resulting in crop losses of up to 15-30% annually. [1]. Conventional disease identification depends heavily on expert visual inspection—a process that is inherently slow, subjective, and largely impractical for smallholder farmers operating in remote areas with limited access to agronomic specialists. This paper provides an end-to-end deep learning-based approach towards automatic mango leaf disease detection along with an Android application for real-time deployment in the field. This work is built upon MangoLeafBD [1], an openly accessible dataset that consists of a total of 4,000 images of RGB color space for seven different disease categories and one healthy class, with each category having 500 samples collected from four separate orchards in Bangladesh. Three types of transfer learning models including VGG16 [2], MobileNetV2 [3], and DenseNet121 [4] were considered after applying two-phase fine-tuning based on pre-trained ImageNet weights. For each of the three types of neural network models tested, a series of preprocessing steps consisting of bilinear resizing to size 224 x 224, channel-wise normalization, and image augmentation (rotation, zoom, brightness adjustment, horizontal flip, and shear) was used to increase model accuracy for diverse real-world images. The training model was deployed using TensorFlow Lite (TFLite), which allowed it to be run in offline mode on mid-end Android phones, without needing internet connectivity. The app lets farmers upload leaf images or take images and get a diagnosis of the leaf diseases, along with possible treatments for them.This work contributes a replicable pipeline linking state-of-the-art deep learning research with practical precision agriculture, particularly for rural communities that currently lack access to timely agronomic advisory services.
Why it matches plant phenotyping methodsマンゴー葉画像から病害状態を推定する深層学習パイプラインとモバイル実装が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractThis paper provides an end-to-end deep learning-based approach towards automatic mango leaf disease detection along with an Android application for real-time deployment in the field.
Abstract. Shortwave infrared (SWIR) UAV imaging remains uncommon despite its sensitivity to canopy water and protein. We report, to our knowledge, the first field validation of a six-band, simultaneously exposed VNIR/SWIR multicamera for plot-scale winter wheat. The payload used narrow bandpass filters at 910, 980, 1100, 1200, 1510, and 1650 nm (FWHM 10–12 nm) and was flown at 30 m AGL, yielding 4 cm GSD. Radiometric calibration used in-flight empirical line calibration with an in-scene gray panel set, followed by independent validation on a material-distinct gray set. ASD spectroradiometer measurements were convolved with Gaussian proxy spectral response functions matched to the nominal filter passbands. Empirical line fits were near-perfect (R2 ≈ 1.000; RMSE = 0.003–0.009). Independent panel validation showed near-unity slopes for five bands from 980–1650 nm (R2 = 0.998–0.999; RMSE = 0.005–0.013). Across 36 canopy plot ROIs, camera-to-ASD agreement remained strong for five bands, with slopes of 0.943–1.079, R2 = 0.58–0.85, and RMSE = 0.010–0.023. Two SWIR normalized ratio indices showed tight cross-sensor agreement: NRI[1100,1200] (R2 ≈ 0.93; RMSE ≈ 0.010) and NRI[1650,1510] (R2 ≈ 0.90; RMSE ≈ 0.017–0.018). Post-hoc filter transmittance measurements revealed secondary long-wavelength throughput in the 910 nm channel, causing compressed slopes and elevated error (MAPE ≈ 33%); this band was excluded from accuracy claims. Panel-anchored, bandpass-aware calibration enables quantitative UAV SWIR reflectance and robust SWIR indices for precision agriculture applications. The workflow also identifies hardware-specific failure modes, supporting reproducible validation and informed redesign of filter-reconfigurable SWIR payloads.
Why it matches plant phenotyping methods冬小麦キャノピーの定量的な反射率・SWIR指数取得を目的に、UAVマルチカメラの校正、独立検証、センサー間比較、故障モード評価を中心的に実施しており、植物フェノタイピング手法の技術検証に該当する。
abstractWe report, to our knowledge, the first field validation of a six-band, simultaneously exposed VNIR/SWIR multicamera for plot-scale winter wheat.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Abstract Timely quantification of crop stress physiology remains challenging because conventional assays are destructive, labor-intensive, and poorly suited for continuous monitoring and field deployment. Here, we report a microneedle-enabled electrochemical biosensing platform with smartphone-based data collection for the in planta monitoring of plant stress that integrates three design innovations in a single architecture: (i) a fully integrated hollow microneedle–microfluidic measurement pathway for sap access, (ii) physical isolation of the metal electrodes from direct tissue contact to reduce insertion-zone abrasion of the sensing interface, improving biocompatibility and potentially lowering fouling pathways, and (iii) lithography-free fabrication of a modular transducer on an additively manufactured substrate. The platform comprises a three-electrode gold (Au) transducer modified with a nanostructured reduced graphene oxide (rGO)–chitosan layer. The biosensing platform enabled dual sensing channels via functionalized glucose oxidase (GOx) and horseradish peroxidase (HRP) for the detection of glucose and water stress-associated hydrogen peroxide (H2O2), respectively. The glucose channel showed a strong linear calibration over the tested range, with Pearson’s r = 0.99, R2 = 0.98, sensitivity of 62.34 μA/mM, and a limit of detection (LOD) of 102.50 μM (∼1.85 mg/dL), while the H2O2 channel exhibited Pearson’s r = 0.99, R2 = 0.99, sensitivity of 3.65 μA/decade, and an LOD of 3.22 μM. Repeatability across measured standards remained high for both channels, with mean coefficients of variation of 1.31% for glucose and 1.16% for H2O2. Ex vivo measurements in plant sap, including standard-addition experiments and comparison with commercial benchmark assays, provided validation of analyte concentration determination in plant-derived samples. In planta measurements on maize plants (Zea mays L.) grown under graded watering treatments revealed statistically significant treatment-dependent glucose and H2O2 signatures over time (p
Why it matches plant phenotyping methods植物体内のグルコースとH2O2を非破壊・連続測定し、水ストレス状態を推定する電気化学センシング基盤の開発と検証が中心であり、植物フェノタイプ取得手法に該当する。
abstractwe report a microneedle-enabled electrochemical biosensing platform with smartphone-based data collection for the in planta monitoring of plant stress
Genetically encoded biosensors are one of the essential tools in biological research. They enable visualization of molecules of interest from the subcellular level to entire organism level in vivo and can be used to monitor the presence of small molecules, gene expression, protein activity, and protein degradation. However, multiplexing fluorescent biosensors in plants is notoriously difficult due to signal bleed-through and strong autofluorescence from chlorophyll. In this study, we investigated the potential of multiplexing biosensors based on the selection of reporter fluorescent proteins. We characterized the emission spectra, fluorescence lifetimes, and relative brightness of diverse fluorescent proteins in plant leaves. We show that selected proteins exhibit comparable brightness, supporting their use in co-expression experiments and reliable quantification of individual signals. To separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing. We identified the channel separation unmixing approach as the most suitable for biosensors. Additionally, we show how unmixing with the selected approach can be applied to separate autofluorescence and five fluorescent proteins. We further validated this approach in virus-infected cells by following organelle dynamics in vivo. Finally, we demonstrate the feasibility of high-throughput segmentation and quantification with a custom MATLAB workflow for nuclei, chloroplasts, and cytoplasm signal analysis. Overall, our work demonstrates that biosensors can be multiplexed, even when their emission spectra overlap.
Why it matches plant phenotyping methods植物組織における蛍光シグナルの分離、画像セグメンテーション、定量化ワークフローを開発・比較・検証しており、植物の細胞・細胞小器官状態を測定する方法が中心である。
abstractTo separate three overlapping signals, we applied two different linear unmixing approaches and compared them to results obtained without unmixing.
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 segmentation and histograms were acquired using MATLAB script ( https://github.com/NIB‐SI/Nuclei‐segmentation ). The parameters used in the script to achieve appropriate segmentation are listed on GitHub, Case 1 ( https://github.com/NIB‐SI/Nuclei‐segmentation ).Open asset ↗NIB‐SI/Nuclei‐segmentationlines:255-341Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published1 Jul 2026SPU - Journal of Science, Technology and Management ResearchCited by 0 · OpenAlex ↗
Crop disease identification is still a big problem in agriculture, which results in large yield losses and food lacks, especially in regions dependent on manual monitoring. Traditional methods of identifying plant diseases are often labor intensive, error-prone, and ineffective in early-stage diagnosis. To overcome these limitations, this study proposes a hybrid machine learning model for accurate crop type identification, disease classification, and severity prediction using image data. The methodology utilizes the PlantVillage dataset, encompassing over 50,000 annotated leaf images across 14 crops. After rigorous preprocessing involving image resizing, normalization, and cleaning, Improved Deep Joint Segmentation is applied to localize disease-affected regions. Feature extraction incorporates color, texture (GLCM, LBP), and shape attributes to enhance classification accuracy. A hybrid approach integrating XGBoost for feature selection and Support Vector Machine (SVM) for classification is proposed to capture both overarching trends and intricate details. Experimental results across four major crops—potato, tomato, corn, and grape—demonstrate superior performance, with the hybrid model achieving 98.6% accuracy, 98.3% precision, 99.0% recall, and 99.1% F1-score. The model outperforms existing approaches, offering a robust, scalable, and accurate solution for early crop disease detection in precision agriculture.
Why it matches plant phenotyping methods画像から病変領域を抽出し、植物病害の分類と重症度を推定する機械学習ワークフローが研究の中心であり、植物状態の表現型計測に該当する。
abstractthis study proposes a hybrid machine learning model for accurate crop type identification, disease classification, and severity prediction using image data.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R2 of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R2 increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments.
Why it matches plant phenotyping methodsマルチソースリモートセンシングと機械学習により、トウモロコシの植物含水量という明示的な植物状態を地域スケールで推定・検証する手法開発が中心である。
abstractthis work develops a regional PMC estimation approach by combining multi-source remote sensing data.
Agriculture is one of the most important sectors contributing to economic development and global food security. However, plant diseases caused by fungi, bacteria, viruses, and other pathogens significantly reduce crop yield and quality, leading to substantial economic losses for farmers. Early and accurate identification of plant diseases is essential for effective crop management and timely application of suitable pesticides. Conventional methods of disease diagnosis rely on manual inspection by agricultural experts, which is time-consuming, labor-intensive, expensive, and often inaccessible to farmers in remote areas. Recent advancements in Artificial Intelligence (AI) and Deep Learning have provided efficient solutions for automating plant disease detection through image analysis. This project, "Plant Disease Identification and Pesticides Recommendation System Using Convolutional Neural Network (CNN) for Crop Protection," presents an intelligent system that automatically identifies plant diseases from leaf images and recommends appropriate pesticides for effective crop protection. The proposed system utilizes a Convolutional Neural Network (CNN), a deep learning model specifically designed for image classification tasks. The CNN model is trained using a large dataset of healthy and diseased plant leaf images collected from publicly available agricultural datasets. During training, the model learns to recognize disease-specific visual features such as color variations, lesion patterns, texture changes, and leaf deformities. Image preprocessing techniques, including resizing, normalization, and data augmentation, are employed to improve the quality of the input images and enhance the overall performance of the model. When a farmer uploads an image of a plant leaf through the system, the trained CNN model analyzes the image and accurately classifies it as either healthy or affected by a specific disease. After identifying the disease, the system recommends suitable pesticides, fungicides, insecticides, or biological treatments based on an agricultural knowledge database. It also provides additional information such as recommended dosage, application method, spraying schedule, safety precautions, and preventive measures to ensure responsible pesticide usage and minimize environmental impact. The proposed system offers several advantages, including rapid disease detection, high classification accuracy, reduced dependence on agricultural experts, optimized pesticide application, lower crop losses, improved productivity, and support for sustainable farming practices. Furthermore, the system can be deployed as a web or mobile application, enabling farmers to access disease diagnosis and treatment recommendations anytime and anywhere using smartphones or other digital devices. Overall, the proposed CNN-based plant disease identification and pesticide recommendation system provides a reliable, cost-effective, and intelligent solution for modern agriculture. By combining image processing, deep learning, and agricultural expertise, the system supports precision farming, enhances decision-making, reduces unnecessary pesticide usage, and contributes to increased crop productivity, environmental sustainability, and long-term food security.
Why it matches plant phenotyping methods葉画像から植物病害の状態をCNNで直接推定する手法が研究の中心であり、病害症状の画像ベース表現型計測に該当する。農薬推薦も含むが、植物病害識別というフェノタイピング要素が明示的である。
abstractpresents an intelligent system that automatically identifies plant diseases from leaf images and recommends appropriate pesticides for effective crop protection.
Wheat grain quality is highly susceptible to environmental fluctuations. This study proposes a complementary two-stage chemometric framework combining Analysis of Variance-Simultaneous Component Analysis (ASCA) and ANOVA-Common Dimensions (AComDim) with near-infrared (NIR) spectroscopy to systematically evaluate spatiotemporal impacts on wheat. A total of 179 samples collected across four Austrian locations over a three-year period were analyzed. In the first stage, ASCA was utilized as a computationally efficient pre-screening tool to systematically evaluate 27 preprocessing protocols, aimed at maximizing target-factor variance and minimizing background noise. In the second stage, AComDim was implemented as the core engine for orthogonal variance decomposition. By embedding ANOVA into a multiblock framework, AComDim successfully preserved joint multivariate variations among variance blocks, overcoming the independent-block limitations of traditional ASCA. By mathematically decoupling distinct sources of variance directly from the untargeted spectral fingerprints, the results revealed that harvest year was the predominant driver of spectral variation (captured in Common Component 1, explaining 45.1% of the total variance). Furthermore, the year-by-location interaction (CC2, 14.5%) and the main effect of growing location (CC3, 14.1%) were successfully and orthogonally extracted. Although their global F -values did not meet the strict 95% statistical significance threshold, they exhibited structured, deterministic spectral patterns clearly distinguishable from random background noise. Loading analysis of broad spectral regions (e.g., 1100–1200 nm and 1350–1450 nm) highlighted environmentally induced macroscopic shifts in carbohydrates, lipids, proteins, and moisture status. This research provides a robust, high-throughput phenotyping framework for quantifying the spatiotemporal sensitivity of cereals, offering essential insights for stabilizing grain quality under changing environmental conditions.
Why it matches plant phenotyping methodsNIRスペクトルから小麦粒の品質・環境応答を抽出する chemometric 手法が研究の中心であり、単なる品質測定ではなく、高スループット表現型解析フレームワークとして開発・適用されている。
abstractThis study proposes a complementary two-stage chemometric framework combining Analysis of Variance-Simultaneous Component Analysis (ASCA) and ANOVA-Common Dimensions (AComDim) with near-infrared (NIR) spectroscopy to systematically evaluate spatiotemporal impacts on wheat.
In Controlled Environment Agriculture (CEA), traditional fixed set-point control is replaced by dynamic control strategies. These strategies enable joint optimization of resource use efficiency and biomass output, leverage electricity price fluctuations to reduce energy costs, and employ targeted environmental stressors to enhance crop quality and physiological resilience. Implementation of dynamic control strategies, however, builds upon real-time monitoring, robust data integration and management, and high-fidelity predictive modeling. These capabilities can be effectively provided through a Digital Twin (DT). This study introduces a novel open-source DT framework designed to support dynamic control strategies in CEA, addressing challenges in scalability, generalizability and interoperability. The framework is centered on the IoT platform ThingsBoard, providing unified, scalable data acquisition and management across heterogeneous sensor and actuator networks through vendor-agnostic integration and standardized interfaces. A significant contribution is its physics-based modeling backend, built on ordinary differential equation models developed in Modelica and exported as Functional Mock-up Units (FMUs). To ensure model accuracy across varying biological conditions, a parameter estimation pipeline is developed to calibrate and adapt these FMUs against experimental data. Building on this, a dedicated simulation backend is implemented to leverage the calibrated FMUs, providing the dynamic predictive capabilities necessary for proactive system control. Furthermore, the framework incorporates a Multirate Moving Horizon Estimation (MMHE) state estimator to estimate critical unmeasured variables, such as plant biomass. This estimator is specifically designed to handle multirate data, maintaining continuous estimates even when certain sensors provide frequent data while others are sparse or infrequent. Demonstrated through a simulation-based case study modeling lettuce growth in a vertical hydroponic farm, the DT framework's architectural feasibility and virtual modeling capabilities are verified. Using synthetic data generated from a known true parameter set, the calibrated growth model achieved a low cross-validation prediction error, with an RMSE of 0.221g and an NRMSE of 6.62% on an independent test set. The MMHE-based state estimator effectively maintained continuous biomass estimates despite sparse synthetic measurements and model mismatch. These findings underscore the framework's potential as a robust and extensible foundation for future physical DT implementations in CEA, enabling a 31 transition toward dynamic, data-driven, and energy-aware operations.
Why it matches plant phenotyping methods植物バイオマスという観測可能な植物形質を、デジタルツインの状態推定器と動的モデルで継続的に推定する方法を開発・検証しており、単なる栽培制御や routine measurement ではない。
abstracta dedicated simulation backend is implemented to leverage the calibrated FMUs, providing the dynamic predictive capabilities necessary for proactive system control.
Fruit diseases caused by fungal, bacterial, and viral pathogens cause devastating pre- and post-harvest losses to Indian agriculture, particularly in Maharashtra where Guava, Mango, Orange, Papaya, and Pomegranate are major horticulture crops. Accurate, early-stage disease identification directly impacts farmer income, food security, and precision crop management. Conventional manual inspection by agronomists is subjective, time-consuming, and not scalable across thousands of orchard acres. Automated deep learning-based image analysis has emerged as a transformative solution, offering high accuracy, speed, and field deployability. Existing deep learning models for plant disease detection are predominantly trained on the real time database, which inadequately represents Indian fruit crop species. Furthermore, published systems focus on binary disease presence detection and lack disease severity grading — a critical requirement for treatment decision-making. The absence of expert-verified datasets and the visual overlap among disease classes (such as Phytophthora vs. Scab in Guava, and Powdery Mildew vs. Ring-spot in Papaya) present significant classification challenges. Limited training data for rare classes such as Stylerandroot (Guava, 310 samples) and Bacterial Blight (Pomegranate, 304 samples) further compounds model generalization. This study presents an end-to-end deep learning framework using VGG-16 and VGG-19 architectures trained on a novel, expert-verified dataset of 7,372 approved images spanning 22 disease/healthy classes across 5 fruit types, after on filed validation. The pipeline includes image preprocessing (CLAHE, Gaussian denoising, normalization), hybrid multi-feature extraction (CNN features, GLCM, LBP, Color Histograms), transfer learning with progressive fine-tuning, and a cascaded severity estimation module. Three algorithms are designed: (1) a Transfer Learning Classification Algorithm using VGG-16/VGG-19 backbone with softmax multi-class head, (2) a Hybrid Feature Fusion Algorithm combining CNN deep features with handcrafted descriptors for improved minority-class performance, and (3) a Cascaded Rule-CNN Severity Estimation Algorithm classifying disease progression into Healthy, Mild, Moderate, and Severe categories. VGG-19 achieved 96.1% overall accuracy, 95.1% precision, 94.5% recall, and a macro F1-score of 0.942, significantly outperforming VGG-16 (94.5% accuracy, F1: 0.918). Mango classification achieved the highest accuracy at 97.8%, while severity estimation reached 91.2% overall accuracy with the Mild category being the most challenging at 86.4%.The proposed system demonstrates that expert-verified, domain-specific datasets combined with transfer learning and hybrid feature fusion significantly advance the state of fruit disease detection for Indian agriculture. This framework provides a scalable, interpretable, and practically deployable solution for precision horticulture.
Why it matches plant phenotyping methods植物画像から病害状態と重症度を推定する深層学習手法、データセット、評価を中心的に開発しており、植物表現型計測の方法論的研究に該当する。
abstractThis study presents an end-to-end deep learning framework using VGG-16 and VGG-19 architectures trained on a novel, expert-verified dataset of 7,372 approved images spanning 22 disease/healthy classes across 5 fruit types
Agriculture remains one of the most essential sectors for sustaining human life and economic stability. However, crop diseases continue to pose a serious threat to agricultural productivity, often leading to significant financial losses for farmers. Traditional disease identification methods rely heavily on manual inspection, which is time-consuming, requires expert knowledge, and is not always accurate. In this paper, a smart crop disease detection system is proposed using machine learning techniques. The system focuses on analyzing leaf images to identify visible symptoms of diseases at an early stage. Image preprocessing techniques are applied to enhance the quality of the input data, followed by feature extraction and classification using an efficient learning model. The proposed approach aims to reduce human effort while improving detection accuracy. The model is trained and tested on a dataset of crop leaf images and demonstrates promising performance in identifying multiple types of plant diseases. The results indicate that the system can serve as a supportive tool for farmers by providing quick and reliable predictions. This approach not only improves productivity but also contributes to sustainable agricultural practices. Future enhancements can further improve real-time detection and expand the system for a wider range of crops
Why it matches plant phenotyping methods葉画像から植物病害の可視症状を抽出・分類する機械学習手法が研究の中心であり、植物状態の表現型推定に該当する。
abstractThe system focuses on analyzing leaf images to identify visible symptoms of diseases at an early stage.
Introduction eaf area index (LAI) and leaf nitrogen accumulation (LNA) are key indicators of wheat growth and nitrogen nutritional status. However, existing prediction methods predominantly rely on single-modal information and single-output models, limiting their ability to characterize the complex structural and physiological traits of crops. This study aimed to develop a multimodal learning framework for the simultaneous and accurate prediction of wheat LAI and LNA. Methods Spectral, image, and canopy structural features were extracted from wheat canopies across different cultivars, nitrogen treatments, and growth stages. A canopy height correction-based preprocessing method was developed to improve the extraction of structural features. A Dual-Output Bayesian Neural Network (DO-BNN) was then constructed to simultaneously predict LAI and LNA. In addition, an Extreme Sample Mining (ESM) strategy and a joint loss function were introduced to strengthen the learning of complementary information across modalities and the intrinsic correlation between the two target variables. Results The DO-BNN achieved its best predictive performance when all feature modalities were fused. The coefficients of determination (R²) for LAI and LNA were 0.89 and 0.77, respectively, while the corresponding relative root mean square errors (RRMSEs) were 0.15 and 0.35. Compared with single-modal and conventional single-output approaches, the proposed method provided more accurate and robust predictions of both wheat growth parameters. Discussion The results demonstrate that integrating spectral, image, and structural information can improve the characterization of wheat canopy traits. By jointly modeling LAI and LNA, the DO-BNN effectively exploited the physiological relationship between crop growth and nitrogen accumulation. The proposed framework provides a promising approach for the high-accuracy, collaborative monitoring of wheat growth and nitrogen nutritional status.
Why it matches plant phenotyping methods小麦キャノピーのスペクトル・画像・構造情報からLAIと葉窒素蓄積を推定するマルチモーダル手法を開発し、前処理、ニューラルネットワーク、性能比較まで中心的に扱っているため。
abstractThis study aimed to develop a multimodal learning framework for the simultaneous and accurate prediction of wheat LAI and LNA.
The paper discusses the preparation of experimental data used to measure the biopotentials of Novosibirskaya 41 wheat seedlings exposed to elevated and lowered temperatures, in order to conduct phenotyping of these plants using cluster analysis. It is noted that such a preparation is necessary for long-term experimental studies that take several calendar days (up to 10 or more), during which metabolic changes in seedling samples occur, affecting their biopotential values. The paper is based on experimental data obtained in 2020 and 2022 and their regression analysis, as reported in [13]. The results of changes in seedling biopotentials depending on their age are briefly described, and an algorithm for calculating corrective biopotential values for each magnification level of the objects is provided. Statistical regressions of changes in biopotential values depending on the need to preserve seedlings of these wheat varieties were obtained. This allowed the development of an algorithm for correcting the initial average biopotentials for these conditions without preliminary regression analysis of the data. Two data sets were generated for assessing the phenotype of the objects: the original data set, obtained through primary processing of changes in these seedling biopotentials under exposure to elevated and lowered temperatures, and the corrected data set, in the partial parameter (smax.c.) of the maximum filtered centered value (cf) of the wheat seedling biopotentials under these conditions. Plant phenotyping was performed based on the data sets using the original Eclaster program, which implements this methodical spectral clustering from the sklearn.cluster library in the Python programming environment. The clustering results presented in the form of a scatterplot demonstrate improved cluster separation for the corrected data.
Why it matches plant phenotyping methods小麦幼苗のバイオポテンシャルを用いた表現型評価のため、データ補正アルゴリズムとクラスタリング解析プログラムを開発・適用しており、表現型取得・抽出手法が研究の中心である。
abstractThe paper discusses the preparation of experimental data used to measure the biopotentials of Novosibirskaya 41 wheat seedlings exposed to elevated and lowered temperatures, in order to conduct phenotyping of these plants using cluster analysis.
Guava cultivation is considerably influenced by foliar and fruit diseases whose overlapping symptoms and environmental variability make accurate field-level diagnosis challenging. Numerous studies have been conducted to find efficient methods of diagnosing plant diseases, but most focus on image-level classification and do not include lesion localization or pixel-level segmentation of the images within a single framework of analysis. This study proposes a comprehensive framework for utilizing automated image analysis to classify guava leaf and fruit diseases at the image level, locate lesions, and segment lesions at the pixel level from multiple images of the same type of disease collected from various growing conditions. The dataset was enriched through three augmentation strategies including standard preprocessing, structured augmentation, and GAN-based synthetic image generation, expanding the effective training data to approximately 7,000 images, while a 5-fold cross-validation strategy guided model selection and final performance was assessed on a held-out test set. The experimental evaluation of multiple state-of-the-art Convolutional Neural Networks (CNNs) for the classification of guava leaf and fruit diseases indicated that the model generated using the ResNet50+DenseNet121 model fusion achieved the highest classification accuracy of 98.20%. For lesion detection and segmentation, YOLOv8-seg outperformed Mask R-CNN, achieving mAP@0.5 of 0.907 and 0.889, and mAP@0.5:0.95 of 0.783 and 0.769 for detection and segmentation, respectively, with a balanced precision–recall profile. The techniques of Explainable AI (XAI) were used to increase the transparency of this model by identifying areas in the image that are significant to the actual lesion. The framework was further designed with practical web-based deployment in mind, evaluating both lightweight and high-capacity models to balance computational efficiency against predictive accuracy. From this research, it was concluded that using model fusion, data augmentation, and segmentation-aware lesion detection would provide a solution for managing guava diseases effectively.
Why it matches plant phenotyping methodsグアバの葉・果実における病斑の分類、位置特定、画素レベル分割を自動化する画像解析フレームワークを開発・評価しており、植物の病害状態の表現型取得が研究の中心である。
abstractThis study proposes a comprehensive framework for utilizing automated image analysis to classify guava leaf and fruit diseases at the image level, locate lesions, and segment lesions at the pixel level
Doubled Haploid (DH) technology significantly accelerates the development of homozygous lines in maize breeding; however, its scalability is constrained by the reliable discrimination of haploid and diploid individuals. The widely used R1-nj anthocyanin marker at the seed stage is susceptible to genetic suppression and environmental variability, leading to high misclassification rates. This limitation has driven a shift toward seedling root morphology as a more robust phenotypic marker, yet it introduces major challenges, including complex image noise and severe class imbalance. In this study, we systematically evaluate the limitations of standard computer vision pipelines and baseline deep learning models for root-based classification. Automated background removal methods (HSV, Rembg) are shown to misinterpret fine root hairs as noise, resulting in significant morphological data loss. Additionally, experiments conducted under a realistic class imbalance (1:5.4) demonstrate that widely used CNN architectures (ResNet50, VGG16, EfficientNetB0, DenseNet121) exhibit strong majority class bias, with haploid recall dropping to 0.00% and 27.7%. These findings reveal a critical limitation in existing approaches and highlight the need for domain-informed datasets and imbalance-aware learning strategies for robust and scalable AI-based maize breeding systems.
Why it matches plant phenotyping methodsトウモロコシの根形態画像を用いた倍加半数体・二倍体分類について、画像前処理と深層学習モデルの限界を体系的に評価しており、表現型取得・抽出手法が中心である。
abstractIn this study, we systematically evaluate the limitations of standard computer vision pipelines and baseline deep learning models for root-based classification.
The widespread impact of plant diseases on agricultural yield demands an intelligent, accessible, and real-time detection solution. This paper presents the Mobile Application for Automated Plant Disease Detection, a lightweight smartphone-based system that leverages a fine-tuned MobileNetV2 Convolutional Neural Network (CNN) to classify plant leaf diseases in real time. The system accepts smartphone camera images, applies preprocessing including resizing to 224×224 pixels and normalisation, and classifies the plant health state as Healthy, Early Blight, Late Blight, Leaf Curl, or Powdery Mildew with associated confidence scores. A Flask API backend hosts the trained model and communicates with a React Native mobile frontend to return classification results within 1.2 seconds on a 4G network connection. Firebase Cloud Messaging delivers real-time push notifications and treatment recommendations directly to the farmer's device. The system is deployed entirely on standard Android and iOS smartphones without any specialised hardware, sensors, or wearable devices. Experimental evaluation on the PlantVillage dataset with over 54,000 annotated leaf images demonstrated classification accuracy exceeding 96%, API response latency below 800 milliseconds, and zero dependency on dedicated agricultural equipment. Usability testing with agricultural practitioners confirmed intuitive operation without prior technical training. These results confirm that the proposed application offers an efficient, portable, and institutionally deployable solution for modern precision agriculture. Keywords—Plant disease detection; MobileNetV2; convolutional neural network; deep learning; precision agriculture; smartphone application; transfer learning; PlantVillage dataset; real-time classification; push notification
Why it matches plant phenotyping methodsスマートフォン画像から植物葉の病害・健康状態を推定するCNNベースの手法とアプリを開発・評価しており、植物病害表現型の取得が中心である。
abstractThis paper presents the Mobile Application for Automated Plant Disease Detection, a lightweight smartphone-based system that leverages a fine-tuned MobileNetV2 Convolutional Neural Network (CNN) to classify plant leaf diseases in real time.
Plant diseases significantly affect agricultural productivity and crop quality, making early detection essential for sustainable farming. This study presents a simple deep learning framework for automated plant disease detection using leaf images. A Convolutional Neural Network (CNN) model was developed and trained on a publicly available plant disease dataset to classify healthy and diseased leaves. Image preprocessing and augmentation techniques were applied to improve model generalization and performance. Experimental results demonstrate that the proposed framework effectively identifies plant diseases with high accuracy while maintaining low computational complexity. The proposed approach can assist farmers and agricultural experts in timely disease diagnosis and crop management.
Why it matches plant phenotyping methods葉画像から植物の健全・罹病状態を推定するCNN手法を開発しており、植物病害の表現型取得・分類が研究の中心であるため。
abstractThis study presents a simple deep learning framework for automated plant disease detection using leaf images.
Three-dimensional (3D) reconstruction based on structure from motion and multi-view stereo (SfM-MVS) is increasingly used in plant phenotyping, but its performance is influenced by crop architecture, viewpoint configuration, and image preprocessing. For compact crops such as peanut, dense branching and severe within-canopy occlusion make reliable reconstruction challenging. This study evaluated the effects of reconstruction pipeline, angular interval, and image preprocessing on 3D reconstruction of peanut plants under controlled rotary imaging. A total of 10,800 RGB images from 30 plants were used to compare representative implementations of incremental and global SfM-MVS pipelines in terms of geometric quality, phenotypic accuracy, and processing efficiency. At the 1° baseline, the tested global pipeline implementation reduced the root mean square reprojection error (RMSRE), point-density coefficient of variation (CV), vertical root mean square error (VRMSE), and the 95th percentile of the absolute point-cloud distance values (P95) by 15.05%, 14.08%, 39.87%, and 33.33%, respectively, and increased average phenotypic accuracy from 96.08% to 97.37%, compared with the tested incremental pipeline implementation. In contrast, the tested incremental implementation showed a lower voxel void ratio and shorter processing time. In both pipelines, increasing the angular interval reduced processing time but also reduced geometric stability, internal voxel filling, and phenotypic accuracy. In the present dataset, angular intervals of 3°–5° provided a favourable balance between reconstruction accuracy and efficiency. Cropping reduced peripheral redundancy, whereas cropping combined with background removal produced the best overall results, with the lowest reprojection error and the highest phenotypic accuracy. These results provide practical guidance for selecting reconstruction pipeline, viewpoint configuration, and preprocessing strategy in close-range indoor 3D phenotyping of peanut plants and crops with similar canopy architectures.
Why it matches plant phenotyping methods落花生の3D表現型取得について、SfM-MVSパイプライン、視点間隔、画像前処理を比較・検証しており、フェノタイピング手法が研究の中心である。
abstractThis study evaluated the effects of reconstruction pipeline, angular interval, and image preprocessing on 3D reconstruction of peanut plants under controlled rotary imaging.
Leaf diseases in banana plants and ornamental crops can significantly reduce productivity and product quality, highlighting the need for accurate early detection methods. This study proposes an image-based classification approach utilizing texture features extracted from the Gray Level Co-occurrence Matrix (GLCM) combined with a Hybrid Stacking model that integrates Random Forest (RF) and Support Vector Machine (SVM). The preprocessing stage involves image resizing and noise reduction, followed by feature extraction using energy, contrast, homogeneity, and correlation parameters. The dataset consists of eight classes of healthy and diseased leaves, collected from both field documentation and secondary sources. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics under a cross-validation scheme. Experimental results show that SVM achieved 89.2% accuracy, RF 88.5%, while the stacking model yielded the best performance with 91.7% accuracy, effectively reducing misclassification among visually similar disease classes. This study demonstrates the effectiveness of combining GLCM features and hybrid stacking models for leaf disease classification, with potential applications in automated plant monitoring systems to support precision agriculture.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する手法の開発・評価が研究の中心であり、GLCM特徴量とRF/SVMの性能を交差検証しているため、植物フェノタイピング手法として収録する。
abstractThis study proposes an image-based classification approach utilizing texture features extracted from the Gray Level Co-occurrence Matrix (GLCM) combined with a Hybrid Stacking model that integrates Random Forest (RF) and Support Vector Machine (SVM).
Agriculture plays a vital role in the economy of many countries, and crop productivity is highly dependent on plant health.Plant diseases can significantly reduce crop yield and quality if not detected at an early stage.Traditional disease identification methods rely on manual inspection by agricultural experts, which can be time-consuming, expensive, and sometimes inaccurate.Recent advancements in image processing and machine learning have enabled automated systems for plant disease detection.This research presents a plant leaf disease detection system based on a Multi-Class Support Vector Machine (SVM) classifier using color, texture, and shape features extracted from leaf images.The proposed approach captures leaf images, performs preprocessing to remove noise and enhance image quality, segments the infected region, and extracts relevant features.These features are then used to train a Multi-Class SVM model capable of classifying different plant diseases.The combination of color, texture, and shape characteristics improves classification accuracy by providing comprehensive information about disease symptoms present on the leaf surface.Experimental analysis demonstrates that the proposed method can effectively identify multiple plant diseases with high accuracy while reducing the dependency on manual diagnosis.The developed system offers a cost-effective and efficient solution for farmers and agricultural professionals, helping in early disease detection and timely treatment recommendations.The proposed approach contributes to the advancement of smart agriculture and precision farming technologies.
Why it matches plant phenotyping methods葉画像から病斑領域を分割し、色・テクスチャ・形状特徴を抽出して植物病害状態を分類する画像ベースの表現型推定手法が研究の中心であるため。
abstractThis research presents a plant leaf disease detection system based on a Multi-Class Support Vector Machine (SVM) classifier using color, texture, and shape features extracted from leaf images.
The world suffers from 10–40% loss in crop yields each year because of plant disease. This threat is serious and growing; it threatens food security, rural livelihoods, and agricultural economies. Advances being made through deep learning, computer vision, and mobile technology have presented a unique opportunity to use leaf images to automatically recognize plant disease. Published classification accuracies on benchmark datasets now exceed 97%, which is an important achievement but achieving high accuracy on a benchmark alone does not indicate that traditional methods will work when deployed in the real world: all four stakeholders (i.e., farmers, agronomists, regulatory authorities, and extension agents) must therefore have the ability to understand, and interpret the output of automatically recognized plant diseases in a way that enhances human expertise rather than replacing it. In this chapter, we provide a compendium of technical deep learning architectures and methods related to Explainable Artificial Intelligence (XAI) for plant disease detection, including convolutional networks, residual architectures, dense architectures, transformer networks, and hybrid models. We also systematically evaluate the explainability methods used in both post-hoc and intrinsic explanation and evaluate the applicability of these methods across a variety of imaging modalities used in agriculture, including RGB, multispectral, and hyperspectral. This chapter characterizes major benchmark datasets; discusses major challenges to their deployment, including class imbalance, domain shift, model size reduction, and human–AI trust calibration; then ends with potential new directions for research in areas such as foundation models (FM), causal interpretable models (Explanations), federated learning, and continual learning to build resilience for each evolving pathogen landscape.
Why it matches plant phenotyping methods植物病害を葉画像から自動認識する画像ベースの表現型推定手法と、その説明可能性・データセット・評価課題を体系的に扱うレビューであり、方法論が中心である。
abstractIn this chapter, we provide a compendium of technical deep learning architectures and methods related to Explainable Artificial Intelligence (XAI) for plant disease detection
Abstract— Crop diseases hit harvests hard, especially when farmers can't spot them early. We’ve been looking at a new deep learning approach that uses MobileNetV2 and mixes structured images with real field photos to catch problems faster. The data covers four classes: Early Blight, Late Blight, Leaf Mold, and healthy leaves. Preprocessing steps like resizing, normalization, augmentation, and cleaning out duplicates really helped the model hold up better. Their MobileNetV2 version hit 91.53% validation accuracy and dropped the loss to 0.235. The training curves stayed steady, and overfitting stayed low. Compared to regular CNNs, this setup gives solid accuracy without needing heavy computing power, which matters when you want something that works right in the field. Keywords—Crop Disease Detection, MobileNetV2, Deep Learning, Transfer Learning, PlantVillage, PlantDoc, Agriculture AI.
Why it matches plant phenotyping methods葉の画像から病害状態を推定する深層学習手法の開発・性能評価が中心であり、植物フェノタイピング方法に該当する。
abstractTheir MobileNetV2 version hit 91.53% validation accuracy and dropped the loss to 0.235.
First stable release of the RGB and NPQ pixel-wise phenotyping pipeline associated with the manuscript "Image-based biomarkers effectively predict salt and drought stress in dwarf tomatoes (Solanum lycopersicum L.)". This repository includes a Python-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data. The workflow is designed for pixel-wise extraction and analysis of image-derived traits, with a specific focus on preserving full spatial distributions rather than relying on image-level summary statistics. The pipeline processes RGB images to compute vegetation indices derived from color channel combinations, and NPQ fluorescence images to extract pixel-level chlorophyll fluorescence metrics. Both data types are integrated with experimental metadata through structured indexing files. The analysis framework is organised into two main stages: (i) data pre-processing and structuring into long-format pixel-wise datasets, and (ii) distribution-based statistical analysis of trait variability across treatments and conditions. The latter includes normalised histograms, Jensen–Shannon and Wasserstein distance metrics, and cluster-based permutation testing to identify statistically significant differences between distributions. A minimal example dataset is provided to enable end-to-end testing of the workflow, including image processing, metadata integration, and statistical analysis. An additional archive containing representative example outputs generated from the example dataset is included to illustrate the structure and format of intermediate and final pipeline outputs. To facilitate computational reproducibility, the repository also includes complete derived outputs generated from the full study dataset, including distribution-comparison results (Jensen–Shannon and Wasserstein distances) and cluster analysis outputs for all evaluated RGB and chlorophyll fluorescence traits. These files are provided as supplementary computational products of the workflow and can be used to verify, inspect, and reproduce the analyses described in the associated manuscript.
Why it matches plant phenotyping methodsRGBおよびNPQ画像から植物形質を画素単位で抽出・解析する再利用可能なパイプラインと再現性資料が中心であり、植物フェノタイピング手法に該当する。
abstractPython-based image analysis pipeline for high-throughput plant phenotyping using RGB and chlorophyll fluorescence (NPQ) imaging data.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 5 Sept 2026
1 Abstract Non-structural carbohydrates (NSCs) are central to plant carbon allocation and physiological regulation, yet their quantification typically relies on destructive biochemical assays that lack spatial resolution. Here, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves. The workflow combined automated hyperspectral segmentation, spectral preprocessing, Partial Least Squares Regression (PLSR), and constrained wavelength selection. Sample-level spectra extracted from 114 strawberry leaf samples grown across three different metabolic conditions were paired with destructive starch measurements and used to train models across the 900–1750 nm spectral range. A constrained greedy band-selection strategy revealed that predictive performance approached a plateau at approximately 12 wavelengths, indicating substantial spectral redundancy within the full hyperspectral dataset. The final reduced-band model achieved a cross-validated coefficient of determination (R 2 ) of 0.771 ± 0.066 and a root mean squared error (RMSE) of 0.743 ± 0.098 mg g −1 fresh weight using repeated stratified 5-fold cross-validation. Pixel-wise application of the final model generated spatial starch-associated maps that preserved pronounced intra-leaf heterogeneity, including vein-associated spatial structure. These results demonstrate that starch-associated spectral information can be reconstructed from a constrained reduced-band SWIR framework while retaining sufficient predictive performance for spatial mapping. The identified wavelength reduction supports the feasibility of deployable multispectral systems for non-destructive carbohydrate sensing in plant phenotyping applications.
Why it matches plant phenotyping methods植物葉のデンプン状態を非破壊推定・空間再構成するSWIR画像計測とケモメトリック解析ワークフローを開発・検証しており、フェノタイピング手法が中心です。
abstractHere, we developed a shortwave infrared (SWIR) hyperspectral imaging workflow for non-destructive estimation and spatial reconstruction of starch-associated variation in strawberry leaves.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
Abstract Automated three-dimensional reconstruction of plant architecture under- pins high-throughput crop phenotyping, yet its deployment in practical field settings is constrained by two fundamental limitations of Neural Radiance Fields (NeRF): degraded geometry under sparse, unstructured image capture, and a complete absence of calibrated uncertainty estimates that would allow practitioners to distinguish reliable geometry from reconstruction artefacts. We present UA-PlantNeRF, a unified framework that resolves both limita- tions through three tightly coupled contributions. First, building on VISAR, our prior intelligent video frame-selection strategy (weights α1 = 0.4, α2 = 0.3, α3 = 0.3), the pipeline identifies maximally informative, non-redundant view- points from raw footage with as few as 15 frames. Second, a dual-head NeRF architecture augmented with Monte Carlo Dropout produces jointly decom- posed aleatoric and epistemic uncertainty alongside each reconstructed voxel, trained under a heteroscedastic negative log-likelihood objective. Third, split conformal prediction—with calibration performed on held-out plants to preserve exchangeability— yields provable, distribution-free per-ray cov- erage guarantees at any user-specified confidence level. Evaluated across three benchmarks (Pheno4D, ROSE-X, and our novel UA-Field greenhouse dataset) at sparsity levels N ∈ {15, 30, 50}, UA-PlantNeRF achieves PSNR 28.7±0.6 dB and SSIM 0.89±0.01 at N = 30, outperforming all sparse-view baselines (p
Why it matches plant phenotyping methods植物の3D構造・アーキテクチャを推定する画像ベースのNeRF手法を開発し、複数ベンチマークで評価しているため、植物表現型計測法が中心です。
abstractEvaluated across three benchmarks (Pheno4D, ROSE-X, and our novel UA-Field greenhouse dataset)
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
High-fidelity 3D reconstruction and precise phenotypic parameter extraction of banana plants are critical for crop growth monitoring and yield estimation in precision agriculture. However, traditional methods encounter significant bottlenecks: LiDAR systems are cost-prohibitive for widespread adoption, while traditional photogrammetry often fails to handle the complex canopy structures, severe occlusions, and weak texture features characteristic of banana leaves. To address these limitations, this article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones. We introduce BN-NeRF, an enhanced Neural Radiance Field method built upon Instant-NGP. Specifically, we integrate three key technical improvements: (1) frame-level geometric calibration to correct camera pose drift caused by handheld motion; (2) sparse geometric anchoring to explicitly constrain depth and scale using sparse point clouds; and (3) thin-leaf prior regularization to suppress artifacts and improve the geometric accuracy of leaf surfaces. Building on this reconstruction, we establish a complete pipeline to recover explicit metric geometry from implicit radiance fields. By combining mesh topological analysis with geodesic algorithms, we achieve automated and precise extraction of key morphological parameters. Extensive experiments were conducted on a dataset of 90 banana plants in a real-world orchard. The results demonstrate that BN-NeRF achieves superior rendering quality (PSNR of 32.4 dB, SSIM of 0.951, and LPIPS of 0.152) while maintaining inference speeds comparable to Instant-NGP. Furthermore, the extracted phenotypic parameters showed strong agreement with manual ground truth across both leaf-level and structural traits. In addition to trait-specific regression performance, the evaluation also includes normalized completeness analysis, calibration-cube-based scale validation, and Bland-Altman agreement analysis, supporting the measurement reliability of BN-NeRF for field phenotyping. This study demonstrates that low-cost smartphone-based acquisition, combined with BN-NeRF, can support accurate field phenotyping of banana plants. In addition, an implemented mobile-cloud system was functionally validated through repeated end-to-end runs on an iPhone 13 client and a cloud workstation.
Why it matches plant phenotyping methodsスマートフォン画像からの3D再構成と植物形態形質抽出を中核とするBN-NeRF手法を開発し、圃場データで精度・再現性を検証しているため。
abstractthis article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones
Accurate and scalable soybean crop health monitoring remains a major challenge in precision agriculture due to environment variability, inconsistent lighting conditions, and significant differences between the ground-level leaf imagery and UAV-based aerial imagery. Most existing deep learning approaches treat these two sensing modalities separately without properly exploring cross-scale feature transferability or measuring the domain gap that exists between the sensing scales. As a result, developing unified and deployment-ready crop health monitoring systems that can effectively leverage the more accessible leaf-level datasets, collected without specialized equipment or regulatory constraints, to improve UAV-scale inference remains difficult. In order to address this limitation, we propose a multi-scale soybean crop health assessment framework that integrates ground-level leaf imagery and UAV-based aerial imagery from the MH-SoyaHealthVision dataset across four health conditions, which include Healthy, Mosaic Virus, Pest attack, and Rust. CLAHE, Gray-World color constancy correction, and illumination normalization is incorporated into a structured pre-processing pipeline and further applied to reduce illumination bias and enhance cross-domain feature consistency. Six deep learning backbones were comprehensively evaluated for leaf-level classification, with MaxViT and ConvNeXt achieving the best performance. Their static weighted ensemble further improved accuracy to 87.08%. Cross-scale evaluation showed that zero-shot leap-to-UAV transfer achieved only 40% accuracy, thus highlighting the presence of a substantial domain shift. Fine-tuning improved UAV classification performance to about 97%, while a supervised contrastive learning framework specifically designed for cross-scale feature alignment further increased accuracy to approximately 98% with better convergence stability. Feature embedding analysis using PCA, t-SNE, and silhouette metrics demonstrated considerable improvements in inter-class separability (0.59 vs. 0.19) and reduced domain discrepancy (0.0336 vs. 0.114) under contrastive learning. These findings suggest that supervised alignment can generate more class-discriminative representations with lower cross-scale domain discrepancy, making them more suitable for scalable multi-scale cross-health monitoring.
Why it matches plant phenotyping methods葉およびUAV画像からダイズの健康状態・病害を推定する画像ベースの表現学習フレームワークを開発し、複数モデル、クロススケール転移、微調整、教師ありコントラスト学習を比較・検証しているため、植物表現型取得法が中心である。
abstractwe propose a multi-scale soybean crop health assessment framework that integrates ground-level leaf imagery and UAV-based aerial imagery
Crop diseases pose a serious threat to agricultural productivity and global food security. Early and accurate detection of plant diseases is essential to minimize yield losses and reduce excessive pesticide usage. Traditional disease identification methods rely heavily on manual inspection by agricultural experts, which is time-consuming, subjective, and impractical for large-scale deployment. Recent advances in deep learning and computer vision have enabled automated, image-based crop disease detection with significantly improved accuracy and scalability. This review critically examines state-of-the-art deep learning techniques employed for crop disease detection using leaf images. It analyses commonly used datasets, preprocessing strategies, neural network architectures, evaluation metrics, and deployment challenges. Furthermore, existing research gaps and future directions toward robust, real-world agricultural applications are identified.
Why it matches plant phenotyping methods葉画像から植物病害を検出する画像ベース手法を対象とした包括的レビューであり、データセット、前処理、モデル、評価指標を体系的に扱うため、植物フェノタイピング手法が中心です。
abstractThis review critically examines state-of-the-art deep learning techniques employed for crop disease detection using leaf images.
RSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization. This approach automates estimation of leaf area index, aboveground dry matter, and grain yield without extensive ground-based calibration. The system couples a Python data interface with a high-performance C simulation engine, enabling efficient regional-scale processing. Validation using independent datasets for rice, wheat, and maize demonstrated robust performance: yield Model Efficiency reached 0.99, with a minimum ME of 0.67 for wheat. The Bayesian prior regularization constrained parameter estimates while maintaining predictive accuracy. Regional applications in South Korea, North Korea, and the U.S. Corn Belt captured spatial yield gradients and inter-annual variability across millions of pixels. RSCM provides a computationally efficient tool bridging process-based modeling and remote sensing for precision agriculture and food security monitoring.
Why it matches plant phenotyping methods衛星データと作物モデルを統合し、LAI・地上部乾物量・収量という植物形質を推定するソフトウェア手法を開発・検証しており、形質取得・推定法が研究の中心である。
abstractRSCM is an open-source, process-based crop simulation framework that integrates satellite-derived vegetation indices directly into parameter estimation via Bayesian Maximum A Posteriori (MAP) optimization.
Deficit irrigation (DI) is a crucial strategy for optimizing water use in arid and semi-arid agriculture, yet its success depends on accurately determining the crop yield response factor (K y ). This study introduces a novel, machine learning-assisted framework for large-scale estimation of sugarcane K y using satellite-derived water stress indicators. fused Landsat 7/8/9 and MODIS data within the Google Earth Engine platform to generate high-resolution daily Crop Water Stress Index (CWSI) maps for the 2023 season in southern Iran. The Random Forest (RF) algorithm was applied to correct biases in land surface temperature (LST), achieving high accuracy (RMSE < 1.0°C, nRMSE < 3%, rMBE ≈ 0%) before CWSI calculation. Ground measurements from 12 field points, including canopy temperature and yield, were used for calibration and validation. The satellite-based CWSI showed strong agreement with field data (RMSE = 0.05, nRMSE = 11%), with values ranging from 0.18 to 0.71 at dekadal scale. Using this CWSI, K y was computed at dekadal, monthly, and seasonal scales, revealing substantial spatiotemporal variability (0.2–1.63) and an average seasonal K y of 1.05. This value is lower than the FAO-66 default of 1.2, indicating that the standard coefficient may prompt over-irrigation without yield benefits. The analysis further identified early July as the period of peak water stress sensitivity, with K y values exceeding 1.82. This ML-enhanced, satellite-based approach provides a robust tool for deriving spatially explicit K y values, offering a significant advancement for precision irrigation planning and water resource management.
Why it matches plant phenotyping methods衛星データと機械学習で作物の水ストレス状態（CWSI）を推定し、地上測定で較正・検証する手法が中心であるため、植物生理状態のセンシング型フェノタイピングとして含める。
abstractThis study introduces a novel, machine learning-assisted framework for large-scale estimation of sugarcane K y using satellite-derived water stress indicators.
Plant diseases are one of the major challenges faced in modern agriculture, as they directly impact crop productivity, food quality, and the overall economic stability of farmers. Various environmental factors such as climate change, excessive moisture, poor soil conditions, and pest attacks contribute to the rapid spread of plant diseases. Traditional methods of disease identification mainly rely on manual inspection by agricultural experts, which is time-consuming, costly, and often inaccurate during the early stages of infection. Therefore, there is a growing need for an automated, fast, and reliable plant disease detection system that can assist farmers in identifying diseases at an early stage and taking appropriate preventive actions. This project presents an intelligent plant disease detection system that utilizes image processing, Machine learning, and machine learning techniques for accurate disease identification in crops such as Onion, Brinjal, Mango, Papaya, and Guava. The system is designed to analyze images of plant leaves captured through cameras or mobile devices. Using advanced image preprocessing methods, the captured leaf images are enhanced and processed to extract important features such as color, texture, and disease patterns. These features are then analyzed using a Convolutional Neural Network (CNN) model, which classifies the plant as either healthy or diseased with high accuracy. If a disease is detected, the system further identifies the specific type of disease affecting the plant and provides suitable recommendations for treatment and prevention. These recommendations include appropriate fertilizers, pesticides, organic supplements, and preventive agricultural practices customized for each crop type. The system also helps farmers understand the severity of the disease and suggests measures to minimize its spread to nearby plants. By providing real-time analysis and accurate predictions, the proposed solution reduces dependency on manual monitoring and expert consultation. The main objective of this project is to support precision agriculture by enabling early disease diagnosis, improving crop management efficiency, and increasing agricultural productivity. The automated detection process saves time, reduces crop losses, minimizes excessive pesticide usage, and promotes sustainable farming practices. Furthermore, this system can be integrated into smart farming applications and mobile-based agricultural support systems, making it accessible and beneficial for farmers in rural and urban areas alike
Why it matches plant phenotyping methods植物葉画像から健康・罹病状態と病害種を推定する画像処理・CNN手法が研究の中心であり、植物病害状態の表現型取得に該当する。ただし処置推奨は付随的である。
abstractThis project presents an intelligent plant disease detection system that utilizes image processing, Machine learning, and machine learning techniques for accurate disease identification in crops such as Onion, Brinjal, Mango, Papaya, and Guava.
This study aims to develop an automated system for classifying vegetable plant diseases using the MobileNet algorithm integrated with a Telegram Bot. The system is designed to assist users, especially farmers, in identifying plant diseases quickly and efficiently through leaf images. The research method applies a Convolutional Neural Network with the MobileNet architecture due to its lightweight and efficient computational performance. The dataset used in this study consists of tomato leaf images obtained from a public dataset on Kaggle, which includes several disease categories and healthy leaves. The system is implemented using Python and integrated with the Telegram Bot API to enable real-time interaction. The process begins when users upload leaf images, followed by image preprocessing and classification using the trained model. The results show that the system is capable of providing accurate classification with good performance and can handle various input conditions. In addition, the integration with Telegram makes the system easily accessible without requiring additional applications. Therefore, this study offers a practical and efficient solution for early detection of plant diseases using deep learning technology.
Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習システムの開発が研究の中心であり、植物の病害状態を直接推定する画像ベースのフェノタイピング手法に該当する。
abstractdevelop an automated system for classifying vegetable plant diseases using the MobileNet algorithm integrated with a Telegram Bot
MaizeTomatoLeafPhysiological trait estimationCalibration / preprocessingWater status / transpiration
Within the soil-plant-atmosphere continuum, water movement is driven by the water potential gradients between these three domains. To have a comprehensive understanding of such water relations, an examination of how plants respond to variations in soil water availability is required. The methodologies employed for measuring water potential in leaf (Ψ leaf ) and soil (Ψ soil ) have undergone a significant evolution; transitioning from qualitative assessments to the use of high-precision digital sensors over the past few decades. The present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor). Additionally, we present the code for processing the raw data files in RStudio.
Why it matches plant phenotyping methods葉の水ポテンシャルを連続測定するセンサー設置、データ処理コード、手順を中心とした植物生理形質の測定プロトコルであり、方法論的貢献が明確。
abstractThe present protocol aims to provide a comprehensive, step-by-step guide from the germination phase of maize and tomato plants to the installation of two sensors that continuously monitor water potential in the leaf (PSY1 psychrometer) and in the soil (TEROS 21 matric potential sensor).
Reproduction assets foundThe paper deposits its authors' R analysis notebook with an example water-potential dataset, the CR800 datalogger program, and an installation video on Zenodo, all publicly accessible.Code · publicthat were missing, zero, or otherwise aberrant. It was also programmed to identify and remove inverted day-night cycle patterns, as well as values that were statistically insignificant.
Figure 9 shows applications of data cleaning on the example dataset. For more details, please check codes that have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino, 2026 ).
Figure 9.
Example of data cleaning using the algorithm.
Green is kept data and red is discarded data.
Conclusion
In summary, the present protocol is not confined to the descriptive monitoring of Ψ
soil
and Ψ
leafOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:452-504Code · public(1) the address of each Teros 21; (2) the data transporting port (“C1” or “C3”); (3) the creation of dataset files to store the recorded soil matric potential and temperature, as well as the voltage of the battery for power supply; (4) the time interval for the data recording.
An example of the program was deposited on Zenodo (
https://doi.org/10.5281/zenodo.17158115 ), with the document name of “Program-CR800”). Before starting, install the software of “Device Configuration Utility” and “PC400” from Campbell Scientific (
https://www.campbellsci.com/devconfig ;
https://www.campbellsci.com/pc400 ). “CRBasic Editor” is integrated inside PC400. For more details about the programming, please reOpen asset ↗Zenodo · 10.5281/zenodo.17158115lines:321-378Dataset · publiculic limitation, soil-root disconnection, and recovery. Consequently, this linkage of the protocol to mechanistic analyses of water transport in the SPAC is more direct.
Ethics and consent
Ethical approval and consent were not required.
Data availability
The datasets and codes to analyze the data have been deposited on Zenodo (
https://doi.org/10.5281/zenodo.20080750 ,
D’Agostino (2026) ).
Data are available under the terms of the Creative Commons Zero v1.0 Universal.
An additional explicative video for the psychrometer installation on leaves is available on Zenodo (
https://doi.org/10.5281/zenodo.17510720 ,
Degand
et al. (2025) ).
The author(s) declare that this video is released under theOpen asset ↗Zenodo · 10.5281/zenodo.20080750lines:505-651Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Analysis of the element composition of plant materials has many applications, including monitoring nutrient status, detecting biogeochemical indications of mineral deposits and assessing the effectiveness of phytoremediation in contaminated soils. Portable X-ray fluorescence spectroscopy (pXRF) delivers the advantage of real time in situ multi-elemental analysis at low cost, but calibration is affected by factors that include the water content of plant organs. The effect of variation in moisture content on pXRF-determined concentrations of heavy metals Zn, Fe, Cu, Sr and Th, and nutrient elements Si, S, K and Ca, have been evaluated using the foliage of A. imperialis, D. excelsa and A. macrorhiza through a controlled continuous drying experiments. A segmented linear relationship between pXRF measurements and moisture content was observed for most elements with a rapid decrease followed by a moderate decrease as moisture content increased. The position of the inflection point is dependent on leaf thickness and energy of the main X-ray peak measured. Calibration issues related to variation in moisture content comprises a combination of dilution and spectral interference effects. Dilution accounts for most of the underestimation of pXRF-determined concentrations for fresh plant samples compared with laboratory methods on dried samples. As moisture contents increase, the relative influence of spectral interferences decreases. The single-layer thickness of plant sample affects the position of inflection point of linearity and the relative contribution of spectral interference effect. This study provides new insights into the effect of moisture on pXRF-determined elemental concentrations and offers practical suggestions and recommendations for in situ analysis of plant samples using pXRF.
Why it matches plant phenotyping methods植物試料の元素濃度を測定するpXRFについて、水分含量による測定誤差と校正特性を評価し、実 in situ 測定への実用的推奨を示す方法検証研究である。
abstractcalibration is affected by factors that include the water content of plant organs
Poplar (Populus) trees are indispensable to various industries and environmental sustainability efforts. They are widely utilized for paper production, timber, and windbreaks, while also playing a significant role in carbon sequestration. Given their economic and ecological importance, the effective management of diseases is crucial. Convolutional Neural Networks (CNNs), renowned for their ability to process visual data, are pivotal in accurately detecting and classifying plant diseases. This study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, ensuring geographic diversity and broader applicability. The dataset includes four disease classes, i.e., " Parsha (Scab) ," " Brown spotting ," " White-Gray spotting ," and " Rust ," which represent common afflictions in these regions. To advance research efforts, this dataset will be made publicly accessible, providing a valuable resource for the scientific community. Leveraging the cutting-edge YOLOv9c model, a state-of-the-art CNN architecture, we applied the Histogram Equalization technique as a preprocessing step to enhance the image quality to increase the accuracy of disease detection. This method not only improves the diagnostic performance of the model but also provides a scalable solution for monitoring and managing poplar diseases. By ensuring the health of poplar trees, this approach supports the sustainability of these critical resources. To our knowledge, this is the first publicly available dataset specifically focused on diseased poplar leaves, making it a significant contribution to global research efforts. It offers an invaluable resource for researchers and practitioners, enabling further advancements in early disease detection and sustainable forestry management.
Why it matches plant phenotyping methodsポプラ葉の病徴を画像から検出・分類する手法と公開データセットが研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採択。
abstractThis study presents a domain-specific dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea
Abstract Chlorophyll estimation is fundamental in plant physiology, crop management, and ecological studies; however, destructive and non-destructive methods are often interpreted interchangeably despite differing measurement principles. The present study compared four chlorophyll estimation approaches—two non-destructive (SPAD meter and GreenSeeker) and two destructive (80% acetone and DMSO extraction)—across eight crop species under uniform field conditions. Significant interspecific variation was observed for all methods. Correlation and regression analyses revealed generally weak relationships among methods, particularly between leaf-level (SPAD, solvent extraction) and canopy-level (GreenSeeker) measurements, reflecting scale-dependent behavior and methodological differences. Moderate associations were observed between SPAD and acetone-extracted chlorophyll for certain traits, whereas GreenSeeker showed poor agreement with solvent-based estimates. Differences between DMSO and acetone extraction further highlighted solvent-specific extraction efficiency. The results demonstrate that chlorophyll estimation methods are not directly interchangeable and should be selected based on study objectives, biological scale, and leaf anatomical characteristics. Species-specific calibration and integration of canopy structural parameters are required to improve cross-method interpretability.
Why it matches plant phenotyping methods複数の葉・キャノピーのクロロフィル推定法を作物種間で比較し、相関、回帰、スケール依存性、互換性を評価しており、植物表現型測定法の技術的検証が中心である。
abstractThe present study compared four chlorophyll estimation approaches—two non-destructive (SPAD meter and GreenSeeker) and two destructive (80% acetone and DMSO extraction)—across eight crop species under uniform field conditions.
Reproduction assets foundThe preprint declares that the datasets generated in this chlorophyll-method comparison study (SPAD, GreenSeeker, acetone and DMSO measurements across eight crop species) are publicly deposited in Figshare under DOI 10.6084/m9.figshare.31817989. This is a paper-specific, publicly actionable phenotype dataset. No authorDataset · publicThe datasets generated during the current study are available in the Figshare repository, https://doi.org/10.6084/m9.figshare.31817989Open asset ↗Figshare · 10.6084/m9.figshare.31817989lines:163-185Code / dataset availability confirmedCrossref · Europe PMC · checked 5 Sept 2026
Introduction Confidence calibration, selective prediction, out-of-distribution scoring, and deep ensembles are mature techniques in machine learning, yet their efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked. Methods Models trained on PlantVillage were evaluated on PlantDoc leaf-level crop images under a parent-image-aware split protocol, and a suite of standard mitigation techniques was applied to characterize the reliability gap. Analyses included temperature scaling and selective prediction for a fine-tuned ResNet-50, quantitative image-level shift analysis, Grad-CAM visualization, simple target-aware adaptation baselines, frozen-feature backbone comparisons, and ensemble baselines. Results In the primary case study, a fine-tuned ResNet-50 suffered a 67.7-percentage-point accuracy collapse upon cross-domain transfer, while mean predicted confidence remained at 79.76%. Post-hoc temperature scaling reduced calibrated ECE to 0.3645 but left selective risk at 80% coverage at 64.30%. Quantitative image-level shift analysis confirmed large-effect-size differences in saturation ( d = 3.90), border edge density ( d = 3.33), and foreground-occupancy proxy ( d = 2.48) between the two domains, while Grad-CAM visualizations showed that the model shifts attention from lesion-centered regions in PlantVillage to background-dominated areas in PlantDoc. Simple target-aware mitigations, including adaptive batch normalization and feature moment matching, improved accuracy from 0.321 to 0.343 and 0.366, respectively, whereas DANN-style adversarial adaptation degraded performance to 0.252. A frozen-feature backbone comparison across five backbones showed that, within the energy-scoring frozen-backbone comparison, DINOv2-S/14 achieved the highest unknown-detection AUROC (0.764) and the lowest selective risk at 80% coverage (0.520), with paired Wilcoxon tests confirming statistically significant accuracy and macro-F1 differences across backbones. Two ensemble baselines were evaluated: a warm-start end-to-end ResNet-50 ensemble reduced calibrated ECE to 0.063 but achieved only 0.666 AUROC, while a lightweight DINOv2 linear-probe ensemble achieved 0.779 AUROC after calibration but under limited epistemic diversity. Discussion Neither ensemble established deployment-grade reliability: the best selective risk at 80% coverage across all configurations remained above 0.51. The principal contribution is a reproducible, deployment-oriented reliability characterization showing that standard post-hoc and lightweight adaptation techniques reduce but do not eliminate the severe reliability gap under controlled-to-field transfer in agricultural computer vision.
Why it matches plant phenotyping methods植物病害画像分類の信頼性・ドメインシフト・校正・選択的予測を体系的にベンチマークしており、病害状態を画像から推定する方法の技術評価が中心である。
abstracttheir efficacy under the severe domain shift encountered when plant disease classifiers move from controlled laboratory imagery to heterogeneous field photographs has not been systematically benchmarked.
Reproduction assets found本文中に内容が明示された植物フェノタイピング関連の補足表と、その公開リンクを確認しました。Supplement · publicSupplementary Table 1 ) was therefore constructed by normalizing all labels to a canonical Crop_Disease format and retaining only those categories for which an unambiguous semantic match existed in both datasets.Open asset ↗lines:335-337Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Nodule color and morphology are key readouts of legume symbiotic performance. However, long-term preservation of post-excavation nodules with intact morphology, color, and microbial cleanliness remains a major challenge. This study developed a two-stage aqueous-phase preservation method (TAPP) that enables rapid structural fixation and long-term chemical stabilization. A comprehensive evaluation was subsequently established, incorporating composite morphological score (0-5), color difference (ΔE) and its piecewise slope over time ([Formula: see text]), and visible contamination grade (0-3). Peanut and soybean nodules from multiple regions and cultivars were tracked for 24 months under five preservation methods: TAPP, FormalinCu, TAPP-Resin, Resin, and AirDry. TAPP showed the best overall preservation, with composite morphological scores of 4.65 ± 0.14 for peanut and 4.63 ± 0.22 for soybean at 24 months, and no visible mold. Color change slowed over time: [Formula: see text] decreased from 1.83 to 1.10 ΔE·month - 1 during 0-1 month to 0.16 ΔE·month - 1 during 12-24 months, yielding final ΔE values of 10.53 ± 1.88 and 10.32 ± 1.93, respectively. Notably, TAPP pretreatment markedly improved resin-embedded samples, demonstrating scalability and flexible deployment. In addition, this study further proposes a stage-wise workflow that integrates on-site pre-fixation, long-distance transport, and long-term storage to enable cross-regional circulation and collaborative phenomics of oxidation-prone, dehydration-sensitive nodules. Together, this work establishes a standardized, traceable workflow to preserve and benchmark legume root nodule phenotypes, supporting cross-laboratory comparability and longitudinal cross-source analyses.
Why it matches plant phenotyping methodsマメ科根粒の形態・色・汚染状態という植物表現型を長期保存し、定量評価・比較する手法と標準化ワークフローが研究の中心であるため。
abstractThis study developed a two-stage aqueous-phase preservation method (TAPP) that enables rapid structural fixation and long-term chemical stabilization.
Abstract Plant diseases are a serious danger to the world’s food security, because they lower agricultural output and increase economic losses. Due to subjectivity, fluctuating lighting, and environmental unpredictability, traditional visual examination techniques are frequently incorrect. The Excess Green (ExG) vegetation index and pseudo-thermal representations produced from RGB pictures are two synthetically developed complementary representations that are integrated with RGB imagery in this study’s lightweight multimodal deep learning system to address these issues. Histogram shifting and pseudo-infrared color mapping are used in a reproducible picture alteration pipeline to create the pseudo-thermal modality, which allows for extra visual signals without the need for specific thermal sensors. In order to classify plant diseases while preserving computational efficiency, the suggested framework uses MobileNetV3-Small backbones to extract modality-specific characteristics. This is followed by feature-level fusion. The publicly accessible Ginger Leaf Dataset, which includes RGB pictures of ginger leaves in four different conditions—Damage-Pest, Dehydrated, Healthy, and Leaf-blight—was used for the experiments. For training, validation, and testing, the dataset was split using a stratified 70:15:15 split. Python-based preprocessing procedures were used to create the extra modalities (ExG and pseudo-thermal representations) from the original RGB images. The experimental results show that the combination of the representations with RGB images can enhance the classification performance compared with the unimodal RGB-based models. Ablation experiments are also conducted to examine the contributions of different modalities to the overall categorization accuracy. The experimental results show that plant disease recognition can be improved with the help of efficient computing by combining lightweight convolutional neural networks with computationally generated visual representations.
Why it matches plant phenotyping methodsRGB画像からExG・疑似熱画像を生成し、植物葉の病害状態を分類するマルチモーダル手法が研究の中心であり、アブレーション評価も実施している。
titleHybrid deep learning-based multimodal framework for plant leaf disease classification using RGB, Excess Green (ExG), and pseudo-thermal representations with MobileNetV2
Reproduction assets foundThe paper's phenotyping experiments use the publicly available Ginger Leaf Dataset (RGB leaf images of four ginger leaf conditions), with a public GitHub repository and dataset website. The authors' derived ExG/pseudo-thermal representations and preprocessing scripts are only available upon request, so they do not yetDataset · publicor multispectral images
IEEE Geosci. Remote Sens. Lett. 2025
10.1109/LGRS.2025.XXXXXXX
Ulku, I., Tanriover, O. O. & Akagündüz, E. Cross-band correlation-aware interactive fusion for multispectral images. IEEE Geosci. Remote Sens. Lett.
10.1109/LGRS.2025.XXXXXXX
(2025).
10. Wong, J. Ginger Leaf Dataset. GitHub Repository (2023). https://github.com/wongjay1941/Ginger-Leaf-Dataset
11.
Bhakta I
A novel plant disease prediction model based on thermal images using modified deep convolutional neural network
Precis. Agric. 2023 24 23 39
10.1007/s11119-022-09927-x
Bhakta, I. et al. A novel plant disease prediction model based on thermal images using modified deep convolutional neural network. Precis.Open asset ↗https://github.com/wongjay1941/Ginger-Leaf-Datasetlines:580-681Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Reflectance spectroscopy offers a powerful approach to integrate high-throughput phenotypic data from herbarium specimens into the digital landscapes of ecology, evolution, and systematics. Because inconsistencies in instrumentation and measurement practices can increase noise and limit dataset compatibility, the International Herbarium Spectral Digitization Working Group (IHerbSpec) has published the Protocol for Spectral Digitization of Herbarium Specimens, currently in version 1.2.1, as an open resource that will continue to evolve through community use and collaboration (https://iherbspec.github.io/protocol). The protocol defines a stepwise measurement workflow, standardized filename conventions, structured metadata tables with controlled vocabularies, and guidance on tissue selection, materials, and instrumentation quality control to ensure that newly generated spectral datasets are robust and comparable. By embedding standardized practices at the point of data collection, it provides a scalable foundation for data synthesis and new quantitative insights into plant diversity across taxonomic, geographic, and temporal scales.
Why it matches plant phenotyping methodsハーバリウム葉の反射スペクトル取得を標準化する測定プロトコルであり、ワークフロー、メタデータ、組織選択、機器品質管理を中心的に扱うため、植物表現型取得法として適格。
abstractReflectance spectroscopy offers a powerful approach to integrate high-throughput phenotypic data from herbarium specimens
Plant diseases remain a major challenge in modern agriculture, directly affecting crop yield, quality, and economic stability. Early and accurate identification of plant diseases is essential for minimizing losses and improving productivity. This paper presents an integrated deep learning-based framework for automated plant disease detection and smart agricultural decision support. The proposed system utilizes a Convolutional Neural Network (CNN) to analyze leaf images and classify them into multiple disease categories based on learned visual patterns such as texture, color variation, and structural features. To enhance practical applicability, the system is designed to operate as part of a broader decision-support pipeline, providing insights that assist in timely intervention. The model is trained on a labeled dataset containing diverse plant disease classes and optimized using standard preprocessing and regularization techniques to improve generalization. Experimental evaluation demonstrates that the model achieves high classification accuracy under realistic conditions while maintaining computational efficiency. The proposed approach reduces dependency on manual inspection and enables scalable deployment in agricultural environments. By combining deep learning with application-oriented design, this work contributes toward the development of intelligent and accessible solutions for precision agriculture.
Why it matches plant phenotyping methods葉画像から植物病害を分類するCNN手法が研究の中心であり、植物の病害状態を直接推定・評価しているため、植物フェノタイピング手法として含める。
abstractThis paper presents an integrated deep learning-based framework for automated plant disease detection and smart agricultural decision support.
Agriculture is an essential part of the worldwide economy, and initial detection of crop disease is essential to avoid substantial yield reduction. Conventional approaches of disease detection are primarily completed manually by specialists, which is expensive and frequently requires human errors. This survey aims to introduce an intelligent deep learning model to identify crop disease and suggest pesticides. This model is established on Convolutional Neural Networks (CNN) and uses the idea of transfer learning to sort the disease from the leaves of crops such as tomato and pomegranate. The proposed model works on the rule of image classification and is accomplished through image preprocessing, feature extraction, and classification employing the pre-trained model MobileNetV2. Once the disease is detected, it is mapped to the dataset.
Why it matches plant phenotyping methods作物葉の画像から病害を分類する深層学習手法が中心で、植物の病害状態を直接推定しているため収載。農薬推薦も含むが、病害検出モデル自体が主要な技術的貢献である。
abstractThis survey aims to introduce an intelligent deep learning model to identify crop disease and suggest pesticides.
Plant disease detection using deep learning is essential for precision agriculture, enabling early and automated crop health monitoring. This study proposes an end-to-end transfer learning pipeline, LeafyVGG-16, for multi-class classification of plant diseases and nutrient deficiencies using a tomato leaf dataset. The framework integrates data preprocessing, augmentation, and a VGG-16 backbone with a two-stage fine-tuning strategy. The proposed model is evaluated against CNN, DenseNet-121, Inception-V3, EfficientNetB0, and ResNet-50, achieving an accuracy of 0.93 with precision, recall, and F1-scores of 0.93, 0.90, and 0.92, respectively. These results demonstrate the effectiveness of transfer learning for fine-grained plant disease recognition. We further evaluate model robustness under adversarial cyber attacks to assess deployment reliability in agricultural systems. Under Fast Gradient Sign Method (FGSM) attacks ( ϵ = 0.01– 0.05), the model shows an accuracy drop of 1%–7.5%, while Projected Gradient Descent (PGD) attacks ( ϵ = 0.05, step size = 0.005, 10 iterations) produce similar degradation, highlighting the model’s vulnerability to adversarial perturbations. These findings highlight potential security and reliability risks in AI-based agricultural decision-making systems. Future work will focus on improving robustness and cyber-resilience and extending this framework to other crops for secure and context-aware deployment in resource-constrained environments.
Why it matches plant phenotyping methods植物葉の画像から病害・栄養欠乏状態を分類する深層学習パイプラインが研究の中心であり、複数モデルとの比較評価と敵対的攻撃下での頑健性検証も実施しているため。
abstractThis study proposes an end-to-end transfer learning pipeline, LeafyVGG-16, for multi-class classification of plant diseases and nutrient deficiencies using a tomato leaf dataset.
Reproduction assets foundThe paper's plant-phenotyping input is the publicly available Tomato-Village Variant-a dataset (4,525 tomato leaf images across 8 disease/deficiency classes), which the authors explicitly cite with a public Kaggle URL. No author analysis code, trained model checkpoints, or supplementary data deposits are mentioned. TheDataset · publicThis study uses the publicly available Tomato-Village
dataset [18], which is designed for real-world tomato disease
detection in agricultural environments.Open asset ↗pdf-raw-page:3 lines:1-59Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Machine learning has become an increasingly important tool for overcoming agricultural challenges by enabling efficient and consistent classification of crop-related data. Training such supervised models requires high quality labeled datasets. This work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species on California's Central Coast: annual sowthistle ( Sonchus oleraceus ) and little mallow ( Malva parviflora ). Hyperspectral imaging provides rich spectral-spatial data cubes that can support the development of deep learning models and autonomous technology for precision weed management. Plants were grown in a greenhouse under five conditions: standard, drought, overwatering, excess fertilizer, and no fertilizer. Custom MATLAB scripts were utilized for preprocessing, including k-means clustering to define regions of interest (ROIs), and extraction of spectral metrics. Data visualization was performed using Wolfram language and MATLAB. The dataset includes both raw and ENVI-formatted hyperspectral cubes and pre-processed MATLAB outputs, supporting spectral feature engineering, benchmark development, and exploratory machine learning workflows for controlled environment stress classification.
Why it matches plant phenotyping methods植物のストレス状態を対象とするハイパースペクトル画像データセットで、ROI抽出・スペクトル指標化と機械学習ベンチマークを中心的に提供しているため、植物フェノタイピング手法・データセットとして適格。
abstractThis work presents a dataset consisting of raw and preprocessed hyperspectral imaging (HSI) files capturing reflectance in the visible to near-infrared range (400-1000 nm) from two problematic weed species
Reproduction assets foundThe paper is a Data in Brief article describing its own hyperspectral imaging dataset of annual sowthistle and little mallow under five abiotic stress treatments, deposited publicly on Zenodo (record 17398082). The dataset includes raw ENVI-format hyperspectral cubes, preprocessed MATLAB outputs (ROI masks, extracted植被Dataset · publicData accessibility
Repository name: Zenodo
Data identification number: zenodo.17398082
Direct URL to data: https://doi.org/10.5281/zenodo.17398082Open asset ↗Zenodo · zenodo.17398082html-lines:92-120Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Phenomic prediction (PP) is a genetic value prediction method based on near infrared spectroscopy (NIRS). Spectra pre-processing is a key step in the analysis pipeline of PP and generally involves chemometrics methods. However, the choice of pre-processing is usually done either arbitrarily or through a search of the optimal set of methods and associated parameters. In this study, we propose to implement a singular value decomposition (SVD) step in the pre-processing pipeline where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths. This way, estimations are based on a few informative, orthogonal and interpretable features of spectra instead of many correlated, uninformative wavelengths. We tested this pre-processing method on five datasets representing four plant species (maize, rice, sorghum and grapevine). Results show that estimating genetic values on components of raw spectra, that are not weighted by their eigenvalues, performs as well as doing it on spectra pre-processed with the best classical chemometrics methods in most cases, while requiring less parameter optimization. Moreover, this SVD step opens up possibilities for better understanding and selecting parts of the spectral information that are relevant for PP. Plain language summary Cultivated plants are the result of a breeding process during which their genetic values are used to select those to breed. Estimating these values requires heavy experimental means and is time consuming. Phenomic prediction is a low cost and high throughput method that is increasingly being used for this purpose. It often uses, as predictors, near infrared spectroscopy measurements that are easy to collect and thus routinely used in many species. However, near infrared spectra generally require pre-processing before being used in prediction. Currently used pre-processing methods arise from the chemometrics community, and still deserve a better in-depth appropriation by geneticists. In this study, we propose a pre-processing approach that performs as well as the best chemometrics pre-processing generally used, reduces computation time, and allows for a better understanding of what parts of spectral information are relevant for prediction. Core Ideas The SVD-based pre-processing performs as well as the best performing classical chemometrics pre-processing in most cases Using the SVD-based pre-processing reduces computing time of genetic value estimation and requires less parameter optimization than using classical chemometrics pre-processing Spectra are composed of chemical and physical information and classical pre-processing methods remove the physical part of the signal It is likely that chemical information is the most important for phenomic prediction even though physical information remains valuable Performance of the SVD-based pre-processing is likely due to a good estimation of the genetic part of spectra and the conservation of physical information of spectra
Why it matches plant phenotyping methods植物のNIRSスペクトルから遺伝的価値を推定するフェノミック予測について、SVDベースの前処理法を提案し、複数植物種のデータセットで既存法と比較検証しているため、フェノタイピング手法が中心である。
abstractIn this study, we propose to implement a singular value decomposition (SVD) step in the pre-processing pipeline where genetic values of spectra are estimated on a set of principal components instead of individual wavelengths.
Agriculture plays a vital role in ensuring food security, yet plant leaf diseases continue to cause significant crop losses due to delayed and inaccurate detection. Traditional manual inspection methods are time-consuming, subjective, and not suitable for large-scale farming. To address these challenges, this paper presents a real-time plant leaf disease monitoring system that combines Deep Learning and Internet of Things (IoT) technologies for fast and accurate disease detection. The proposed system continuously captures leaf images using IoT-enabled camera modules and processes them through an advanced image preprocessing pipeline to remove noise and enhance relevant features. A Convolutional Neural Network (CNN), optimized using transfer learning techniques, is employed to automatically extract features and classify plant leaves into healthy or diseased categories, including multiple disease types. The model is further optimized for deployment on edge devices such as Raspberry Pi, enabling low-latency and real-time predictions directly in the field without heavy computational requirements. The system provides instant alerts, confidence scores, and disease insights through a user-friendly dashboard, helping farmers take timely preventive actions. Experimental analysis demonstrates high accuracy, robustness under varying environmental conditions, and scalability across different crops. By enabling early detection and reducing unnecessary pesticide usage, the proposed solution contributes to sustainable agriculture, improved crop productivity, and cost-effective farm management.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類するリアルタイム画像ベース手法とシステムが研究の中心であり、植物の病害状態を直接推定するため収録対象。
abstractthis paper presents a real-time plant leaf disease monitoring system that combines Deep Learning and Internet of Things (IoT) technologies for fast and accurate disease detection.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Eggplant (Solanum melongena L.) is a widely cultivated vegetable crop worldwide, occupying an important position in the agricultural industries of Asia, the Middle East, and Southern Europe. Its significance extends beyond agricultural economics to diverse dimensions such as dietary nutrition, rendering it of considerable research and application value. Traditional crop phenotyping methods suffer from low efficiency, substantial manual errors, and a tendency to damage tender seedlings, while existing three-dimensional phenotyping techniques face challenges including strong background interference and large data volumes. These dual constraints limit the accuracy and application feasibility of seedling phenotyping. To address these issues, this study proposes a non-destructive phenotyping method for eggplant seedlings, with the improvement of the PointNet++ architecture as its core and point cloud background purification as a key preprocessing step, aiming to enhance eggplant breeding efficiency and seedling screening accuracy. The raw point clouds first undergo background purification to actively remove seedling tray points, thereby improving point cloud purity and reducing data size. Concurrently, based on the PointNet++ model, we develop an improved point cloud segmentation model, EggplantPointNet++, by introducing multi-scale residual blocks, integrating channel attention mechanisms, incorporating a global context module, and refining the feature propagation layer. In conjunction with the DBSCAN clustering algorithm, this approach achieves semantic and instance segmentation of eggplant seedling point clouds, with certain improvements in segmentation accuracy and model efficiency under small-scale and occluded scenarios. To validate the technical effectiveness, multiple comparative experiments and ablation studies were conducted. The results demonstrate that EggplantPointNet++ outperforms the original model, background purification preprocessing provides positive gains, and each improved module contributes positively. The final model achieves improvements in core metrics including Recall and F1-score. Based on the segmented point cloud data, this study calculates core phenotypic parameters including plant height, stem diameter, cotyledon angle, and cotyledon area. Using the technical system established in this study, we completed the time-series measurement of three-dimensional morphological changes in eggplant seedlings during the cotyledon stage, providing quantitative references for seedling growth assessment and superior plant selection.
Why it matches plant phenotyping methodsナス幼苗の3D点群から形質を抽出する非破壊フェノタイピング手法を開発し、比較実験・アブレーションで技術性能を検証しているため、方法が中心的である。
abstractthis study proposes a non-destructive phenotyping method for eggplant seedlings
Background/Objectives: One of the most widely produced and consumed crops in the world, tomatoes are often threatened by various leaf diseases, including early blight, late blight, and leaf mold, which can result in significant yield losses if not detected and managed promptly. Due to reliance on manual inspection, tomato leaf diseases are often detected late, resulting in significant crop loss. The goal of this research is to develop a deep learning (DL) model that accurately classifies tomato leaf diseases. The model is trained using supervised learning on the PlantVillage dataset, which includes labelled images of tomato leaves under different conditions. Method: Gaussian Blurring-Gaussian Mixture Models (GB-GMM) are a preprocessing method used to enhance the image quality. While EfficientNet and VGGNet architectures are utilised for accurate classification, data augmentation is used to boost model robustness. A standard train-validation-test split is used to assess the model. Findings: The results demonstrate that the proposed method, implemented in Python, performs better than the others in terms of accuracy (94.3%), precision (93.7%), recall (92.5%), and F1-score (93.1%) in VGGNet architectures. These results indicate that the proposed model is very effective overall and produces balanced predictions. In the future, the system may be integrated with drone and IoT technologies for automatic disease warnings and real-time field surveillance. Novelty: The Multivariable Grey Prediction Evolution Algorithm (MGPEA) is included for illness trend forecasting to enhance predictive power further. This technology facilitates large-scale, sustainable agricultural management, minimizes human inspection, and enables prompt disease response. Keywords: Tomato Leaf Diseases, Crop Health, Gaussian Mixture Models, Multivariable Grey Prediction Evolution Algorithm, EfficientNet, VGGNet
Why it matches plant phenotyping methodsトマト葉画像から病害状態を分類する深層学習手法の開発・評価が研究の中心であり、植物の病害表現型を直接推定している。
abstractThe goal of this research is to develop a deep learning (DL) model that accurately classifies tomato leaf diseases.
Plant factories have evolved from automated cultivation facilities into data-driven crop production systems. Over the last decade, artificial intelligence has been applied to non-destructive crop monitoring, sensor correction, nutrient-solution diagnosis, growth prediction, environmental control, digital twins, and product-level inspection. This review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces. The main argument is that plant-factory AI should not be understood only as image-based phenotyping; practical systems require an integrated intelligence stack connecting visual perception, sensor calibration, nutrient modeling, control, remote operation, and industrial inspection. Remaining challenges include dataset scarcity, model generalization, sensor drift, explainability, energy-aware control, and closed-loop decision-making.
Why it matches plant phenotyping methods植物工場におけるAI技術レビューで、非破壊的な作物モニタリング、機械視覚、深層学習、センサー補正など、植物形質取得に関わる方法を中心的に扱っている。
abstractThis review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces.
Abstract The effective optimization of tomato pruning robots was hindered by the lack of accurate simulation models for the shearing process of tomato stems and precise calibration and optimization methods for bonding parameters to predict shearing force. This paper proposed a simulation model along with a bonding parameter calibration method. Taking shearing force as the evaluation metric, a two-level factorial experiment was conducted to screen for significant parameters. A steepest ascent experiment was employed to determine the optimal range of these significant parameters. Then a Box-Behnken design was implemented, and the optimal combination of bonding parameters was derived based on the established regression model. Finally, comparative experiments were conducted to validate the simulation's shearing performance under this optimal parameter set. The results show that the optimal combination for the tomato stem model bonding parameters was a normal stiffness x 3 = 2.05×10⁸ N·m⁻³, tangential stiffness x₈=1.62×10⁸ N·m⁻³, and a bonding radius x₂₁=3.26×10⁻⁴ m. The optimized model reduced the shearing force simulation error by 75.8 and 43.7 percentage points compared to the traditional and pre-optimization models. These results demonstrate that the calibrated parameters of the simulation model are accurate and reliable. It can provide valuable parameters for optimizing the design of a tomato pruning robot.
Why it matches plant phenotyping methodsトマト茎のせん断力を推定する離散要素シミュレーションモデルと結合パラメータ校正法を開発し、実験で性能検証しており、植物器官の測定・推定手法が中心である。
abstractThis paper proposed a simulation model along with a bonding parameter calibration method.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Accurate seed-yield prediction is essential for optimizing nitrogen (N) management in buffalograss seed production. However, current UAV-based approaches often rely directly on vegetation indices (VIs), which provide limited physiological insight and not transfer well across growing seasons. To address this limitation, we developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC), representing crop structural and physiological status, respectively. Field experiments were conducted from 2022 to 2024 under seven N application rates. Using data from 2022 and 2023, we calibrated a quadratic PH-CNC model and then evaluated its predictive performance with an independent 2024 dataset. We also compared this framework with a conventional direct VI-based model. The trait-based model explained 89% of the variation in seed yield during calibration and showed better cross-year predictive performance than the VI-based model (R 2 = 0.70, NRMSE = 17% versus R 2 = 0.52, NRMSE = 22%). In addition, the model captured the decline in seed yield under excessive N input, indicating that it reflected biologically meaningful crop responses. These results demonstrated that combining structural and physiological traits can provide a more robust and interpretable alternative to conventional VI-based methods for UAV-based yield prediction. This framework has practical potential for improving precise and sustainable N management in buffalograss seed production.
Why it matches plant phenotyping methodsUAV由来の草高と群落窒素濃度を統合した形質ベース予測法を開発し、独立年データで検証・従来法と比較しており、表現型取得と解析手法が中心である。
abstractwe developed a trait-based yield prediction methold that integrates UAV-derived plant height (PH) and canopy nitrogen concentration (CNC)
Three-dimensional (3D) phenotyping of plants plays a crucial role for understanding plant growth, yield prediction, and disease control. We present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants. To acquire data, a small commercially available Unmanned aerial vehicle (UAV) captures images of a selected plant. Apart from placing ArUco markers, the entire image acquisition process is fully autonomous, controlled by a self-developed Android application running on the drone's controller. The reconstruction task is particularly challenging due to environmental wind and downwash of the UAV. Our proposed pipeline supports the integration of arbitrary state-of-the-art 3D reconstruction methods. To mitigate errors caused by leaf motion during image capture, we use an iterative method that gradually adjusts the input images through deformation. Motion is estimated using optical flow between the original input images and intermediate 3D reconstructions rendered from the corresponding viewpoints. This alignment gradually reduces scene motion, resulting in a canonical representation. After a few iterations, our pipeline improves the reconstruction of state-of-the-art methods and enables the extraction of high-resolution 3D meshes.
Why it matches plant phenotyping methodsUAV画像から植物個体の高解像度3D形状を再構成する手法を開発しており、植物フェノタイピングの取得・抽出方法が研究の中心である。
abstractWe present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants.
Tree barriers are the key factors of current transmission line failures, early discovery of tree barriers hidden dangers, the implementation of forest clearing tasks on transmission lines is the current power inspectors need to pay attention to the key issues. The article is based on the inclined photogrammetry technology to obtain the three-dimensional data of transmission lines, and construct a real-time database to realize the standardized management of three-dimensional data. Then the Mean Shift algorithm is used to preprocess the remote sensing data, and the forest diameter measurement system and transmission line forest clearing program are designed. QDN Power Supply Bureau was selected as a research sample to verify the effectiveness of the application of the above methods. The study showed that the RMSE of the breast diameter monitoring results ranged from 4.30% to 5.05%, and the reduction of forced outage rate of transmission lines of 110kV and above in the power grid could be up to 72.28%, and the overall work efficiency was improved by about 5.14 times. Therefore, actively realizing the optimization of transmission line forest clearing tasks can ensure the stable operation of transmission lines and provide basic support for ensuring the power supply of the grid.
Why it matches plant phenotyping methods傾斜写真測量とMean Shift処理により樹木の胸高直径を推定する測定システムを構築し、RMSEで検証しており、植物形質の取得・評価が実質的な方法貢献として含まれる。
abstractThe article is based on the inclined photogrammetry technology to obtain the three-dimensional data of transmission lines, and construct a real-time database to realize the standardized management of three-dimensional data.
To address the low efficiency of manual inspection for corn sowing quality, which is labor-intensive and time-consuming, this study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision. A high-clearance mobile platform equipped with a ZED 2i stereo camera and an industrial computer was developed to acquire RGB images and depth information of corn seedlings in the field in real time. Using the YOLOv11-Pose model, plant keypoints were detected and localized; combined with camera calibration and 3D reconstruction techniques, inter-plant distances were computed automatically. A sowing-quality evaluation framework was then established to enable automated analysis of the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation. Experimental results indicate that the system operates effectively under three preset plant spacings (15 cm, 20 cm, and 25 cm), achieving a keypoint-detection mAP@0.5 of 0.990 and an mAP@0.5:0.95 of 0.989. In sowing-quality evaluation, the qualified indices produced by the system were 77.83%, 80.36%, and 82.46%, respectively, and the Quality of Feed Index (QFI), Multiple Index (MUL), Miss Index (MI), and coefficient of variation showed trends consistent with manual measurements. The proposed method enables efficient, nondestructive detection of seedling-stage plant spacing and sowing quality, providing reliable technical support for precision sowing and field management.
Why it matches plant phenotyping methods3Dマシンビジョン、姿勢検出、校正、3D再構成を組み合わせ、トウモロコシ個体間距離を自動推定・検証する手法が研究の中心である。
abstractthis study proposes an automatic seedling-stage plant-spacing measurement method based on three-dimensional machine vision.
The essential economic contribution of agriculture to developing nations helps maintain food security which serves as the foundation of their economic systems. Farmers face a major difficulty because they need to identify crop diseases at an early stage through accurate methods because these diseases will cause major crop losses if they remain undetected. The existing methods for detecting diseases require experts to conduct manual inspections which develop into a process that consumes excessive time and incurs high costs while becoming unsuitable for implementation in extensive agricultural operations. The project proposes a Crop Disease Detection System which uses Deep Learning techniques as a solution to these existing challenges while supporting the Sustainable Development Goals 2 Zero Hunger. The system uses Convolutional Neural Networks (CNNs) for automatic detection and classification of crop diseases through its analysis of leaf images. The dataset includes images of healthy and diseased crop leaves which researchers obtained from both public databases and real-world environments. The images undergo preprocessing through three steps which include resizing and normalization and augmentation to achieve model accuracy and robustness improvements. The proposed solution supports sustainable farming through its early disease detection capabilities and precision agriculture functions which lead to better crop yields and decreased food shortages. The project shows how deep learning functions as an effective agricultural tool while demonstrating how artificial intelligence enables sustainable solutions which help achieve the zero-hunger objective.
Why it matches plant phenotyping methods葉画像から作物の健全・罹病状態をCNNで自動検出・分類する方法が研究の中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractThe project proposes a Crop Disease Detection System which uses Deep Learning techniques as a solution to these existing challenges
Conventional approaches to measuring stomatal conductance (gs) and transpiration often rely on instruments that interfere with plant physiology. Porometers, for example, restrict natural leaf movement, apply pressure, and introduce dry airflow that can alter stomatal behaviour, thereby reducing the relevance of such measurements. Prior studies report discrepancies among devices attributable to such interferences (Toro et al. 2019). To minimise artefacts, transpiration should be estimated remotely without physical contact, which theoretically can be achieved via a thermal leaf energy-balance approach that infers gs from leaf temperature, radiative load, and boundary-layer terms. In this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely. Approaches to estimate stomatal conductance based on the energy-balance equation were developed recently to aid phenotyping of plantss. Most methods either imposed rapid changes in air humidity to perturb transpiration and, consequently, leaf temperature (Driever et al. 2023), or relied on ‘dry’ and ‘wet’ reference surfaces (as in Leinonen et al. 2006) to compute stress indices (Vialet-Chabrand and Lawson 2020). These methods require reference materials to assess surface temperatures under maximum and zero transpiration, showing the effect of longwave radiation. However, reference-material methods were constrained by heterogeneity in light interception caused by variation in leaf angle and orientation, because reference surfaces could not reorient like real leaves (Zhang et al. 2025). In this study, we addressed this challenge by using thermal imaging and 3D photogrammetry to capture leaf temperature and geometry noninvasively, allowing parameter estimation for each leaf individually. Here, ρ is the density of air (kg m−3), cp is the specific heat capacity of air (J kg−1 K−1) and rHR is the parallel resistance to heat and radiative transfer on the leaf surface (s m−1), s is the slope of the curve relating saturating water vapour pressure to temperature (Pa °C−1). TL and TA are leaf and air temperatures (°C), respectively, δe is air vapour pressure deficit (Pa), γ is the psychrometric constant (Pa K−1) and rva is the boundary layer resistance to water vapour (s m−1) (Supporting Information S2: Equation S1). The net radiative energy Rn in the energy-balance term was obtained from the same 3D light interception model, which integrates measured direct and lateral scattered irradiance (W m−2) (Supplement Material and Methods, File S2). Stomatal conductance gs (m s−1) is the inverse of stomatal resistance rs (s m−1). To experimentally obtain a wide range of gs values, we grew eggplant (Solanum melongena L.) plants in hydroponic units in growth chambers under four sets of environmental conditions (Table 1). Thirty-day-old plants (4–5-leaf stage) were placed on balances (Supplementary Materials and Methods, File S2). Units were sealed with plastic film to minimise evaporation. Mass loss attributable to transpiration was logged automatically every 30 s. To induce short-term changes in stomatal conductance, we imposed an acute osmotic stress by delivering a saline NaCl solution with high electrical conductivity (60 mS cm−1) to the root zone, producing a steep drop in root osmotic potential. This created rapid physiological and morphological responses that altered incident irradiance at the leaves, leaf temperature, and consequently energy balance, stomatal conductance and transpiration. We chose this stressor for operational simplicity. Any perturbation that modifies transpiration dynamics and thus gas exchange could have served our purpose. The total transpiration of a leaf, Et (kg s−1), is the product of the total conductance to water vapour from the mesophyll to the atmosphere, gv (m s−1), calculated from the estimated stomatal resistance rs (s m−1) and the boundary layer conductance gva (m s−1), the difference between water vapour concentration inside the leaf Cvs (dimensionless), and in surrounding air Cva (dimensionless), the leaf area A (m2), and the density of water ρw (kg/m3) (Jones 1992). Estimated stomatal conductance was obtained from leaf energy balance calculation (Equation 1). Boundary-layer conductance was computed from measured wind speed and leaf dimensions (leaf area, length, width) extracted from structure-from-motion 3D reconstructions (Supporting Information S1: Equation S6; Grace et al. 1980). Transpiration was then calculated for each leaf at each thermal 3D imaging time point, and whole-plant transpiration for comparison with gravimetric logs was the sum of all per-leaf estimates. As a non-invasive approach, we evaluated the plausibility or our model derived stomatal conductance (Equation 2) indirectly by comparing calculated and measured whole plant transpiration. We emphasise that this is not a direct validation of gs. Rather, the close agreement between modelled and measured transpiration across the wide range of environmental treatments, both stressed and non-stressed, provides confidence that the inferred gs is realistic. RGB and thermal images acquired before, during, and after stress application enabled dynamic tracking of leaf position and temperature (Supplementary Material and Methods, File S2). As expected, osmotic stress application had immediate effects on morphology and physiology. While control leaves maintained an angle of around 110° throughout, osmotic shock induced immediate turgor loss and drooping in all environments except one (Figure 1A,B). Leaf angles recovered to pre-stress positions within 1 h, indicating adaptation to the osmotic shock and restoration of turgor. Only environment 4 (high light, low air temperature and low humidity) maintained turgor during stress. Angle shifts were most pronounced in older leaves, which drooped and reduced light interception; younger leaves better preserved structure and turgor (Supporting Information S1: Figure S2). These angle changes also altered incident irradiance at the leaf surface (Supporting Information S1: Figure S3). These morphological responses coincided with increases in leaf temperature, consistent with altered water fluxes and stomatal regulation after stress. Across environments, plants showed a uniform rise in leaf temperature following osmotic stress, regardless of initial temperature (Supporting Information S1: Figure S4). This response held across leaf ages, encompassing older (Figure 1C) and younger (Figure 1D) leaves. Stomatal conductance estimated with our method followed the same pattern, dropping rapidly after osmotic shock in both older (Figure 1E) and younger (Figure 1F) leaves (Supporting Information S1: Figure S5). We estimated no stomatal conductance recovery to pre-stress conditions over the time course of stress exposure. Model-estimated and gravimetrically measured transpiration showed identical time courses across all four environmental conditions (Figure 1G–J). Transpiration rates did not recover to the same extent as leaf turgor, indicating long-term effects of the osmotic shock. Across environments and time points, correlation between model estimated and measured whole-plant transpiration was high (Figure 1K). In this study, stomatal conductance (gs) is a model-derived quantity inferred from the same physically constrained framework and model (leaf temperature, boundary-layer conductance and vapour pressure deficit). Since we did not measure gs directly, we cannot validate gs directly. Instead, we used a non-invasive check via transpiration. Model predictions closely tracked measured transpiration across the four controlled environments. This agreement increases confidence that the inferred gs is realistic, while we acknowledge that transpiration agreement alone is not a rigorous validation and cannot fully rule out compensating errors. Our study demonstrated the potential of our approach to estimate transpiration accurately by combining 3D imaging and thermography with physiological modelling without the use of reference materials that imitate real leaves. This remote approach enables simultaneous assessment of morphological and physiological responses to stress, yielding a more integrated view on plant transpiration and gas exchange. In contrast to chamber and porometer measurements or IR methods requiring wet and dry references or calibration plates, our workflow is reference-free. Absorbed shortwave radiation is derived from measured irradiance and a 3D reconstruction of leaf geometry, with no external reference materials. Moreover, remote measurements avoid continuous pressure from clamp-on porometers, permitting long-term observation and capture of rapid stress responses without sustained damage or microclimate artifacts. Further, the approach is not limited by any clamp on sensors and as such enables multi-leaf tracking. Applied to crop canopies, this approach could improve understanding of canopy processes that influence productivity and enable remote estimation of canopy transpiration. Future research could further improve by replacing our strong saline solution stress by gradual soil drying to depict a more realistic and natural stress while testing the approach under long-term conditions. Recent studies indicate that, with rising atmospheric CO2 concentrations, breeding for reduced stomatal conductance could increases WUE without affecting photosynthetic capacity (Srivastava et al. 2024). As such, remote systems for high-throughput plant phenotyping (HTP) are required to scan vast quantities of plants. We see a potential use of our system for such purposes to quickly estimated whole plant and individual leaf transpiration, as initial image capturing is very fast. A large bottleneck in our work was 3D model generation speed and manual extraction of leaf parameters from these 3D models. Both could be streamlined with more automated software, possibly including neural network solutions. The authors have nothing to report. The authors declare no conflict of interest. The data that support the findings of this study are available from the corresponding author upon reasonable request. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Why it matches plant phenotyping methods3D画像、熱画像、光遮断モデル、エネルギーバランスモデルを統合し、葉ごとの蒸散と気孔コンダクタンスを非侵襲的に推定する手法を開発・評価しており、植物表現型取得が研究の中心である。
abstractIn this study, we combine 3D plant models, light interception models, and thermal imaging to solve the energy-balance equation of individual leaves, estimating transpiration entirely remotely.
The cultivation of apples is affected by various apple plant diseases. These diseases, if not identified and treated on time, may lead to considerable losses in yield. Early detection is highly essential in order to provide early warnings to farmers and to help in identifying diseases at an early stage so that further action can be done to prevent the spread of disease as these diseases cannot be identified through naked eyes in their early stages. This leads to less wastage of yield. This paper proposes a comparison among the deep learning models such as LetNet, AlexNet, VGG, Resnet, Inception Net, and DensNet, for the efficient classification of leaf diseases of the apple plant. The model is trained on the Plant Village Dataset (Updated) taken from kaggle, which contains both healthy and diseased leaf images of apple plants. In this, images go through various preprocessing techniques like resizing, normalizing, and augmenting images in order to increase the robustness of the model. AlexNet attained the maximum classification accuracy in initial trials but had the largest number of parameters, didn't use modern regularization, and hence got at risk of overfitting at some early stage. The paper improved their performance by using a hybrid architecture which consisted of MobileNetV3 and ResNet50 because MobileNetV3 offered efficient extraction of features with little computational expense. It was further complemented by the depth features offered by ResNet50. The main aim for proceeding with the idea of hybrid architecture was not only to improve generalization but also to prevent overfitting. The hybrid model is implemented using streamlit. This web interface allows the users to upload images of leaves and get real-time results predicting whether the leaves are affected by a disease or not. The system demonstrates high classification accuracy and effective differentiation among visually similar diseases. However, the model's performance in terms of empirical data analysis is influenced by dataset quality, computational resource demands, and its limited ability to generalize in the presence of sparse data. Despite these challenges, the proposed solution provides a scalable and accessible tool to assist farmers and agricultural experts in early disease detection and management.
Why it matches plant phenotyping methodsリンゴ葉の画像から病徴・病害状態を推定する画像ベースの植物フェノタイピング手法を開発・比較しており、分類モデルと実装が研究の中心である。
abstractThis paper proposes a comparison among the deep learning models such as LetNet, AlexNet, VGG, Resnet, Inception Net, and DensNet, for the efficient classification of leaf diseases of the apple plant.
Unmanned aerial vehicles (UAVs) have become indispensable tools in precision agriculture and plant phenotyping, enabling the rapid, non-destructive assessment of crop traits across space and time. Equipped with RGB, multispectral, thermal, and other sensors, UAVs provide detailed information on canopy structure, physiology, and stress responses that can guide management decisions and accelerate breeding programs. Despite these advances, the downstream processing of UAV imagery remains technically demanding. Converting orthomosaics into standardized, biologically meaningful data often requires a combination of photogrammetry, geospatial analysis, and custom scripting, which can limit reproducibility and accessibility across research groups. We present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports, supporting both research and applied crop breeding needs. Alongside the basic structure and functioning of drone2report, we also present five case studies that illustrate practical applications common in UAV-/drone-phenotyping of plants: (i) thresholding to remove background noise and highlight regions of interest; (ii) monitoring plant phenotypes over time; (iii) extracting information on plant height to detect events like lodging or the falling over of spikes; (iv) integrating multiple sensors (cameras) to construct and optimize new synthetic indices; (v) integrate a trained deep learning network to implement a classification task. These examples demonstrate the tool’s ability to automate analysis, integrate heterogeneous data and models, and support reproducible computation of agronomically relevant traits. drone2report streamlines orthorectified UAV-image processing for precision agriculture by linking orthomosaics to standardized, plot-level outputs. Its modular, configuration-driven design allows transparent workflows, easy customization, and integration of multiple sensors within a unified analytical framework. By facilitating reproducible, multi-modal image analysis, drone2report lowers technical barriers to UAV-based phenotyping and opens the way to robust, data-driven crop monitoring and breeding applications.
Why it matches plant phenotyping methods植物表現型取得のためのUAV画像処理ソフトウェアを開発し、植物高・倒伏などの形質抽出、マルチセンサー統合、再現可能な解析ワークフローを中心的に提示している。
abstractWe present drone2report, an open-source python-based software that processes orthomosaics from UAV flights to generate vegetation indices, summary statistics, derived subimages, and text (html) reports
Reproduction assets foundThe paper explicitly states that the code and data to reproduce its five case studies (thresholding, temporal vegetation indices, height analysis, multi-sensor index optimization, deep learning classification) are publicly available in the authors' GitHub repository, and the DRONE2REPORT software itself is released as Code · publicThe code and data to reproduce these case studies
can be found at https://github.com/ne1s0n/paper-drone2report (accessed on 13 April
2026).Open asset ↗ne1s0n/paper-drone2reportpdf-page:6 lines:1-59Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 5 Sept 2026
Three-dimensional seed phenotyping requires imaging systems capable of achieving micron-level resolution across a centimeter-level field of view (FOV), a goal constrained by the resolution–FOV trade-off in conventional light field architectures. This paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation. At the hardware level, we develop a tunable-focus lens module that enables flexible adjustment of the effective focal length, combined with a custom-designed microlens array (MLA). A mathematical model is established to analyze the interdependencies among FOV, lateral resolution, depth of field (DOF), and system configuration, guiding the design of individual optical components. On the computational side, we propose a hybrid aberration correction strategy: first, a co-calibration of lens and MLA aberrations based on line-feature detection; second, a conditional generative adversarial network (cGAN) with attention-guided residual learning to enhance sub-aperture images, achieving a PSNR of 34.63 dB and an SSIM of 0.9570 on seed datasets. Experimentally, the system achieves a resolution of 6.2 lp/mm at MTF50 over a 2–3 cm FOV, representing a 307% improvement over the initial configuration (1.52 lp/mm). The reconstruction pipeline combines epipolar plane image (EPI) analysis with multi-view consistency constraints to generate dense 3D point clouds at a density of approximately 1.5 × 104 points/cm2 while preserving spectral and textural features. Validation on bitter melon and rice seeds demonstrates accurate 3D reconstruction and accurate extraction of morphological parameters across a large area. By integrating optical and computational design, this work establishes a reconfigurable imaging framework that overcomes the resolution–FOV limitations of conventional light field systems. The proposed architecture is also applicable to robotic vision and biomedical imaging.
Why it matches plant phenotyping methods種子の3D形態形質を取得する光学・計算イメージングシステムの開発と検証が研究の中心であり、フェノタイピング手法として明確に適格。
abstractThis paper presents a hardware–software co-optimized framework that integrates a reconfigurable optical system with computational imaging pipelines to address this limitation.
In this study, a hybrid architecture that combines the feature-extraction capability of CNN and the classification power of RF is proposed to focus on the correct detection of plant diseases, which is Convolutional Neural Network-Random Forest (CNN-RF). Data acquisition and preprocessing, which consisted of image normalization, augmentation, and resizing to make sure that the models could fit the data and enhance generalization, started with the methodology. The CNN element was trained to automatically learn discriminative features on the plant leaf images, which were then inputted into an RF classifier which was optimized by hyperparameter optimization. The performance measurement utilized conventional measures, such as accuracy, precision, recall, and F1-score and the Receiver Operating Characteristic (ROC) curve analysis. It has been proven by experimental results that the hybrid CNN-RF model is better than the standalone CNN model and RF model. The proposed model attained an accuracy of 96.3, precision of 95.8, recall of 96.7 and F1-score of 96.2, which was better than CNN (93.5% accuracy) and RF (88.4% accuracy) baselines. The tuning of hyperparameters was demonstrated to be of great benefit to the outcomes of classification as illustrated in the tuning heat map. The hybrid model had a close Area Under the Curve (AUC) of 1.0 on the ROC curve, which is ideal sensitivity and specificity.
Why it matches plant phenotyping methods植物葉画像から病害を識別するCNN-RF手法の開発と性能比較が研究の中心であり、植物の病害状態を直接推定しているため。
abstracta hybrid architecture that combines the feature-extraction capability of CNN and the classification power of RF is proposed to focus on the correct detection of plant diseases
Rice (Oryza sativa) is a vital global food crop, despite its importance, rice production is often hindered by leaf diseases, such as Leaf Scald and Bacterial Leaf Blight, which can significantly reduce yields. Conventional disease diagnosis techniques are often error-prone and inefficient. To address this, A deep learning-based approach using EfficientNetB4 architecture was proposed and combined with a Gaussian filter for enhanced image preprocessing. The Gaussian filter reduces noise and enhancing image, while EfficientNetB4 leverages its optimized depth, width, and resolution scaling for accurate classification. The dataset consists of 2,627 rice leaf images categorized into six classes and divided into training, validation, and testing. Preprocessing includes resizing images to$224 \times 224$pixels, data augmentation, and Gaussian filtering with$5 \times 5$kernel and standard deviation value is 1. The model is evaluated using$\text{F 1}$-score, precision, recall, and accuracy. Results demonstrate that EfficientNetB4 with Gaussian filtering achieves 97.62 % accuracy, outperforming the unfiltered model 96.19 %. This highlights the efficacy of Gaussian filtering in improving feature extraction and classification performance for rice leaf diseases.
Why it matches plant phenotyping methodsイネ葉の病害状態を画像から分類する深層学習手法が中心であり、画像前処理と分類性能を評価しているため、植物病害フェノタイピング手法として含める。
abstractResults demonstrate that EfficientNetB4 with Gaussian filtering achieves 97.62 % accuracy, outperforming the unfiltered model 96.19 %.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 15 Sept 2026
Introduction Reliable identification of plant diseases from leaf images is essential for effective crop monitoring and the prevention of yield deterioration. With the growing adoption of deep learning in agricultural applications, convolutional neural network–based classifiers have demonstrated notable success in visual plant disease recognition. Methods In this study, we propose PlantPathNet , a purpose-built deep learning architecture for plant disease classification. Input images are transformed from the RGB to the HSV color space to enhance the representation of disease-related visual features. A novel Cross-layer Feature Integration Module (CFIM) is introduced to effectively aggregate discriminative features across multiple network depths. Additionally, an efficient channel attention mechanism based on ECANet is incorporated to emphasize disease-relevant representations. The model is optimized using a composite loss function combining modified softmax loss and center loss to address class imbalance and improve feature separability. Results Extensive experiments conducted on the PlantVillage dataset demonstrate that PlantPathNet outperforms several state-of-the-art models, including ResNet-50, Inception-V3, DenseNet121, VGG16, and Vision Transformer-based approaches. The proposed model achieves an overall accuracy of 99.57%, precision of 99.52%, recall of 99.54%, F1-score of 99.53%, and an AUROC of 99.84%. Discussion The results indicate that the integration of HSV-based preprocessing, CFIM, and channel attention significantly enhances classification performance. The proposed framework provides a robust and efficient solution for automated plant disease diagnosis and has strong potential for real-world agricultural applications.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する深層学習手法を新規開発し、既存モデルとの比較検証も行っているため、植物フェノタイピング手法が中心である。
abstractwe propose PlantPathNet , a purpose-built deep learning architecture for plant disease classification.
Near- and short-wave infrared spectroscopy has become increasingly relevant for the assessment of plant composition, yet the reproducibility of chemometric models across different instruments remains a major limitation. This work proposes a methodological approach for calibration transfer from a benchtop hyperspectral imaging (HSI) system to a portable SWIR spectrometer, using powdered grapevine leaves as a controlled case study. The strategy relies on Direct Standardization (DS), a technique that transforms spectra acquired under different conditions into a common space. By applying DS to a representative subset of samples, models trained under laboratory conditions can be adapted to spectra acquired with portable devices. Two complementary modelling strategies were adopted: an Error-Correcting Output Codes Support Vector Machine (ECOC-SVM) classifier, used to assess qualitative improvements in class discrimination before and after DS, and an eXtreme Gradient Boosting (XGB) regression model, developed for quantitative prediction of elemental concentrations measured by micro-XRF. Validation on an independent set of homogenized leaf powders confirmed that DS markedly reduced inter-instrument spectral divergence, improving class separability and enabling accurate regression of macro- and micronutrients (Fe, Mn, P, S, Zn, Ca, K, Si). Although limited to laboratory-scale samples, the study demonstrates that calibration transfer is effective in harmonizing spectral domains. The proposed workflow provides a reproducible and scalable methodology for cross-instrument adaptation, with potential applicability to diverse agricultural products and portable spectroscopy platforms.
Why it matches plant phenotyping methods植物葉のスペクトルから元素濃度を推定する測定ワークフローを対象に、装置間キャリブレーション転移を開発・検証しており、植物形質取得法が研究の中心である。
abstractThis work proposes a methodological approach for calibration transfer from a benchtop hyperspectral imaging (HSI) system to a portable SWIR spectrometer, using powdered grapevine leaves as a controlled case study.
The analysis of plant growth and development in controlled agricultural environments has become increasingly important to ensure sustainable and efficient food production. Traditionally, plant monitoring relied on manual observations and basic statistical approaches, which provided limited insights into the complex interactions between environmental factors and plant physiology. To address these challenges, this study proposes a machine learning–driven analytical framework designed for comprehensive plant development analysis. The system integrates data preprocessing, exploratory data analysis, classification, regression, and hybrid deep learning approaches within a unified pipeline. Classification algorithms such as Support Vector Classifier (SVC), Bernoulli Naive Bayes (BNC), and Multinomial Naive Bayes (MNC) are employed to identify plant growth stages. Regression models, including Decision Tree Regressor (DTR), Support Vector Regressor (SVR), and Ridge Regressor (RR), are utilized to estimate growth-related parameters. Furthermore, two hybrid models are introduced to enhance predictive performance. The Deep Feature Probabilistic Classifier (DFPC) combines Feed Forward Neural Networks (FFNN) with Gaussian Naive Bayes (GNB) for improved classification accuracy, while the Hybrid Deep Ridge Predictor (HDRP) integrates FFNN with Ridge Regressor to achieve precise regression outcomes. Experimental results demonstrate that the DFPC model attains an accuracy of 94.42%, whereas the HDRP model achieves an R² score of 0.999. These findings highlight the effectiveness of combining deep learning and machine learning techniques for accurate plant growth analysis and informed decision-making in controlled agricultural systems.
Why it matches plant phenotyping methods植物の成長段階と成長関連パラメータを推定する機械学習・深層学習パイプラインが研究の中心であり、表現型推定手法の開発に該当する。
abstractClassification algorithms such as Support Vector Classifier (SVC), Bernoulli Naive Bayes (BNC), and Multinomial Naive Bayes (MNC) are employed to identify plant growth stages.
Multispectral / hyperspectralLeafCalibration / preprocessingSegmentationVisualization / data management
Hyperspectral imaging (HSI) allows researchers to study plant traits non-destructively. By capturing hundreds of narrow spectral bands per pixel, it reveals details about plant biochemistry and stress that standard cameras miss. However, processing this data is often challenging. Many labs still rely on loosely organized collections of lab-specific MATLAB or Python scripts, which makes workflows difficult to share and results difficult to reproduce. MVOS_HSI is an open-source Python library that provides an end-to-end workflow for processing leaf-level HSI data. The software handles everything from calibrating raw ENVI files to detecting and clipping individual leaves based on multiple vegetation indices (NDVI, CIRedEdge and GCI). It also includes tools for data augmentation to create training-time variations for machine learning and utilities to visualize spectral profiles. MVOS_HSI can be used as an importable Python library or run directly from the command line. The code and documentation are available on GitHub. By consolidating these common tasks into a single package, MVOS_HSI helps researchers produce consistent and reproducible results in plant phenotyping
Why it matches plant phenotyping methods葉レベルHSIの校正・葉検出・切り出しを含む再現可能な植物表現型解析用ソフトウェアであり、手法が中心。
abstractMVOS_HSI is an open-source Python library that provides an end-to-end workflow for processing leaf-level HSI data.
Reproduction assets foundThis is a software paper describing MVOS_HSI, the authors' open-source Python library for hyperspectral plant-phenotyping preprocessing (calibration, leaf segmentation/clipping, augmentation, spectral plotting). The authors' code is explicitly and publicly available on GitHub at the allowed URL, making it a paper-phenyCode · publicyping.
K eywords Hyperspectral imaging ⋅ \cdot
Plant phenotyping ⋅ \cdot
Data preprocessing ⋅ \cdot
Vegetation indices ⋅ \cdot
Data augmentation ⋅ \cdot
Python
Table 1: Code Metadata for MVOS_HSI
Nr.
Code metadata description
Metadata
C1
Current code version
v0.2.1
C2
Permanent link to code/repository used for this code version
https://github.com/MVOSlab-sdstate/mvos_hsi
C3
Permanent link to Reproducible Capsule
N/A
C4
Legal Code License
MIT License
C5
Code versioning system used
git
C6
Software code languages, tools, and services used
Python 3.x; NumPy, SciPy, Matplotlib
C7
Compilation requirements, operating environments & dependencies
Standard scientific Python environment on Windows, LinOpen asset ↗MVOSlab-sdstate/mvos_hsi · mvos_hsilines:1-122Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 15 Sept 2026
Contemporary agrobiotechnology research increasingly relies on automated methods for capturing and interpreting morphophysiological and spectral plant characteristics - a field known as digital phenotyping. This approach aims to identify stable differences between genotypes cultivated under non-identical environmental conditions. We previously introduced StatFaRmer, an open-source tool that we further develop here for comprehensive analysis of temporal phenotypic datasets, with a primary focus on crops such as soybean (Glycine max). The tool implements automated data preprocessing procedures, including synchronization of timestamps across samples and removal of noise artifacts and outliers. These features are particularly relevant for multi-month experiments involving assessments of growth parameters, fluctuations in photosynthetic apparatus area, or other biometric indicators. Support for standardized data formats (XLSX, CSV) ensures compatibility with common phenotyping systems, simplifying cross-platform integration. Thus, the tool can integrate with widely used HTPP platforms (e. g., Traitmill, HyperAIxpert, Plant Accelerator), enabling data from diverse sources to be analyzed within a single pipeline. For soybean experiments, StatFaRmer provides customizable analysis of variance (ANOVA) with visualization of diagnostic parameters (normality of distribution, homogeneity of variances) and evaluation of effect significance between user-defined groups. An example application compares growth parameters across 20 soybean cultivars under controlled stress: the tool automatically aggregated data with uneven measurement frequencies (from 1 hour to 3 days), identified anomalies in hypocotyl elongation dynamics, and computed statistical significance between groups (p < 0.01).The tool has been tested on large-scale datasets (over 2,000 measurements per experiment). StatFaRmer is implemented as a Shiny-based web application, with step-by-step deployment guides for Windows and Linux. All processing stages - from raw data to final plots - are documented to ensure transparency and compliance with research reproducibility standards. Thus, StatFaRmer offers a specialized solution for statistical hypothesis testing in soybean digital phenotyping, reducing data preparation time and minimizing risks of error when handling non-stationary time series.
Why it matches plant phenotyping methods植物デジタルフェノタイピング用の時系列解析ツールを開発・拡張し、前処理、異常値除去、統計解析、再現可能なワークフローを提供しているため、フェノタイピング手法が中心である。
abstractWe previously introduced StatFaRmer, an open-source tool that we further develop here for comprehensive analysis of temporal phenotypic datasets
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Field / plotPhotogrammetry / SfM / MVSRootWhole plant / canopy / plot / fieldCalibration / preprocessing2D/3D reconstructionRoot system architecture
Accurate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracyccu remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding in downstream analysis.
Why it matches plant phenotyping methods根の3D画像フェノタイピングにおけるカメラ校正・撮像条件を体系的に評価し、再構成精度と再現性を改善する技術指針を提示しており、フェノタイプ取得手法が研究の中心である。
abstractThis work improves the repeatability and accuracy of 3D root imaging for phenotyping pipelines by giving useful calibration guidelines.
Spectral sensing plays a crucial role in agriculture, environmental monitoring, and material characterization, providing non-destructive insights into chemical composition and physiological status of plants. Commercial spectrometers, however, are often expensive and lack modularity, which limits their adoption in educational, research and field applications. The developed open-source spectral measurement platform addresses this gap by offering a low-cost, customizable solution for reflectance analysis in plant leaves and diverse materials. The system integrates a Hamamatsu C12880MA miniature spectrometer with dual illumination: a white LED and a 3 mm incandescent bulb, covering 340-850 nm. Control and acquisition are managed by a Teensy 3.2 microcontroller through a Python interface that enables calibration and data storage. The modular enclosure, fabricated via RepRap-class fused filament-based 3D printing and PVC components, ensures flexibility and reproducibility. Validation tests demonstrated accurate wavelength alignment when exposed to laser sources, with peaks detected at 407 nm and 663 nm, and mean squared errors of 0.0131 and 0.0208, respectively. Ambient light comparisons with a commercial OSHP spectrometer yielded an MSE of 0.0105 ( ≈ 10%), indicating strong agreement. Reflectance measurements using a ColorChecker Classic confirmed consistency in 340-850 nm, validating the suitability of the device for plant reflectance analysis and material identification in both scientific research and educational settings.
Why it matches plant phenotyping methods植物葉の反射スペクトルを取得する低コスト計測プラットフォームを開発し、校正・商用機器比較で検証しており、植物フェノタイピング手法が中心である。
abstractThe developed open-source spectral measurement platform addresses this gap by offering a low-cost, customizable solution for reflectance analysis in plant leaves and diverse materials.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Grapevine cluster architecture is a key selection target in breeding programs because it influences disease susceptibility, yield stability and juice quality. High-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits, yet the influence of plant organ reflectance and data partitioning strategies on trait prediction remains poorly understood. In this study, we evaluated how hyperspectral reflectance from different grapevine organs contributes to the prediction of cluster architecture and juice quality traits in two clonal populations of Riesling and Pinot. Using partial least squares regression (PLSR), we assessed the prediction accuracy of eight cluster architecture and six juice quality traits under two data partitioning strategies. Models based on cluster reflectance outperformed those using dry leaf reflectance for most traits, except for pH. Partitioning the dataset by cluster type increased trait variance and improved predictions for number of berries (R² = 0.53), berry diameter (R² = 0.79), and total acidity (R² = 0.48). Visible, red-edge and NIR spectra were most informative regions to predict the traits studied. Together, our results highlight the importance of organ-specific data and appropriate calibration strategies to improve phenomic models for the development of scalable proxies for grapevine improvement. Highlight Spectral phenomics reveals that prediction accuracy in grapevine depends on organ spectral signatures and traits, with cluster reflectance outperforming leaves, informing new phenotyping strategies for breeding improvement.
Why it matches plant phenotyping methodsブドウの器官反射スペクトルとPLSRを用いて、房構造および果汁品質形質の予測性能を評価することが中心であり、スペクトル表現型解析手法の検証・応用に該当する。
abstractHigh-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits
Cotton productivity plays a crucial role in the global agricultural economy; however, various leaf diseases significantly threaten crop yield and fiber quality. Early and accurate disease detection is essential for effective crop management, yet traditional inspection methods are time-consuming, labor-intensive, and dependent on expert knowledge, often leading to inconsistent results. Conventional machine learning approaches also face limitations in real-world agricultural environments due to variations in lighting conditions, complex backgrounds, and similarities between disease symptoms. To address these challenges, this research proposes an intelligent framework called Cotton Plant Disease Identification Using ResMobNet with Attention-Guided Localization and Severity Analysis (CPDI-RMN). The proposed system integrates advanced image preprocessing, hybrid feature extraction, deep learning classification, and attention-based localization to create a comprehensive disease detection framework. Initially, cotton leaf images are collected from a comprehensive dataset and preprocessed through image resizing, noise removal, contrast enhancement, and Min–Max normalization to improve visual quality and ensure stable model training. Data augmentation techniques such as rotation, flipping, zooming, and brightness adjustment are applied to enhance dataset diversity and improve model robustness against overfitting. For feature enhancement, contour visualization and geometric feature representation are combined with texture analysis using the Gray-Level Co-occurrence Matrix (GLCM) and Laplacian filtering. The core of the framework is the ResMobNet hybrid architecture, which integrates ResNet-50, EfficientNet-B3, and MobileNet-V2 to capture multi-scale spatial and texture features while maintaining computational efficiency. Gradient-Weighted Class Activation Mapping (Grad-CAM) is employed to generate attention maps for disease localization, followed by segmentation to isolate infected regions. Disease severity is then quantified by calculating the percentage of infected leaf area and classifying it into mild, moderate, and severe categories. Experimental results using five-fold cross-validation demonstrate that the CPDI-RMN model achieves 98.85% classification accuracy, outperforming CNN, ANN, ResNet, and MobileNetV2 models. Additionally, the attention-based localization achieves 96.8% Intersection over Union and 98.0% Dice Score, indicating highly accurate disease region detection. Overall, the proposed framework provides a reliable and scalable solution for intelligent cotton disease monitoring and supports precision agriculture through data-driven crop management.
Why it matches plant phenotyping methodsワタ葉画像から病変領域を抽出し、感染面積率に基づいて病害重症度を定量化する画像解析手法が研究の中心であるため、植物フェノタイピング手法として含める。
abstractThe proposed system integrates advanced image preprocessing, hybrid feature extraction, deep learning classification, and attention-based localization to create a comprehensive disease detection framework.
Abstract Temporal hyperspectral imaging enables non-destructive monitoring of agricultural stress through spectral signatures evolving across extended observation periods, yet processing high-dimensional spatial-spectral-temporal sequences remains computationally prohibitive for real-time deployment. Traditional machine learning methods sacrifice temporal information through dimensionality reduction, while hybrid deep learning architectures combining convolutional and recurrent networks suffer from optimization pathologies at component boundaries. We introduce PSUMamba, a dual-path bidirectional Mamba architecture that processes 204-band hyperspectral sequences across eight timepoints through linear-complexity state space models, achieving 95.05% accuracy with 99.00% AUC-ROC using 153,268 parameters. The architecture maintains perfect specificity (100%) with 93.67% sensitivity while out-performing Vision Transformer with 39-fold fewer parameters and 37.5% reduced training time. Separate spectral and temporal pathways with adaptive fusion enable specialized biochemical and physiological feature extraction without quadratic attention overhead. Ablation studies confirm temporal features dominate classification under experimental conditions, with dual-path fusion providing superior probabilistic calibration (97.35% AUC) over single-path variants. Statistical comparisons demonstrate significant improvements over PLS-DA (∆=12.07%, p=0.0001), 3D CNN (∆=15.48%, p=0.0042) and 1D CNN-LSTM (∆=33.77%, p
Why it matches plant phenotyping methods植物ストレス状態を時間分解ハイパースペクトル画像から推定する計算・センシング手法を開発し、既存手法との比較およびアブレーションで検証しているため、植物フェノタイピング手法が中心である。
titlePSUMamba: Dual-Path Bidirectional Mamba for Plant Stress Monitoring via Temporal Hyperspectral Imaging
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
3D plant phenotyping has garnered significant interest for its ability to quantify key structural traits such as plant volume and canopy architecture. However, standard monocular 3D reconstruction techniques suffer from inherent scale ambiguity, requiring an additional step to recover the true metric scale of the plants. Existing scale recovery methods, whether based on precisely fabricated 3D objects or planar patterns such as checkerboards, have been successfully applied in controlled environments but face practical constraints in certain real-world scenarios: some require costly fabrication or pre-reconstruction calibration, which can limit throughput in dynamic field environments. Here, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach that addresses these specific constraints and provides a complementary solution for high-throughput, mobile, and field-based phenotyping. MagicRing features a simple red ring printed on A4 paper with a known diameter. By leveraging color-based segmentation and geometric curve fitting, our approach automatically detects the ring within 3D point clouds, recovers the metric scale, and establishes a standardized world coordinate system without the need for pre-calibration. Its planar, isotropic design ensures robustness even under significant occlusion. We demonstrate the utility of MagicRing through MobilePheno3D, an integrated smartphone-based pipeline that performs fully automated 3D reconstruction, scale recovery, and phenotypic extraction from video sequences. This system, which was validated across multiple plant species, including vegetables, wheat, rice, and maize in both indoor and field settings, reliably reconstructs aboveground and root structures and supports continuous growth monitoring. MagicRing decouples data collection from data analysis, enabling a workflow transition from conventional step-by-step, scene-specific calibration toward more scalable, high-throughput 3D plant phenotyping.
Why it matches plant phenotyping methods植物の3D形態形質を抽出するためのスケール復元法とスマートフォン型フェノタイピング・パイプラインを開発し、複数植物種・環境で検証しており、手法が研究の中心である。
abstractHere, we present MagicRing, a novel, affordable, and physically reliable post-reconstruction scale recovery approach
Agriculture plays a significant role in the economic development of many countries. Plant diseases severely affect crop productivity and quality, leading to economic loss for farmers. Early and accurate disease detection is essential to improve yield and ensure food security. This paper proposes a deep learning-based plant disease detection system using Convolutional Neural Network (CNN). The system classifies leaf images into healthy and diseased categories. The proposed model performs image preprocessing, feature extraction, and classification to provide accurate predictions. Experimental results show that the model achieves high accuracy across multiple plant species. The system can be deployed as a web-based application for real-time disease prediction.
Why it matches plant phenotyping methods植物の葉画像から健全・罹病状態を推定するCNN手法の開発が研究の中心であり、病害状態の画像ベースフェノタイピングに該当する。
abstractThis paper proposes a deep learning-based plant disease detection system using Convolutional Neural Network (CNN).
Accurate fruit detection in citrus orchards is essential for yield estimation, precision harvesting, and automated orchard monitoring. Although UAV-based imaging has become a powerful tool in precision agriculture, publicly available datasets for orange fruit detection remain scarce, particularly those integrating multispectral data under real field conditions. This lack of open resources limits the development and benchmarking of robust deep-learning models for cross-spectral and illumination-invariant detection. We present CampanetaOrangeFruit, a dataset acquired with a DJI Mavic 3 Multispectral UAV flying at 14 m above ground level over a commercial citrus orchard in Corbera, Valencia, Spain. The dataset comprises 550 synchronized captures (RGB + four multispectral bands: R, G, RE, NIR) for a total of 2750 images and 301,232 annotated orange instances. Each image includes YOLOv5-format annotations generated through a homography-based reprojection process, ensuring geometric consistency across spectral modalities. CampanetaOrangeFruit uniquely provides pixel-aligned, cross-spectral UAV imagery with fine-grained fruit-level annotations, enabling research on fruit detection, yield estimation, and domain adaptation in real-world orchard environments. It represents a valuable benchmark for advancing deep-learning approaches in precision agriculture and sustainable citrus production.
Why it matches plant phenotyping methods柑橘果実を対象としたUAV画像データセットとアノテーションを提供し、果実検出・収量推定モデルの開発およびベンチマークを中心課題とするため、植物フェノタイピング用データセットとして採用する。
abstractpublicly available datasets for orange fruit detection remain scarce
MaizePotatoSoybeanLeafClassificationObject detectionCalibration / preprocessingSegmentationStress / disease detectionVisualization / data management
Background: Plant diseases significantly reduce global crop productivity, creating an urgent demand for intelligent, automated diagnostic systems in agriculture. Traditional manual inspection is labor-intensive, subjective and often ineffective in detecting early or latent symptoms. This study presents a multi-class classification and severity estimation framework for ten plant disease categories: Maize brown spot, maize rust, maize healthy, potato early blight (Alternaria solani), potato late blight (Phytophthora infestans), potato healthy, soybean mosaic virus (SMV), soybean pod mottle virus (SPMV), soybean sudden death syndrome (SDS/SBS) and soybean healthy. The objective is to develop a robust hybrid deep learning model capable of accurate early detection and quantitative severity assessment to support precision agriculture. Methods: A hybrid architecture combining convolutional neural networks (CNN) with LSTM and BiLSTM networks was implemented. The preprocessing pipeline included leaf segmentation, binary masking, defect localization and edge detection to enhance lesion visibility. CNN layers extracted spatial and textural features, while recurrent layers modeled contextual dependencies within feature representations. Performance was evaluated using Precision, Recall, F1-score, defect percentage estimation, convergence analysis and t-SNE visualization. Result: Results demonstrated stable convergence with decreasing loss (0.8-1.2) and improved feature clustering. Defect severity ranged from 0.00% (Soybean healthy) to 87.93% (Maize brown spot). The framework enables early detection (0.29-5% infection), reduces yield loss, minimizes chemical overuse and promotes sustainable smart agriculture systems.
Why it matches plant phenotyping methodsCNN-LSTM/BiLSTMによる葉画像からの病害検出と病徴重症度推定手法の開発が中心であり、植物状態を直接推定している。
abstractThis study presents a multi-class classification and severity estimation framework for ten plant disease categories
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Robust plant disease detection in real-world agricultural environments remains challenging due to dynamic environmental conditions. Accurate and reliable disease identification is essential for precision agriculture and effective crop management. Although computer vision and Artificial Intelligence (AI) have shown promising results in controlled settings, their performance often drops under lesion scale variability, inter- and intra-class similarity among diseases, class imbalance, and illumination fluctuations. To overcome these challenges, we propose a Heterogeneous Feature Aggregation Network (HFA-Net) that brings together architectural improvements, illumination-aware preprocessing, and training-level enhancements into a single cohesive framework. To extract richer and more discriminative features from the early layers of the network, HFA-Net introduces a multi-scale, multi-level feature aggregation stem. The Reduction-Expansion (RE) mechanism helps preserve important lesion details while adapting to variations in scale. Considering real agricultural environments, an Illumination-Adaptive Contrast Enhancement (IACE) preprocessing pipeline is designed to address illumination variability in real agricultural environments. Experimental results show that HFA-Net achieves 96.03% accuracy under normal conditions and maintains strong performance under challenging lighting scenarios, achieving 92.95% and 93.07% accuracy in extremely dark and bright environments, respectively. Furthermore, quantitative explainability analysis using perturbation-based metrics demonstrates that the model’s predictions are not only accurate but also faithful to disease-relevant regions. Finally, Grad-CAM-based visual explanations confirm that the model’s predictions are driven by disease-specific regions, enhancing interpretability and practical reliability.
Why it matches plant phenotyping methods植物病害の症状領域を画像から診断する深層学習・前処理フレームワークの開発が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractwe propose a Heterogeneous Feature Aggregation Network (HFA-Net)
Hyperspectral imaging provides detailed spectral information for non-destructive plant disease diagnosis, yet its use is limited by high dimensionality of the original spectra, as well as insufficient and imbalanced data records. These issues hinder the extraction of weak pathological signals and ultimately reduce model applicability. To overcome these challenges, this study proposes SPECGAN, a Generative Adversarial Network with a Temporal-Domain Feature Pyramid Fusion (TD-FPNF) and a residual attention mechanism. SPECGAN forms a general framework for both sensitive band extraction and data augmentation. A multi-scale convolutional module captures local narrow-band features related to biochemical changes, as well as global broadband trends linked to physiological structure. The residual attention mechanism further enhances subtle disease cues by adaptively reweighting multi-level fused features and suppressing background noise. SPECGAN accurately identifies key discriminatory bands based on gradient saliency analysis of the discriminator, while generating high-quality synthetic samples to alleviate data scarcity. Experiment results demonstrate that the sensitive bands concentrate in the green peak (520-550 nm) and red-edge (680-720 nm) regions, consistent with disease-induced physiological changes. By only inputting the top 20 bands (8% of the spectrum), the MLP classifier achieves 96.22% accuracy. Under a 14.6:1 imbalance scenario, generating 1500 synthetic samples boosts performance by 6%-13%. Overall, SPECGAN provides an efficient and interpretable approach for early diagnosis of rice bacterial leaf blight.
Why it matches plant phenotyping methods植物病害の症状・生理状態を対象に、ハイパースペクトル画像から感受性バンドを抽出し、データ拡張も行うSPECGAN手法を開発しており、病害表現型の取得・解析が中心である。
abstractthis study proposes SPECGAN, a Generative Adversarial Network with a Temporal-Domain Feature Pyramid Fusion (TD-FPNF) and a residual attention mechanism.
Abstract Background: Next-generation models of fire behavior and smoke production rely on gridded, 3D inputs of wildland fuel complexes. We used a hierarchically scaled sampling design to characterize canopy and surface fuels that are common to prescribed burning programs in the southeastern and western US. Sampling included airborne laser scanning, terrestrial laser scanning, close-range photogrammetry, and destructive field sampling. The objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support Results: Using our integrated, co-located methods, we produced hierarchically-scaled datasets detailing the structure and composition of canopy and surface fuels across 9 southeastern pine sites, 5 western pine sites, and 4 western grassland sites. These are now publicly available at within the Wildland Fire Science Initiative data repository (https://doi.org/10.60594/W4859C). In this paper, we detail methods and the repository structure. Conclusions: The study was designed to evaluate and advance methods for 3D fuel characterization and to provide consistently scaled and labelled datasets for model training and evaluation. More specifically, machine learning models can be used to parse 3D point clouds collected from ALS, TLS, and structure-from-motion photogrammetry into fuel objects and metrics. Calibration with field plots will allow our hierarchically-scaled datasets to be used as the foundation for synthetic fuelbed mapping, starting with fine-scale objects such as individual shrubs or downed wood and scaling to vegetation patches and operational burn units.
Why it matches plant phenotyping methodsALS、TLS、SfMと現地観測を統合して植物群落の3D構造・燃料特性を取得し、手法の評価・改良と公開データセット構築を主目的としているため、植物形質計測法が中心である。
abstractThe objective of this study was to use a combination of airborne laser scanning (ALS), terrestrial laser scanning (TLS), structure-from-motion photogrammetry (SfM), and field observations to create co-located 3D datasets of live and dead understory fuels for use in wildland fuel mapping and prescribed burn decision support
Reproduction assets foundThe paper's hierarchically scaled ALS/TLS/SfM point clouds, field fuel measurements, and analysis scripts are explicitly stated to be open source and archived in the Wildland Fire Science Initiative data repository (DOI 10.60594/W4859C), a paper-specific public asset directly reproducing this study's phenotyping/fuel-3Dataset · publicThe datasets and analysis scripts for this study are open source and are being archived with the Wildland Fire Science Initiative data repository (doi.org/10.60594/W4859C), including project metadata, methods documentation and data libraries (Prichard and Rowell 2025).Open asset ↗Wildland Fire Science Initiative data repository · 10.60594/W4859Clines:384-403Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Accurate monitoring of crop nitrogen status is essential to optimize fertilization management and reduce nitrate losses in intensive horticultural systems. This study aimed to calibrate crop monitoring tools based on ion-selective electrodes and remote sensing indices for nitrogen status assessment in horticultural crops.Field experiments were conducted during the 2025 growing season on broccoli and watermelon grown under Mediterranean conditions and subjected to different nitrogen fertilization levels. Crop nitrogen status was assessed using complementary approaches. Multispectral satellite imagery and UAV-based hyperspectral data were used to calculate vegetation indices related to chlorophyll and nitrogen status, including NDRE, GNDVI, TCARI and OSAVI. These indices were calibrated against leaf nitrogen concentration and nitrate content determined by conventional laboratory analyses. In parallel, xylem sap was extracted from leaves and analyzed using ion-selective electrodes to determine nitrate concentration.Strong relationships were observed between nitrogen supply, spectral indices and nitrate concentration in xylem sap, enabling the development of calibration models for real-time crop nitrogen monitoring. The integration of proximal sensing with remote sensing improved the robustness of nitrogen diagnostics across crops and growth stages.These results highlight the potential of combining ion-selective electrodes and remote sensing tools as decision-support systems for optimized nitrogen management.
Why it matches plant phenotyping methods植物の窒素状態を対象に、イオン選択電極・衛星/UAVリモートセンシングの校正モデルを開発・検証しており、表現型取得手法が研究の中心である。
abstractThis study aimed to calibrate crop monitoring tools based on ion-selective electrodes and remote sensing indices for nitrogen status assessment in horticultural crops.
The regulation of apoplastic pH is critical for plant growth and development, affecting processes such as nutrient uptake, cell wall expansion and intercellular signaling. Conventional methods for measuring apoplastic pH, including pH indicators in growth media and ion-selective electrodes, often fall short of providing the spatial resolution and accuracy needed for detailed studies. Here we present a protocol for the quantitative imaging of apoplastic pH in Arabidopsis thaliana roots using confocal microscopy combined with the fluorescent pH probe 8-hydroxy-pyrene-1,3,6-trisulfonic acid trisodium salt, also called pyranine. This approach addresses the limitations of genetic sensors and traditional pH measurement techniques by offering a nontoxic, cost-effective and precise method for pH assessment at cellular resolution via ratiometric confocal imaging. In addition, we introduce an updated Fiji plugin for ratiometric image conversion. The new plugin enhances workflow efficiency by automating image processing while offering several options for customization, thereby ensuring reliable and reproducible results. The full procedure, from staining to image analysis, can be completed within ~2-4 h, depending on the number of samples and imaging depth. This protocol provides a robust tool for plant physiologists to investigate apoplastic pH dynamics with high spatial resolution and accuracy in plant tissues.
Why it matches plant phenotyping methods植物根のアポプラストpHを細胞解像度で定量画像化する手法と、画像解析プラグインを開発・提示しており、植物状態の取得法が中心である。
abstractHere we present a protocol for the quantitative imaging of apoplastic pH in Arabidopsis thaliana roots using confocal microscopy combined with the fluorescent pH probe 8-hydroxy-pyrene-1,3,6-trisulfonic acid trisodium salt, also called pyranine.
Abstract Background Apoplastic pH is a central regulator of plant growth, development, and environmental adaptation, influencing cell expansion, nutrient uptake, and extracellular signaling. Many studies have successfully used HPTS to monitor relative changes in apoplastic pH in plants. At the same time, research increasingly targets pH-dependent biochemical and biophysical processes. Many enzymatic activities, ion binding events, and receptor–ligand interactions depend on defined proton concentrations. Accordingly, the development of reliable approaches to measure absolute pH in living tissues is gaining importance. Methods A calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging. The approach integrates a simplified two-point normalization strategy with an in-vitro derived sigmoidal calibration model, thereby minimizing the need for extensive in-vivo calibration curves. Confocal imaging was performed using HPTS excited at two wavelengths followed by ratiometric image processing. Data analysis is supported by a custom Fiji plugin, Ratio2pH, which converts ratiometric images into pixel-resolved maps of absolute pH. Results In vitro characterization revealed a robust, non-linear relationship between normalized HPTS ratios and pH, enabling accurate pH estimation within the physiologically relevant range of pH 5.0–7.0. When applied in-vivo to Arabidopsis thaliana roots, the workflow yielded extracellular pH estimates consistent with the pH of the incubation medium and detected reproducible pH shifts in response to pharmacological treatments. Conclusions This workflow enables reproducible, spatially resolved measurement of absolute apoplastic pH in living plant tissues. By combining a simplified calibration strategy with accessible image analysis tools, it facilitates quantitative extracellular pH measurements and their integration into biochemical and biophysical analyses.
Why it matches plant phenotyping methods生きた植物組織の絶対アポプラストpHを画像から定量する校正ワークフローを開発・検証し、Fijiプラグインも提供しているため、植物状態の取得法が中心である。
abstractA calibration-based workflow was developed to enable quantitative assessment of absolute apoplastic pH using ratiometric HPTS imaging.
Reproduction assets foundThe paper deposits its authors' analysis code and data publicly: the Ratio2pH Fiji plugin (Zenodo 10.5281/zenodo.15599805), a Python script for sigmoidal calibration curve fitting (Zenodo 10.5281/zenodo.17303477), and source data files and raw confocal images (Freidata 10.60493/t29wb-7my86). The Zenodo 15658668 ratiom�Code · publicThe Python Script for generating a user-defined sigmoidal calibration curve is available at Zenodo: https://doi.org/10.5281/zenodo.17303477Open asset ↗Zenodo · 10.5281/zenodo.17303477lines:175-235Dataset · publicSource data files and raw images are uploaded at Freidata, the data server of the University of Freiburg, available under https://doi.org/10.60493/t29wb-7my86Open asset ↗Freidata · 10.60493/t29wb-7my86lines:175-235Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Service crops are grown to provide ecosystem services in viticulture, but their adoption remains limited due to their competition with grapevine for soil resources. To identify trade-offs between services, the effect of service crops management strategies on grapevine performances still need further research. This dataset presents data from two experiments conducted to study the effect of service crops management on soil resources and grapevine performances. The inter-row vegetation was sampled in two Mediterranean vineyards using quadrats for biomass estimation. In addition, an unmanned aerial vehicle (UAV) was regularly flown over the vineyards for a period spanning more than four years in total over the two vineyards. The dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs. The raw data consists of image series captured by two UAVs during each flight campaign, including RGB and multispectral imagery. Images were acquired between 2021-06-10 and 2022-07-29 for the first vineyard, and between 2023-06-08 and 2025-03-12 for the second vineyard. Based on these raw data, the processed data comprises spatial vectors, raster layers, and dense point clouds generated from UAV images using a Structure from Motion (SfM) photogrammetry workflow, at a 5 cm spatial resolution. The raster layers and dense point clouds provide specific information on vineyard characteristics for each UAV flight date, including elevation, vegetation indices, visible and near-infrared reflectance, and canopy height. In addition, the processed data include measurements of vegetation dry biomass, as well as separate measurements of dry biomass and leaf area measured for selected service crops species. This dataset can be reused for the calibration and/or evaluation of classification algorithms aimed at discriminating vines from the inter-row vegetation, or as part of a larger dataset to explore relationships between remotely-sensed vegetation indices and field-measured vegetation biomass or surface.
Why it matches plant phenotyping methodsUAV画像とSfM処理により、植生指数・樹冠高・バイオマス等の植物形質を取得した再利用可能なデータセットで、分類アルゴリズムの校正・評価用途も明示されており、植物フェノタイピング手法・データ基盤が中心です。
abstractThe dataset presented here includes both raw data acquired during fieldwork and processed data derived from this raw inputs.
Reproduction assets foundThe paper is a Data in Brief article describing a public dataset on Research Data Gouv (doi: 10.57745/MXM55R) containing UAV RGB/multispectral imagery, SfM-derived rasters and point clouds, and field-measured vegetation biomass/leaf-area data from two Mediterranean vineyards — directly the paper's phenotyping inputs. ADataset · publicollected in vineyards located in southern France near Montpellier (43°32.5243′N, 3°50.8240′E). Data are stored on Research Data Gouv, a remote storage solution curated by the French Department of Research.
Data accessibility
Repository name: Research Data Gouv
Data identification number: doi: 10.57745/MXM55R
Direct URL to data: https://doi.org/10.57745/MXM55R
Related research article
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Value of the Data
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The fine scale imaging of vineyards (i.e., 5 cm resolution) allows for classification of the vegetation in the vineyard inter-rows, and subsequent exploration of its respective dynamics.
•Open asset ↗Research Data Gouv · 10.57745/MXM55Rlines:1-47Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
This article describes a multi-sensor dataset collected during the TIRAMISU (Thermal InfraRed Anisotropy Measurements in India and Southern eUrope) campaign at the Nawagam research site in Gujarat, India, during the 2023 monsoon season. The objective was to acquire continuous ground-based optical and thermal measurements over a homogeneous rice canopy across different crop growth stages. The dataset integrates several complementary components. Thermal data were acquired with an Optris longwave infrared camera (8-14 µm) at high temporal resolution, capturing canopy temperature dynamics throughout the diurnal cycle. Optical data were obtained with a Micasense RedEdge-M multispectral sensor, providing imagery in Blue, Green, Red, RedEdge, and Near-Infrared bands with radiometric corrections. An Apogee radiometer supplied reference radiometric temperature. Meteorological measurements included air temperature, humidity, wind speed and direction, and net radiation. Ancillary field measurements comprised Leaf Area Index (LAI), plant height, emissivity sampling, hyperspectral observations, and crop stage information. The datasets are provided with metadata and processing workflows, including calibration procedures for optical reflectance and thermal radiance. Together, these components form a comprehensive record of canopy-atmosphere interactions over a homogeneous rice field. The datasets can support research on optical and thermal directional anisotropy, canopy radiative transfer, emissivity characterization, and crop biophysical parameter estimation. In addition, they are relevant for applications in vegetation monitoring, agricultural water stress assessment, and surface energy balance studies. By combining optical, thermal, and meteorological observations, the resource is suited for multidisciplinary investigations in remote sensing, agronomy, and environmental sciences.
Why it matches plant phenotyping methods光学・熱画像、校正手順、処理ワークフロー、LAIや草丈などの植物形質を含む再利用可能な作物キャノピーデータセットが研究の中心であり、植物表現型取得基盤として適格。
abstractThe dataset integrates several complementary components.
Reproduction assets foundThe paper is a Data in Brief describing the TIRAMISU rice-canopy dataset (thermal/multispectral images, meteorological, ancillary LAI/height, hyperspectral, emissivity) publicly deposited at doi.org/10.6096/1028, including processing scripts (Thermal_CSV_to_Image.py, MicaSense notebook) for reproducibility.Dataset · publicRepository name: Optical, Thermal Infrared, and Meteorological Dataset from the Thermal InfraRed Anisotropy Measurements in India and Southern eUrope (TIRAMISU) Rice Canopy Experiment
Data identification number: doi.org/10.6096/1028
Direct URL to data: https://doi.org/10.6096/1028
Instructions for access: Publicly accessible repository; representative subsets provided with metadata and processing scripts.
Related research article
Pinnepalli, C., Roujean, J.-L., Irvine, M., et al. [ 1 ]. Measuring and modelling directional effects in the frame of TIRAMISU. ISPRS Annals, X–3–2024 , 325–330. https://doi.orgOpen asset ↗doi.org · 10.6096/1028lines:49-77Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Mar 2026Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
Accurate, non-destructive estimation of chlorophyll content is essential for monitoring crop physiological status and supporting precision cultivation management. This study investigated the feasibility of combining leaf spectral reflectance with deep learning models to estimate chlorophyll content in hydroponically grown wasabi (Eutrema japonicum), while also deriving insights applicable to other leafy crops. A total of 179 leaf samples were collected under diverse nutrient conditions, including variations in pH, sulfur levels, and macronutrient composition across two growing seasons. Spectral data were preprocessed using second-order trend removal followed by a fractional-order derivative (FOD) transformation based on the Grünwald-Letnikov definition to enhance subtle spectral features. Three regression models-a one-dimensional convolutional neural network (1D-CNN), a Vision Transformer (ViT), and a Swin Transformer (SWIN)-were evaluated. The 1D-CNN achieved the highest accuracy when low-order fractional derivatives (0-0.4) were applied, highlighting its sensitivity to localized spectral variations, whereas SWIN performed best with minimally processed original spectra, and ViT showed relatively stable performance across preprocessing methods. These findings indicate that optimal preprocessing strategies depend on model architecture, providing practical guidance for selecting suitable combinations of spectral preprocessing and deep learning models when designing chlorophyll monitoring systems for wasabi and other crops.
Why it matches plant phenotyping methodsスペクトル反射と深層学習を組み合わせ、ワサビ葉のクロロフィル含量という植物形質を非破壊推定する手法を開発・比較評価しており、フェノタイピング手法が中心である。
titleNon-destructive estimation of chlorophyll content in wasabi (Eutrema japonicum) leaves using spectral reflectance and deep learning models
Monitoring plant health is an important factor in maintaining agricultural productivity. Manual identification of leaf diseases requires expert knowledge and is prone to errors due to visual similarities among disease symptoms. This study aims to develop a plant leaf disease classification system based on digital images using a Convolutional Neural Network (CNN) approach. The dataset consists of plant leaf images representing three disease classes: Corn–Common rust, Potato–Early blight, and Tomato–Bacterial spot. Prior to model training, the images undergo preprocessing steps including image resizing and pixel normalization. The performance of the CNN model is evaluated using a testing dataset that is not involved in the training process, employing accuracy, confusion matrix, precision, recall, and F1-score as evaluation metrics. Experimental results show that the proposed model achieves a test accuracy of 95.56%, with balanced performance across all disease classes. In addition to quantitative evaluation, the trained model is implemented in a Streamlit-based application, allowing users to upload plant leaf images and obtain disease classification results interactively. The findings indicate that the CNN-based approach is effective for plant leaf disease classification and has potential application as an early decision-support system for plant health monitoring.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定するCNN分類法を開発し、独立テストデータで性能評価しているため、植物フェノタイピング手法が中心である。
abstractThis study aims to develop a plant leaf disease classification system based on digital images using a Convolutional Neural Network (CNN) approach.
Reproduction assets foundThe paper's phenotyping input is a publicly available PlantVillage image dataset (900 leaf images across three disease classes) obtained from Kaggle, with an explicit authors' URL. No author analysis code or trained model is publicly deposited.Dataset · publicleaf disease images obtained from the PlantVillage Dataset, which is publicly available through the Kaggle platform [17]. The dataset is
organized using a folder-based class structure, where each folder represents a specific leaf disease category. In this study, three disease
classes are used—Corn–Common rust, Potato–Early blight, and Tomato–Bacterial spot—with 300 images per class, resulting in a total of
900 images.Open asset ↗Kagglepdf-page:3 lines:1-51Code / dataset availability confirmedCrossref · checked 15 Sept 2026
Published14 Feb 2026International Journal of Engineering Trends and TechnologyCited by 0 · OpenAlex ↗
The study is a design and development of a strong disease detection system of cucumber and grape leaves with noisy image data, focusing on the ability to withstand salt-and-pepper and Gaussian noises. The image datasets used in agriculture are usually affected by noise because of changes in light, sensor defects, and environmental conditions, which may lead to lower diagnostic accuracy. In order to address this, the proposed system incorporates high noise reduction methods whereby a median filter and a Gaussian filter are used to restore the image quality without compromising on the important leaf texture information. After processing, colour, texture, and shape are used to extract features, which are effective in extracting disease-specific visual representations. These fine features are then trained on various optimized machine learning models, such as Light Gradient Boosted Machine (LGBM), Quantum Support Vector Machine (QSVM), a Modified Random Forest (MRF) with adaptive weighted features, and a Multi-SVM classifier with a custom kernel to map nonlinear features. Through experimental analyses, the proposed ensemble framework is shown to be highly accurate, robust, and noise-tolerant as opposed to the traditional frameworks. The hybrid method is effective in recognizing the significant cucumber diseases and grapes, including powdery mildew, downy mildew, and anthracnose, which will be utilized in the noisy agricultural conditions in the real world. In general, this system offers a noise-resistant, reliable, and computationally efficient system to detect early signs of plant diseases, which can be used in sustainable crop monitoring and precision farming.
Why it matches plant phenotyping methods植物葉の画像から病徴を推定する画像処理・特徴抽出・機械学習システムの設計開発が中心であり、植物病害状態のフェノタイピング手法に該当する。
abstractThe study is a design and development of a strong disease detection system of cucumber and grape leaves with noisy image data
Reproduction assets foundThe paper uses two public Kaggle leaf-image datasets (cucumber and grape) as its phenotyping inputs; both are publicly accessible with URLs given in the references. No author code or model checkpoints are reported.Dataset · publicGrape Disease Dataset, which was collected on Kaggle
[17], is an extensive collection of images created for the
classification and analysis of different diseases in grape
leaves.Open asset ↗Kagglepdf-raw-page:7 lines:1-64Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Accurate three-dimensional (3D) nuclear instance segmentation is a prerequisite for quantitative phenotyping in volumetric microscopy, yet remains challenging in densely packed tissues, irregular nuclear morphologies, and across heterogeneous imaging modalities. Here we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation that combines a residual attention 3D U-Net architecture with a reversible gradient-field representation for robust centroid-aware instance reconstruction. NucVerse3D is trained end to end in 3D using modality-agnostic preprocessing and isotropic scale normalization, enabling deployment across confocal microscopy, two-photon microscopy, light-sheet microscopy, micro–computed tomography, and scanning electron microscopy volumes. We benchmarked NucVerse3D on seven volumetric datasets spanning multiple species and tissues, comprising more than forty thousand manually annotated nuclei, including newly released ground-truth datasets of mouse liver tissue (control and hepatocellular carcinoma) and Drosophila brain glial nuclei. Across datasets, NucVerse3D achieved consistently high precision, recall, F1-score, and average precision, and outperformed the state-of-the-art methods particularly in dense and irregular settings, while remaining competitive on simpler cases. A single generalized model trained on pooled data matched the performance of dataset-specific models, and ablation experiments demonstrated that preprocessing and scale normalization substantially contribute to performance under strict intersection-over-union criteria. To demonstrate the biomedical utility of NucVerse3D, we applied it to three-dimensional liver images from a mouse model of hepatocellular carcinoma (HCC). High-fidelity, nucleus-by-nucleus segmentation enabled the quantification of the Nuclear Decoupling Score (NDS), which captures deviations in nuclear DNA–volume coupling at the single-nucleus level. NDS analysis revealed a progressive increase in nuclear abnormalities within tumor regions, forming spatially coherent domains of dysregulated nuclei and highlighting NDS as a potential quantitative biomarker of dysplastic and tumor tissue. Together, NucVerse3D provides a robust and generalizable solution for 3D nuclear instance segmentation and enables quantitative nuclear phenotyping across imaging modalities. Highlights - NucVerse3D provides accurate 3D nuclear instance segmentation across modalities - Residual attention and gradient fields enable robust separation of dense nuclei - New 3D annotated datasets of mouse liver and Drosophila brain are released - A generalized model achieves performance comparable to dataset-specific training - 3D nuclear phenotyping reveals spatially organized nuclear abnormalities in HCC
Why it matches plant phenotyping methods3D核インスタンスセグメンテーション手法を開発し、多数のデータセットでベンチマークするとともに、核形態状態の定量的フェノタイピングへ応用しているため。
abstractHere we present NucVerse3D, a deep-learning framework for generalized 3D nuclei instance segmentation
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 5 Sept 2026
BACKGROUND: Precise, non-destructive phenotyping of saffron during vegetative growth is critical for optimizing corm yield and accelerating breeding programs, yet systematic 3D measurements have remained elusive due to extreme morphological challenges: ultra-narrow leaves, severe mutual occlusion, and prostrate growth architecture. Traditional single-view imaging systems fail to resolve individual leaves under such conditions, limiting phenotypic analysis to whole-canopy descriptors. Here, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction. RESULTS: > 0.94, MAPE < 6%), achieving accuracy benchmarks established for broad-leaved crops using commercial-grade hardware at 100 × lower cost. Systematic voxel sensitivity analysis across nine scales identified optimal preprocessing parameters (2 cm voxel size) balancing measurement precision with computational efficiency, addressing a critical reproducibility gap in plant phenotyping. Exploratory longitudinal tracking revealed that above-ground biomass was correlated with final corm yield (r = 0.68, P < 0.001), with mid-vegetative canopy volume also showing strong correlation (r = 0.52, P < 0.01), suggesting potential resource allocation trade-offs between vegetative expansion and storage organ development. CONCLUSIONS: This work demonstrates that organ-level 3D phenotyping of narrow, overlapping leaves is achievable using low-cost imaging hardware and transparent methodological workflows. Complete documentation of algorithmic parameters and hardware specifications enables direct replication and adaptation to other narrow-leaved crops (wheat, rice, onion, leek), democratizing access to high-throughput phenotyping in resource-limited settings. The workflow advances plant phenomics by demonstrating that methodological transparency and cost-effectiveness need not compromise measurement precision, opening new avenues for phenotype-to-genotype mapping and predictive breeding in underutilized crops.
Why it matches plant phenotyping methods低コストの双眼カメラとSfM-MVSによるサフラン葉の器官レベル3D形質取得ワークフローを開発し、精度検証、再現性、パラメータ最適化まで扱っており、植物フェノタイピング手法が研究の中心である。
abstractHere, we developed a specialized organ-level 3D phenotyping workflow specifically designed for narrow, overlapping leaves using a low-cost dual-camera rotary acquisition system integrated with open-source Structure-from-Motion Multi-View Stereo (SfM-MVS) reconstruction.
Abstract Unmanned aerial systems (UAS) equipped with multispectral and RGB sensors offer valuable data for monitoring crop health and assessing disease severity. However, the wide range of available photogrammetric software complicates software selection for high‐throughput plant phenotyping. This study compares the consistency of three widely used software packages, OpenDroneMap (ODM), Agisoft Metashape, and Pix4Dmapper in processing UAS‐acquired imagery for wheat ( Triticum aestivum L.) phenotyping. Over two seasons at Oklahoma State University research sites, imagery from a DJI Phantom 4 Pro Multispectral was used to generate eight vegetation indices (VIs), which were evaluated using correlation () and root mean square error (RMSE). Normalized VIs showed strong consistency across software, with values between 0.85 and 0.99 and RMSEs ranging from 0.004 to 0.07. Non‐normalized indices exhibited greater variability but retained high correlations ( > 0.76). Ground‐truth validation used single‐view imagery and disease severity ratings. Bayesian models quantified spectral measurement differences, their distributions (mean, standard deviation, skewness, and excess kurtosis) across processing approaches, and evaluated disease classification performance using ordinal logistic regression. Normalized VIs were highly consistent across software (posterior median differences <0.01 units, overlapping 95% highest density intervals), while single‐view imagery showed 15%–25% higher pixel‐level variability than software outputs. Non‐normalized indices showed greater processing sensitivity. RGB indices demonstrated near to perfect consistency. Disease classification accuracy ranged from 35% to 48% with minimal software differences (<2%). All three software produce biologically consistent results, ensuring stable genotype rankings regardless of processing choice. ODM performed comparably to proprietary alternatives while offering cost‐effectiveness, transparency, and reproducibility advantages.
Why it matches plant phenotyping methodsUAS画像から抽出する小麦表現型について、複数の写真測量ソフトウェアの一貫性・誤差・再現性を比較検証しており、フェノタイピング手法の技術評価が中心です。
abstractThis study compares the consistency of three widely used software packages, OpenDroneMap (ODM), Agisoft Metashape, and Pix4Dmapper in processing UAS‐acquired imagery for wheat ( Triticum aestivum L.) phenotyping.
PURPOSE: Multispectral remote sensing plays an increasingly vital role in precision agriculture, with the green area index (GAI) being a key parameter due to its relevance for yield formation. However, sensor-specific GAI calibration is labor-intensive and time-consuming, contrasting with the rapid advancement of UAV-based spectral sensors and their short market life spans. Therefore, this study investigated exemplarily the feasibility of transferring GAI calibrations between two UAV-based sensors. METHODS: A multi-year, multi-crop dataset was used to evaluate three strategies for cross-calibrating MicaSense RedEdge-MX data to GAI produced by published Sequoia models rather than by destructive sampling: (1) band-to-band, (2) ratio-to-ratio, and (3) ratio-to-GAI. Each approach was tested using crop-specific and universal models. To assess the impact of prediction errors, GAI time series were generated for two crops over two years to compute radiation interception and radiation use efficiency (RUE), emphasizing that plausible RUE values provide an indirect verification. RESULTS: All methods showed high predictive accuracy (R² = 0.83–0.97), but only the ratio-to-GAI approach provided stable GAI dynamics and reliable RUE estimates, especially at low canopy densities. This approach benefited from the combined use of multiple spectral ratios and the inclusion of an additional band not provided by the Sequoia sensor. It also leveraged the RedEdge-MX’s superior wavelength positions for universal GAI calibration, resulting in minimal differences between crop-specific (R² = 0.88–0.99) and universal models (R² = 0.87–0.99). The extensive dataset revealed date-specific and phenology-driven changes in sensor correlations, emphasizing that concise, ratio-based GAI calibrations may be more robust than complex models. CONCLUSION: These findings underline the importance of efficient cross-calibration strategies in a fast-evolving UAV sensor landscape.
Why it matches plant phenotyping methodsUAVマルチスペクトルセンサー間の交差校正を開発・評価し、GAIという植物キャノピー形質を推定する方法が研究の中心である。複数戦略の精度比較と時系列・RUEによる検証も行っている。
abstractTherefore, this study investigated exemplarily the feasibility of transferring GAI calibrations between two UAV-based sensors.
Rooting systems of plants perceive environmental stimuli and flexibly regulate their growth. Therefore, understanding stimulus perception and response mechanisms is essential for optimizing cultivation. During the transition from aquatic to terrestrial environments, land plants have acquired mechanisms to adapt to gravitational force on land. Thus, elucidating gravity responses of rhizoids in bryophytes, early diverging land plants, provides important insights into how gravity-response mechanisms were established during land plant evolution. Analyzing rhizoid morphology under microgravity, where gravitational effects are largely eliminated, provides an effective approach to examine the gravity-response mechanisms that evolved after terrestrialization. In this study, to elucidate microgravity effects on rhizoid growth of Physcomitrium patens , we analyzed 3D datasets obtained by refraction-contrast micro-CT using synchrotron radiation after fixation and embedding of samples from the Space Moss experiment conducted on the International Space Station. Because each CT volume contains numerous rhizoids, we optimized a WEKA-based machine-learning segmentation approach by improving preprocessing, training, and postprocessing steps, resulting in a significantly improved segmentation accuracy. Comparison of 3D morphological indices between manually segmented rhizoids and predicted results supported the validity of the proposed method for morphological analysis. Morphological analyses revealed that, compared with both ground and artificial 1 × g conditions, rhizoid elongation and gravitropic responses were suppressed under microgravity, leading to reduced vertical growth. These findings indicate that gravity plays a fundamental role in rhizoid morphogenesis, and their absence affects growth orientation and elongation. This study provides foundational data for research on the rooting systems of bryophytes in space.
Why it matches plant phenotyping methodsマイクロCT画像からコケ植物の根茎の3D形態を抽出する機械学習セグメンテーション法を開発・最適化し、手動セグメンテーションとの比較で妥当性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す。
abstractwe optimized a WEKA-based machine-learning segmentation approach by improving preprocessing, training, and postprocessing steps, resulting in a significantly improved segmentation accuracy.
Sensor-based datasets often have limited features because continuous sensor deployment is expensive and complex. This study aims to develop a Membership Function-based Feature Extraction (MFFE) technique that operates without dependency on class variables to enhance small-sized sensor-based plant datasets. The research utilizes two sensor-based tomato disease datasets - TomEBD and TPMD, which have been collected in real-time. To address the dataset imbalance, the KMeans-SMOTE technique is applied. Feature extraction is performed using reformed triangular and gaussian membership functions, where all parameters are computed solely from the training data to prevent information leakage and biased evaluation. The enhanced datasets are classified using two optimized models: Optimized Kernel Extreme Learning Machine (OKELM) and Optimized Radial Basis Function Neural Network (ORBFNN), both tuned using the Optuna framework. The proposed technique is further validated on eight benchmarking non-plant-based datasets. Among all models, the TMF-ORBFNN achieved the highest accuracy across both plant-disease and benchmark datasets. Further, statistical analysis using the Friedman test and post-hoc Bonferroni-Dunn test showed that TMF-ORBFNN performed significantly differently from its counterparts. The time complexity of the proposed approach has also been analysed. The proposed MFFE technique provides effective feature extraction in small, sensor-based datasets without class-variable dependency. Enhancing and classifying plant-disease datasets using the proposed TMF-ORBFNN model will help farmers take timely actions to prevent crop diseases and reduce pesticide use.
Why it matches plant phenotyping methods植物病害データから病害状態を抽出・分類する特徴抽出法と分類ワークフローが研究の中心であり、センサベースの植物病害フェノタイピング手法として適格です。
abstractThis study aims to develop a Membership Function-based Feature Extraction (MFFE) technique that operates without dependency on class variables to enhance small-sized sensor-based plant datasets.
Orange crop production is highly vulnerable to fungal, bacterial, and nutrient-related diseases that significantly reduce yield quality and economic productivity. Traditional manual inspection methods are time-consuming, subjective, and often ineffective for early disease diagnosis, creating a need for automated and intelligent monitoring solutions. This study proposes a deep learning–based image-driven framework designed to accurately detect major orange crop diseases using high-resolution leaf and fruit images captured in real field conditions. The methodology integrates image enhancement, segmentation using K-means clustering and Canny edge detection, and preprocessing steps such as resizing, normalization, augmentation, and class balancing. A curated dataset of 3,000 images across six classes—including canker, greening, melanose, black spot, nutrient deficiency, and healthy samples—was used to train multiple CNN architectures (AlexNet, VGG19, and Xception) and a fuzzy rank-based ensemble model. Experimental results demonstrate that the proposed enhanced framework outperforms conventional methods, achieving 96.51% accuracy with the ensemble model, while individual models such as Xception and VGG19 achieve 92.25% and 90.34% accuracy, respectively, confirming its effectiveness for precision disease monitoring in orange orchards.
Why it matches plant phenotyping methodsオレンジ葉・果実画像から植物の病徴・病害状態を推定する画像解析・深層学習フレームワークが研究の中心であり、病害フェノタイピング手法に該当する。
abstractThis study proposes a deep learning–based image-driven framework designed to accurately detect major orange crop diseases using high-resolution leaf and fruit images captured in real field conditions.
Strawberries are among the most economically significant fruits in the United States, generating over $2 billion in annual farm-gate sales and accounting for approximately 13% of the total fruit production value. Plant phenotyping plays a vital role in selecting superior cultivars by characterizing plant traits such as morphology, canopy structure, and growth dynamics. However, traditional plant phenotyping methods are time-consuming, labor-intensive, and often destructive. Recently, neural rendering techniques, notably Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have emerged as powerful frameworks for high-fidelity 3D reconstruction. By capturing a sequence of multi-view images or videos around a target plant, these methods enable non-destructive reconstruction of complex plant architectures. Despite their promise, most current applications of 3DGS in agricultural domains reconstruct the entire scene, including background elements, which introduces noise, increases computational costs, and complicates downstream trait analysis. To address this limitation, we propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions. This approach produces more accurate geometric representations while substantially reducing computational time. With a background-free reconstruction, our algorithm can automatically estimate important plant traits, such as plant height and canopy width, using DBSCAN clustering and Principal Component Analysis (PCA). Experimental results show that our method outperforms conventional pipelines in both accuracy and efficiency, offering a scalable and non-destructive solution for strawberry plant phenotyping.
Why it matches plant phenotyping methods植物の3D再構成、背景除去、クラスタリングを統合し、草丈やキャノピー幅を自動推定するフェノタイピング手法の開発が中心である。
abstractwe propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions.
Abstract. Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10 d filtered timeseries: (1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and (2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: an inital estimate immediately after the 10 d reference period, four subsequent estimates every 10 d using new observations, and a final consolidated estimate 90 d later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsMODIS/VIIRSから植物キャノピー状態であるFPARを推定するNRTフィルタリング・相互較正手法とデータセットの開発、品質評価が中心であり、単なる農業モニタリングへの routine measurement ではない。
abstractThis paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers.
Reproduction assets foundThe paper describes its own global NRT 500 m 10 d filtered FPAR dataset (MODIS and intercalibrated VIIRS timeseries with QA layers), explicitly stated to be publicly and freely available via the JRC Data Catalogue DOI and directly downloadable from the ASAP server, with visualization in the ASAP Warning Explorer. This衍Dataset · publicThe NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://doi.org/10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50 ( Seguini et al. , 2025 ) .Open asset ↗10.2905/1aac79d8-0d68-4f1c-a40f-b6e362264e50lines:158-173Dataset · publicor can be directly downloaded from the following server https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR/ (last access: 30 September 2025).Open asset ↗lines:245-257Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Cassava remains a critical food-security crop across Africa and Southeast Asia but is highly vulnerable to diseases such as cassava mosaic disease (CMD) and cassava brown streak disease (CBSD). Traditional diagnostic approaches are slow, labor-intensive, and inconsistent under field conditions. This review synthesizes current advances in combining unmanned aerial vehicles (UAVs) with deep learning (DL) to enable scalable, data-driven cassava disease detection. It examines UAV platforms, sensor technologies, flight protocols, image preprocessing pipelines, DL architectures, and existing datasets, and it evaluates how these components interact within UAV–DL disease-monitoring frameworks. The review also compares model performance across convolutional neural network-based and Transformer-based architectures, highlighting metrics such as accuracy, recall, F1-score, inference speed, and deployment feasibility. Persistent challenges—such as limited UAV-acquired datasets, annotation inconsistencies, geographic model bias, and inadequate real-time deployment—are identified and discussed. Finally, the paper proposes a structured research agenda including lightweight edge-deployable models, UAV-ready benchmarking protocols, and multimodal data fusion. This review provides a consolidated reference for researchers and practitioners seeking to develop practical and scalable cassava-disease detection systems.
Why it matches plant phenotyping methodsUAV画像と深層学習によるカッサバ病害の検出手法を中心に、センサー、撮影プロトコル、画像処理、モデル、データセット、性能指標を体系的にレビューしているため、植物の病害状態を対象とするフェノタイピング手法レビューに該当する。
abstractThis review synthesizes current advances in combining unmanned aerial vehicles (UAVs) with deep learning (DL) to enable scalable, data-driven cassava disease detection.
Abstract - The rapid advancement of unmanned aerial vehicles (UAVs) and deep learning techniques has significantly transformed crop monitoring and precision agriculture. Among various crops, soybean plays a crucial role in global food and oilseed production, making timely and accurate crop health assessment essential. This review presents a comprehensive analysis of UAV-based soybean crop health evaluation methods, with a particular focus on image-based disease detection and stress monitoring using deep learning models. Recent progress in convolutional neural networks, patch-level image analysis, attention mechanisms, and lightweight architectures is systematically examined. The paper discusses commonly used UAV imaging modalities, preprocessing strategies, model architectures, and evaluation practices reported in the literature. Furthermore, existing challenges such as environmental variability, computational complexity, data imbalance, and real-world deployment constraints are critically analyzed. Based on the reviewed studies, potential research directions are identified, emphasizing efficient patch-level learning, interpretable health mapping, and scalable field-level assessment. This review aims to provide researchers and practitioners with a clear understanding of current trends, limitations, and future opportunities in UAV-assisted soybean crop health monitoring. Key Words: - Unmanned Aerial Vehicles (UAVs), Soybean Crop Health Monitoring, Precision Agriculture, Deep Learning
Why it matches plant phenotyping methodsUAV画像と深層学習によるダイズの病害・ストレス評価手法を体系的にレビューしており、植物状態の画像ベース取得・推定方法が中心である。
abstractThis review presents a comprehensive analysis of UAV-based soybean crop health evaluation methods, with a particular focus on image-based disease detection and stress monitoring using deep learning models.
Industrial hemp cultivation is expanding and requires reliable monitoring for legal compliance and agricultural management. This paper presents a standardized UAV-based multisensor framework designed for Cannabis sativa L. It integrates RGB, multispectral, and thermal imaging as core modules, with hyperspectral and LiDAR as optional extensions. The framework sets protocols for sensor integration, flight planning, field measurements, and annotation, ensuring datasets that meet EU altitude limits (≤120 m AGL). Multi-altitude and multi-time-of-day acquisitions are proposed to capture spatial and diurnal variability. These data improve model robustness for phenotyping, stress detection, and THC compliance verification. Potential applications include precision agriculture, breeding, regulatory monitoring, environmental assessment, and illicit crop detection. Open-access datasets generated through this framework will support reproducibility, machine learning development, and collaboration among researchers, farmers, and regulators.
Why it matches plant phenotyping methodsUAVマルチセンサーフレームワークの設計、取得プロトコル、アノテーション、オープンデータセット開発が中心で、植物表現型やストレスを測定する方法論的貢献が明確です。
abstractThis paper presents a standardized UAV-based multisensor framework designed for Cannabis sativa L.
This study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data. The dataset simulates phenotypic analysis scenarios of maize seeds under controlled laboratory conditions, with the ambient temperature maintained at 20-25°C. Comprehensive testing was conducted using 12 different maize varieties. Approximately 200 seed samples were collected per variety, resulting in a total sample size of about 2400, each subjected to hyperspectral and RGB image acquisition. Preprocessing steps included noise reduction, background removal, band selection, and modality alignment. To ensure the accuracy and reliability of the experimental data, HHIT software and Python were utilized for data processing. This dataset plays a significant role in seed variety classification, phenotypic analysis, precision agriculture, and machine learning applications.
Why it matches plant phenotyping methodsトウモロコシ種子のマルチモーダル画像を収集・前処理した再利用可能なデータセットであり、種子の表現型解析を主要目的としているため、フェノタイピング手法・データセット研究に該当する。
abstractThis study employed an HY-6010-S hyperspectral imaging system, covering a spectral range of 400-1000 nm, combined with an RGB industrial camera to acquire multimodal data.
Reproduction assets foundThe paper is a Data in Brief article depositing its own multimodal maize seed hyperspectral and RGB image dataset (2400 seeds, 12 varieties) on Mendeley Data, with a direct public URL and DOI given in the article.Dataset · publicRepository name: Mendeley Data
Data identification number: doi: 10.17632/4n4xbnx8sr.1
Direct URL to data: https://data.mendeley.com/datasets/4n4xbnx8sr/1Open asset ↗Mendeley Data · 10.17632/4n4xbnx8sr.1html-lines:1-110Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
Published1 Jan 2026ISPRS Journal of Photogrammetry and Remote SensingCited by 6 · OpenAlex ↗
Three-dimensional (3D) plant modeling is fundamental for precise phenotyping analysis. In this study, a high-throughput, multi-stage 3D reconstruction pipeline is proposed to efficiently generate point clouds with real-world physical scales. The pipeline comprises five core components: data acquisition, semantic segmentation, sparse reconstruction, dense reconstruction, and phenotypic trait extraction. To enhance the accuracy of plant structure identification, the SegFormer semantic segmentation model is employed for pixel-level segmentation, thereby guiding the subsequent reconstruction stages to focus specifically on plant regions. In feature-based sparse reconstruction, the scarcity of texture information often results in an insufficient number of matching point pairs, leading to failures in camera parameter estimation. Furthermore, the reconstructed point clouds frequently lack consistency with real-world scale. To address these challenges, a calibration-constrained sparse reconstruction method, Sparse Reconstruction from Calibrated Images (SRCI) was proposed. By integrating precise calibration results computed in a custom world coordinate system, SRCI circumvents the limitations of traditional feature matching in scenarios with scarce features, thereby resolving camera pose estimation failures caused by insufficient matching pairs and generating sparse point clouds with true physical scale. Subsequently, CL-MVSNet was employed to generate dense point clouds. The validation experiments are conducted from three perspectives: visual comparison, phenotypic accuracy assessment, and reconstruction accuracy evaluation. First, five groups of rapeseed plants are selected to perform visual comparisons between the proposed reconstruction pipeline and other advanced reconstruction software. The results demonstrate that the proposed reconstruction pipeline achieves superior performance in terms of visual quality. Additionally, three phenotypic parameters of rapeseed plants—plant height, leaf width, and chord length are manually measured and compared with the corresponding phenotypic parameters extracted from the reconstructed point clouds. The analysis revealed mean absolute errors of 4.93 mm, 3.16 mm, and 6.02 mm; root mean square errors of 6.38 mm, 4.56 mm, and 8.35 mm; and coefficients of determination of 0.98, 0.94, and 0.93, respectively. To further validate the generalization performance and accuracy of the proposed method, four additional plant categories with progressively increasing complexity were selected for accuracy evaluation. For the first three plant categories, the Chamfer distances between the reconstructed point clouds and ground truth point clouds were all within 2.4 mm. In the most complex rice reconstruction experiments, the Chamfer distances between the reconstructed point clouds and ground truth point clouds were all within 9 mm, while other methods failed to achieve effective reconstruction. The proposed high-throughput, automated, and high-quality 3D reconstruction framework provides reliable technical support and data resources for genetic research applications, including gene localization, quantitative trait locus analysis, and genome-wide association studies. • We propose a Plant-to-Camera system for high-quality 3D plant reconstruction within 6 minutes, showing strong generalizability. • Our Sparse Reconstruction from Calibrated Images (SRCI) method prevents failures in feature-scarce scenes. • We develop SFNet, a feature descriptor module that fuses multi-frequency to enhance plant feature representation.
Why it matches plant phenotyping methods植物の3D再構成と形質抽出パイプラインを開発し、植物形質および再構成精度を検証しており、フェノタイピング手法が中心である。
abstracta high-throughput, multi-stage 3D reconstruction pipeline is proposed to efficiently generate point clouds with real-world physical scales
Stomatal conductance (gs) is indicative of plant carbon dioxide uptake via photosynthesis and water loss via transpiration, making it a crucial plant biophysical trait. Direct measurement of gs is labor-intensive and usually not scalable to large fields. Using manual measurements to estimate parameters of gs models is even more labor-intensive and prone to sampling errors. This study aimed to develop an automated pipeline for gs measurement and model calibration using thermal imagery data, which not only disentangles the impacts of genotype-specific stomatal traits and environmental conditions but also enables the prediction of gs in new environments. The methodology involved using simulated thermal imagery data generated from a 3D biophysical model to train a machine learning model that could be applied to real thermal images to predict stomatal model parameters and gs itself. The method was evaluated by comparing predictions against manual gs measurements, all of which were not part of the model training process, as the model was trained against only simulated images. When compared against manual gs measurements using a porometer, the prediction R2 was 0.7, which is likely comparable to the accuracy of the manual porometer-based gs measurements (relative to a leaf gas exchange system). The developed pipeline enables high-throughput gs model parameter calibration and gs estimation.
Why it matches plant phenotyping methods熱画像と機械学習を用いて植物の気孔コンダクタンスを推定・モデル較正するパイプラインを開発し、手動測定と比較検証しており、植物フェノタイプ取得法が研究の中心である。
abstractThis study aimed to develop an automated pipeline for gs measurement and model calibration using thermal imagery data
ABSTRACT Sample preparation is an important first step to obtain high quality mass spectrometry imaging (MSI) data. Preparing plant tissues is especially challenging for MSI of thin tissues along the lateral dimensions. The unique challenges involved with plant tissues, such as fragile cell walls, hydrophobic barriers, and specific tissue structures, often lead to inefficiency and difficulties in sample preparation. Imprinting plant tissues onto porous polytetrafluoroethylene (pPTFE) sheet has been widely used to extract internal metabolites in leaves and petals while keeping spatial resolution for MSI. However, pressure applications were typically made manually using a vise or pliers leading to low reproducibility and resolution in MS images. In this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting. To evaluate the performance of the new device, Lemna minor fronds, Arabidopsis thaliana , and Bacopa monnieri leaves were imprinted onto the pPTFE with PNP, vise, or pliers, and matrix‐assisted laser desorption/ionization (MALDI) MSI was obtained on the imprints. The PNP showed dramatic improvements in reproducibility and image quality compared to manual pressure application tools.
Why it matches plant phenotyping methods植物組織の空間的な代謝物情報を再現性よく取得するための空気圧式インプリンティング装置を開発し、手動法と性能比較している。植物表現型取得に関わる試料調製・イメージング手法が中心である。
abstractIn this study, we introduce a home‐built pneumatic press (PNP) that has been designed to precisely control the pressure application parameters during imprinting.
Reproduction assets foundThe paper's MALDI-MSI data (imzML files of imprinted Lemna minor, Arabidopsis, and Bacopa tissues) are openly deposited in a paper-specific METASPACE project, as stated in the Data Availability Statement. No author analysis code or trained models are disclosed.Dataset · publicData Availability Statement
The data that support the findings of this study are openly available in METASPACE (https://metaspace2020.eu/project/pnp_ptfe_imprinting_plant).Open asset ↗METASPACE · pnp_ptfe_imprinting_planthtml-lines:230-307Code / dataset availability confirmedOpenAlex · Europe PMC · checked 15 Sept 2026
High-throughput plant phenotyping with hyperspectral imaging (HSI) is pivotal for accelerating crop improvement to address global food security. Adoption is limited by a data-processing bottleneck, forcing a trade-off between costly, inflexible commercial software and programming-intensive open-source libraries. To overcome this barrier, we developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface. The platform introduces spectrally guided segmentation strategies (Range Averaging, Difference Enhancement) and a spectral Fractional-Order Differencing (FOD) preprocessor to enhance extraction of subtle, physiologically relevant features. Across diverse in-house and public datasets, FOD-preprocessed spectra improved model performance over conventional pipelines, yielding 87.35% accuracy for tomato maturity and R 2 = 0.878 for fruit firmness. In cross-software benchmarks, PlantSpecLab matched the accuracy of ENVI and code-based Python pipelines while reducing end-to-end workflow time by >90% (>80 min to ∼8 min). PlantSpecLab provides a transparent, efficient analytical environment that lowers the technical barrier to HSI analysis. This enables researchers to prioritize biological interpretation while minimizing computational overhead.
Why it matches plant phenotyping methods植物のハイパースペクトル画像から表現型特徴を抽出・モデル化するオープンソース基盤を開発し、既存ソフトウェアとの性能・処理時間を比較検証しているため、フェノタイピング手法が中心である。
abstractwe developed PlantSpecLab, an open-source, no-code platform that unifies the HSI workflow from image processing to modeling within a single interactive interface.
Reproduction assets foundThe authors explicitly state the PlantSpecLab source code (the platform used for all phenotyping analyses in the paper) is publicly available on GitHub under an MIT license, with a versioned release archived alongside the data.Code · publicsis. Jingye Liu: Data curation. Chu Zhang: Supervision, Writing—review & editing. Wei Xu: Supervision, Funding acquisition, Writing—review & editing.
Data and code availability
All data and code that support the findings of this study will be made publicly available upon publication. The PlantSpecLab source code is available at https://github.com/Another-Train/PlantSpecLab (MIT License), with a versioned release archived alongside the data.
Funding
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62265015 and 32360750), the Xinjiang Uygur Autonomous Region Key R&D Program (Grant No. 2023B02028-3), and the Finance Plan Project of the 8th Division of the Open asset ↗Another-Train/PlantSpecLablines:458-487Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Radiometric calibration of multispectral imagery plays a critical role in the determination of vegetation-related features. This radiometric calibration strongly depends on a proper sensor configuration when acquiring images, the shutter speed being a critical parameter. The objective of the present study was to appraise the influence of shutter speed on the reflectance in the visible and near-infrared (NIR) spectral regions registered by a low-cost multispectral sensor (MAPIR Survey3) on a homogeneous field of turfgrass ( Cynodon dactylon L. -Poaceae) and on the vegetation index (VI) values calculated from them, under different solar radiation conditions. For this purpose, 10 shutter speed configurations were tested in field campaigns with variable solar radiation values. The main results demonstrated that the reflectance in the green spectral region was more sensitive to shutter speed than that of the red and NIR spectral regions, particularly under high solar radiation conditions. Moreover, VIs calculated using the green band were more sensitive to slow shutter speeds, thus presenting a higher probability of providing meaningless artifact values. In conclusion, this study provides shutter speed recommendations under different illumination conditions to optimize the reflectance and the VI sensitivity within the image, which can be applied as a simple method to optimize image acquisition from unmanned aerial vehicles under varying solar radiation conditions.
Why it matches plant phenotyping methods低コスト multispectral センサーによる植物の反射率・植生指数推定について、シャッター速度と照明条件の影響を評価し、画像取得条件を最適化する方法を示しており、フェノタイピング手法が中心である。
abstractThe objective of the present study was to appraise the influence of shutter speed on the reflectance in the visible and near-infrared (NIR) spectral regions registered by a low-cost multispectral sensor (MAPIR Survey3)
Hyperspectral remote sensing has shown great promise for early detection of plant diseases, yet its adoption is often hindered by spectral variability, noise, and distribution shifts across acquisition conditions. In this study, we present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection, combining pixel-wise correction, curve-wise normalization and smoothing, and channel-wise standardization. The pipeline was evaluated on an experiment on early detection of stem rust ( Puccinia graminis f. sp. tritici Eriks. and E. Henn.) of wheat ( Triticum aestivum L.). The pipeline implementation enhanced the classification models accuracy raising F1-scores of logistic regression, support vector machines and Light Gradient Boosting Machine from 0.67-0.75 (raw spectra) to 0.86-0.94. Notably, it enabled reliable detection of asymptomatic infections as early as 4 days after inoculation, which was not achievable without preprocessing. The framework demonstrates potential for generalization beyond plant pathology, suggesting applicability to a range of hyperspectral remote sensing tasks such as vegetative health monitoring, environmental assessment, and material classification through improved signal interpretability and robustness. This work lays the groundwork for advancing hyperspectral image processing by proposing a reproducible, scalable pipeline that could be adapted for integration into unmanned and satellite imaging systems.
Why it matches plant phenotyping methods小麦茎锈病の無症状感染を対象に、ハイパースペクトル画像の前処理パイプラインを開発・評価し、植物病害状態の早期推定性能を検証しているため、植物フェノタイピング手法が中心である。
abstractwe present a systematic preprocessing pipeline tailored for hyperspectral data in plant disease detection
Reproduction assets foundThe paper's data availability statement points to a public Google Drive repository containing the study's hyperspectral datasets used for wheat stem rust early detection. No separate author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://drive.google.com/drive/folders/1vpKPlPw5uK5AnKctaE2oYCuOaRFX4-yN .Open asset ↗lines:616-634Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published17 Dec 2025Journal of Scientific Research, Education, and Technology (JSRET)Cited by 0 · OpenAlex ↗
This study develops a corn leaf disease detection system using the YOLOv11 algorithm to overcome the limitations of manual identification, which is often subjective and slow. The dataset from Roboflow was converted to the object detection format with four classes (Leaf Spot, Blight, Rust, Healthy), annotated with bounding boxes, split in a 70:20:10 proportion, and optimized through preprocessing and data augmentation. The model was trained for 150 epochs, yielding an average precision of 0.785, a recall of 0.662, and an mAP@0.5 of 0.717 from 80 test images. The Healthy class performed superiorly (mAP 0.988), while the Leaf Spot class was the lowest (mAP 0.471) due to the variation of complex lesions. The confusion matrix confirmed prediction consistency. The main advantage is the detection of specific disease locations via bounding boxes, complementing previous classification approaches. This system has the potential to support automatic diagnosis and effective precision agriculture management.
Why it matches plant phenotyping methodsトウモロコシ葉の病徴を画像から検出・局在化するYOLO手法の開発と性能評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。
abstractThis study develops a corn leaf disease detection system using the YOLOv11 algorithm
Rice production is increasingly under threat by a serious fungal disease in the Chidambaram region of Cuddalore district, especially false smut, sheath blight, and brown spot, which are becoming more severe under global climate change. Usually, farmer do their inspections at a later stage, which causes critical damage to the rice crops. This manual inspection is error-prone, time-consuming, and subjective. In these situations, AI-enabled tools and methods are essential for accurate and timely rice disease prediction. This research introduces a novel approach using deep learning–driven image classification framework for accurate detection of rice plant diseases (DLDICF-ADRPD). The DLDICF-ADRPD undergoes three different stages, namely data collection, data preprocessing, feature extraction, detection and classification of diseases. This combination leads to an efficient and robust disease classification system. The series of experiments was conducted to assess the proposed DLDICF-ADRPD performance using large dataset of rice leaf images from different disease types and growth phases, obtained from the publicly accessible Kaggle datasets. When compared to other existing disease prediction models, our DLDICF-ADRPD model performs better. Overall, the suggested DLDICF-ADRPD design greatly increases the reliability and accuracy of disease recognition, supporting global food security and sustainable agriculture.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定する深層学習による画像分類手法の開発・性能評価が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThis research introduces a novel approach using deep learning–driven image classification framework for accurate detection of rice plant diseases (DLDICF-ADRPD).
Objectives: This article focuses on designing and developing an AI-based plant disease identification system using machine learning and image processing techniques to identify crop diseases and health from images of the leaf. The main objectives include developing a real-time automated investigation tool reachable to farmers, creating datasets of both healthy and disease plant images, designing an easy-to-use interface of the application comparing existing solutions. Methods: This study observed mixed-method approach as research methodology. The method merged with qualitative feedback and quantitative surveys. The data set contains images of healthy and infected plant leaves gathered from multiple crops to train the machine learning models for classification. The experiment used image preprocessing and feature extraction for better accuracy, and performance parameters like response time and usability were assessed. The model’s performance was compared with present gold-standard Pashu Poshan, apps—Plantix and Leaf Doctor—aiming on localization, prediction capability and response time. The usability tests were applied to 80 stakeholders, consisting of farmers of both small and medium scale. Findings: Proposed system got an accuracy rate approximately 90% in plant disease detection, average response time of less than one minute, surpassing other available systems that required time of 20–60 seconds for some elementary or basic recognition. The user assessment provides a usability score of 4.5 out of 5, where nearly 87% of participants valued the software as easy to use. As most rural areas face limited internet connection, the system’s offline feature offered significant advantages. Novelty: Unlike other systems with general disease identification and poor interfaces, the proposed system, integrated with voice commands, disseminates real-time localized treatment guidance projecting prediction based on weather circumstances. It also joins NGOs and local agricultural teams giving extended support such as funding and crop insurance. This wide-ranging integration of intelligent automation, approachability and user adaptation makes Plant Guard a valuable and novel solution for technology enabled agriculture. Keywords: Machine Learning, Plant disease detection, Image processing, Smart agriculture, Crop disease identification Introduction
Why it matches plant phenotyping methods葉画像から植物病害を検出する画像処理・機械学習システムの開発、データセット作成、性能比較が研究の中心であり、植物の病害状態を直接推定するため対象範囲に含める。
abstractThis article focuses on designing and developing an AI-based plant disease identification system using machine learning and image processing techniques to identify crop diseases and health from images of the leaf.
The farming of citrus is a crucial component of Pakistan’s fruit-based agricultural economy. But, the foliar diseases citrus canker, black spot, and greening have been posing a constant threat on citrus’s productivity. An optimal solution is an early and accurate detection of these diseases to improve the productivity. Therefore, this paper proposes an automated citrus leaf disease detection and classification framework based on deep convolutional neural networks (DCNNs). The proposed solution has five stages: image acquisition (dataset), preprocessing, data augmentation, deep feature extraction and optimization, and disease classification. Firstly, the images are obtained from a public dataset downloaded from Kaggle. Secondly, preprocessing techniques are used to improve the image quality and shape, thirdly the data augmentation techniques are used to enhance the model generalization, fourthly pre-trained models DenseNet-121, MobileNet, and InceptionV3 with transfer learning technique to extract deep features, and finally Adam optimizer and categorical cross-entropy loss function are used to fine tune the pre-trained models for classifications. The proposed model is evaluated on accuracy, precision, recall, and F1-score metrics. All the models demonstrated robust performance while DenseNet-121 achieved the best performance. The evaluation results assured the robustness of the use of transfer learning-based DCNN in citrus leaf disease detection.
Why it matches plant phenotyping methods柑橘葉の病害状態を画像から直接検出・分類する深層学習ワークフローが研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractTherefore, this paper proposes an automated citrus leaf disease detection and classification framework based on deep convolutional neural networks (DCNNs).
Reproduction assets foundThe paper's phenotyping input is a public Kaggle citrus leaf image dataset (654 RGB images of healthy, blackspot, canker, and greening leaves) explicitly cited with a URL matching an allowed URL. No author code or trained models are reported as publicly available.Dataset · publictaset is essential. Additionally, the dataset must be prepared
so that our model can fully comprehend the data. The model will then be able to effectively
use that dataset for learning. A random sample of infected and healthy leaves images from the
datasets shown in Figure 1. The details of the images are provided in Table 1.
1
https://www.kaggle.com/datasets/sourabh2001/citrus-leaves-dataset?resource=downloadOpen asset ↗Kaggle · sourabh2001/citrus-leaves-datasetpdf-raw-page:3 lines:1-48Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
With unmanned aerial vehicles (UAVs), agricultural monitoring has developed into a new phase of innovation providing remedies to precision farming. The common traditional agricultural methods are based on manual inspection and few observations on the ground using sensors that may be inaccurate and time-consuming. New technologies such as drones and AI provide us with an opening of large scale, early detection, but most systems currently only seek pests or diseases and are usually specific to a single type of crop in controlled laboratory conditions. Drone-operated AI system, which combines RGB and, where feasible, multispectral cameras and a YOLOv8 pipeline to detect pests and crop diseases simultaneously across a variety of crops. We are developing it to be used in the real world: we load in data fields, laboratories, and the internet, perform preprocessing, transfer learning, and make the inference to be lightweight enough to execute on edge computers. The introduction of agricultural monitoring systems based on the use of UAVs builds on the peculiarities of quadcopters and fixed-wing UAVs. Quadcopters are used when conducting detailed field surveys or spot checks, allowing high-resolution imaging to be used in order to complete precise inspections, whereas fixed-wing UAVs are used when it comes to covering extensive areas and long-range capabilities. These UAVs can gather extensive data and conduct biological and chemical analyses due to sophisticated IoT devices and sensors, such as multispectral and hyperspectral cameras, GPS modules, and real-time communication tools. Our hybrid machine learning model (HMLM) has more accuracy and predictive capabilities, with an amazing score of 98.74 and hence, our machine learning model is doing the right job of 98.74 accurate classification and thereby yielding high accurate yields by predicting crop management. This research will contribute to the sustainability of agricultural practices as well as yield protection by providing timely, precise and scalable detection. The model proposed can potentially enable farmers with action-oriented insights, losses can be alleviated, and food security objectives can be achieved in areas where there are high susceptibility rates to pests and diseases.
Why it matches plant phenotyping methodsUAV画像とYOLOv8を用いて作物病害を検出する技術の開発が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。
abstractDrone-operated AI system, which combines RGB and, where feasible, multispectral cameras and a YOLOv8 pipeline to detect pests and crop diseases simultaneously across a variety of crops.
Accurate prediction of photosynthetic parameters is pivotal for precision viticulture, as it enables non-invasive monitoring of plant physiological status and informed management decisions. In this study, spectral reflectance data were used to predict key photosynthetic parameters such as assimilation rate (A), effective photosystem II (PSII) quantum yield (ΦPSII), and electron transport rate (ETR), as well as stem and leaf water potential (Ψstem and Ψleaf), in Vitis vinifera (cv. Müller-Thurgau) grown in an experimental vineyard in Lower Franconia (Germany). Measurements were obtained on 25 July, 7 August, and 12 August 2024 using a LI-COR LI-6800 system and a PSR+ hyperspectral spectroradiometer. Various machine learning models (SVR, Lasso, ElasticNet, Ridge, PLSR, a simple ANN, and Random Forest) were evaluated, both as standalone predictors and as base learners in a stacking ensemble regressor with a Random Forest meta-learner. First derivative reflectance (FDR) preprocessing enhanced predictive performance, particularly for ΦPSII and ETR, with the ensemble approach achieving R2 values up to 0.92 for ΦPSII and 0.85 for A at 1 nm resolution. At coarser spectral resolutions, predictive accuracy declined, though FDR preprocessing provided some mitigation of the performance loss. Diurnal patterns revealed that morning to mid-morning measurements, particularly between 9:00 and 11:00, captured peak photosynthetic activity, making them optimal for assessing vine vigor, while midday water potential declines indicated favorable timing for irrigation scheduling. These findings demonstrate the potential of integrating hyperspectral data with ensemble machine learning and FDR preprocessing for accurate, scalable, and high-throughput monitoring of grapevine physiology, supporting real-time vineyard management and the use of cost-effective sensors under diverse environmental conditions.
Why it matches plant phenotyping methodsハイパースペクトル測定と機械学習によるブドウの光合成・水ポテンシャル推定が研究の中心であり、複数モデルの性能評価と前処理比較も実施しているため。
abstractspectral reflectance data were used to predict key photosynthetic parameters such as assimilation rate (A), effective photosystem II (PSII) quantum yield (ΦPSII), and electron transport rate (ETR), as well as stem and leaf water potential (Ψstem and Ψleaf)
MultimodalLiDAR / point cloudAnnotation / quality controlClassificationObject detectionCalibration / preprocessingSegmentationTracking
Plant phenomics, the comprehensive study of plant phenotypes, has gained prominence as a vital tool for understanding the intricate relationships between genotypes and the environment. Image-based plant phenomics has progressed rapidly, and three-dimensional (3D) phenotyping is a valuable extension of traditional 2D phenomics. However, the increased data dimensionality poses challenges to feature extraction and phenotyping. In recent decades, deep learning has led to remarkable progress in revolutionizing 3D phenotyping. Therefore, this review highlights the importance of using deep learning in 3D plant phenomics. It systematically overviews the capabilities of deep learning for 3D computer vision, covering 3D representation, classification, detection and tracking, semantic segmentation, instance segmentation, and generation. Additionally, deep learning techniques for 3D point preprocessing (e.g., annotation, downsampling, and dataset organization) and various plant phenotyping tasks are discussed. Finally, the challenges and perspectives associated with deep learning in 3D plant phenomics are summarized, including (1) benchmark dataset construction by using synthetic datasets and methods such as generative artificial intelligence and unsupervised or weakly supervised learning; (2) accurate and efficient 3D point cloud analysis by leveraging multitask learning, lightweight models, and self-supervised learning; and (3) deep learning for 3D plant phenomics by exploring interpretability, extensibility, and multimodal data utilization. The exploration of deep learning in 3D plant phenomics is poised to spur breakthroughs in a new dimension of plant science.
Why it matches plant phenotyping methods3D植物フェノミクスにおける深層学習手法を体系的にレビューしており、植物形質の抽出・推定手法が中心である。
abstractTherefore, this review highlights the importance of using deep learning in 3D plant phenomics.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset can be downloaded from https://github.com/Jinlab-AiPhenomics/Mazie3D.Open asset ↗Jinlab-AiPhenomics/Mazie3Dhtml-lines:332-336Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Computers and Electronics in Agriculture.
In-orchard apple size grading remains challenging under occlusions, variable illumination, and irregular fruit morphology. We present a contact–curvature strategy that integrates bending sensors into fin–ray flexible fingers to estimate fruit size during grasping, unifying grasp–and–grade operations. Neural Radiance Fields (NeRF) reconstruction provides offline ground-truth curvature for calibration, yielding strong linear agreement between sensor readings and true curvature (R2=0.9852). Using temporal curvature features across approach, grasp, and steady phases, a gradient-boosting regression model predicts fruit diameter with R2=0.9577 and RMSE = 1.19 mm on the test set. In laboratory conditions, the system achieved an overall grading accuracy of 98.0 % for 200 apples classified into four grades, with a processing capacity of approximately 6 apples·min⁻¹, meeting real-time requirements. In a small-scale orchard pilot study, the system maintainedR2=0.94 andRMSE=1.27 mm, achieving 96 % grading accuracy versus 77 % for a camera-only approach. Compared with vision-only sizing methods, contact-curvature sensing demonstrates inherent robustness to occlusion and illumination while better tolerating morphological irregularities. A methylene–blue protocol confirmed non–destructive operation. Contact–curvature sensing is robust to occlusions/illumination and can, in principle, extend to other near–spherical crops.
Why it matches plant phenotyping methods果実径という植物器官形質を、接触・曲率センサーと回帰モデルで推定する手法を開発し、校正・精度検証・圃場評価まで行っており、表現型取得法が研究の中心である。
abstractWe present a contact–curvature strategy that integrates bending sensors into fin–ray flexible fingers to estimate fruit size during grasping, unifying grasp–and–grade operations.
Why it matches plant phenotyping methods乾燥イネ組織のSEM画像取得・処理・解析プロトコル自体が中心で、植物細胞壁形態などの表現型観察を可能にする方法開発である。
abstractTo overcome the challenges surrounding SEM micrograph preparation, dried rice stems were used to develop a specific set of protocols for processing dried plant samples.
This study aimed to compare in-situ normalized difference vegetation index (NDVI) measurements with satellite-derived NDVI data for rice paddies field in southern region of South Korea and to develop calibration equations for different growth stages using both linear and non-linear regression models. The in-situ NDVI was measured with a portable leaf index meter (Crop Circle ACS-435), and satellite-derived NDVI was obtained from Landsat-8/9 and Sentinel-2 images. All values represented daily average NDVI across a five-year period (2020–2024). Analysis showed that satellite-derived NDVI values were generally lower than in-situ values, primarily because of atmospheric and spatial resolution differences. Both satellite platforms exhibited a strong positive correlation with ground-based NDVI, although stage-specific differences were observed. Landsat-8/9 outperformed in the pre-heading stage, whereas Sentinel-2 performed better in the post-heading stage. For Landsat-8/9, the mean absolute percentage error (MAPE) decreased substantially from 38.6 % before correction to 16.7 % after applying the calibration equations, whereas for Sentinel-2 it decreased from 22.1 % to 15.3 %. This study establishes a foundation for improving the accuracy and reliability of satellite-based NDVI through in-situ calibration, with potential applications in agricultural productivity, environmental monitoring, and climate change adaptation.
Why it matches plant phenotyping methodsイネの成長段階ごとのNDVIという植物状態を対象に、衛星NDVIを現地測定で比較・校正し、誤差低減を検証しているため、表現型取得法の技術的検証が中心です。
abstractcompare in-situ normalized difference vegetation index (NDVI) measurements with satellite-derived NDVI data for rice paddies
Field / plotStomata / guard-cell complexPhysiological trait estimationCalibration / preprocessingStomatal traitsWater status / transpiration
The development of commercially available porometers has allowed for higher throughput measurement of stomatal conductance, but a body of evidence has suggested a persistent positive bias in their measurements relative to "reference" measurements from instrumentation based on infra-red gas analysis. We compiled a data set comprised of 25 angiosperm species, across a range of field conditions and found that the LI-COR LI-600, an open flow-through porometer, produced an exponentially increasing bias relative to the LI-COR LI-6800 infra-red gas analyser-based instrument in response to increasing stomatal conductance and decreasing relative humidity. This bias was minimal at lower stomatal conductance (below roughly 0.25 mol m -2 s -1 ), but was pronounced for larger values. We hypothesised that this bias is the result of the assumption of a constant air temperature throughout the flow stream used by the instrument software to estimate stomatal conductance from raw sensor measurements. We relaxed this assumption, and applied psychrometrics to augment the typical gas exchange equations with an additional energy balance constraint to solve for the temperature change throughout the air flow stream. We found that including this temperature difference corrects the computed transpiration and stomatal conductance values, and brings the porometer measurement into agreement with that of the infra-red gas analysis-based system. Software is provided to apply the correction to LI-600 output files. For future instrument design iterations, explicit measurement of temperature variation in the flow stream provides a potential opportunity for improvement in measurement accuracy at high stomatal conductance.
Why it matches plant phenotyping methods気孔コンダクタンス測定器の系統誤差を検証し、物理モデルと補正ソフトウェアで植物生理形質の測定精度を改善する方法研究である。
abstractThe development of commercially available porometers has allowed for higher throughput measurement of stomatal conductance, but a body of evidence has suggested a persistent positive bias in their measurements relative to "reference" measurements from instrumentation based on infra-red gas analysis.
Field / plotMultimodalMultispectral / hyperspectralRaman / spectroscopyWhole plant / canopy / plot / fieldCalibration / preprocessingGrowth / development / phenology
This data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo), a native species from the arid and semi-arid regions of Southern Zacatecas, Mexico. In particular, Stenocereus spp. are important cacti in the region due to its nutritional properties, role as an economic resource, and cultural significance.It is worth emphasising that these cacti traditionally grow wild (i.e., without deliberate cultivation); accordingly, controlled cultivation is uncommon and remains understudied. With the aim of producing a formal, comprehensive analysis and compendium, the data were collected across multiple phenological stages to provide a complete representation of the plant development cycle, from vegetative growth through to fruiting. To achieve this, the collection process combined high-resolution multispectral imaging with field spectrometry in the 400-700 nm range. Standardized acquisition protocols were applied in field conditions to capture consistent reflectance data, and environmental variables such as illumination, temperature, and geographic coordinates were recorded for each session to ensure reproducibility. The dataset integrates several components: (i) multispectral images that provide spatial information on canopy and structural characteristics, (ii) field spectral signatures with detailed reflectance values for each sampled plant, and (iii) metadata describing phenological stage, acquisition date and time, environmental conditions, and equipment settings. For subsequent analysis, data was preprocessed and normalized to enable reliable comparisons between growth stages and across acquisition sessions, resulting in a clean, structured resource ready for computational analysis. In this regard, this dataset has been organized to facilitate its direct application across multiple research and development contexts. Specifically, potential applications include the training and validation of machine learning and computer vision models for automated phenological stage classification, harvest time estimation, and development of species-specific vegetation indices. Moreover, owing to its standardized design, the resource can serve as a benchmark for comparing methods, validating algorithms, and supporting reproducible workflows in precision agriculture and remote sensing. Beyond Stenocereus queretaroensis, the documented acquisition and preprocessing methodology can be replicated or adapted to generate similar multimodal datasets for other climate-resilient crops, particularly those cultivated in arid and semi-arid regions. This could enable comparative analyses across species and provide a reference for extending multimodal sensing approaches to underrepresented plants of ecological and economic importance.
Why it matches plant phenotyping methods植物の生育段階・生理・構造特性を対象に、標準化されたマルチスペクトル画像とフィールド分光データを収集・前処理した再利用可能なデータセットであり、ベンチマークやアルゴリズム検証を目的とするため、フェノタイピング手法が中心です。
abstractThis data article presents a multimodal, non-invasive dataset documenting the physiology and growth stages of Stenocereus queretaroensis (pitayo)
Reproduction assets foundThe paper's own multimodal phenotyping dataset (multispectral/RGB images, spectral signatures, NDVI products, metadata, and example MATLAB scripts) is publicly deposited on Mendeley Data with explicit direct URL and DOI.Dataset · public) at ∼1750 m a.s.l., under semi-arid temperate conditions with spring temperatures ranging 20–33°C. The data were collected from the Unit Academic of Electrical Engineering Plantel Jalpa.
Data accessibility
Repository name: Multimodal_Cactaceae_Dataset_25
Data identification number: doi:10.17632/skw8tjc82f.1
Direct URL to data: https://data.mendeley.com/datasets/skw8tjc82f/1
Instructions for accessing these data: click on the direct URL to obtain the multimodal data from Mendeley Dataset Repository.
Related research article
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These data provide a unique, non-invasive resource for studying Stenocereus spp. physiology. The integrated collection of high-resolution multOpen asset ↗Mendeley Data · doi:10.17632/skw8tjc82f.1lines:32-58Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Background: Mango leaf diseases reduce fruit yield and quality, requiring early detection for effective management. Traditional methods rely on manual inspection, which is slow, subjective and error-prone. Deep learning, especially Convolutional Neural Networks (CNNs), offers automation but faces challenges. These include class imbalance, poor dataset generalization and limited real-world scalability. This study develops a robust CNN model to improve mango leaf disease classification. Methods: A dataset of 2,494 mango leaf images from the Mendeley database was used. Images were categorized into anthracnose, bacterial canker, cutting weevil, dieback and healthy. Preprocessing involved image resizing, normalization and data augmentation to enhance model performance. The dataset was split into 80% training, 10% validation and 10% testing. A six-layer CNN with ReLU activation, max-pooling, dropout (0.5) and fully connected layers was trained for 25 epochs. The model used Adam optimizer and categorical cross-entropy loss. Result: The model achieved 98.03% training accuracy and 97.77% validation accuracy over 25 epochs. It had a low validation loss (0.0485), indicating good generalization. The confusion matrix showed high precision and recall across all classes. The overall classification accuracy was 96.53%, with a macro-average F1-score of 96.57%. Anthracnose and Dieback were perfectly classified. Bacterial canker had a lower precision (0.8500), suggesting minor misclassifications. AUC analysis showed good disease separation, with Cutting Weevil achieving the highest AUC (0.72). This CNN model can automate mango disease detection, reducing reliance on manual inspections. It can be useful for smart farming systems and mobile applications for real-time disease diagnosis. Future work will focus on expanding the dataset, optimizing for mobile use and integrating environmental factors for better disease prediction.
Why it matches plant phenotyping methodsCNNによるマンゴー葉画像からの病害状態分類が研究の中心であり、植物の病徴を直接推定する画像ベースのフェノタイピング手法を開発・評価している。
abstractThis study develops a robust CNN model to improve mango leaf disease classification.
Potato leaf diseases pose a serious challenge to global food security, often leading to considerable yield losses if not detected promptly. The growing maturity of deep learning has enabled automated, high-precision plant disease recognition, even on devices with limited computational resources. In this study, several lightweight convolutional neural network (CNN) models—MobileNetV3 (Small and Large), EfficientNet-Lite, ShuffleNet, and SqueezeNet—are comparatively assessed for the task of potato leaf disease classification. The models were trained under identical preprocessing and fine-tuning conditions, incorporating checkpoint-based training for stability. Among the evaluated networks, ShuffleNet delivered the highest overall performance with 99% accuracy, 0.97 precision, 0.99 recall, and an F1-score of 0.98, making it well-suited for real-time field deployment. EfficientNet-Lite also demonstrated a strong balance between speed and accuracy (91.9%), outperforming both MobileNet variants. Conversely, SqueezeNet, though the most compact model, recorded lower metrics (76% accuracy), indicating limited feature discrimination capability. This analysis underscores the balance between efficiency, robustness, and predictive accuracy, providing practical insights for deploying deep learning models in precision agriculture and low-resource environments.
Why it matches plant phenotyping methodsジャガイモ葉の病徴を画像から分類する軽量CNN手法を比較・評価しており、植物病害状態のフェノタイピング手法が中心である。
abstractThe models were trained under identical preprocessing and fine-tuning conditions, incorporating checkpoint-based training for stability.
Abstract This work presents a lightweight imaging-based AI model for rapid, point-of-care diagnosis of potato leaf diseases. Using the PlantVillage dataset comprising approximately 3,000 labeled images across three classes— Healthy , Early Blight , and Late Blight —a transfer learning approach was implemented with the MobileNetV2architecture. The dataset was split into 80% training and 20% validation sets, with preprocessing and augmentation to enhance generalization. Trained for 10 epochs using the Adam optimizer (learning rate = 0.001), the model achieved a training accuracy of 96.8% and a validation accuracy of 93.7% , with respective losses of 0.13and 0.21 . Class-wise evaluation confirmed balanced precision and recall across all categories, while external testing yielded correct disease identification with 57.2% confidence . The model demonstrates that high diagnostic accuracy can be achieved on basic hardware, making it suitable for low-resource agricultural settings. Compared to complex multimodal architectures, this MobileNetV2-based design offers fast inference, minimal computational demand, and strong generalization—establishing an efficient foundation for real-time, AI-driven plant disease diagnostics.
Why it matches plant phenotyping methodsジャガイモ葉の病害状態を画像から推定するAI診断モデルの開発・検証が研究の中心であり、植物表現型の取得方法に該当する。
abstractThis work presents a lightweight imaging-based AI model for rapid, point-of-care diagnosis of potato leaf diseases.
This data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India). It focuses on broad-leaf legume species (mungbean, common bean, cowpea, and lima bean). The dataset, generated by PlantEye(R) F600 technology, captures multispectral 3D scans of plant canopies. It includes 223 scans, providing detailed organ-level segmentation annotations for embryonic leaves, leaves, petioles, stems, and whole plants. The dataset fills a critical gap in plant phenomics research by offering a base of annotated data to support AI model development efforts in 3D computer vision. Data preprocessing, annotation procedures, and potential applications in crop research disciplines are further discussed. The dataset, preprocessing code, annotations, and a MIAPPE-compliant data sheet are also presented via the GitHub repository for further updates and expansion.
Why it matches plant phenotyping methods植物フェノタイピングプラットフォームで取得した3D点群と器官レベル注釈を提供するデータセットで、再利用可能な画像解析・AI開発基盤が中心です。
abstractThis data descriptor presents novel, annotated 3D point cloud plant scans generated by a high-throughput phenotyping platform (LeasyScan, ICRISAT, India).
Reproduction assets foundThe paper's own annotated 3D point cloud dataset (223 scans of legumes with organ-level segmentation annotations), raw scanner data, MIAPPE metadata, and preprocessing/cuboid-generation/baseline-evaluation code are publicly deposited on Figshare and mirrored on GitHub.Code · publicinto this software. All the code and data are also available as the GitHub (https://github.com/kit-pef-czu-czOpen asset ↗GitHubpdf-page:2 lines:1-58Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 6 Sept 2026
This study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops—including maize, wheat, and rice—and designed to overcome the inefficiency and subjectivity of manual measurements and the high costs of laser-based phenotyping. A panoramic video of the seed is captured and processed through frame sampling to extract multi-view images. Structure-from-Motion (SFM) is employed for sparse reconstruction and camera pose estimation, while 3D Gaussian Splatting (3DGS) is utilized for high-fidelity dense reconstruction, generating detailed point cloud models. The subsequent point cloud preprocessing, filtering, and segmentation enable the extraction of key phenotypic parameters, including length, width, height, surface area, and volume. The experimental evaluations demonstrated a high measurement accuracy, with coefficients of determination (R2) for length, width, and height reaching 0.9361, 0.8889, and 0.946, respectively. Moreover, the reconstructed models exhibit superior image quality, with peak signal-to-noise ratio (PSNR) values consistently ranging from 35 to 37 dB, underscoring the robustness of 3DGS in preserving fine structural details. Compared to conventional multi-view stereo (MVS) techniques, the proposed method can achieve significantly improved reconstruction accuracy and visual fidelity. The key outcomes of this study confirm that the 3DGS-based pipeline provides a highly accurate, efficient, and scalable solution for digital phenotyping, establishing a robust foundation for its application across diverse crop species.
Why it matches plant phenotyping methods3DGSを用いた種子の3D再構成・点群処理・形質抽出パイプラインを開発し、精度を評価しており、植物表現型取得手法が研究の中心である。
abstractThis study introduces a versatile seed 3D reconstruction method that is applicable to multiple crops
Strawberries are among the most economically significant fruits in the United States, generating over $2 billion in annual farm-gate sales and accounting for approximately 13% of the total fruit production value. Plant phenotyping plays a vital role in selecting superior cultivars by characterizing plant traits such as morphology, canopy structure, and growth dynamics. However, traditional plant phenotyping methods are time-consuming, labor-intensive, and often destructive. Recently, neural rendering techniques, notably Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS), have emerged as powerful frameworks for high-fidelity 3D reconstruction. By capturing a sequence of multi-view images or videos around a target plant, these methods enable non-destructive reconstruction of complex plant architectures. Despite their promise, most current applications of 3DGS in agricultural domains reconstruct the entire scene, including background elements, which introduces noise, increases computational costs, and complicates downstream trait analysis. To address this limitation, we propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions. This approach produces more accurate geometric representations while substantially reducing computational time. With a background-free reconstruction, our algorithm can automatically estimate important plant traits, such as plant height and canopy width, using DBSCAN clustering and Principal Component Analysis (PCA). Experimental results show that our method outperforms conventional pipelines in both accuracy and efficiency, offering a scalable and non-destructive solution for strawberry plant phenotyping.
Why it matches plant phenotyping methodsイチゴ植物の3D再構成、背景除去、形質推定を一体化した新規フェノタイピング手法の開発・評価が中心であり、植物高と樹冠幅を自動推定して精度・効率を比較している。
abstractwe propose a novel object-centric 3D reconstruction framework incorporating a preprocessing pipeline that leverages the Segment Anything Model v2 (SAM-2) and alpha channel background masking to achieve clean strawberry plant reconstructions.
ABSTRACT RGB‐D cameras are widely used in indoor robots. However, their ranging capability for agricultural robots under natural lighting still needs to be evaluated. Especially in the field of robotics‐based high‐throughput crop phenotyping, the measurement accuracy of phenotypic parameters is deeply related to the ranging performances of RGB‐D cameras. In this paper, we propose a depth‐ranging evaluation framework and an online ranging compensation strategy for RGB‐D cameras on phenotyping robots. The goal is to acquire high‐quality depth‐ranging performances for plant phenotyping tasks. First, we evaluate ranging performances of RealSense D435i and Kinect V2 under typical phenotyping scenes with different lighting conditions, verify their feasibility on different maize organ observations in different growth periods, and give the optimal observation ranging areas. Second, we employ image brightness to reflect the lighting situations, and propose a novel ranging compensation strategy to decrease the lighting influences in real‐time. The results of sufficient field experiments show that RealSense D435i has better ranging performances than Kinect V2 for crop phenotyping, especially for open‐field, in‐row, and close‐range observations. The optimal ranging area of RealSense D435i is within a region of [0.16–1.2] m. However, Kinect V2 is not suitable for field phenotyping robots due to significant interference from natural sunlight, limited measurement range, and instability in depth measurements under outdoor conditions. In addition, we also verify that our online depth error compensation strategy can effectively reduce the influences of lighting intensity and target distance on the depth ranging of RGB‐D cameras. Although we test and verify our ranging evaluation framework and ranging error compensation strategy with two old‐fashion cameras, the framework and strategy are generic and applicable to other new RGB‐D cameras.
Why it matches plant phenotyping methods植物フェノタイピングロボット向けRGB-Dカメラの測距評価フレームワークとオンライン補償手法を開発・検証しており、表現型取得の技術性能が研究の中心である。
abstractwe propose a depth‐ranging evaluation framework and an online ranging compensation strategy for RGB‐D cameras on phenotyping robots.
Abstract Rice cultivation is a significant source of agricultural methane (CH₄), yet routine quantification still relies heavily on gas chromatography (GC), which limits throughput and field deployment. Here, we evaluated a portable tunable-diode-laser absorption spectroscopy (TDLAS) detector (PMD) as an alternative to GC for measuring CH₄ released from pot-grown rice plants under field conditions. Weekly closed-chamber samples from five cultivars were analyzed in parallel by GC (FID/MS) and the PMD. Standard gas tests showed an excellent linear relationship for the PMD (R 2 = 0.9995), indicating a near-ideal response. Across field samples, GC and PMD were strongly associated (R 2 = 0.9943). Bland–Altman analysis revealed a mean bias (GC − PMD) of 8.55 with 95% limits of agreement − 9.16 to 26.26, and Lin’s concordance correlation coefficient was 0.991, evidencing near-perfect agreement despite a slight systematic offset. A simple calibration with a linear regression eliminated the bias and narrowed the limits of agreement, while preserving the high correlation. Residual analyses suggested a modest influence of CO₂ (but not N₂O) on between-method differences. Taken together, the PMD provides rapid, robust, and labor-efficient CH₄ measurements that closely match GC when a fixed calibration is applied, enabling high-throughput phenotyping of rice genotypes and management practices in both laboratory and field settings. This calibrated, portable approach lowers barriers to large-scale screening for low-emission rice, supporting climate-smart crop improvement.
Why it matches plant phenotyping methods稲品種のメタン放出という植物状態を測定する携帯型TDLAS法を開発・校正し、GCとの一致性を検証しており、フェノタイピング手法が中心である。
titleDevelopment and validation of a portable TDLAS system for gas chromatography–level quantification of methane emissions from rice
The detection of virus infection attacking plants mainly depends on polymerase chain reaction (PCR) testing. Nevertheless, the COVID-19 pandemic, during which the availability of the PCR test was limited, highlights the need for reliable alternative methods for detecting viruses. An effective technique for diagnosing plant disease involves the use of an electronic nose (e-nose) that can detect volatile organic compounds (VOCs) emitted by plants. However, the extensive use of e-noses is limited by the noise that can come from temperature and humidity changes. In order to address this limitation, this research focused on optimising filtering techniques to improve e-nose performance in detecting pepper yellow leaf curl virus (PYLCV) infected chilli plants. The samples were taken from commercial plantations, ensuring that those infected grew in a controlled environment, and ensuring PYLCV detection in diverse conditions. The methods of Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), and Savitzky-Golay (SG) filtering were used for the purpose of noise filtering. The optimisation of each filtering technique was performed, such as cutoff frequency for the FFT, the decomposition levels and types of mother wavelets for the DWT, and the polynomial degree and number of windows for the SG filter. The optimisation was performed using a deep neural network (DNN). As a result, the DWT symlet4 level 10 with a specific filter length outperformed the FFT and SG method, with DNN accuracy reaching 97.8% and increasing the accuracy of the unfiltered signal by 5.4%. The result was then validated with other classification models. This proves that with a suitable filtering technique, the e-nose can be a reliable instrument for plant disease detection.
Why it matches plant phenotyping methods植物が放出するVOCを電子鼻で測定し、植物ウイルス感染状態を推定する信号処理・分類手法の最適化と検証が研究の中心であるため、植物病害フェノタイピング手法として収載する。
abstractthis research focused on optimising filtering techniques to improve e-nose performance in detecting pepper yellow leaf curl virus (PYLCV) infected chilli plants.
In the domain of plant morphological studies, three-dimensional scanning technologies have brought about a paradigm shift in the field of leaf structure modelling. Nevertheless, the high cost and operational complexity of these systems act as significant barriers to widespread adoption. To address this issue, a single-image 3D reconstruction pipeline was developed, focusing on potato leaves and optimized for use with mobile phone cameras. The algorithm begins with image preprocessing to enhance quality, followed by leaf instance segmentation to isolate the target leaf. Subsequently, a precise 2D leaf contour is extracted to capture planar geometry. Subsequently, 3D spatial features are recovered from a single image to infer depth information. Contour back-projection is a process that Links the extracted 2D contour to the estimated 3D space. The discretization of the 3D contour is enabled by boundary sampling, thereby facilitating the generation of an initial 3D polygonal mesh. Finally, mesh surface refinement is applied to optimize model accuracy and visual fidelity. The methodology employed in this study successfully reconstructed potato and other crop leaves, demonstrating minimal deviation in key morphological shape descriptors and negligible error in surface area measurements in comparison to the ground truth. The reconstructed models exhibited high geometric congruence with the original leaves. This demonstrates the potential of our technique to broaden the accessibility of conventional modelling approaches and to advance methodologies within the field of crop phenotyping.
Why it matches plant phenotyping methods単一画像から葉の3D形状を再構成し、形態形状記述子や表面積を推定・検証する手法開発であり、植物フェノタイピングが中心である。
abstracta single-image 3D reconstruction pipeline was developed, focusing on potato leaves and optimized for use with mobile phone cameras.
ClassificationObject detectionCalibration / preprocessingGrowth / development / phenology
Plant electrical signals are closely related to light conditions, and changes in light intensity lead to variations in the amplitude, frequency, and other characteristics of plant electrical signals. Therefore, real-time analysis of the relationship between plant electrical signals and light factors is crucial for monitoring plant growth status. In this study, Aloe Vera was chosen as the experimental subject, and electrical signal data were collected under different light intensities, followed by preprocessing including wavelet threshold denoising. Furthermore, a hybrid model architecture combining one-dimensional convolutional neural networks (1D-CNNs) and lightweight gated recurrent units (GRUs) was proposed to address the temporal signal characteristics of plant electrical signals and edge computing requirements. The 1D-CNN module extracts local spatial features, which are then modeled in time by the optimized GRU module with channel pruning. Model compression was achieved through parameter quantization. Finally, the computational and storage modules of the model were deployed on an FPGA development board using hardware description language for simulation verification. The results indicate that the system achieved a classification accuracy of 90.1%, a detection time of 43.2 ms, and a power consumption of 4.95 W, demonstrating the comprehensive advantages in terms of accuracy, response speed, and power consumption. This approach effectively improves data processing speed and reduces system power consumption while maintaining high classification accuracy, thereby providing technical support for the development of plant growth monitoring technologies.
Why it matches plant phenotyping methods植物電気信号から成長状態に関連する特徴を分類するCNN-GRUモデルを開発し、FPGA実装・性能検証まで行っており、植物状態の取得・推定手法が研究の中心である。
abstracta hybrid model architecture combining one-dimensional convolutional neural networks (1D-CNNs) and lightweight gated recurrent units (GRUs) was proposed to address the temporal signal characteristics of plant electrical signals and edge computing requirements
Abstract - We present a comprehensive study exploring quantum machine learning (QML) approaches for plant disease detection from leaf images and compare them against well-established classical counterparts. Specifically, we implement and analyze Quantum Support Vector Machines (QSVMs) vs classical SVMs, and Quantum Convolutional Neural Networks (QCNNs) vs classical CNNs. Using the widely used PlantVillage and complementary field datasets, we describe image preprocessing, classical baseline architectures, quantum data-encoding strategies, circuit-level QSVM and QCNN designs for near-term quantum devices, and hybrid training procedures. Where possible, we review literature-reported performance and propose a reproducible experimental pipeline for empirical evaluation on simulated/noisy quantum backends. We discuss expected strengths and limitations of quantum approaches (expressivity, kernel advantages, resource constraints), provide detailed evaluation metrics and ablations, and propose directions for real-device experiments and field deployment. Key takeaways: QSVM/quantum-kernel methods can provide superior separability on certain feature maps and small-to-medium-sized datasets, while QCNNs show promise as compact feature extractors for hybrid pipelines — but both approaches currently require careful circuit design and error-mitigation to outperform well-tuned classical models in realistic field settings. Key Words: QCNN, Plant Disease, SVM, CNN, QSVM
Why it matches plant phenotyping methods葉画像から植物病害を検出する量子・古典画像解析手法を比較し、前処理、モデル設計、評価指標、アブレーションを含む再現可能なパイプラインを扱っており、植物表現型（病害状態）の推定手法が中心である。
abstractWe present a comprehensive study exploring quantum machine learning (QML) approaches for plant disease detection from leaf images and compare them against well-established classical counterparts.
This research presents a hybrid Convolutional Neural Network–Support Vector Machine (CNN-SVM) approach for accurate plant disease detection, integrating CNN’s feature extraction capabilities with SVM’s robust classification performance. The methodology began with data acquisition and preprocessing, including image normalization, augmentation, and resizing to ensure model compatibility and improve generalization. The CNN component was trained to automatically extract discriminative features from plant leaf images, which were subsequently fed into an SVM classifier optimized through hyperparameter tuning. Performance evaluation employed standard metrics, including accuracy, precision, recall, and F1-score, alongside the Receiver Operating Characteristic (ROC) curve analysis. Experimental results demonstrate the hybrid CNN-SVM model’s superiority over standalone CNN and SVM models. The proposed model achieved an accuracy of 96.3%, precision of 95.8%, recall of 96.7%, and F1-score of 96.2%, outperforming the CNN (93.5% accuracy) and SVM (88.4% accuracy) baselines. Hyperparameter tuning was shown to significantly enhance classification results, as visualized in the tuning heat map. The ROC curve for the hybrid model exhibited an Area Under the Curve (AUC) close to 1.0, indicating excellent sensitivity and specificity.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定するCNN-SVM手法の開発・比較・性能評価が研究の中心であり、植物フェノタイピング手法として適格。
abstractThis research presents a hybrid Convolutional Neural Network–Support Vector Machine (CNN-SVM) approach for accurate plant disease detection
ABSTRACT Early detection of plant stress is crucial for minimizing crop loss and promoting sustainable food production. Traditional methods often fail to identify stress indicators before visible symptoms emerge. Normal images primarily capture visible signs of stress, which become apparent only after significant damage has occurred, limiting timely intervention and leading to lower accuracy in early detection due to their inability to capture hidden stress markers. In contrast, thermal images reveal temperature variations that indicate stress at an earlier stage, even before it becomes visible to the human eye, allowing for improved accuracy by identifying subtle physiological changes. The proposed system involves collecting and preprocessing thermal images of plants under varying stress conditions, enabling the detection of underlying stress through these temperature variations. A MobileNetV3 model, known for its lightweight architecture, speed, and efficiency, is trained on these thermal images to classify them into stress and non‐stress categories. The experimental results compare three deep learning models—MobileNetV3, DenseNet, and VGG16 for plant stress classification using thermal images. MobileNetV3 achieved the highest accuracy, with an average F1‐score of 0.67, significantly outperforming DenseNet and VGG16. MobileNetV3 strikes an optimal balance between accuracy and computational efficiency, outperforming more complex models while maintaining lower processing demands. This makes it particularly suitable for real‐time, on‐device applications. The proposed system harnesses these advantages to provide farmers and agronomists with an automated, non‐invasive solution for real‐time plant health monitoring.
Why it matches plant phenotyping methods熱画像から植物ストレス状態を推定し、複数の深層学習モデルを比較・評価する手法が研究の中心であるため、植物フェノタイピング手法として含める。
abstractThe proposed system involves collecting and preprocessing thermal images of plants under varying stress conditions, enabling the detection of underlying stress through these temperature variations.
3D phenotyping of plants plays a crucial role for understanding plant growth, yield prediction, and disease control. We present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants. To acquire data, a small commercially available UAV captures images of a selected plant. Apart from placing ArUco markers, the entire image acquisition process is fully autonomous, controlled by a self-developed Android application running on the drone's controller. The reconstruction task is particularly challenging due to environmental wind and downwash of the UAV. Our proposed pipeline supports the integration of arbitrary state-of-the-art 3D reconstruction methods. To mitigate errors caused by leaf motion during image capture, we use an iterative method that gradually adjusts the input images through deformation. Motion is estimated using optical flow between the original input images and intermediate 3D reconstructions rendered from the corresponding viewpoints. This alignment gradually reduces scene motion, resulting in a canonical representation. After a few iterations, our pipeline improves the reconstruction of state-of-the-art methods and enables the extraction of high-resolution 3D meshes. We will publicly release the source code of our reconstruction pipeline. Additionally, we provide a dataset consisting of multiple plants from various crops, captured across different points in time.
Why it matches plant phenotyping methodsUAV画像から植物個体の高解像度3D形状を再構成する手法と、風による葉の動きを補正する技術を開発しており、植物表現型取得が中心です。データセット提供も含みます。
abstractWe present a pipeline capable of generating high-quality 3D reconstructions of individual agricultural plants.
The automation of plant disease identification can significantly enhance agricultural productivity by enabling early intervention. This research proposes a deep learning-based multi-class image classification system using the MobileNet V3 architecture to identify 38 distinct plant disease categories. Using a publicly available image dataset from Kaggle, this study conducted a full model development pipeline including data preprocessing, exploratory data analysis, transfer learning, training with validation, and deployment via a Gradio interface. The system was evaluated using accuracy and multi-class log loss metrics and demonstrates promising results for real-time agricultural applications.
Why it matches plant phenotyping methods植物画像から病害状態を推定する画像分類手法の開発・検証が研究の中心であり、植物病害表現型の取得方法に該当する。
abstractThis research proposes a deep learning-based multi-class image classification system using the MobileNet V3 architecture to identify 38 distinct plant disease categories.
Flow cytometry (FCM) and genome sequencing are complementary methods for estimating plant genome size (GS). However, discrepancies between the GS estimates derived from genome assemblies and FCM create ambiguity regarding the accuracy of these approaches. Approximately 12,000 plant GS measurements have been reported, with hardly any of them based on genome assemblies. Currently, FCM is the most frequently used method. Accurate GS estimation by FCM relies on internal standards with known GS values. However, previous GS calibrations, often based on incomplete reference genome assemblies, have led to significant discrepancies in GS estimates. Historically, the GS of a diploid plant species was estimated by doubling the size of a consensus genome assembly. However, consensus assemblies collapse homologous chromosomes into a single sequence, typically favouring the larger haplotype and potentially overestimating GS, especially in highly heterozygous species. Here, we applied haplotype-resolved genome assemblies to accurately recalibrate the reference standards. We utilized a recent gapless, telomere-to-telomere (T2T) consensus and the most complete phased genome assemblies of the Nipponbare rice as a primary standard to recalibrate five commonly used plant standards. Using the consensus genome as a reference revealed an overestimation of over 30% in widely used previous GS estimates for Pisum sativum and Nicotiana benthamiana , approximately 18% for Arabidopsis thaliana , and 5% for Sorghum bicolor and Gossypium hirsutum . The GS estimates based on phased haplotype assemblies suggested an additional 6%–7% overestimation. Haplotype-resolved genome assemblies allow the recalibration of GS estimates with the potential to yield more accurate values by capturing haplotype-specific variations previously missed in consensus assemblies.
Why it matches plant phenotyping methods植物のゲノムサイズ推定に用いるフローサイトメトリー標準の再校正が研究の中心であり、測定精度の検証・改善に該当する。
titleRe-calibration of flow cytometry standards for plant genome size estimation
Fluorescence lifetime measurements offer crucial insights into molecular interactions and fluorophore microenvironments, with significant potential to advance research in areas like photosynthesis and field phenotyping. Currently, these measurements are largely confined to controlled laboratory settings. To overcome this limitation, we developed a portable fluorescence lifetime imaging system utilizing a dual-tap CMOS sensor. Acknowledging the unique noise characteristics of CMOS sensors and the temperature-dependent noise challenges inherent in on-site measurements, we propose a dedicated denoising method for CMOS-based fluorescence lifetime images. This procedure significantly improves the accuracy of fluorescence lifetime data and enhances image clarity, enabling on-site measurements of intact plants.
Why it matches plant phenotyping methods携帯型蛍光寿命イメージングシステムとCMOS画像の専用デノイジング法を開発し、植物の光合成関連状態をオンサイトで測定可能にすることが中心的な方法論的貢献である。
abstractwe developed a portable fluorescence lifetime imaging system utilizing a dual-tap CMOS sensor.
Satellite remote sensing is widely used for Leaf Area Index (LAI) retrievals, but calibration and validation efforts require ground Plant Area Index (PAI) values that may be converted to LAI. Digital Cover Photography (DCP) presents an affordable means for covering large regions (∼2 × 33² km²) and multiple years. The Soil Moisture Active Passive Validation Experiment conducted from 2019–2022 (SMAPVEX19–22) utilized DCP data to study vegetation impacts on soil moisture retrievals in forests. This work reports on the DCP tool “EzPAI”, its outputs for SMAPVEX19–22, and clear sky PAI bias identification and its correction. EzPAI features sky condition tracking, multi-tier data screening, data quality flagging, and two-corner thresholding. We found that PAI is overestimated in clear conditions, and our bias correction approach reduced this by 0.2 on average. Massachusetts (‘MA’) and New York (‘MB’) networks attained comparable results for cloudiness (∼67 %) and poor data quality (21 %). Benchmark comparisons to other approaches (DCP tools, Sentinel-2 LAI, LAI-2200c) showed good agreement and performance. EzPAI showed similar results for in situ and Sentinel-2 LAI comparisons, achieving R∼0.9, and some bias (MD= <0.53). For comparisons to the coveR DCP tool the correlation was 0.73 and bias 0.26. For summer PAI totals, CoveR, coverPy, EzPAI and LAI-220c obtained nearly identical results: 4.08±0.35 4.08±0.33, 4.12±0.33 and 4.16±0.77. Differences may be explained in part due to image quality issues (noted at needleleaf canopies), the LAI-2200c data being noisy (σₛₚᵣᵢₙg=0.43, σₛᵤₘₘₑᵣ=0.72), the different measurement modalities used (satellite, handheld hemispherical, DCP), and our assumption of a constant extinction coefficient (k = 0.65) across 144 site-years. PAI values ranged from 2.73 to 5.16 (3.92 average) and 2.44 to 4.96 (3.80 average) for MA and MB, respectively. Processing time on a Dell Precision Laptop 7560 was 0.87 per image, which was about 3x speedier than coveR.
Why it matches plant phenotyping methods植物面積指数（PAI）を画像から推定するDCP処理ツールEzPAIの開発、品質管理・バイアス補正、他手法とのベンチマークを中心に扱っており、植物フェノタイピング手法が明確に中心である。
abstractThis work reports on the DCP tool “EzPAI”, its outputs for SMAPVEX19–22, and clear sky PAI bias identification and its correction.
Fusarium head blight (FHB) is one of the most serious wheat diseases and mainly infects the ear, affecting the yield and quality of wheat worldwide. Segmentation of FHB infection in wheat ear based on unmanned aerial vehicle (UAV) images is feasible and significant in ensuring timely control measures and maintaining food security. The high flight altitude of UAV allows for rapid image acquisition but results in blurred textures and details, and the variability of field environment leads to missed and false segmentation. To address these problems, we first executed the super-resolution (SR) of high-altitude UAV images, and then FHB infection was segmented using a deep gate network. Specifically, an SR network called hierarchical context aggregation network (HCAN) was developed to generate clear textures and detailed characteristics of wheat efficiently through the successive fusion of various contexts. HCAN was superior to the current state-of-the-art methods with a peak signal-to-noise ratio of 29.056 dB and a structural similarity index of 0.9142. Meanwhile, a reception enrichment gate network (REGN) was applied to segment FHB infection in wheat ear through the integration of dual-gate mechanism and multi-scale convolution. REGN gained superior results to those of other segmentation networks with a mean intersection over union of 77.93 %, mean pixel accuracy of 87.43 %, and mean Dice coefficient of 87.06 %. Indistinct edges, missed segmentation, and false segmentation were dramatically alleviated in high-density, overlapping, shaded and overexposed wheat because local and neighboring gate operations enhanced the representation and reception field, and multi-scale convolution could enrich the reception diversity. In sum, the proposed approach provided a reliable, efficient, and accurate determination of FHB infection in wheat on the basis of UAV images and could be extended to the analysis of other diseases or crops.
Why it matches plant phenotyping methodsUAV画像からコムギ穂のFHB感染状態を抽出する超解像・セグメンテーション手法を開発し、性能評価しており、植物病害表現型の取得方法が中心である。
abstractREGN gained superior results to those of other segmentation networks with a mean intersection over union of 77.93 %, mean pixel accuracy of 87.43 %, and mean Dice coefficient of 87.06 %.
Functional diversity can be assessed remotely from optical sensors using vegetation index-based plant traits. Without effective corrections, employed reflectance values are affected by absorption and scattering processes in the atmosphere and on the ground, which modify radiance and irradiance values used for the reflectance retrieval. Additionally, the anisotropic nature of vegetation canopies induces observation and illumination angle-dependent reflectance variations. Often, however, the reflectance retrieval is not accurate enough to compensate for these effects in the atmosphere and on the surface, resulting in uncertain reflectance values. Furthermore, the effects in retrieved reflectance values propagate into derived products, like the vegetation indices used for calculating functional diversity, where they manifest as apparent differences between temporally close observations of the same area. A key to compensating for these effects lies in the capacity and consideration of several processing steps, such as atmospheric, topographic, and anisotropy correction. To date, it is unknown how these effects and their correction influence the estimation of functional richness. Here, we estimate functional richness based on three differently retrieved reflectance datasets in the overlapping area of three consecutively acquired flight lines with short temporal differences but with three distinct acquisition geometries. We analyze how atmospheric, topographic, and anisotropy effects influence functional richness estimates and how functional richness varies due to different observation and illumination angles. We show that reflectance data before correction for atmospheric, topographic, and anisotropy effects yield up to 15% larger median functional richness estimates compared to data after respective corrections. We discuss under which circumstances comprehensive data processing can reduce between-observation differences. Furthermore, we show that resulting functional richness estimates correlate with the number of shaded pixels (r 2 ≈ 0.7). Consequently, observations in the solar principal plane with more or fewer shadows can lead to larger or smaller functional richness estimates and to differences compared to observations perpendicular to the solar principal plane. We conclude with recommendations concerning best-suited data processing and acquisition geometry for reliable and repeatable assessments of functional richness from optical remote sensing data and discuss applications to aerial and space-based observations of functional diversity.
Why it matches plant phenotyping methods航空画像分光の反射率補正と取得ジオメトリが植物形質に基づく機能的豊かさの推定へ与える影響を検証し、信頼性・再現性のための処理と取得条件を提案しており、植物表現型推定手法が中心である。
titleData processing and acquisition geometry impact the estimation of plant trait-based functional richness from airborne imaging spectroscopy
Photogrammetry / SfM / MVSLiDAR / point cloudCalibration / preprocessingSegmentation
Accurate point cloud segmentation for plant organs is crucial for 3D plant phenotyping. Existing solutions are designed problem-specific with a focus on certain plant species or specified sensor-modalities for data acquisition. Furthermore, it is common to use extensive pre-processing and down-sample the plant point clouds to meet hardware or neural network input size requirements. We propose a simple, yet effective algorithm KDSS for sub-sampling of biological point clouds that is agnostic to sensor data and plant species. The main benefit of this approach is that we do not need to down-sample our input data and thus, enable segmentation of the full-resolution point cloud. Combining KD-SS with current state-of-the-art segmentation models shows satisfying results evaluated on different modalities such as photogrammetry, laser triangulation and LiDAR for various plant species. We propose KD-SS as lightweight resolution-retaining alternative to intensive pre-processing and down-sampling methods for plant organ segmentation regardless of used species and sensor modality.
Why it matches plant phenotyping methods植物器官の3D点群セグメンテーションと、センサー・種に依存しないサブサンプリング手法を開発・評価しており、植物フェノタイピングのための形態抽出手法が中心である。
abstractAccurate point cloud segmentation for plant organs is crucial for 3D plant phenotyping.
Diagnosis of banana plant disease is a crucial aspect of sustaining the harvest of crops and their quality. Visual inspection of certain diseases like Black Sigatoka, Panama disease, and aphids is not easy and can lead to misjudgments. Generally, traditional deep learning approaches have been previously used but they have not performed well in addressing issues of class imbalance, sensitive disease differentiation and noisy images obtained in the field. Furthermore, most models are based on a collection of predetermined preprocessing methods and single-path networks that limit their ability to generalize to a wide variety of environments. Current methods of deep learning tend to achieve reasonable overall performance but fail to perform well on key performance indicators such as recall and F1-score when considering underrepresented and overlapping classes, such as Yellow and Black Sigatoka. Such constraints impede efficient field implementation, as diseases of minority classes are often falsely classified. To overcome these deficiencies, we develop a novel Duel-Path Attention Fusion Network (DPAFNet) that is trained utilizing adaptive quantum monarch butterfly optimization (AQMBO). The concept behind the proposed model is to feed MaxViT and HorNet-S two feature extractors to deliver global contextual details and minute-scale textural features. The traditional filters which do a reasonable job in handling dynamic noise and contrast are replaced by a learnable preprocessing unit. The cross-layer fusion attention encourages interclass discriminative learning of diseased plants. The suggested model has been trained and tested on an open-source dataset of Mendeley banana disease, which includes 5,170 images in 7 disease categories and 1 control condition. The accuracy, F1-score and MCC of 98.6% and 0.93 and 0.87 respectively (achieved experimentally) demonstrate the superiority of DPAFNet over baseline models such as EfficientNetB0 (accuracy 95.0%), DenseNet121 and ResNet50 (accuracy 93.50% and 92.0% respectively). As can be seen, the model had a 0.26-0.48 increase in F1-score in the challenging Panama disease category. These results prove that the proposed architecture can be successfully used to achieve high-accuracy disease classification in smart agriculture that is robust and prepared for field implementation.
Why it matches plant phenotyping methodsバナナ植物の病徴画像から病害状態を推定する深層学習手法を開発し、データセットとベースラインで性能検証しており、植物フェノタイピング手法が中心である。
abstractTo overcome these deficiencies, we develop a novel Duel-Path Attention Fusion Network (DPAFNet) that is trained utilizing adaptive quantum monarch butterfly optimization (AQMBO).
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 suggested model has been trained and tested
on an open-source dataset of Mendeley banana disease, which includes 5,170 images in 7
disease categories and 1 control condition.Open asset ↗pdf-page:1 lines:1-55Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published8 Sept 2025Workshop Proceedings of the 54th International Conference on Parallel ProcessingCited by 0 · OpenAlex ↗
Field / plotGrowth chamberLiDAR / point cloudMultispectral / hyperspectralX-ray / CTCalibration / preprocessing
This paper presents a systemic framework designed to advance multi-modal plant phenotyping research through the strategic use of high-performance computing (HPC) in agricultural science. Our framework addresses the unique challenges in high-throughput plant phenotyping (HTP) research, which integrate multi-mode imaging sensors such as RGB, hyperspectral, LiDAR, thermal, and X-ray computed tomography (CT) across indoor and outdoor environments. Our primary objective is to transform raw sensor data into research-ready plant phenotypical traits. This directly supports downstream agricultural research, such as in plant breeding, crop management, etc. The framework is structured into two main phases. The initial data processing occurs on non-HPC systems, utilizing operating system and license-specific software due to the specialized algorithms and knowledge required for each imaging data pipeline. The subsequent HPC phase refines these processed datasets into tabular formats, preparing them for statistical analysis in agronomy, plant science, and bioinformatics. The current implementation of the second phase utilizes a hybrid parallelization model across HPC nodes and threads. However, its performance could be significantly enhanced by implementing more efficient algorithms and optimizing resource allocation. We discuss current bottlenecks, including technical challenges related to sensors, imaging platforms, and computational pipelines, and propose immediate solutions.
Why it matches plant phenotyping methodsHPCを用いて多モーダルセンサーデータから植物形質を抽出する計算フレームワークが研究の中心であり、植物フェノタイピング基盤として該当する。
abstractThis paper presents a systemic framework designed to advance multi-modal plant phenotyping research through the strategic use of high-performance computing (HPC) in agricultural science.
Color accuracy and consistency in remote sensing imagery are crucial for reliable plant health monitoring, precise growth stage identification, and stress detection. However, without effective color correction, variations in lighting and sensor sensitivity often cause color distortions between images, compromising data quality and analysis. This study introduces a novel in-flight color correction approach for RGB imagery using cooperative dual unmanned aerial vehicle (UAV) flights integrated with a color chart (CoF-CC). The method employs a master UAV equipped with an RGB camera for image acquisition and a synchronized secondary UAV carrying a ColorChecker (X-Rite) chart, ensuring persistent visibility of the chart within the imaging field of the master UAV for the calculation of a color correction matrix (CCM) for in-flight image correction. Field experiments validated the method by analyzing cross-sensor color consistency, assessing color measurement accuracy on field-grown rice leaves, and demonstrating its practical applications using rice maturity estimation as an example. The results indicated that the CCM significantly enhanced color accuracy, with a 66.1 % reduction in the average CIE 2000 color difference (ΔE), and improved color consistency among the six RGB sensors, with a 70.2 % increase in the intracluster distance. CoF-CC subsequently reduced ΔE from 18.2 to 5.0 between the corrected rice leaf color and ground-truth measurements, indicating that the color differences were nearly perceptible to the human eye. Moreover, the corrected imagery significantly enhanced the rice maturity prediction accuracy, improving the R 2 from 0.28 to 0.67. In summary, the CoF-CC method standardizes RGB images across diverse lighting conditions and sensors, demonstrating robust performance in color analysis and interpretation under open-field conditions.
Why it matches plant phenotyping methods植物葉の色および成熟度を推定するためのUAV画像色補正法を開発し、圃場で精度検証と成熟度推定への適用を行っており、フェノタイピング手法が中心である。
abstractThis study introduces a novel in-flight color correction approach for RGB imagery using cooperative dual unmanned aerial vehicle (UAV) flights integrated with a color chart (CoF-CC).
Reproduction assets foundThe paper's UAV image datasets are explicitly deposited on GitHub (RiceUAVImageData); labelme is a generic third-party tool, not a paper-specific asset.Dataset · publicThe UAV image datasets used in this study are openly available in the GitHub repository at https://github.com/GaryLXQ/RiceUAVImageData .Open asset ↗GaryLXQ/RiceUAVImageData · GaryLXQ/RiceUAVImageDatalines:179-198Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Root hair counting is a specialized aspect of plant biology and agronomy research that offers valuable insights into plant health, nutrient uptake, and overall growth potential. Root hairs are tiny extensions from the root epidermis that significantly increase the surface area and constitute roughly 70% of the total root area of a plant root system, enhancing the ability of plants to absorb water and nutrients from the soil. Understanding the importance of root hair counting involves looking at various aspects of plant physiology and soil–plant interactions. Despite these benefits, counting root hairs, especially manually, can be tedious, time-consuming, and, more often, inaccurate due to differences in the perception of individuals. Therefore, we have proposed a novel method for root hair counting and further observed an improvement in root hair count measurements when utilizing image super-resolution as a preprocessing step. Our approach of counting root hairs can tackle real-world challenges and be able to count overlapping hairs as well. By visualizing the rhizosphere in binary space, we can see a considerable increase in root hair count from 37 to 68 when counting manually to our approach for Bell pepper, and from 44 to 88, when counting manually to our method for Arabidopsis root images. To the best of the authors’ knowledge, this research study is specifically designed for root hair counting and measurement improvement using super-resolution, is the first of its kind, and has yet to be acknowledged.
Why it matches plant phenotyping methods植物の根毛数という形態形質を画像から自動計測する新規手法を開発し、超解像前処理による測定改善も検証しており、フェノタイピング手法が中心である。
titleA Novel Approach for Plant Root Hair Counting and its Improvement via Image Super-Resolution
Ray-tracing models enable the assessment of light quantity and quality intercepted by plant organs, supporting biological studies in growth chambers with varying light conditions. However, their validation within canopies and clear usage methods remain limited. This work establishes a reliable method for using these models. The method includes i) accounting for the intensity and spectrum of light sources in the calibration procedure; ii) a generic calibration strategy using a few well-placed light measurement points based on chamber geometry. It evaluates the method to simulate light phylloclimate at the organ scale across biologically relevant wavebands of contrasted widths and properties. Using the SEC2 light simulation framework, three virtual experiments were conducted in a growth chamber, with and without rose plants. Inputs included chamber geometry, material optical properties, lamp emissions, and digitised plant mock-ups. Simulations were compared with spectral measurements at various chamber positions and sensor orientations, both without plants and inside a canopy. Results showed high accuracy in replicating spatial light variability, with RMSE ranging 0.011 to 0.021 and 0.014–0.038 μmol m⁻²s⁻¹nm⁻¹ across different wavebands and sensor orientations, for vertical and horizontal transects, respectively. Applying this approach to a case study demonstrated its effectiveness in formulating new biological hypotheses regarding the role of local light in regulating bud outgrowth. This was achieved by highlighting differences in phylloclimate induced by variations in plant architecture. This work thus provides a comprehensive framework for facilitating the application of ray-tracing models in growth chamber studies.
Why it matches plant phenotyping methods植物器官スケールの光環境を推定するレイトレーシング手法を開発・較正し、実測値との検証まで行っており、植物の器官状態・アーキテクチャに関わる再利用可能な計測ワークフローが中心です。
abstractThis work establishes a reliable method for using these models.
This study proposes a reinforcement learning-based hyperspectral feature band selection method, Reinforcement Intelligence for Spectral Enhancement (RISE), for the non-destructive detection of sugar content in citrus. The band selection problem is modelled as a Markov decision process, and an optimal feature optimisation strategy is learned through a deep Q network. A total of 120 citrus pulp samples with sugar content ranging from 6.40 to 10.81 °Brix were collected. Hyperspectral data (388.34–1036.34 nm) containing 256 continuous bands were collected in the experiment, and compared with the traditional CARS (9 bands selected) and BOSS (10 bands selected) algorithms. The results show that the RISE algorithm selected 19 characteristic bands that obtained the best prediction performance (R² = 0.84, RPD = 2.51) on the PLSR model, and maintained consistent performance across multiple prediction models including SVR (R² = 0.85, RPD = 2.57), Random Forest (R² = 0.84, RPD = 2.47) and XGBoost (R² = 0.84, RPD = 2.53). The visualization of the spatial distribution of sugar content in citrus fruits based on the RISE algorithm revealed a gradient distribution feature that decreases from the outside to the inside. The study confirms the application potential of the RISE algorithm in non-destructive testing of agricultural product quality and provides a new technical path for hyperspectral imaging technology in agriculture.
Why it matches plant phenotyping methods柑橘果实糖含量是植物器官性状，研究核心是开发并比较基于高光谱数据的特征波段选择与无损性状预测方法，而非例行测量。
abstractThis study proposes a reinforcement learning-based hyperspectral feature band selection method, Reinforcement Intelligence for Spectral Enhancement (RISE), for the non-destructive detection of sugar content in citrus.
Field / plotWhole plant / canopy / plot / fieldClassificationCalibration / preprocessingGrowth / development / phenology
Understanding and monitoring the phenological phases of trees is essential for ecological research and climate change studies. In this work, we present a comprehensive evaluation of state-of-the-art convolutional neural networks (CNNs) and transformer architectures for the automated classification of the flowering phase of Tilia cordata Mill. (small-leaved lime) based on a large set of real-world images acquired under natural field conditions. The study introduces a novel, automated image quality filtering approach using an XGBoost classifier trained on diverse exposure and sharpness features to ensure robust input data for subsequent deep learning models. Seven modern neural network architectures, including VGG16, ResNet50, EfficientNetB3, MobileNetV3 Large, ConvNeXt Tiny, Vision Transformer (ViT-B/16), and Swin Transformer Tiny, were fine-tuned and evaluated under a rigorous cross-validation protocol. All models achieved excellent performance, with cross-validated F1-scores exceeding 0.97 and balanced accuracy up to 0.993. The best results were obtained for ResNet50 and ConvNeXt Tiny (F1-score: 0.9879 ± 0.0077 and 0.9860 ± 0.0073, balanced accuracy: 0.9922 ± 0.0054 and 0.9927 ± 0.0042, respectively), indicating outstanding sensitivity and specificity for both flowering and non-flowering classes. Classical CNNs (VGG16, ResNet50, and ConvNeXt Tiny) demonstrated slightly superior robustness compared to transformer-based models, though all architectures maintained high generalization and minimal variance across folds. The integrated quality assessment and classification pipeline enables scalable, high-throughput monitoring of flowering phases in natural environments. The proposed methodology is adaptable to other plant species and locations, supporting future ecological monitoring and climate studies. Our key contributions are as follows: (i) introducing an automated exposure-quality filtering stage for field imagery; (ii) publishing a curated, season-long dataset of Tilia cordata images; and (iii) providing the first systematic cross-validated benchmark that contrasts classical CNNs with transformer architectures for phenological phase recognition.
Why it matches plant phenotyping methods植物の開花フェノロジーを画像から分類する手法を開発・比較評価し、画像品質フィルタ、モデルベンチマーク、データセットを中心的に扱うため、植物フェノタイピング手法研究として含める。
abstractThe study introduces a novel, automated image quality filtering approach using an XGBoost classifier trained on diverse exposure and sharpness features to ensure robust input data for subsequent deep learning models.
• In this study, we take tomato as an example and propose an improved deep learning combined with clustering algorithm for plant point cloud segmentation. First, a multi-temporal tomato point cloud dataset is constructed by combining multi-view image sequences with neural radiation field (NeRF), and preprocessing and labeling are completed; second, single channel attention (SCA) and global feature aggregation module (GFA) are introduced into the PointNet++ model, respectively, to construct the TomatoSegNet model, which improves the tomato dataset's semantic segmentation performance, while the edge filter was added to the DBSCAN algorithm to improve it and enhance the instance segmentation performance of canopy leaves; finally, a total of six phenotypic parameters were extracted based on the segmented organs. The experimental results show that the TomatoSegNet model has an average precision (mP) of 97.82%, an average recall (mR) of 98.62%, an average F1 score (mF1) of 97.97%, an average intersection and merger ratio (mIoU) of 96.84%, and an overall accuracy (OA) of 94.22% in the tomato dataset, which proves that the use of semantic segmentation algorithms feasibility of stem and leaf segmentation; the improved DBSCAN algorithm achieved an instance segmentation accuracy of 96.03% for leaves, which improved the segmentation accuracy of overlapping leaves; the coefficients of determination between the measured and calculated values of the six phenotypic parameters (plant height, stem thickness, leaf inclination, leaf length, leaf width, and leaf area) were 0.983, 0.903, 0.916, 0.962, 0.951, and 0.978. The method proposed in this study realizes the accurate segmentation and extraction of phenotypic parameters from the 3D point cloud of plants, which provides a valuable reference for automated phenotypic analysis of plants.. In plant phenotyping research, accurate organ segmentation and phenotype extraction is the key to accelerate the process of big data analysis and intelligent breeding.In this study, we take tomato as an example and propose an improved deep learning combined with clustering algorithm for plant point cloud segmentation. First, a multi-temporal tomato point cloud dataset is constructed by combining multi-view image sequences with neural radiation field (NeRF), and preprocessing and labeling are completed; second, single channel attention (SCA) and global feature aggregation module (GFA) are introduced into the PointNet++ model, respectively, to construct the TomatoSegNet model, which improves the tomato dataset's semantic segmentation performance, while the edge filter was added to the DBSCAN algorithm to improve it and enhance the instance segmentation performance of canopy leaves; finally, a total of six phenotypic parameters were extracted based on the segmented organs. The experimental results show that the TomatoSegNet model has an average precision (mP) of 97.82%, an average recall (mR) of 98.62%, an average F1 score (mF1) of 97.97%, an average intersection and merger ratio (mIoU) of 96.84%, and an overall accuracy (OA) of 94.22% in the tomato dataset, which proves that the use of semantic segmentation algorithms feasibility of stem and leaf segmentation; the improved DBSCAN algorithm achieved an instance segmentation accuracy of 96.03% for leaves, which improved the segmentation accuracy of overlapping leaves; the coefficients of determination between the measured and calculated values of the six phenotypic parameters (plant height, stem thickness, leaf inclination, leaf length, leaf width, and leaf area) were 0.983, 0.903, 0.916, 0.962, 0.951, and 0.978. The method proposed in this study realizes the accurate segmentation and extraction of phenotypic parameters from the 3D point cloud of plants, which provides a valuable reference for automated phenotypic analysis of plants.
Why it matches plant phenotyping methods3D点群の生成、深層学習・クラスタリングによる器官分割、6種類の植物表現型抽出を中心に開発・検証した研究であり、植物フェノタイピング手法が明確に中核である。
abstractpropose an improved deep learning combined with clustering algorithm for plant point cloud segmentation
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 6 Sept 2026
Abstract In recent years, non-destructive and non-invasive methods for 3D plant reconstruction have gained importance in plant phenotyping. Morphological traits reflect a plant’s physiological status and serve as key indicators for precision agriculture, crop protection, and food quality assessment. Accurate and efficient 3D modelling enables objective, repeatable monitoring of plant development and health, supporting data-driven decision-making in agricultural and food research. This study presents a cost-effective and flexible photogrammetric methodology for analysing plant morphological traits under controlled laboratory conditions. The system includes an industrial RGB camera mounted on a robotic arm, a rotating platform with an adjustable plant holder, and stable illumination. The key steps involved camera calibration, exposure optimisation, fine-tuning of evaluation algorithm parameters (tweaks), setting the optimal camera-to-object distance, and reducing computational load for 3D model evaluation. Comparative testing revealed that the most effective calibration strategy integrated simultaneous calibration, pre-calibrated parameters, and adaptive fitting, ensuring high reconstruction accuracy and consistent model quality. The optimal acquisition parameters were a 50 milliseconds exposure time, a tweak value of 0.9, and a 16 cm camera-to-object distance. Using more camera positions with fewer frames per position proved more efficient than the reverse. The optimal configuration consisted of three height levels with 40 frames each. Automation and data reduction led to a 75% decrease in processing time, reducing the scan time from 8 minutes to 2.7 minutes per plant. The developed method proved to be a reliable, reproducible, and affordable tool for routine 3D analysis of plant morphology via close-range photogrammetry.
Why it matches plant phenotyping methods植物形態を取得するSfM-MVSフォトグラメトリ法と撮像・校正・処理条件を開発、比較検証しており、植物フェノタイピング手法が研究の中心である。
abstractThis study presents a cost-effective and flexible photogrammetric methodology for analysing plant morphological traits under controlled laboratory conditions.
Maize, as one of the most important crops, plays a key role in phenotypic research, which promotes the development of precision agriculture and the in-depth exploration of the gene-phenotypic association mechanism. However, traditional phenotyping methods relying on manual measurements or two-dimensional images have significant limitations in terms of efficiency and accuracy, particularly in effectively analyzing the complex three-dimensional structures of plants. To address these challenges, this paper proposes a high-precision automatic segmentation method for maize stem and leaf organs based on 3D point clouds. The method consists of the following three core modules: (1) point cloud rotation correction preprocessing using a directed bounding box; (2) coarse segmentation of the stem and leaf using a dynamic root-shoot radius adjustment strategy; (3) fine segmentation incorporating dynamic misclassification detection and re-clustering mechanisms. The proposed method was systematically evaluated on maize plant point cloud data at multiple growth stages, and compared with manually annotated results. Experimental results show that the method achieved an average precision of 0.944, average recall of 0.915, Micro-F1 score of 0.920, and average overall accuracy of 0.935, demonstrating excellent segmentation performance. Furthermore, seven key phenotypic parameters, including plant height, crown diameter, stem height, stem diameter, number of leaves, leaf length, and leaf width, were automatically extracted, with the results showing highly significant correlations with manual measurements. This study provides effective technical support for high-precision 3D segmentation of maize stem and leaf organs and automated phenotypic analysis, laying a solid foundation for high-throughput plant phenotyping research and 3D reconstruction applications.
Why it matches plant phenotyping methodsトウモロコシの3D点群から茎葉を自動分割し、複数の表現型形質を抽出する手法の開発と手動測定との検証が研究の中心である。
abstractthis paper proposes a high-precision automatic segmentation method for maize stem and leaf organs based on 3D point clouds.
Aiming to address the problems of asynchronous acquisition time of multiple sensors in the crop phenotype acquisition system and high cost of the acquisition equipment, this paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol, realizing the synchronous acquisition of three types of crop data: visible light images, thermal infrared images, and laser point clouds. The paper innovatively proposed the Difference Structural Similarity Index Measure (DSSIM) index, combined with statistical indicators (average point number difference, average coordinate error), distribution characteristic indicators (Charm distance), and Hausdorff distance to characterize the stability of the system. After 72 consecutive hours of synchronization testing on the timing boards, it was verified that the root mean square error of the synchronization time for each timing board reached the ns level. The synchronous trigger acquisition time for crop parameters under time synchronization was controlled at the microsecond level. Using pepper as the crop sample, 133 consecutive acquisitions were conducted. The acquisition success rate for the three phenotypic data types of pepper samples was 100%, with a DSSIM of approximately 0.96. The average point number difference and average coordinate error were both about 3%, while the Charm distance and Hausdorff distance were only 1.14 mm and 5 mm. This system can provide hardware support for multi-parameter acquisition and data registration in the fast mobile crop phenotype platform, laying a reliable data foundation for crop growth monitoring, intelligent yield analysis, and prediction.
Why it matches plant phenotyping methods作物表現型取得のための低コスト・高速・時刻同期システムを開発し、可視画像、熱画像、レーザ点群の取得性能と安定性を検証しており、表現型取得法が中心である。
abstractthis paper developed a low-cost crop phenotype synchronous acquisition system based on the PTP synchronization protocol
LiDAR / point cloudOrgan identificationCalibration / preprocessingSegmentation
To address the issues in existing 3D point cloud facet generation networks, specifically, the tendency to produce a large number of empty facets and the uncertainty in facet count, this paper proposes a novel deep learning framework for robust facet segmentation. Based on the generated facet set, two exploratory applications are further developed. First, to overcome the bottleneck where inaccurate empty-facet detection impairs the downsampling performance, a facet-abstracted downsampling method is introduced. By using a learned facet classifier to filter out and discard empty facets, retaining only non-empty surface facets, and fusing point coordinates and local features within each facet, the method achieves significant compression of point cloud data while preserving essential geometric information. Second, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed. The network first predicts the edge scores for the facets to determine the seed facets. The facets are then iteratively expanded according to adjacent-facet similarity until a complete organ region is enclosed, thereby enhancing the accuracy of segmentation across semantic boundaries. Finally, the proposed facet segmentation network is trained and validated using a synthetic dataset. Experiments show that, compared with traditional methods, the proposed approach significantly outperforms both downsampling accuracy and instance segmentation performance. In various crop scenarios, it demonstrates excellent geometric fidelity and semantic consistency, as well as strong generalization ability and practical application potential, providing new ideas for in-depth applications of facet-level features in 3D point cloud analysis.
Why it matches plant phenotyping methods作物3D点群から器官を抽出・分割する画像解析手法を開発し、合成データで検証しており、植物形態の取得が中心的な技術貢献です。
abstractSecond, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed.
This study presents an end-to-end workflow Segment Any Strawberry Plant (SASP) for estimating strawberry canopy volume from multi-view images. The approach utilizes several recent advances in computer vision and 3D reconstruction. First, a Planar-based Gaussian Splatting Reconstruction (PGSR) method is employed to generate high-fidelity 3D point clouds of strawberry plants, offering improved geometric consistency compared to standard 3D Gaussian Splatting. Next, the Segment Any 3D Gaussians (SAGA) framework is adapted with fully automated prompts derived from YOLO (You Only Look Once) detection and color-based prompt selection which can eliminate the need for manual user input in the segmentation process. The resulting point clouds of plant canopies are calculated via concave hull to estimate their volumes. A reference box of known volume is included in the scene as a calibration object, mapping computed volumes from the virtual 3D space into real-world measurements. Experimental evaluations show that the proposed method achieves high segmentation quality and offers volume estimates across multiple plant shapes. This end-to-end pipeline addresses both the labor-intensive nature of manual canopy measurements and the computational complexity of large-scale 3D reconstructions, offering a potential for high-throughput phenotyping and yield prediction in future strawberry cultivation studies.
Why it matches plant phenotyping methodsイチゴ植物の3D画像から樹冠体積を推定するエンドツーエンド手法を開発・評価しており、植物形態形質の取得が研究の中心である。
abstractThis study presents an end-to-end workflow Segment Any Strawberry Plant (SASP) for estimating strawberry canopy volume from multi-view images.
Low-cost multispectral sensors have recently been commercialized, paving the way for decision support in a wide variety of agricultural applications (fertilization, grass cover management, etc.). Such sensors have seldom been tested for agricultural applications taking into account practical constraints (external environment, expected accuracy, etc.). This study proposes to investigate the measurement characteristics of the “AS7265x” (by AMS) which, with 18 spectral bands ranging from blue to near infrared light and a low cost, presents a real potential for a wide range of applications in agriculture, and especially in viticulture. This work proposes a holistic approach including tests of the “AS7265x” in controlled conditions to validate the measurement characteristics in terms of accuracy, repeatability and reproducibility as well as experiments carried out in the field to validate the potential of the sensor in assessing agronomic information in outdoor conditions. Three different “AS7265x” sensors were tested in controlled conditions to assess the accuracy and the repeatability of the 18 different spectral bands as well as the reproducibility of the measurements from one sensor to another. To assess the sensor’s suitability for field applications, two experiments were conducted using different vegetation indices (VIs). The first experiment involved proximal sensing to estimate weed coverage on the soil surface, categorized into five classes ranging from class 1 (0–20%) to class 5 (80–100%), and to evaluate plant vigor using the NDVI. The second experiment utilized contact mode with active light to estimate the nitrogen status of grapevine leaves using four adapted VIs: BGI, SIPI, NI_Tian, and NI_Wang. These two applications were chosen to explore the potential of the large diversity of wavebands available on the sensor as well as different acquisition modes (proximal detection, contact detection). For both experiments, results were compared to specific sensors intended as reference for the considered measurements. Regarding measurement characteristics in controlled conditions, results show that although accurate, the sensors present some bias specific to each spectral band and each sensor. These drawbacks require each sensor to be specifically calibrated before use which may limit their dissemination in agriculture. Once calibrated, results of proximal NDVI measurements performed with the sensor are consistent when compared to a Greenseeker (R²=0.87 for NDVI < 0.75). The sensor also allows to discriminate significantly (p < 0.05) two levels of vine vigour when plants were grown in different conditions. Regarding the measurement of nitrogen status, the sensor shows a good correlation (R² = 0.78) between NI_Wang index and the NBI reference value observed with the Dualex sensor. This work highlights the potential of “AS7265x” sensor to access objective agronomic information. It also highlights its limitations when it comes to acquire reference data. In this case, the quality of the sensor requires a calibration procedure specific to each sensor for each waveband.
Why it matches plant phenotyping methods低コストマルチスペクトルセンサーの精度・反復性・再現性を検証し、NDVIによるブドウ樹勢および葉の窒素状態の推定に適用している。センサー校正と植物形質取得が研究の中心である。
abstractThis study proposes to investigate the measurement characteristics of the “AS7265x” (by AMS)
In recent years, phenomic prediction has emerged as a new method in plant breeding that has been shown to have great potential. However, there are still many open questions regarding its practical application. For example, in the field of spectroscopy, it is standard practice to optimize the preprocessing of spectra, which so far has only been done to a limited extent for phenomic prediction. In this study, we therefore used three different data sets of soybean, triticale and maize to identify the best combinations of Savitzky–Golay filter parameters for preprocessing near‐infrared spectra for phenomic prediction. We tested 677 combinations of polynomial order, derivative and window size and evaluated them with Monte Carlo cross‐validation. Our results showed that the predictive ability can be improved with the right settings. However, there was no global optimum that gave the best results for all data sets. Even for different traits within the same data set, different combinations of parameters were necessary to achieve the highest predictive ability. Nevertheless, we show that some combinations generally result in a very low predictive ability and should not be used for preprocessing. In addition, we used the normalized discounted cumulative gain to assess whether preprocessing affected the ranking of individuals, which revealed no major changes in the top 1%, 10% or 20% of predicted individuals. Taken together, our results show the potential of preprocessing near‐infrared spectroscopy data to improve the phenomic predictive ability, but there appears to be no global optimum of parameter settings across data sets and traits.
Why it matches plant phenotyping methods植物育種におけるNIRSスペクトル前処理を最適化し、複数作物・形質で交差検証して予測性能と個体順位への影響を評価しており、表現型予測手法が研究の中心である。
abstractwe therefore used three different data sets of soybean, triticale and maize to identify the best combinations of Savitzky–Golay filter parameters for preprocessing near‐infrared spectra for phenomic prediction.
The early and accurate detection of plant diseases plays a pivotal role in enhancing crop health and ensuring food security in modern agriculture. Traditional disease diagnosis techniques often rely on visual inspection by experts, which may be subjective, time-consuming, and inaccessible to farmers in remote regions. To overcome these limitations, this project introduces an intelligent plant disease detection system that leverages deep learning and image analysis to identify symptoms directly from leaf images. The proposed methodology begins with the collection of a curated dataset comprising various plant species exhibiting both healthy and diseased conditions. The images undergo preprocessing to enhance quality and ensure consistency, followed by feature extraction using Convolutional Neural Networks (CNNs). Transfer learning is applied to improve model generalization and reduce the training time by utilizing pre-trained models. The system is integrated into a Flask-based web application, enabling users to upload leaf images and receive instant disease diagnoses along with treatment suggestions and suitable fertilizers. Evaluation of the model has shown high classification accuracy across multiple disease classes, affirming its potential to support precision agriculture. The solution is designed to be lightweight, user-friendly, and deployable in real-world agricultural settings, aiming to assist farmers with timely and informed interventions to mitigate crop loss and promote sustainable farming practices.
Why it matches plant phenotyping methods葉画像から植物の病徴を深層学習で抽出・分類するシステム開発が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractthis project introduces an intelligent plant disease detection system that leverages deep learning and image analysis to identify symptoms directly from leaf images.
Reproduction assets foundThe paper's only qualifying paper-specific asset is the PlantVillage leaf-image dataset, which the authors explicitly state they used for training and evaluation. No author analysis code, trained model checkpoints, or supplementary data deposit is mentioned with an availability statement or URL.Dataset · publicThis project uses the PlantVillage dataset, a
well-known open-source repository that contains labeled images of healthy and diseased plant leaves.Open asset ↗PlantVillagepdf-page:3 lines:1-55Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 6 Sept 2026
Abstract This paper presents a novel approach to automated plant disease detection and classification using advanced image processing and deep learning techniques. Early detection of plant diseases is crucial for sustainable agricultural practices and food security. Our proposed system leverages convolutional neural networks (CNNs) to analyze leaf images and accurately identify various plant diseases across multiple crop species. The methodology includes image preprocessing, segmentation, feature extraction, and classification using a custom CNN architecture. The system was trained and validated on a diverse dataset containing 38,000 images spanning 14 crop species and 26 diseases. Experimental results demonstrate 97.89% classification accuracy, outperforming existing methods. The system is implemented as a lightweight mobile application allowing farmers to diagnose plant diseases in real-time using only a smartphone camera, potentially reducing crop losses and pesticide usage through early intervention. This research contributes to precision agriculture by providing an accessible, cost-effective tool for disease management in both developed and developing agricultural contexts.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する画像解析・CNN手法の開発と検証が研究の中心であり、植物フェノタイピング手法に該当する。
abstractThis paper presents a novel approach to automated plant disease detection and classification using advanced image processing and deep learning techniques.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract This paper introduces an efficient IoT-based framework for predicting crop damage within smart agricultural systems, focusing on the integration of Internet of Things (IoT) sensor data with advanced machine learning (ML) and ensemble learning (EL) techniques. The primary objective is to develop a reliable decision support system capable of forecasting crop health status classifying crops as healthy, pesticide-damaged, or affected by other stressors while addressing a critical challenge: the presence of missing data in real-time agricultural datasets. To overcome this limitation, the proposed approach incorporates robust data imputation strategies using both traditional ML methods and powerful EL models. Techniques such as K-Nearest Neighbors, linear regression, and ensemble-based imputers are evaluated for their effectiveness in reconstructing incomplete data. Furthermore, Bayesian Optimization is applied to fine-tune EL classifiers including XGBoost, CatBoost, and LightGBM (LGBM), enhancing their predictive performance. Extensive experiments demonstrate that XGBoost outperforms all other models, achieving an average sensitivity of 88.1%, accuracy of 89.56%, precision of 83.4%, and F1-score of 84.8%. CatBoost and LGBM also deliver competitive results, with CatBoost achieving 90.50% accuracy and LGBM reaching 90.23%. In addition, the imputation capability of the XGBoost model is validated through a low Mean Squared Error (MSE) of 0.0213 and a high R-squared (R 2 ) value of 0.99, confirming its effectiveness for both prediction and data recovery tasks. The key contributions of this innovative work include the design of a low-cost, power-efficient, and scalable crop damage prediction system, the integration of real-time IoT data with optimized ensemble learning, and a comprehensive evaluation of imputation techniques to enhance model robustness. This framework is particularly suited for deployment in resource-constrained agricultural environments, advancing the field of smart farming through intelligent, data-driven solutions.
Why it matches plant phenotyping methodsIoTセンサーデータから作物の健康・損傷状態を推定する機械学習ワークフローが研究の中心であり、植物状態の取得・分類法として実質的に評価されている。
abstractThe primary objective is to develop a reliable decision support system capable of forecasting crop health status classifying crops as healthy, pesticide-damaged, or affected by other stressors
Recent advancements in data science and artificial intelligence have significantly transformed plant sciences, particularly through the integration of image recognition and deep learning technologies. These innovations have profoundly impacted various aspects of plant research, including species identification, disease detection, cellular signaling analysis, and growth monitoring. This review summarizes the latest computational tools and methodologies used in these areas. We emphasize the importance of data acquisition and preprocessing, discussing techniques such as high-resolution imaging and unmanned aerial vehicle (UAV) photography, along with image enhancement methods like cropping and scaling. Additionally, we review feature extraction techniques like colour histograms and texture analysis, which are essential for plant identification and health assessment. Finally, we discuss emerging trends, challenges, and future directions, offering insights into the applications of these technologies in advancing plant science research and practical implementations.
Why it matches plant phenotyping methods植物画像処理と深層学習の手法・パイプラインを扱うレビューで、病害検出や生長モニタリングなど植物状態の画像ベース推定を方法論として整理している。
titleThe pipelines of deep learning-based plant image processing.
Abstract. Extensive urban expansion has significantly impacted green spaces leading to the degradation of urban vegetation. Hence, monitoring variations in vegetation using remote sensing methods is essential. However, 2D remote sensing methods have drawbacks as they lack vertical structures in urban areas, shadows caused by buildings, cloud cover and require substantial preprocessing to encounter these limitations. This study focuses on identifying and quantifying changes in Malminkartano, Helsinki during the leaf-off and leaf-on seasons for the year 2022. The research utilized terrestrial laser scanning (TLS) and UAV-photogrammetry datasets for change detection in urban vegetation and point cloud-based algorithms for seasonal variations such as C2C, C2M, and M3C2. Notably, many existing methods involve rasterizing point clouds as DSM which results in the loss of significant information. Therefore, this paper investigates the potential of utilized datasets in detecting changes directly on point clouds. However, there are uncertainties associated with point clouds including data registration, point density, weather effects, and misalignment therefore this study aims to take these limitations into account. The results from TLS and UAV-photogrammetry demonstrated competence in identifying the maximum growth of urban vegetation up to 2.0 m and 2.8 m respectively. However, the accuracy assessment of data corresponded to a 4 cm difference in both datasets at a 95% confidence threshold and potential vertical height differences accounted for the difference in change detection. This study underscores data processing uncertainties associated with registration, vertical height, and data noise and proposes the integration of point clouds with different sensors for completeness and improved change detection in urban vegetation.
Why it matches plant phenotyping methodsUAVフォトグラメトリと地上レーザースキャンを比較し、点群処理によって都市植生の成長量・高さ変化を定量化する技術的評価が中心である。
abstractThis study focuses on identifying and quantifying changes in Malminkartano, Helsinki during the leaf-off and leaf-on seasons for the year 2022.
Accurate estimation of plant disease severity is pivotal for effective management and decision-making. Field experiments were conducted to understand the correlation and predict the yellow mosaic disease severity in yard-long beans using visible image indices. A total of 45 visible / Red Green Blue (RGB) indices were derived from the RGB images and correlated with disease severity, and also used as inputs for predicting disease severity using nine machine learning (ML) models. Out of 143 genotypes screened based on final disease severity 3, 18, 18, 17, 34 and 53 genotypes were grouped in immune, resistant, moderately resistant, moderately susceptible, susceptible and highly susceptible categories, respectively. Model performances was evaluated using R 2 , d-index, mean bias error, and normalized Root Mean Square Error (n-RMSE) metrics. Results revealed that 34 indices exhibited significant correlations (p 2 and d-index values exceeding 0.92 and 0.98, respectively, in calibration, and 0.88 and 0.96 in validation, underscoring their effectiveness in predicting YMD severity using RGB images only. Random Forest (RF), Cubist, XGBoost (XGB), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM) emerged as the five top-performing models for predicting YMD severity using visible indices in yard-long beans. These findings hold practical implications for timely disease management strategies, expediting breeding programs, and aiding policy planners and farmers in making well-informed decisions.
Why it matches plant phenotyping methodsRGB画像から可視画像指標を抽出し、機械学習で植物病害の重症度を推定・検証する手法が研究の中心であり、植物状態の定量的フェノタイピングに該当する。
abstractA total of 45 visible / Red Green Blue (RGB) indices were derived from the RGB images and correlated with disease severity, and also used as inputs for predicting disease severity using nine machine learning (ML) models.
Plant disease classification plays a vital role in advancing modern agriculture, transitioning from traditional manual diagnosis to intelligent, automated systems powered by machine learning. Historically, identification of plant diseases relied on visual inspections, expert advice, and lab tests— methods that were accurate for small-scale use but often subjective, slow, and inconsistent. These limitations resulted in delayed treatment and substantial crop losses, highlighting the inefficiency and high cost of conventional approaches, especially at scale. To address this, the proposed system introduces an innovative machine learning-based solution capable of accurately classifying plant diseases using sparse and categorical IoT data. It incorporates comprehensive data preprocessing techniques, including handling missing values, label encoding, and class imbalance correction using the Synthetic Minority Oversampling Technique (SMOTE), ensuring a high-quality dataset for model training. The classification pipeline integrates multiple models—Gaussian Naive Bayes, Support Vector Machines, K-Nearest Neighbors, and a novel Decision Tree Classifier. Among these, the Decision Tree model demonstrated superior performance, achieving an accuracy of 99.07% with precision, recall, and F1-scores consistently exceeding 98%, confirming its robustness and reliability. This research is significant in offering real-time, data-driven diagnostics that enable early disease detection and precise pesticide recommendations. It not only improves crop yield and reduces financial losses but also promotes environmentally sustainable agriculture by limiting excessive chemical usage. By overcoming the limitations of traditional methods—such as subjectivity, delay, and lack of scalability—this system presents a transformative approach to plant disease management through advanced machine learning, marking a pivotal shift toward precision agriculture.
Why it matches plant phenotyping methods植物病害をIoTデータから機械学習で自動分類する手法が研究の中心であり、感染植物の病害状態を推定する植物フェノタイピング手法に該当する。
abstractthe proposed system introduces an innovative machine learning-based solution capable of accurately classifying plant diseases using sparse and categorical IoT data.
Abstract. Crop monitoring is crucial for precision agriculture, providing insights for optimizing yield and managing resources effectively. This study explores the fusion of Unmanned Aerial Vehicle (UAV) and Sentinel-2 (S2) satellite imagery for monitoring the crop by analyzing vegetation indices and canopy height information from the temporal dataset. Brovey Transform (BT) and Principal Component Analysis (PCA) fusion techniques are used to fuse the UAV and satellite images, aiming to leverage the high spatial resolution of UAV imagery with the broader spectral range of S2 data. Five key vegetation indices, including NDVI, GNDVI, SAVI, EVI, and LAI, were calculated from UAV, S2, and fused imagery in various temporal dates. Canopy height was derived from UAV data, and statistical analyses, including coefficient of determination (R2), Pearson correlation coefficient, and Root Mean Square Error (RMSE), were performed to assess relationships between canopy height and vegetation indices across the fused images and UAV and S2 images. Results indicate that fused imagery significantly enhances crop health metrics' accuracy and spatial relevance, with high R2 values and strong correlations between vegetation indices of fused images and UAV images, suggesting enhanced predictive power in monitoring crop health. Our findings highlight the advantages of fusing UAV and S2 imagery for comprehensive crop condition assessment, demonstrating that fused images provide a robust tool for monitoring crop vigor and stress levels. This approach offers valuable support for timely, data-driven decisions in crop management practices.
Why it matches plant phenotyping methodsUAV・衛星画像の融合により植生指数とキャノピー高を推定し、相関・誤差で性能評価しており、植物状態の取得手法が中心です。
abstractThis study explores the fusion of Unmanned Aerial Vehicle (UAV) and Sentinel-2 (S2) satellite imagery for monitoring the crop by analyzing vegetation indices and canopy height information from the temporal dataset.
Plant diseases are a major problem for farmers and gardeners because they impact the plant’s yield and health. Several Deep Learning (DL) approaches are established by researchers worldwide for detecting plant leaf diseases. However, attaining accurate outcomes at the early stage is challenging. An innovative approach for the detection of disease in plant leaves is proposed in this research. The images are initially obtained from the datasets and the anisotropic diffusion is utilized for denoising in the preprocessing module. Subsequently, the U-Net is exploited for segmenting the plant leaf. The image augmentation is implemented utilizing color change, scaling, and rotation. The feature extraction is processed for mining Local Ternary Pattern (LTP), and Haralick features namely correlation, Inverse Difference Moment (IDM), Angular Second Moment (ASM), and entropy. Finally, the detection of diseases in the plant leaf is performed by the Dense Maxout Forward Harmonic Network (DenMxFH-Net) approach. The DenMxFH-Net is introduced by combining the Harmonic Analysis, Deep Maxout Network (DMN), and DenseNet technique. Furthermore, the DenMxFH-Net approach obtained the lowest False Positive Rate (FPR) of 7.9%. Further, the DenMxFH-Net recorded an accuracy of 92.9%, a True Positive Rate (TPR) of 94.9%, a precision of 91.8%, and an [Formula: see text]-score of 93.3%.
Why it matches plant phenotyping methods植物葉の病害状態を画像から抽出・分類する画像解析手法を提案し、前処理、葉領域分割、特徴抽出、分類器の性能を評価しているため、病害フェノタイピング手法が中心である。
abstractAn innovative approach for the detection of disease in plant leaves is proposed in this research.
Objective . Plant Positron Emission Tomography (PET) is a new and efficient imaging technique which aims at providing a quantitative analysis of plant stress, enabling personalized crop management and maximizing productivity. However, a highly performant classification system for noisy dynamic plant PET images faces the challenge of retrieving noise-free datasets and encoding both spatial and temporal representations within a unified model. Approach . To overcome these limitations, we introduce an innovative hybrid model that combines denoising and classification for dynamic plant PET images. Initially, we compute a precise solution for the denoising problem of noisy dynamic plant PET images using a modified optimization method coupled with deep convolutional neural networks. Subsequently, this solution is unfolded into a deep network known as the predictor-corrector network (PCNet). To optimize the PCNet without requiring a noise-free dynamic training set, we propose a novel unsupervised learning method. Finally, the sequence of noise-reduced dynamic plant PET images is further fed into a unique classification system, encoding spatial representations of images and temporal representations of multivariate time series into a unified spatiotemporal representation and generating a prediction. Main results . The experimental results underscore the necessity of the denoising procedure and highlight the superiority of the proposed PCNet over existing competing denoising methods, demonstrating the effectiveness of the proposed classification system. Notably, the classification performance between the two classes achieves an averaged accuracy of 0.852, an averaged precision of 0.838, an averaged recall of 0.959, and an averaged F1-score of 0.880. Significance . The ability of the proposed method to reduce noise intensity and effectively encode spatiotemporal representations overcomes the limitations of existing methods. This advancement may have substantial implications for other noisy dynamic image classification.
Why it matches plant phenotyping methods植物PET画像のノイズ除去と時空間分類を統合した手法を開発・評価しており、植物ストレスの定量解析を目的とする画像ベースのフェノタイピング手法が中心である。
abstractwe introduce an innovative hybrid model that combines denoising and classification for dynamic plant PET images.
Remote sensing technology and machine learning methods are being scaled up globally to predict nutrient content based on spectral data. However, there is a lack of rigorous comparison of co-benefit delivery across different factors, which leads to unstable accuracy of the final model owing to insufficient analysis of the factors influencing the prediction model. In particular, for nutrients (e.g. phosphorus), visual symptoms are not obvious or have a certain lag. Therefore, a Three-Level Meta-Analysis model was proposed in this study to extract and analyse a large number of studies, delving into the analysis of various influencing factors and filling the current knowledge gap. Through global synthesis, a Three-Level Meta-Analysis was applied to seven validated datasets of field observations from multispectral remote sensing, including 32 effect sizes, and 46 datasets of field observations from hyperspectral remote sensing, including 630 effect sizes. We thoroughly explored the heterogeneity of a Three-Level Meta-Analysis using the new machine learning method Meta-Forest, while also using Meta-Cart to explore the interaction effects between moderating variables. Through a comprehensive analysis of the literature published over the past 25 years, we determined the importance of matching preprocessing and regression methods for predicting plant phosphorus spectral responses. The combination of pretreatment and regression methods is particularly important for regional-scale phosphorus concentration prediction. Baseline calibration is effective in removing background noise at the regional scale; however, it cannot solve the problem of redundancy between hyperspectral data. It is necessary to combine a regression method that can effectively deal with redundancy between data to improve the accuracy of the model. Nonlinear non-parametric regression can better deal with the complex nonlinear relationship between phosphorus concentration and spectral data and can resist the influence of the quantity and quality of the data itself and the heterogeneity of the study area; therefore, it has excellent prediction ability. The type of spectrometer is crucial for predicting regional phosphorus concentrations using multispectral data, especially when collecting data using drones. This study provides guidance for fully utilising spectral data and establishing a fast, efficient, and non-destructive prediction model for plant P concentrations, revealing the optimal selection of data preprocessing and regression methods.
Why it matches plant phenotyping methods植物リン濃度という明示的な形質をスペクトルから推定する手法について、複数研究・データセットを用いて前処理、回帰法、分光計の性能や組合せを比較・統合したメタ分析であり、フェノタイピング手法の技術評価が中心である。
titlePreprocessing and regression approaches alter the spectral estimation accuracy of plant phosphorus content—A three-level meta-analysis
Arbuscular mycorrhizal (AM) fungi, ubiquitously distributed across diverse terrestrial ecosystems, establish symbiotic associations with the majority of vascular plants, fulfilling essential physiological and ecological functions. Mycorrhizal development represents the initiation of host-fungus interactions and serves as a metric for assessing mutualistic efficacy. However, mycorrhizal detection underscores the urgent need to develop cost-effective, efficient, and environmentally benign dyestuff. Therefore, wild-collected and laboratory-grown roots of Medicago sativa were selected. Six reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one. Concurrently, root characteristics were quantified, and interrelationships among root traits, image quality, and colonization indices were analyzed to unravel the mechanism of their interactions. The findings demonstrated that wild roots exhibited pronounced lignification, achieving a mycorrhizal colonization rate of 100 %, which was better than the two laboratory groups. And the fungal community displayed a markedly greater colonization intensity compared to the Claroideoglomus etunicatum. Evaluation of the six reagents revealed distinct staining efficacy, with significant variations in image clarity, gray-level co-occurrence matrix (GLCM) indices, and colonization parameters across treatments. Specifically, aniline blue proved ineffective, while Sudan IV showed selective binding. Notably, black ink in glacial acetic acid achieved optimal mycorrhizal detection efficacy. Moreover, correlation matrix identified microscopic image quality as critical determinant of quantification accuracy, influenced by both reagent types and root properties, and AvgDiam exerted the most substantial impact (|R| > 0.75).
Why it matches plant phenotyping methods植物根のAM菌根菌感染状態を染色とコンピュータビジョンで定量する手法の開発・比較評価が中心であり、単なる生物学的測定ではない。
abstractSix reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one.
Abstract: The early and accurate identification of plant diseases plays a critical role in enhancing agricultural productivity and minimizing crop losses. In this study, a deep learning-based solution has been developed using Convolutional Neural Networks (CNNs) to automate the classification of plant leaf diseases. A total of 17,088training images and 4,273test images were used, covering multiple disease categories. The system employs an image preprocessing pipeline that includes data normalization and augmentation techniques such as rotation, flipping, and shifting to improve the robustness of the model against real-world variations. A custom CNN architecture was constructed using sequential layers including convolutional, max pooling, dropout, and global average pooling layers, followed by dense layers for classification. The model was trained using the Adam optimizer and categorical cross-entropy loss function over 50 epochs. It achieved a classification accuracy of approximately 93.4% on the test dataset. The results demonstrate the model's ability to generalize across different disease types with high reliability. Visual validation through test samples further confirmed its effectiveness. The trained model is exportable for integration into real-time agricultural advisory systems. This study showcases a scalable and lightweight architecture suitable for deployment in resourceconstrained environments, including mobile and IoT platforms. The proposed system has the potential to support farmers in making timely and informed decisions for crop disease management.
Why it matches plant phenotyping methodsCNNによる葉画像からの植物病害分類手法が研究の中心であり、植物の病害状態を直接推定しているため、植物フェノタイピング手法として適格です。
abstracta deep learning-based solution has been developed using Convolutional Neural Networks (CNNs) to automate the classification of plant leaf diseases.
Abstract Timely identification and treatment of plant diseases are essential for boosting agricultural productivity and reducing economic losses. In this study, we present an innovative deep learning framework for patient-automated plant disease detection using a Hierarchical Wasserstein Convolutional Neural Network (HW-CNN). Specifically, we introduce depth-separable convolutions for computational cost savings and a new Hierarchical Wasserstein Distance (HWD) loss function which improves classification by leveraging inter-class relationships. The model was trained and validated on a large dataset containing 53200 Images across 38 different diseases in 14 different species of plants. Additionally, the proposed methodology provides a detailed description of the preprocessing steps (transformation of colour space to H, S, and V, pixel masking of green pixels), feature extraction using Hu moments, Haralick texture features, and colour histograms. The HW-CNN architecture is based on depth-separable convolutions which have been shown to yield very good performance with fewer parameters. The HWD loss function also helps build a more suitable loss landscape that enables the model to generalise across different types of diseases. The HW-CNN outperformed classical machine learning models (SVM, Random Forest, and Logistic Regression) and other deep learning architectures with an accuracy of 99.19%.The experimental results show that the HW-CNN has an accuracy of 99.19%. The experimental results showed similar improvements in performance, while significantly reducing complexity compared to existing methods. Throwing light on the effectiveness of advanced deep learning techniques to overcome significant obstacles in plant disease detection, including serendipitous symptoms and climate differences. The novel HW-CNN architecture forms a scalable, low-power circuitry with high energy efficiency that can tremendously benefit real-world scenario applications such as agriculture, reduce potential crop losses, and improve food security in society.
Why it matches plant phenotyping methods植物病害の画像から病徴・病害状態を推定する深層学習手法を開発・検証しており、植物フェノタイピング手法が中心です。
abstractwe present an innovative deep learning framework for patient-automated plant disease detection using a Hierarchical Wasserstein Convolutional Neural Network (HW-CNN).
The germination rate of maize seeds is a critical indicator for ensuring high-quality sowing and suitability for food processing. To address the limitations of traditional germination tests, a rapid and non-destructive evaluation method based on near-infrared (NIR) spectroscopy combined with Gaussian Process Regression (GPR) was developed. Various spectral data preprocessing techniques were applied, and a hybrid kernel function integrating Gaussian and Linear kernels was constructed. Particle Swarm Optimization (PSO) was used to optimize the kernel parameters. The PSO-GPR model achieved excellent performance, with determination coefficients (R 2 ) of 1.000 and 0.9899 for the training and validation sets, respectively. The root mean square errors (RMSE) were 0.0059 and 0.0033, and the residual predictive deviation (RPD) reached 9.3, outperforming PLSR and SVM models. This study provides a novel strategy for the non-destructive evaluation of crop seed quality and contributes to developing smart agricultural practices.
Why it matches plant phenotyping methodsトウモロコシ種子の発芽率という植物状態を、NIR分光とGPRで非破壊推定する手法を開発しており、表現型取得・推定法が研究の中心である。
abstracta rapid and non-destructive evaluation method based on near-infrared (NIR) spectroscopy combined with Gaussian Process Regression (GPR) was developed.
Field / plotFlowerFruitLeafWhole plant / canopy / plot / fieldClassificationCalibration / preprocessingTrackingGrowth / development / phenology
Abstract Plant phenology plays a fundamental role in shaping ecosystems, and global change‐induced shifts in phenology have cascading impacts on species interactions and ecosystem structure and function. Detailed, high‐quality observations of when plants undergo seasonal transitions such as leaf‐out, flowering and fruiting are critical for tracking causes and consequences of phenology shifts, but these data are often sparse and biased globally. These data gaps limit broader generalizations and forecasting improvements in the face of continuing disturbance. One solution to closing such gaps is to document phenology on field images taken by public participants. iNaturalist, in particular, provides global‐scale research‐grade data and is expanding rapidly. Here we utilize over 53 million field images of plants and millions of human annotations from iNaturalist—data spanning all angiosperms and drawn from across the globe—to train a computer vision model (PhenoVision) to detect the presence of fruits and flowers. PhenoVision utilizes a vision transformer architecture pretrained with a masked autoencoder to improve classification success, and it achieves high accuracy on held‐out test images for flower (98.5%) and fruit presence (95%), as well as a high level of agreement with an expert annotator (98.6% for flowers and 90.4% for fruits). Key to producing research‐ready phenology data is post‐calibration tuning and validation focused on reducing noise inherent in field photographs, and maximizing the true positive rate. We also develop a standardized set of quality metrics and metadata so that results can be used effectively by the community. Finally, we showcase how this effort vastly increases phenology data coverage, including regions of the globe where data have been limited before. Our end products are tuned models, new data resources and an application streamlining discovery and use of those data for the broader research and management community. We close by discussing next steps, including automating phenology annotations, adding new phenology targets, for example leaf phenology, and further integration with other resources to form a global central database integrating all in situ plant phenology resources.
Why it matches plant phenotyping methods植物の開花・結実状態をフィールド画像から推定する視覚モデルを開発・検証し、校正、品質指標、研究用データ資源まで整備しており、フェノタイピング手法が中心である。
abstractto train a computer vision model (PhenoVision) to detect the presence of fruits and flowers
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Cassava ( Manihot esculenta Crantz) is a critical food security crop for sub-Saharan Africa. Efforts to improve cassava through breeding have expanded over the past decade. Crop growth models (CGM) are becoming common place in breeding efforts to expand the inference of evaluations of breeding germplasm to environments that have not been tested and to prepare for breeding for adaptation to future climates. We parameterized a CGM, the CROPGRO-MANIHOT-Cassava model in the DSSAT family of models, using data on 67 clones from the International Institute of Tropical Agriculture cassava breeding program evaluated from 2017 to 2020 and over eight locations in Nigeria using trial and error parameter adjustments and the General Likelihood Uncertainty Estimation method. Our objectives were to assess the feasibility of this large-scale calibration in the context of a cassava breeding program and to identify systematic biases of the model. For each cultivar we calculated the Pearson correlation between model prediction and observation across the environments, as well as root mean squared error and d statistics. As a result of calibration, the correlation coefficient increased from –0.03 to +0.08, the RMSE dropped from 21 t ha -1 to 5 t ha -1 while d increased from 0.23 to 0.44. We found that the model underestimated root yield in dry environments (low precipitation and high temperature) and overestimated root yield in wet environments (high precipitation and low temperature). Our experience suggests both that CGM calibration could become a routine component of the cassava breeding data analysis cycle and that there are opportunities for model improvement.
Why it matches plant phenotyping methodsキャッサバの根収量という植物形質を予測する作物成長モデルを大規模に較正し、観測値との性能比較で検証しており、モデル手法が研究の中心である。
abstractWe parameterized a CGM, the CROPGRO-MANIHOT-Cassava model in the DSSAT family of models, using data on 67 clones from the International Institute of Tropical Agriculture cassava breeding program evaluated from 2017 to 2020 and over eight locations in Nigeria using trial and error parameter adjustments and the General Likelihood Uncertainty Estimation method.
Three-dimensional seed phenotyping demands imaging systems that simultaneously achieve the micron-level resolution in centimeter-level field-of-view (FOV), a challenge exacerbated by the intrinsic resolution-FOV trade-off in conventional light field architectures. This paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research. At the hardware level, we develop a tunable-focus lens group containing main lens with tunable lens, enabling the flexible adjustment on the effective focal length, coupled with a custom microlens array. A mathematic model is analyzed with FOV, lateral resolution, DOF, lens parameters and system configurations, which also help us on individual optical component design (i.e., MLA design). Computationally, we propose a hybrid aberration correction strategy: First, co-calibration of lens and microlens array aberrations via line-feature detection is developed. Subsequently, a conditional generative adversarial network (cGAN) with attention-guided residual learning enhances sub-aperture images, attaining PSNR 34.63 dB and SSIM 0.9570 on seed test. Experimentally, system achieves 6.2lp/mm resolution at MTF50 over 2~3cm FOV, a 307% improvement over 1.52 lp/mm at initial configurations. The reconstruction pipeline synergizes epipolar plane image (EPI) analysis with multi-view consistency constraints from the sub-aperture array, generating dense 3D point clouds surface (approximately 1.5×10^4 points/cm²) that preserve spectral-textural features at the mean time. Experimental validation of bitter melon seeds and rice grain seeds demonstrates that the accurate morphological parameters are extracted in large area and present in high-fidelity 3D reconstruction. This hardware/software co-optimization framework demonstrates unprecedented dynamic adjustability of resolution-FOV trade-off, overcoming the inherent limitations of conventional light-field systems, while establishing a field-reconfigurable scalable architecture for next-generation phenotyping, with potential extensions to robotic vision and biomedical imaging applications.
Why it matches plant phenotyping methods種子の3D表現型取得を目的に、可変焦点光学系、ライトフィールド撮像、計算再構成を統合したシステムを開発し、性能検証と形態パラメータ抽出を行っており、表現型取得法が研究の中心である。
abstractThis paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research.
ABSTRACT Apoplastic pH is a key regulator of plant development and environmental responses, influencing processes such as cell expansion, nutrient uptake, and intercellular signaling. Accurate tools for measuring absolute pH values at high spatial resolution are therefore essential, yet limiting. Here, we present a novel calibration-based workflow for the in vivo quantification of absolute apoplastic pH using the fluorescent pH indicator HPTS. While HPTS has previously been used primarily to track relative pH changes, our novel methodology enables precise absolute pH measurement through a simplified calibration strategy and a tailor-made Fiji Plugin. This approach offers a non-invasive, reproducible tool for investigating absolute extracellular pH with high spatial resolution, expanding the methodological toolbox available to plant physiologists.
Why it matches plant phenotyping methods植物根のアポプラストpHという生理状態を、生体イメージング・校正ワークフロー・Fijiプラグインにより絶対値として定量する方法開発が中心である。
abstractHere, we present a novel calibration-based workflow for the in vivo quantification of absolute apoplastic pH using the fluorescent pH indicator HPTS.
Three-dimensional seed phenotyping demands imaging systems that simultaneously achieve the micron-level resolution in centimeter-level field-of-view (FOV), a challenge exacerbated by the intrinsic resolution-FOV trade-off in conventional light field architectures. This paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research. At the hardware level, we develop a tunable-focus lens group containing main lens with tunable lens, enabling the flexible adjustment on the effective focal length, coupled with a custom microlens array. A mathematic model is analyzed with FOV, lateral resolution, DOF, lens parameters and system configurations, which also help us on individual optical component design (i.e., MLA design). Computationally, we propose a hybrid aberration correction strategy: First, co-calibration of lens and microlens array aberrations via line-feature detection is developed. Subsequently, a conditional generative adversarial network (cGAN) with attention-guided residual learning enhances sub-aperture images, attaining PSNR 34.63 dB and SSIM 0.9570 on seed test. Experimentally, system achieves 6.2lp/mm resolution at MTF50 over 2~3cm FOV, a 307% improvement over 1.52 lp/mm at initial configurations. The reconstruction pipeline synergizes epipolar plane image (EPI) analysis with multi-view consistency constraints from the sub-aperture array, generating dense 3D point clouds surface (approximately 1.5×10^4 points/cm²) that preserve spectral-textural features at the mean time. Experimental validation of bitter melon seeds and rice grain seeds demonstrates that the accurate morphological parameters are extracted in large area and present in high-fidelity 3D reconstruction. This hardware/software co-optimization framework demonstrates unprecedented dynamic adjustability of resolution-FOV trade-off, overcoming the inherent limitations of conventional light-field systems, while establishing a field-reconfigurable scalable architecture for next-generation phenotyping, with potential extensions to robotic vision and biomedical imaging applications.
Why it matches plant phenotyping methods種子の3D形質を取得・抽出する光学系、計算再構成、検証を中心に開発した植物フェノタイピング手法研究。
abstractThis paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research.
Abstract. Climate change and extreme weather events pose challenges to food security, emphasizing the need for reliable and timely monitoring of crop and rangeland conditions. For this purpose, long-term consistent Earth Observation datasets on vegetation conditions are typically used in early warning and crop yield forecast systems. However, the near-real-time (NRT) production of high quality datasets and the need to guarantee long-term records present various challenges. To address these, we present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications. Our dataset combines MODIS-FPAR (Collection 6.1) and VIIRS-FPAR (Collection 2) data, ensuring continuity from 2000 to well beyond 2030. We applied a robust filtering approach based on the Whittaker smoother to produce reliable FPAR estimates in NRT, accounting for sparse and irregular spaced observations due to cloud cover. The dataset is composed of two 10-day filtered timeseries: 1) MODIS-FPAR for 2000 to 2023, being the reference dataset, and 2) intercalibrated VIIRS-FPAR for 2018 onward. While several methods can effectively smooth and gap-fill FPAR data (i.e., using observations before and after the estimation date), our method is designed for optimal filtering in NRT (i.e., using only prior observations). Our approach yields six successive estimates of the same FPAR data point with increasing quality: a inital estimate immediately after the 10-day reference period, four subsequent estimates every 10 days using new observations, and a final consolidated estimate 90 days later. The implemented filtering ingests the available FPAR observations and their original quality assessment (QA) layers. To avoid unrealistic extrapolation when observations are sparse, we impose constraints, season and location specific, to FPAR estimates. We then intercalibrated the VIIRS-FPAR with the MODIS-FPAR filtered timeseries, using a mean difference correction approach, to ensure consistency between both series. This paper describes the filtering and intercalibration method used, the quality assessment of resulting timeseries, and details the obtained products and the corresponding QA layers. The NRT FPAR dataset is publicly available through the Joint Research Centre Data Catalogue, https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50 (Seguini et al., 2025).
Why it matches plant phenotyping methodsFPARという作物・植生キャノピーの明示的な状態量を対象に、MODIS/VIIRSデータのNRTフィルタリング、相互校正、品質評価を開発・記述しており、単なる農業利用ではなく再利用可能な測定データセットと抽出手法が中心である。
abstractwe present a NRT global dataset of Fraction of Photosynthetically Active Radiation (FPAR) at 500 m resolution, optimized for agricultural applications.
Reproduction assets foundThe paper's own filtered and intercalibrated MODIS/VIIRS FPAR dataset (the paper's core output) is explicitly described as open and freely available in near real time via the JRC Data Catalogue and the ASAP website, both of which appear in allowed_urls. No author analysis code is mentioned.Dataset · publicec.europa.eu/, last
access: 30 September 2025) early warning system. The
FPAR dataset is accompanied by associated quality layers
and has a temporal resolution of 10 d, a time step often
used in operational agricultural monitoring. The dataset
is open and freely available in NRT through the Joint
Research Centre Data Catalogue (https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50, last
access: 30 September 2025) and on the ASAP website
(https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR, last access: 30 September 2025). This paper
has the following specific objectives: (i) to introduce the
method used to produce a long-term archive of NRT filtered
FPAR Open asset ↗1aac79d8-0d68-4f1c-a40f-b6e362264e50pdf-raw-page:3 lines:1-86Dataset · publichas a temporal resolution of 10 d, a time step often
used in operational agricultural monitoring. The dataset
is open and freely available in NRT through the Joint
Research Centre Data Catalogue (https://data.jrc.ec.europa.eu/dataset/1aac79d8-0d68-4f1c-a40f-b6e362264e50, last
access: 30 September 2025) and on the ASAP website
(https://agricultural-production-hotspots.ec.europa.eu/data/MO6_FPAR, last access: 30 September 2025). This paper
has the following specific objectives: (i) to introduce the
method used to produce a long-term archive of NRT filtered
FPAR data; (ii) to present the intercalibration performed
between the filtered MODIS-FPAR and the filtered VIIRS-
FPAR; (iii) to evaluate tOpen asset ↗ASAP websitepdf-raw-page:3 lines:1-86Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 14 Sept 2026
Published1 Jun 2025International Journal of Applied Earth Observation and GeoinformationCited by 1 · OpenAlex ↗
Solar-induced chlorophyll fluorescence (SIF) from hyperspectral imaging is strongly associated with agricultural indices, particularly leaf chlorophyll content (LCC), in crop phenotyping. However, confounding factors such as spectral resolution (SR), canopy structure, and illumination reduce SIF sensitivity and complicate its association with these indices. This study focused on examining the effect of SR and mitigating the influence of canopy structure and illumination to enhance SIF accuracy. This study employed theoretical simulations, experimental validation, and two field experiments comparing devices with varying SRs to extract SIF at 687 nm and 761 nm (SIF Red and SIF NIR ). Initially, SCOPE model was used to simulate radiance trends across different SRs. Secondly, narrow-band imager with 7 nm full width at half maximum (FWHM) were then compared with radiance from sub-nanometer and ASD spectrometers (0.3 nm and 3 nm FWHM). Finally, to modify SIF measurements, canopy structure was quantified using fluorescence escape fraction (f esc ), while absorbed photosynthetically active radiation (APAR) and intensity component from hue-saturation-intensity color model (I HSI ) were used to account for illumination. And driver factor for SIF extraction was explored. Results showed a strong relationship (R 2 ≥ 0.95) between original simulated radiance from sub-nanometer spectrometers and radiance resampled to 3, 5, and 7 nm FWHM. SIF NIR from the narrow-band imager correlated with the other spectrometers (R ≥ 0.64). While lower SR reduced SIF sensitivity, the narrow-band imager still showed potential for estimating LCC. Modified SIF NIR by f esc , APAR, and I HSI , improved correlations with LCC (R = 0.77, 0.74, and 0.86) compared to unmodified SIF NIR (R = 0.65). Estimation model based on SIF modified by f esc , APAR and I HSI presented R P 2 of 0.73, 0.75, 0.84 and outperformed SIF (R P 2 = 0.52), and photosynthetically active radiation and APAR showed R of 0.52 and 0.72 with SIF NIR , which indicated that disentangling confounding factors could enhance the sensitivity of SIF and structural effect governed SIF strongly than illumination effect. This study demonstrated the feasibility of narrow-band imagers for capturing spatial–temporal SIF variability by addressing confounding factors and can provide guidance for narrow-band instrument development for SIF extraction.
Why it matches plant phenotyping methods狭帯域ハイパースペクトル画像からSIFを抽出し、冠層構造・照明の影響を補正して葉緑素含量を推定する方法を、シミュレーションと実験で検証・改良しており、植物表現型取得が中心である。
abstractThis study focused on examining the effect of SR and mitigating the influence of canopy structure and illumination to enhance SIF accuracy.
Three-dimensional seed phenotyping demands imaging systems that simultaneously achieve the micron-level resolution in centimeter-level field-of-view (FOV), a challenge exacerbated by the intrinsic resolution-FOV trade-off in conventional light field architectures. This paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research. At the hardware level, we develop a tunable-focus lens group containing main lens with tunable lens, enabling the flexible adjustment on the effective focal length, coupled with a custom microlens array. A mathematic model is analyzed with FOV, lateral resolution, DOF, lens parameters and system configurations, which also help us on individual optical component design (i.e., MLA design). Computationally, we propose a hybrid aberration correction strategy: First, co-calibration of lens and microlens array aberrations via line-feature detection is developed. Subsequently, a conditional generative adversarial network (cGAN) with attention-guided residual learning enhances sub-aperture images, attaining PSNR 34.63 dB and SSIM 0.9570 on seed test. Experimentally, system achieves 6.2lp/mm resolution at MTF50 over 2~3cm FOV, a 307% improvement over 1.52 lp/mm at initial configurations. The reconstruction pipeline synergizes epipolar plane image (EPI) analysis with multi-view consistency constraints from the sub-aperture array, generating dense 3D point clouds surface (approximately 1.5×10^4 points/cm²) that preserve spectral-textural features at the mean time. Experimental validation of bitter melon seeds and rice grain seeds demonstrates that the accurate morphological parameters are extracted in large area and present in high-fidelity 3D reconstruction. This hardware/software co-optimization framework demonstrates unprecedented dynamic adjustability of resolution-FOV trade-off, overcoming the inherent limitations of conventional light-field systems, while establishing a field-reconfigurable scalable architecture for next-generation phenotyping, with potential extensions to robotic vision and biomedical imaging applications.
Why it matches plant phenotyping methods種子の3D形態形質を取得する光学・計算イメージングシステムを開発し、性能評価と実データでの検証を行っており、植物フェノタイピング手法が研究の中心である。
abstractThis paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research.
Three-dimensional seed phenotyping demands imaging systems that simultaneously achieve the micron-level resolution in centimeter-level field-of-view (FOV), a challenge exacerbated by the intrinsic resolution-FOV trade-off in conventional light field architectures. This paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research. At the hardware level, we develop a tunable-focus lens group containing main lens with tunable lens, enabling the flexible adjustment on the effective focal length, coupled with a custom microlens array. A mathematic model is analyzed with FOV, lateral resolution, DOF, lens parameters and system configurations, which also help us on individual optical component design (i.e., MLA design). Computationally, we propose a hybrid aberration correction strategy: First, co-calibration of lens and microlens array aberrations via line-feature detection is developed. Subsequently, a conditional generative adversarial network (cGAN) with attention-guided residual learning enhances sub-aperture images, attaining PSNR 34.63 dB and SSIM 0.9570 on seed test. Experimentally, system achieves 6.2lp/mm resolution at MTF50 over 2~3cm FOV, a 307% improvement over 1.52 lp/mm at initial configurations. The reconstruction pipeline synergizes epipolar plane image (EPI) analysis with multi-view consistency constraints from the sub-aperture array, generating dense 3D point clouds surface (approximately 1.5×10^4 points/cm²) that preserve spectral-textural features at the mean time. Experimental validation of bitter melon seeds and rice grain seeds demonstrates that the accurate morphological parameters are extracted in large area and present in high-fidelity 3D reconstruction. This hardware/software co-optimization framework demonstrates unprecedented dynamic adjustability of resolution-FOV trade-off, overcoming the inherent limitations of conventional light-field systems, while establishing a field-reconfigurable scalable architecture for next-generation phenotyping, with potential extensions to robotic vision and biomedical imaging applications.
Why it matches plant phenotyping methods種子の3D形態形質を取得する光学・計算イメージングシステムを開発し、性能評価と実種子での検証を行っており、フェノタイピング手法が研究の中心である。
abstractThis paper presents a co-optimized framework integrating a dynamically reconfigurable optical system with computational imaging pipelines, to meet the demand from the variety of seed phenotyping research.
Accurate diagnosis of apple diseases is vital for tree health, yield improvement, and minimizing economic losses. This study introduces a deep learning-based model to tackle issues like limited datasets, small sample sizes, and low recognition accuracy in detecting apple leaf diseases. The approach begins with enhancing the CycleGAN-M network using a multi-scale attention mechanism to generate synthetic samples, improving model robustness and generalization by mitigating imbalances in disease-type representation. Next, an improved YOLOv8s-KEF model is introduced to overcome limitations in feature extraction, particularly for small lesions and complex textures in natural environments. The model's backbone replaces the standard C2f structure with C2f-KanConv, significantly enhancing disease recognition capabilities. Additionally, we optimize the detection head with Efficient Multi-Scale Convolution (EMS-Conv), improving the model's ability to detect small targets while maintaining robustness and generalization across diverse disease types and conditions. Incorporating Focal-EIoU further reduces missed and false detections, enhancing overall accuracy. The experiment results demonstrate that the YOLOv8s-KEF model achieves 95.0% in accuracy, 93.1% in recall, 95.8% in precision, and an F1-score of 94.5%. Compared to the original YOLOv8s model, the proposed model improves accuracy by 7.2%, precision by 6.5%, and F1-score by 5.0%, with only a modest 6MB increase in model size. Furthermore, compared to Faster RCNN, ResNet50, SSD, YOLOv3-tiny, YOLOv6, YOLOv9s, and YOLOv10m, our model demonstrates substantial improvements, with up to 30.2% higher precision and 18.0% greater accuracy. This study used CycleGAN-M and YOLOv8s-KEF methods to enhance the detection capability of apple leaf diseases.
Why it matches plant phenotyping methodsリンゴ葉の病斑・病害を画像から検出する深層学習モデルの改良と性能比較が研究の中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstractThis study introduces a deep learning-based model to tackle issues like limited datasets, small sample sizes, and low recognition accuracy in detecting apple leaf diseases.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits a portion of the apple leaf disease dataset and source code in a public GitHub repository, which directly supports this paper's CycleGAN-M augmentation and YOLOv8s-KEF detection experiments.Code · publicA portion of the dataset and source code is available on GitHub at https://github.com/Lijun-Gao/Apple-Leaf-Disease-Detection .Open asset ↗https://github.com/Lijun-Gao/Apple-Leaf-Disease-Detection · Lijun-Gao/Apple-Leaf-Disease-Detectionlines:158-169Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
The Distributed System for Scientific Collections (DiSSCo) is a research infrastructure to integrate European natural science collections (NSCs) digitally. The aim is to facilitate and enhance the access, management and analysis of collection assets in one unified digital collection. The Machine Annotation Services (MAS) are essential components of DiSSCo’s Digital Specimen Architecture (DSArch). These services automate the annotation of digital objects to enable labeling and categorization of NSC's digital assets. To further advance this, a Machine Learning as a Service (MLaaS) approach was developed which provides researchers with the access to pre-trained machine learning models for complex tasks such as instance segmentation and morphological analysis of datasets. MLaaS enhances the DiSSCo’s scalability and flexibility and allows the integration of machine learning tools in close alignment with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This study employs DiSSCO's MLaaS framework for the quantitative analysis of herbarium specimens. Machine learning models such as Mask R-CNN and YOLO11 are comparatively applied to detect and generate the pixel-level masks of plant organs in herbarium sheets. Subsequently, these models are used to reconstruct the scale in the herbarium sheet and to calculate the surface area of identified plant organs. Based on our finding that YOLO11 performs better than the Mask R-CNN for our use case, we deployed a YOLO11-based service as MAS in DSArch to open up natural science collections on scale for research fields such as plant phenology and climate change science.
Why it matches plant phenotyping methodsハーバリウム画像から植物器官を検出・セグメンテーションし、面積を定量化する機械学習手法とサービスを開発・比較しており、植物表現型の取得が中心である。
abstractMachine learning models such as Mask R-CNN and YOLO11 are comparatively applied to detect and generate the pixel-level masks of plant organs in herbarium sheets.
There is growing interest in hyperspectral imaging to complement observation needs and techniques required to capture the critical zone dynamics. It is already widely used in remote sensing satellite imagery, for regional-scale monitoring of canopies (Asner et al. 2004), or suspended sediment transport (Yepez et al. 2017). Spectral imaging offers dense, remote and non-intrusive measurement coverage. Its implementation at fixed-station for fine temporal monitoring would ensure maximum temporal coverage to study the phenology and functioning of ecosystems (vegetation-water-soil interactions) and watersheds (sediment dynamics), at integrative scales (e.g. watershed outlets), or over experimental plots. On-site hyperspectral data also enable links to the regional scale through cross-comparison with data from space (de Moura et al. 2017). It would then enables to better control measurement biases, offering in that way better opportunity for standardizing observables, as required by international research infrastructures. In recent years, both technological progresses and applications for commercial uses made these kind of cameras more reliable, compact, and affordable, making feasible on-site hand-held or UAV-based experiments (Stuart et al. 2019). Nevertheless, deployment for continuous monitoring remains uncommon, and limited to specific applications (de Moura et al. 2017, Woodgate et al. 2020), due to a still high instrumental complexity and costs. Furthermore, correct data exploitation requires a complete mastery of the calibration, acquisition, normalization and processing chain, that can be complex with “black-box” commercial systems. The development of a dedicated spectral camera is thus preferred. Such a camera is developed within the program TERRA FORMA from the French Agency for Research (Longuevergne et al. 2022). This program aims to implement integrated socio-ecosystem observatories, in support of the French RZA and OZCAR infrastructures, by developing and deploying a dozen types of state-of-the-art sensors dedicated to environmental monitoring at national-scale until 2029. A part of this project is dedicated to the deployment up to 20 spectral cameras, within two scientific topics: monitoring of plant canopies, monitoring of suspended sediment dynamics in rivers. monitoring of plant canopies, monitoring of suspended sediment dynamics in rivers. The instrumental solution we are implementing is based on developments carried out at IPAG since 2016 in compact spectral imaging for spaceborne Earth Observation (Gousset et al. 2019, Le Coarer et al. 2021). In addition to its compactness and optical simplicity, the main advantage of this kind of camera lies in its ability to acquire all spectral and spatial information in a single acquisition (“snapshot”) of a fraction of a second. By opposite to pushbroom or linescanner concepts, which require tens of seconds of exposure under stable illumination conditions. The TERRA FORMA camera complements these instruments with a frugal, less expensive solution, suitable for deployment as a stand-alone fixed station or for handle-held/UAV acquisitions on the field. Since May 2024, we integrated and tested in laboratory an operational camera (Fig. 1), with the following specifications: Field of view 22 by 12°, for 365 by 200 pixels 1 cm / pixel at 9 m distance 42 spectral channels between 400 and 780 nm (up to 850) Spectral resolution 10 nm (up to 6 nm) 10 x 10 x 6 cm, 0.6 kg, powered by LiPo battery Field of view 22 by 12°, for 365 by 200 pixels 1 cm / pixel at 9 m distance 42 spectral channels between 400 and 780 nm (up to 850) Spectral resolution 10 nm (up to 6 nm) 10 x 10 x 6 cm, 0.6 kg, powered by LiPo battery We carried out a first field test in August 2024 at the eLTER site Lautaret / Roche Noire (French Alps). During this single day of acquisition, we acquired data over the landscape jointly to a reference commercial non-imaging spectrometer. This last is shown on Fig. 2, demonstrating a good adequacy between hyperspectral data from the camera and reference spectra. The next steps for 2025 are on site campaigns, lasting 3 to 6 months at fixed stations on pilot sites. On the biodiversity topic: acquisition during a full growing season in a snow-covered mountain grassland equipped with a flux tower should enable: To compare the series of data from hyperspectral imagery with the installed multi-spectral NDVI sensor (only two channels in red and near infrared). To compare spectral measurements with balances of radiative fluxes, and with CO 2 and H 2 O exchanges in the soil-plant-atmosphere continuum. To identify the best optical proxies for inferring vegetation water status and CO 2 fixation capacity during a season. To compare the series of data from hyperspectral imagery with the installed multi-spectral NDVI sensor (only two channels in red and near infrared). To compare spectral measurements with balances of radiative fluxes, and with CO 2 and H 2 O exchanges in the soil-plant-atmosphere continuum. To identify the best optical proxies for inferring vegetation water status and CO 2 fixation capacity during a season. Mid-term objective is to be able to increase the effective footprint of the tower, then to be able to infer canopy function and structure using imagery, through integrated and continuous measurement of several biodiversity parameters at the same time, complementary to data collected as part of the eLTER and ICOS infrastructures. On the hydrology topic: another camera will be deployed on hydrological stations (campus of Grenoble, then Galabre river (Legout et al. 2021)). The aggregation of data should enable: To identify optical proxies for quantifying suspended solids concentrations. To evaluate the robustness of this approach in a concentration range from 0 to a few tens of g/l, currently well measured by the combined turbidimetry and sampling approach (Navratil et al. 2011). To identify optical proxies capable of discriminating between the different types of suspended solids transported in rivers during floods. To apply an approach based on these optical proxies to trace the sources of suspended solids using mixture models, and compare these results with those obtained using the spectro-colorimetric manual suspended solids tracing method implemented on the Galabre site since 2013 (Legout et al. 2013). To identify optical proxies for quantifying suspended solids concentrations. To evaluate the robustness of this approach in a concentration range from 0 to a few tens of g/l, currently well measured by the combined turbidimetry and sampling approach (Navratil et al. 2011). To identify optical proxies capable of discriminating between the different types of suspended solids transported in rivers during floods. To apply an approach based on these optical proxies to trace the sources of suspended solids using mixture models, and compare these results with those obtained using the spectro-colorimetric manual suspended solids tracing method implemented on the Galabre site since 2013 (Legout et al. 2013). The final objective is to be able to complement in situ techniques (turbidimetry) and river sampling with a remote, robotized measurement method, providing better temporal coverage of flood episodes, more reliable than submerged sensors.
Why it matches plant phenotyping methods植物キャノピーの状態・機能を測定する専用ハイパースペクトルカメラを開発・試験しており、植物フェノタイピング用の取得基盤が研究の中心である。
abstractThe development of a dedicated spectral camera is thus preferred.
Accurate prediction of apple soluble solids content (SSC) is essential for fruit quality evaluation. Aiming at the problem of spectral aberration caused by the variation of fruit diameter in the existing visible/near infrared spectroscopy (VIS/NIR) detection, a novel spectral correction strategy was proposed in this study. By systematically analysing the correlation law between light intensity attenuation and fruit size in different wavelength bands (600-1000 nm), it was found that the traditional single-parameter correction models (exponential function method, hyperbolic sine function method) had limitations of applicability in a wide spectral range. Based on this, this paper innovatively proposed the band-specific segmented extinction correction method, established the mapping relationship between spectral intervals and size compensation parameters, and realised the multi-band synergistic correction. Experiments showed that the extinction coefficient method based on the exponential function and the diameter transformation method based on the hyperbolic sine function exhibited significant results in spectral correction for specific bands, resulting in a 6 %-10 % improvement in modelling accuracy at global size. However, when we introduced the band-specific segmented extinction correction, the spectra were effectively corrected over the entire band range, the light intensity differences between samples of different sizes were significantly reduced, and the modelling accuracy at global size jumped by 15 %. Specifically, the partial least squares regression (PLSR) model had a coefficient of determination (R 2 ) of 0.90 and a root mean square error (RMSE) of 0.55, and the convolutional neural network (CNN) model had the R 2 of 0.95 and the RMSE of 0.44, after corrected for the band-specific segmented extinction. Finally, this paper set up additional validation experiments to test the calibration effect of the three methods, and the results showed that band-specific segmented extinction correction method improved the modelling effect of the model most significantly. Therefore, the band-specific segmented extinction correction method proposed could effectively reduce the effect of apple diameter on the transmission spectrum and further improve the apple SSC's prediction accuracy.
Why it matches plant phenotyping methodsリンゴ果実の可溶性固形分を推定するVIS/NIR分光法について、果径補正アルゴリズムを開発し、追加検証実験で性能を比較しており、植物形質取得法が研究の中心である。
abstracta novel spectral correction strategy was proposed in this study
Early detection of plant diseases is critical for sustainable agriculture and reducing crop losses. This study presents a real-time monitoring system integrating IoT and machine learning for the early detection of basal bulb rot disease in shallots. The system combines image data captured by an ESP32-CAM and soil pH data from a sensor to provide timely alerts to farmers. The images undergo preprocessing using Gaussian filtering and histogram equalization, while pH data is smoothed using a moving average filter. Features such as color, texture, shape, and pH dynamics are extracted and analyzed using a hybrid classification model comprising MobileNetV2 for image-based disease identification and Random Forest for soil pH classification, fused at the decision level. The models were optimized for edge deployment using TensorFlow Lite and field-tested under solar-powered conditions. Experimental results demonstrate a hybrid model accuracy of 93.7%, with recall and specificity of 94.1% and 92.8%, respectively. The system responds within 450 milliseconds, making it suitable for real-time applications. This solution offers a low-cost, scalable, and accurate method for precision agriculture, reducing dependence on manual inspections and enabling proactive disease management.
Why it matches plant phenotyping methodsタマネギの病徴を画像から分類・検出する手法と、リアルタイム監視システムの開発・評価が研究の中心であり、植物の病害状態を直接推定するため含める。土壌pH分類は補助的だが、画像による病害表現型推定が明確である。
abstractThis study presents a real-time monitoring system integrating IoT and machine learning for the early detection of basal bulb rot disease in shallots.
Background: Agriculture is very important for human existence since time immemorial. Approximately sixty percent of the world’s population is dependent on agriculture and its allied activities. Due to plant diseases every year farmers bear heavy economic loss which can be reduced by having an early detection system for plant disease. Conventional plant disease detection techniques are laborious and based on chemical and analytical testing. In this work, we suggest a deep learning method to precisely detect plant diseases by applying the VGG19 convolutional neural network. Methods: Modifications specifically designed for the classification of plant diseases were applied to the VGG19 model. By first freezing the bottom layers of the pre-trained VGG19 model-which had been trained on the ImageNet dataset-and then fine-tuning the upper layers to better fit the PlantVillage dataset, transfer learning was used. Resizing, standardization and data augmentation are a few of the image preprocessing approaches that were used to increase the variety of the dataset and boost model performance. The efficacy of the model was assessed through the use of metrics like F1-score, recall, accuracy and precision. Result: The constructed model performed well in the classification of plant diseases, with over 95% accuracy on the test set. The success of the model in generalizing across different plant disease categories was largely attributed to the application of transfer learning and data augmentation. The findings show that deep learning techniques-in particular, the use of VGG19-can significantly enhance agricultural practices and decision-making processes by helping to quickly and accurately identify plant illnesses. VGG9-based image processing offers an accurate and automated solution for plant disease identification, achieving 98% accuracy in classifying healthy and diseased leaves. By integrating mobile applications, drones and smart farming cameras, farmers can detect diseases early and take timely action, improving crop health and yield. Future advancements in dataset expansion and real-time processing will further enhance its effectiveness in precision agriculture.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定するVGG19ベースの画像解析手法を開発・評価しており、病害表現型の取得が中心です。
abstractIn this work, we suggest a deep learning method to precisely detect plant diseases by applying the VGG19 convolutional neural network.
Reproduction assets foundThe paper's plant disease detection experiments are performed entirely on the public PlantVillage leaf image dataset (14,103 images of apple, grape, potato, tomato used here), which is openly available via the Hughes & Salathé arXiv reference. No author-specific code, trained model, or supplement is disclosed.Dataset · publicWe have gathered plant images from an open-source
database named PlantVillage. The PlantVillage dataset
comprises 54,303 photos and 38 classes representing
14 distinct plant species, of which 12 are healthy and 26
are diseased (Hughes and Salathe, 2015).Open asset ↗PlantVillagepdf-raw-page:3 lines:1-58Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published14 May 2025INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTCited by 0 · OpenAlex ↗
Crop diseases can cause significant yield losses and threaten food security if not diagnosed and treated in time. In this paper, we present a real-time web application for crop disease detection using deep learning techniques, primarily Convolutional Neural Networks (CNNs). The application allows farmers to upload leaf images through a Streamlet interface, which are then analyzed by a trained CNN model to detect diseases. The system provides fast, accurate predictions along with suggestions for treatments, promoting smart and sustainable farming. For scalability and maintainability, it makes use of a modular backend, picture preprocessing methods, and the Plant Village dataset. Performance is evaluated using accuracy, precision, recall, and user feedback. Key Words: Crop Disease Detection, Convolutional Neural Network, Deep Learning, Streamlit, Smart Farming, Plant Village, AI in Agriculture
Why it matches plant phenotyping methods植物葉画像から病害状態をCNNで推定する手法とWebアプリケーションの開発・評価が研究の中心であり、植物フェノタイピング手法に該当する。
abstractwe present a real-time web application for crop disease detection using deep learning techniques, primarily Convolutional Neural Networks (CNNs).
ABSTRACT Late leaf spot (LLS), caused by Nothopassalora personata , is the most damaging foliar disease in peanut production worldwide, leading to significant yield losses if not properly managed. Accurate disease severity assessment is crucial for evaluating fungicide efficacy and implementing effective management strategies. This study aimed to develop and validate an automated image analysis model, LLS-SevEst , for quantifying LLS severity in peanut leaves. A dataset of 190 scanned leaf images was analyzed using three approaches: a fixed threshold-based segmentation, morphological preprocessing, and K-means clustering. Exploratory analyses revealed distinct brightness patterns between healthy and diseased tissues, guiding the development of classification functions. The threshold-based model yielded high false positive rates due to its inability to account for natural leaf variation, while the morphological preprocessing method improved segmentation marginally but still required manual adjustments. The K-means clustering approach achieved superior segmentation by objectively differentiating healthy tissue, lesions, and background, and showed high potential for automated, reproducible disease severity estimation. Future work should focus on integrating deep learning and expanding the dataset to improve model robustness and adaptability to other foliar pathosystems.
Why it matches plant phenotyping methods落花生葉の病斑から葉面病害重症度を自動推定する画像解析手法を開発・比較検証しており、植物表現型（病害状態）の取得が研究の中心です。
abstractThis study aimed to develop and validate an automated image analysis model, LLS-SevEst , for quantifying LLS severity in peanut leaves.
The aim of this paper is to address the lack of standard methodologies for the assessment of 3D point clouds. We present a methodology to realistically assess the accuracy of 3D point clouds, enabling the evaluation in a full 3D context rather than based on isolated points. Additionally, it introduces three significant innovations: a) it bridges the gap related to the unknown error of the reference ground-truth point cloud; b) it provides separate metrics for location error and reconstruction error; and c) it introduces a procedure to compute the location error that eliminates the bias in the selection of point-pair picking between the DGT points and their corresponding pairs in the point cloud being assessed. The geometry and structure of trees are related to the vegetative parameters and productivity in fruit orchards. In consequence, obtaining a precise and accurate geometric characterization of canopies is of interest for implementing site-specific management strategies that optimize input rates and minimize the costs and environmental risks of agricultural operations. Among the different sensing technologies, sensors based on the principle of light detection and ranging (LiDAR) have emerged as the primary choice for accurate geometric characterization of orchards. However, to make informed orchard management decisions based on LiDAR-derived geometric and structural data, it is essential to assess the accuracy of LiDAR-based scanning systems. Unfortunately, there is currently a lack of standard methodologies to evaluate the accuracy of LiDAR-based systems in agricultural environments. This research paper presents a novel methodology to assess the location error and the reconstruction error of 3D point clouds in full 3D context. The methodology involves comparing LiDAR-derived point clouds to an accurate high-resolution 3D digital ground truth (DGT) obtained using digital photogrammetric techniques. One of the main difficulties when using a reference point cloud to assess point cloud errors is the selection of the points to be compared so that they can be considered as corresponding point pairs. When developing the methodology, four procedures of point pair selection and distance calculation were compared. The best performing procedure was selected and proposed as a standard for accuracy assessment of 3D point clouds. The proposed procedure minimizes the error attributed to the selection of the corresponding point pairs between the assessed point cloud and the reference DGT point cloud. Subsequently, the proposed methodology was tested and validated by assessing the accuracy of 46 different point clouds. The conclusions regarding the accuracy, applicability, and practical utility of the proposed methodology are supported by the determination of reconstruction errors and location errors in 46 point clouds obtained with the 3 different MTLS systems operated with different settings. The proposed methodology will be very useful for scanning system manufacturers, researchers, advisors and eventually advanced farmers to quantify the errors committed when characterizing tree canopies. This is crucial to enable accurate management operations in the framework of Precision Agriculture based on canopy variability. Furthermore, the methodology is expected to facilitate the design of new applications requiring high accuracy to be implemented in the near future.
Why it matches plant phenotyping methods果樹キャノピーの3D形状・構造を測定するLiDAR点群について、誤差評価手法を開発し、46点群で検証しており、植物表現型取得の技術的評価が中心である。
abstractThis research paper presents a novel methodology to assess the location error and the reconstruction error of 3D point clouds in full 3D context.
ABSTRACT Crop diseases pose a significant threat to global food security, leading to substantial yield losses and economic repercussions. Timely intervention and efficient management of many disorders depend on their prompt and precise detection. Deep learning techniques have transformed computer vision in recent years and provided potential solutions for automated crop disease diagnosis. The proposed framework of dual RNN‐based crop disease detection (D‐RNN‐based CDD) encompasses several crucial stages aimed at harnessing deep learning methods for precise and efficient disease identification in agricultural crops. First, preparation methods are used to improve the raw images quality. This involves the contrast transformation to enhance visibility and the application of a Gaussian filter to reduce noise, ensuring smoother images suitable for subsequent processing. After preprocessing, image segmentation is done using a hyper softmax patch‐based convolutional neural network (HSP‐CNN) approach, dividing the images into smaller patches for localised analysis. This segmentation method allows the model to focus on specific regions of interest, facilitating accurate identification of diseased areas. Following segmentation, the next step is feature extraction that captures pertinent characteristics indicative of crop diseases. Shape features, such as area, convexity, centroid and perimeter, are extracted to quantify the disease symptoms. Colour features capture RGB components in terms of mean, median and standard deviation. Moreover, texture features are extracted to analyse the patterns and structures associated with different diseases using modified texture orientation‐based multitexon (MTOM) features enabling extraction at multiple scales. These extracted features offer comprehensive representations of the underlying characteristics of diseased regions within crop images. Finally, classification is done using a dual RNN (D‐RNN) comprising two triple hidden layers assisted in RNN (THA‐RNN) models. One THA‐RNN model trains the segmented images, while the other captures the extracted feature set and trains on it. Leveraging the capabilities of THA‐RNNs, adept at analysing sequential information (features), the classifier accurately predicts the presence or absence of crop diseases. For Dataset 1, the developed THA‐RNN model achieved the maximum value of 0.955, outperforming other models, such as GoogLeNet (0.911), LeNet (0.921), CNN (0.894), Bi‐LSTM (0.877), DNN (0.895), RNN (0.873), RESNET (0.886) and ANN (0.870).
Why it matches plant phenotyping methods植物画像から病変領域をセグメンテーションし、症状の形状・色・テクスチャ特徴を抽出して疾病状態を分類する手法が研究の中心であるため、植物病害フェノタイピング手法として含める。
abstractimage segmentation is done using a hyper softmax patch‐based convolutional neural network (HSP‐CNN) approach, dividing the images into smaller patches for localised analysis.
Field / plotWhole plant / canopy / plot / fieldCalibration / preprocessingGrowth / time-series analysisGrowth / development / phenology
Long-term phenological data in alpine regions are often limited to a few locations and thus, little is known about climate-change-induced plant phenological shifts above the treeline. Because plant growth initiation in seasonally snow-covered regions is largely driven by snowmelt timing and local temperature, it is essential to simultaneously track phenological shifts, snowmelt, and near-ground temperatures. In this study, we make use of ultrasonic snow height sensors installed at climate stations in the Swiss Alps to reveal the phenological advance of grassland ecosystems and relate them to climatic changes over 25 years (1998-2023). When snow is absent, these snow height sensors additionally provide information on plant growth at a uniquely fine temporal scale. We applied a two-step machine learning algorithm to separate snow- from plant-height measurements, allowing us to determine melt-out for 122 stations between 1560 and 2950 m a.s.l., and to extract seasonal plant growth signals for a subset of 40 stations used for phenological analyses. We identified the start of growth and calculated temperature trends, focusing particularly on thermal conditions between melt-out and growth initiation. We observed an advance of green-up by -2.4 days/decade coinciding with strong warming of up to +0.8°C/decade. Although the timing of snowmelt has not changed significantly over the study period in this focal region, phenological responses to early melt-out years varied due to differing influences of photoperiodic and thermal constraints, which were not equally important across elevations and communities. Phenological shifts of alpine grasslands are thus likely to become even more pronounced if snowmelt timing advances in the future as predicted. As climate change continues to reshape mountain ecosystems, understanding the interplay between phenological changes and species turnover will be essential for predicting future biodiversity patterns and informing conservation strategies in alpine regions.
Why it matches plant phenotyping methods超音波雪高センサーと機械学習により雪と植物高を分離し、植物成長・フェノロジーを抽出する方法が研究の中心であるため。
abstractWe applied a two-step machine learning algorithm to separate snow- from plant-height measurements, allowing us to determine melt-out for 122 stations between 1560 and 2950 m a.s.l., and to extract seasonal plant growth signals for a subset of 40 stations used for phenological analyses.
This paper presents a comprehensive MATLAB-based deep learning framework for early detection and classification of plant diseases using Convolutional Neural Networks (CNNs). Our approach processes plant leaf images through a systematic pipeline consisting of acquisition, preprocessing, segmentation, and feature extraction. We apply a custom CNN model to classify leaves into various disease categories and a healthy class. The proposed system demonstrates high accuracy and scalability, promoting precision agriculture and sustainable farming through timely diagnosis and intervention.
Why it matches plant phenotyping methods葉画像の取得・前処理・セグメンテーション・特徴抽出とCNNによる植物病害分類が研究の中心であり、植物の病害状態を画像から推定する手法開発に該当する。
abstractThis paper presents a comprehensive MATLAB-based deep learning framework for early detection and classification of plant diseases using Convolutional Neural Networks (CNNs).
We present a groundbreaking Do-It-Yourself (DIY) acoustic volumetry platform that redefines quantitative measurement by eliminating the conventional microphone. Our design exploits the inherent acoustic–electrical properties of a dynamic microphone cartridge mounted on a sealed chamber—representing the theoretically simplest possible acoustic volumeter. By focusing on resonance peak shifts, our streamlined sensor —utilizing an easily assembled circuit built solely from off-the-shelf audio connectors to split the sound card output between sensor excitation and response recording—delivers rapid, precise volumetric measurements with as few as three frequency points in several seconds. Calibration using both linear and logarithmic models demonstrated a robust correlation between resonance peak shifts and sample volume, yielding root mean square errors (RMSE) of 1.980 μL and 1.662 μL, respectively. Notably, when applied to a ten-grain assay, these values correspond to an average error of less than 0.2 μL per wheat grain, thereby affirming the device’s precision across a diverse range of sample volumes. An exclusive Python-based freeware, distributed globally, provides an intuitive interface for calibration and measurement, ensuring that this cost-effective and modular approach is accessible to researchers worldwide. This innovative method not only simplifies traditional volumetry techniques but also paves the way for further optimization, marking a significant advancement for applications across a broad spectrum of scientific fields.
Why it matches plant phenotyping methods音響容積測定プラットフォームとPythonソフトウェアを開発し、コムギ粒の体積測定で精度を評価しているため、植物器官形質の取得法が中心である。
abstractWe present a groundbreaking Do-It-Yourself (DIY) acoustic volumetry platform
Abstract— Plant leaf diseases significantly impact agricultural productivity, leading to substantial crop losses if not detected early. This paper introduces a deep learning approach for automatically identifying and classifying potato leaf diseases using image data. By utilizing transfer learning with MobileNetV2, a lightweight yet efficient CNN, the system accurately distinguishes between Early Blight, Late Blight, and Healthy leaves. The process includes dataset collection, preprocessing, model training, and real-time web application deployment for user interaction. Experimental results highlight high prediction accuracy and practical feasibility, making this system a valuable asset for precision agriculture and crop protection. Keywords— Plant leaf Disease Detection, Convolutional Neural Networks (CNNs), Image Classification, Transfer Learning, MobileNetV2, Image Preprocessing, Crop Protection, Deep Learning.
Why it matches plant phenotyping methodsジャガイモ葉の病害状態を画像から分類する深層学習手法が研究の中心であり、植物病害表現型の取得・推定に該当する。
abstractThis paper introduces a deep learning approach for automatically identifying and classifying potato leaf diseases using image data.
Abstract- Agricultural productivity is greatly affected by plant diseases, leading to significant economic losses worldwide. Early detection and proper diagnosis of plant diseases are critical for maintaining healthy crops and ensuring food security. Traditional disease detection methods rely on human expertise and manual inspection, which are labor-intensive and prone to errors. Automated plant disease detection using machine learning techniques provides a more efficient and accurate solution. This paper presents an approach that leverages image processing and deep learning algorithms to identify plant diseases from leaf images. The study compares the performance of VGG, Support Vector Machines (SVM), and Random Forest classifiers to determine the most effective model for disease classification. The proposed system utilizes advanced image preprocessing techniques, including noise removal and color normalization, to enhance model performance. The results indicate that the VGG model with transfer learning achieves superior accuracy compared to traditional machine learning models, making it an optimal choice for real-world applications. The proposed system provides real-time, cost-effective solutions for farmers, enabling them to detect diseases early and take preventive actions. Additionally, a mobile application is developed to allow farmers to capture and analyze plant images instantly, ensuring accessibility even in remote agricultural areas. By integrating artificial intelligence with precision agriculture, this research aims to minimize crop losses, enhance disease management, and contribute to sustainable farming practices. Future improvements include expanding the dataset, incorporating environmental parameters, and integrating real-time IoT-based monitoring for enhanced decision-making. Keywords— Plant Disease Detection, Machine Learning, Deep Learning, VGG, Image Processing, Agricultural Technology.
Why it matches plant phenotyping methods葉画像から植物病害を推定する画像・深層学習手法が研究の中心であり、分類器比較、画像前処理、実用システム開発を含むため植物フェノタイピング方法論として適格です。
abstractThis paper presents an approach that leverages image processing and deep learning algorithms to identify plant diseases from leaf images.
Rice is a staple food crop for more than half of the world's population, playing a crucial role in global food security. However, the cultivation of rice is frequently threatened by a range of diseases, ranging which can negatively affect food supply chains and result in large yield losses. In order to ensure sustainable development of rice and secure food resources, it is crucial to understand and deal with rice plant diseases. Rice plant diseases can be broadly classified into fungal, bacterial, viral, and nematode diseases. Among these, fungal diseases are the most prevalent and destructive, with rice blast, sheath blight, and brown spot being particularly notorious. In recent years, the rapid advancements in deep learning have opened new avenues for addressing complex problems in various domains. This paper introduces a novel deep learning-based model for the detection and classification of Rice Plant Disease (RPD) using a combination of Densely Convolutional Neural Network (DenseNet) and Multi-Layer Perceptron (MLP), termed the DenseNet-169-MLP model. The input consisted of pictures of afflicted rice leaves on a white backdrop. Following the required preprocessing and cleaning of the collected data which may involve fixing missing values, harmonizing the data format, as well as getting rid of noise. Find pertinent characteristics in the dataset that can be used to distinguish between various illnesses. This involves techniques such as image processing to extract features from images of affected rice plants. Based on the anticipated disease class, group rice diseases into several categories. This can help with identifying disease trends, putting suitable management plans into place, and giving farmers focused advice. A variety of Deep Learning techniques were used to train the dataset including AlexNet,VGG16 and DenseNet-169-MLP. DenseNet-169-MLP achieved an accuracy of 94.05 when applied on the Rice plant disease dataset.
Why it matches plant phenotyping methods罹病葉画像からイネの病害状態を推定する深層学習手法が論文の中心であり、植物病害表現型の画像ベース推定に該当する。
abstractThis paper introduces a novel deep learning-based model for the detection and classification of Rice Plant Disease (RPD) using a combination of Densely Convolutional Neural Network (DenseNet) and Multi-Layer Perceptron (MLP), termed the DenseNet-169-MLP model.
Abstract Complex omics approaches and high-throughput phenotyping generate large, heterogeneous datasets that make linking molecular signatures to plant traits challenging. To address this challenge, here we introduce panomiX, a user-friendly toolbox for multi-omics integration, designed to enable non-experts to apply advanced computational methods with ease. panomiX automates data preprocessing, variance analysis, multi-omics prediction, and interaction modeling through machine learning, revealing meaningful molecular interactions and synergies. We applied panomiX to a tomato heat-stress experiment combining image-based phenotyping, transcriptomics, and Fourier-transform infrared spectroscopy data, with the aim of identification of condition-specific, cross-domain relationships between gene expression, metabolite levels, and phenotypic traits. Our approach identified a network of such connections, with those linking photosynthesis traits with stress-responsive kinases in elevated temperatures among most significant ones. By simplifying complex analyses and improving interpretability, panomiX offers a platform to accelerate the discovery of trait emergence in plants and select specific candidate genes based on multi-omics analyses.
Why it matches plant phenotyping methods植物形質データを含むマルチオミクス統合用ツール panomiX を開発・提示し、画像ベース表現型データを統合解析する再利用可能な計算ワークフローを示しているため、表現型取得そのものより解析ツールが中心的な方法論的貢献である。
abstracthere we introduce panomiX, a user-friendly toolbox for multi-omics integration
Reproduction assets foundThe paper's computational analysis assets are publicly available: the panomiX toolbox source code (GitHub) and its deployed Shiny app, plus the authors' rnaseq-mapper pipeline used to process this study's RNA-seq data. No public deposit of the paper-specific phenotype/FTIR/RNA-seq datasets is stated in the supplied.Code · publicThe source
code for the platform is available on GitHub: https://github.com/NAMlab/panomiX-tool. The
repository contains all the necessary R scripts for data processing, visualization, and machine
learning prediction.Open asset ↗NAMlab/panomiX-toolpdf-page:4 lines:1-42Code · publicThe source code is managed with a GitHub repository connected to
the Shinyapps.io via ‘rsconnect’ [53]: https://szymanskilab.shinyapps.io/panomiX/.Open asset ↗pdf-page:4 lines:1-42Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published14 Apr 2025International Journal on Advanced Electrical and Computer EngineeringCited by 0 · OpenAlex ↗
Early detection of diseases in tomato plants is crucial for sustainable agriculture and food security. This paper presents a comprehensive plant disease detection system that uses image processing and machine learning techniques to identify diseases in tomato plants and recommend appropriate pesticides. The proposed system consists of multiple modules, including image preprocessing, feature extraction, disease classification, and pesticide recommendation. Experimental results show high accuracy in disease detection and demonstrate the system's potential to assist farmers in improving crop yield and minimizing losses.
Why it matches plant phenotyping methodsトマト植物の画像から病害を検出・分類する画像処理および機械学習ワークフローが研究の中心であり、植物の病害状態を直接推定しているため。農薬推薦も含むが、病害表現型の取得・抽出手法が明示されている。
abstractThis paper presents a comprehensive plant disease detection system that uses image processing and machine learning techniques to identify diseases in tomato plants
This study introduces a method for optimizing flight modes using unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR) technology, aiming for the efficient and accurate estimation of crop phenotypes in wide-narrow row planting patterns, for cotton. It proposes specialized flight plans that take into account the unique growth stages of cotton and recommends the s-along flight path, which is derived from a detailed analysis of the cross flight path, to facilitate effective and precise data collection. A comprehensive phenotypic index, labeled as ‘P', and a fitting function are developed to describe the relationship between flight parameters, paths, and digital elevation model (DEM) data. The study also introduces two flight strategies, one focusing on accuracy and the other on efficiency, utilizing a sophisticated multi-objective optimization method. Comparative analyses show that the s-along flight path significantly improves efficiency without sacrificing accuracy, compared to traditional cross flight path techniques. The use of high-precision prior DEM data greatly enhances the precision in estimating critical phenotypic parameters such as plant height (PH) and leaf area index (LAI), especially during key stages of canopy growth. By carefully adjusting flight height, speed, and overlap during different growth stages, an ideal balance is achieved between the precision and efficiency of data collection. These strategies markedly increase the accuracy of estimating phenotypic features (P > 0.75) and efficiency (by 42 %–44 %). This research highlights the potential of these approaches in facilitating large-scale phenotypic data collection for precision agriculture, demonstrating their ability to simultaneously improve data quality and operational efficiency. Future research will aim to expand the applicability and robustness of these methods across various planting conditions and crops, further enhancing essential tools for the advancement of precision agriculture practices and development.
Why it matches plant phenotyping methodsUAV LiDARの飛行経路・パラメータ最適化と多目的最適化を開発し、綿の草丈・葉面積指数などの表現型推定精度と効率を評価しており、表現型取得法が研究の中心である。
abstractThis study introduces a method for optimizing flight modes using unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR) technology, aiming for the efficient and accurate estimation of crop phenotypes
As UAV-based hyperspectral remote sensing technology becomes increasingly prevalent in agriculture and forestry, the estimation of plant nutrient content through hyperspectral data has become crucial for enhancing the efficiency of precision agricultural management. Hyperspectral imaging technology, capable of capturing the spectral characteristics of plants, shows significant potential for estimating nitrogen content. However, the preprocessing of hyperspectral images remains a challenge in practical applications, particularly with canopy images of Torreya Grandis, where mixed pixels lead to inaccuracies in extracting canopy pixel reflectance. To address this, we developed a novel Simple Linear Iterative Clustering- Enhanced Vegetation Index (SLIC-EVI) method specifically tailored for Torreya Grandis. Furthermore, we proposed a new approach that combines Hyperspectral Multiscale Transformation (HMT) with a squeeze-excitation convolutional neural network (seCNN). The HMT-seCNN model transforms one-dimensional spectral data into a three-dimensional map, optimizing the capabilities of CNN and the learning potential of the SE module, thereby significantly enhancing prediction performance. Experimental results demonstrate that the correlation between canopy pixel reflectance, as extracted by the SLIC-EVI method, and nitrogen content reached 0.744, substantially higher than the correlations achieved with NDVI (0.303) and EVI (0.551). This indicates that the SLIC-EVI method provides superior precision. In estimating nitrogen content, the HMT-seCNN model showed enhanced accuracy and generalization capabilities compared to traditional models that rely on local spectral features. In the test set, the HMT-seCNN model achieved a coefficient of determination (R2) of 0.765, a root mean square error (RMSE) of 2.281, and a relative prediction deviation (RPD) of 2.06, outperforming other methods. These findings underscore the benefits of integrating global spectral features with deep learning technology to enhance the accuracy of nitrogen content estimation. The main conclusions of this study include: (1) The SLIC-EVI method offers significant advantages over traditional methods in extracting reflectance; (2) The combination of global spectral features with the deep learning HMT-seCNN technique effectively enhances the predictive ability for Torreya Grandis nitrogen content; (3) The SE channel attention mechanism plays a crucial role in enhancing model performance.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像から樹冠窒素含量を推定する画素抽出法（SLIC-EVI）と深層学習モデル（HMT-seCNN）を開発・比較検証しており、植物形質推定手法が研究の中心である。
abstractwe developed a novel Simple Linear Iterative Clustering- Enhanced Vegetation Index (SLIC-EVI) method specifically tailored for Torreya Grandis.
Rapid and accurate plant phenotyping is vital to plant breeding and monitoring. Hyperspectral imaging (HSI) is the popular phenotypic technique to acquire spectral and spatial information of plants. However, the close-range HSI of plant canopies is greatly affected by the complex interaction of canopy geometry with illumination, which leads to biased or contaminated spectral information. Thus, the mitigation of these effects is imperative but challenging. In this study, a three-dimensional (3D) spectral compensation method on close-range canopy HSI was proposed, to correct the reflectance of canopies affected by imaging distance and leaf angle. First, the hyperspectral and depth images of canopies were registered and fused to generate hyperspectral 3D point clouds. Next, the full-spectrum reflectance on canopies was compensated based on the depth and angle information provided by the hyperspectral 3D point clouds. Then, the performance of spectral compensation results was evaluated by cluster analysis, spectral curve validation, and chlorophyll regression. Results on two plant types (perilla and tea seedlings) showed that after spectral compensation, the reflectance variations within canopies reduced greatly, the reflectance of whole canopies became more homogeneous, with a dominant cluster accounting for over 67% pixels of canopies. And using the mean reflectance curves of in vitro flattened leaves as the reference, the canopy reflectance after compensation were closer to the reference level, that the Euclidean Distance (ED) between them reduced by 50.6%. The determination coefficient (R²) for chlorophyll regression after compensation reached 0.75, increasing about 17% compared to that before compensation. The overall results demonstrated that the proposed 3D spectral compensation method was effective in mitigating the effects of imaging distance and leaf angle on plant canopies in close-range HSI. This could further facilitate the revelation of plant optical characteristics, which is of high significance for the accurate close-range plant phenotyping.
Why it matches plant phenotyping methods植物キャノピーの近接ハイパースペクトル画像に対する3Dスペクトル補償法を開発し、複数の評価で性能検証しており、表現型取得・抽出手法が研究の中心である。
abstractIn this study, a three-dimensional (3D) spectral compensation method on close-range canopy HSI was proposed, to correct the reflectance of canopies affected by imaging distance and leaf angle.
High-resolution temporal spectral data captured by unmanned aerial vehicles (UAVs) have become increasingly important in predicting crop yields. Effective preprocessing of these temporal datasets is crucial for improving yield estimation accuracy and facilitating the broader application of predictive models. Despite its growing importance, a comprehensive guide detailing the preprocessing procedures for UAV temporal data is currently lacking. Consequently, this research is dedicated to constructing a robust preprocessing framework tailored to UAV time series spectral remote sensing data, with a particular emphasis on assessing its impact on the accuracy of yield predictions. We developed a multi-level threshold segmentation (MLT) method specifically for rice particle swarm optimization (ricePSO). Three field experiments were executed under diverse nutritional regimes to contrast the efficacy of yield predictions derived from UAV temporal dynamic threshold segmentation against those achieved through temporal data smoothing. Results showed that the ricePSO multi-level threshold segmentation outperformed the conventional Otsu threshold segmentation method, enhancing yield prediction accuracy by 1–11 %. Meanwhile, data smoothing effectively reduced errors in the temporal data acquisition process. Combining MLT, Gaussian smoothing, and the Bidirectional Long Short-Term Memory (Bi-LSTM) model resulted in the highest yield prediction accuracy, with an R² value of 87.52 %. Overall, this study achieved improvements in yield prediction accuracy through the use of multilevel dynamic threshold segmentation and data smoothing, providing new strategies for the preprocessing of temporal multispectral remote sensing data from UAV.
Why it matches plant phenotyping methodsUAVマルチスペクトル時系列データから作物収量を推定するための前処理・セグメンテーション手法を開発し、既存手法と比較検証しており、植物フェノタイピング手法が研究の中心である。
abstractthis research is dedicated to constructing a robust preprocessing framework tailored to UAV time series spectral remote sensing data
Precision Agriculture relies significantly on yield data obtained from combine harvesters, which constitutes a pivotal tool for optimizing crop productivity. Despite its potential, challenges in data accuracy persist, necessitating the development of novel automated postprocessing protocols for yield data refinement. In this paper, different automatic postprocessing protocols were evaluated using remote sensing data, specifically Sentinel-2 satellite imagery. Various automatic postprocessing protocols were applied to a dataset spanning 946 hectares over a four-year period. Commercial sensors on combine harvesters acquired the yield data. The analysis included global (field-level) adjustments and local adjustments at a finer scale (40 × 40 m² level), employing interval mean ± n·(standard deviation) calculations. Three n values (1, 1.5, and 2.5) were tested, resulting in thirteen distinct postprocessing variations. Finally, a mean filter was also applied. The results demonstrated that the yield correlation with satellite data increased with the reduction of yield variability at the pixel level (10 m). The best results were obtained using n = 1 with a 3 × 3 mean filter, where Sentinel-2 pixels remained unaffected, and the average Root Mean Square Error (RMSE) during validation was 0.572 t·ha⁻¹. In addition, the geostatistical parameters (coefficient of variation, semivariance, and range within a 10 m pixel) reached optimal values. Finally, the postprocessing uncertainty was determined to be 0.200 t·ha⁻¹. These results validate the efficacy of a novel postprocessing protocol for refining yield data in rice crops. The integration of pixel-level data from combine harvesters with Sentinel-2 imagery emerges as a promising approach for optimizing crop management, offering valuable insights for the advancement of Precision Agriculture.
Why it matches plant phenotyping methods稲の収量という植物形質を対象に、収量データの自動ポストプロセッシング手法を開発・評価し、衛星データとの相関やRMSEで検証しているため、方法論が中心である。
abstractnecessitating the development of novel automated postprocessing protocols for yield data refinement
Precision Agriculture relies significantly on yield data obtained from combine harvesters, which constitutes a pivotal tool for optimizing crop productivity. Despite its potential, challenges in data accuracy persist, necessitating the development of novel automated postprocessing protocols for yield data refinement. In this paper, different automatic postprocessing protocols were evaluated using remote sensing data, specifically Sentinel-2 satellite imagery. Various automatic postprocessing protocols were applied to a dataset spanning 946 hectares over a four-year period. Commercial sensors on combine harvesters acquired the yield data. The analysis included global (field-level) adjustments and local adjustments at a finer scale (40 × 40 m² level), employing interval mean ± n·(standard deviation) calculations. Three n values (1, 1.5, and 2.5) were tested, resulting in thirteen distinct postprocessing variations. Finally, a mean filter was also applied. The results demonstrated that the yield correlation with satellite data increased with the reduction of yield variability at the pixel level (10 m). The best results were obtained using n = 1 with a 3 × 3 mean filter, where Sentinel-2 pixels remained unaffected, and the average Root Mean Square Error (RMSE) during validation was 0.572 t·ha⁻¹. In addition, the geostatistical parameters (coefficient of variation, semivariance, and range within a 10 m pixel) reached optimal values. Finally, the postprocessing uncertainty was determined to be 0.200 t·ha⁻¹. These results validate the efficacy of a novel postprocessing protocol for refining yield data in rice crops. The integration of pixel-level data from combine harvesters with Sentinel-2 imagery emerges as a promising approach for optimizing crop management, offering valuable insights for the advancement of Precision Agriculture.
Why it matches plant phenotyping methods収量という植物形質の取得データを対象に、衛星データと組み合わせた自動ポスト処理プロトコルを開発・評価し、RMSEや検証結果で有効性を検証しているため、方法論が中心である。
abstractnecessitating the development of novel automated postprocessing protocols for yield data refinement
Medicinal plants are vital to both agriculture and healthcare but are highly susceptible to various leaf diseases, which can significantly reduce yield and quality. Traditional methods for identifying plant diseases rely on visual inspection by farmers or experts, often leading to inaccuracies and delays due to human error, especially in rural or resource-limited areas where expert knowledge is scarce. Climate change is altering disease patterns, making traditional detection methods less reliable over time. project aims to develop an AI-powered system using Convolutional Neural Networks (CNNs) to automatically detect medicinal plant leaf diseases, offering rapid and accurate diagnosis via a user-friendly web application. The system integrates image preprocessing, model training, and real-time treatment recommendations, providing valuable guidance but requiring reliable internet connectivity .However, the system's performance depends on the quality and diversity of the dataset and may face challenges in generalizing across different environments and adapting to local agricultural practices or resources. Keywords—Plant disease detection, Resnet, Convolutional Neural Network, image processing, agriculture technology, Dense Net
Why it matches plant phenotyping methods植物の葉の病気状態を画像から推定するCNN手法の開発が中心であり、植物表現型（病徴・病害状態）の取得に該当する。
abstractproject aims to develop an AI-powered system using Convolutional Neural Networks (CNNs) to automatically detect medicinal plant leaf diseases
Intercropping is a key cultivation strategy for safeguarding national food and oil security. Accurate early-stage yield prediction of intercropped soybeans is essential for the rapid screening and breeding of high-yield soybean varieties. As a widely used technique for crop yield estimation, the accuracy of 3D reconstruction models directly affects the reliability of yield predictions. This study focuses on optimizing the 3D reconstruction process for intercropped soybeans to efficiently extract canopy structural parameters throughout the entire growth cycle, thereby enhancing the accuracy of early yield prediction. To achieve this, we optimized image acquisition protocols by testing four imaging angles (15°, 30°, 45°, and 60°), four plant rotation speeds (0.8 rpm, 1.0 rpm, 1.2 rpm, and 1.4 rpm), and four image acquisition counts (24, 36, 48, and 72 images). Point cloud preprocessing was refined through the application of secondary transformation matrices, color thresholding, statistical filtering, and scaling. Key algorithms—including the convex hull algorithm, voxel method, and 3D α-shape algorithm—were optimized using MATLAB, enabling the extraction of multi-dimensional canopy parameters. Subsequently, a stepwise regression model was developed to achieve precise early-stage yield prediction for soybeans. The study identified optimal image acquisition settings: a 30° imaging angle, a plant rotation speed of 1.2 rpm, and the collection of 36 images during the vegetative stage and 48 images during the reproductive stage. With these improvements, a high-precision 3D canopy point-cloud model of soybeans covering the entire growth period was successfully constructed. The optimized pipeline enabled batch extraction of 23 canopy structural parameters, achieving high accuracy, with linear fitting R2 values of 0.990 for plant height and 0.950 for plant width. Furthermore, the voxel volume-based prediction approach yielded a maximum yield prediction accuracy of R2 = 0.788. This study presents an integrated 3D reconstruction framework, spanning image acquisition, point cloud generation, and structural parameter extraction, effectively enabling early and precise yield prediction for intercropped soybeans. The proposed method offers an efficient and reliable technical reference for acquiring 3D structural information of soybeans in strip intercropping systems and contributes to the accurate identification of soybean germplasm resources, providing substantial theoretical and practical value.
Why it matches plant phenotyping methodsインタクロップ大豆の3D画像取得・点群処理・形質抽出パイプラインを中心に最適化し、構造形質の精度検証と収量予測まで行っているため、植物フェノタイピング手法研究に該当する。
abstractThis study focuses on optimizing the 3D reconstruction process for intercropped soybeans to efficiently extract canopy structural parameters throughout the entire growth cycle
Virtual laser scanning (VLS) [1] has been used intensively for method development and machine learning in forestry, e.g., for quantification of leaf angle distribution [2], aboveground biomass prediction [3], or leaf-wood segmentation [4]. So far, these applications have been limited to mono-temporal VLS acquisitions where scenes were simplified to being completely static. However, forests are inherently dynamic environments with processes occurring at different timescales and rhythms, such as wind-induced movement, response to varying water potential, seasonal changes, stress-induced changes or growth.Given the increasing availability of multi- and hyper-temporal point cloud data [5] as well as the potential of cloud-to-cloud data fusion [6], we propose virtual laser scanning of dynamic scenes (VLS-4D) [7] to develop methods for monitoring vegetation movement, tree health, and forest growth. Unlike real-world data collection, which is limited by time or equipment to one or a few scenarios, VLS-4D allows the creation of many different scenarios. This is achieved by combining different scene compositions and dynamics, acquisition modes and sensor settings. Furthermore, VLS-4D includes perfect reference data of the underlying scene, including semantic labels, geometry and changes (e.g., as deformation/movement values or change labels). Such data is usually difficult, time-consuming or impossible to obtain when working with real point clouds, or it is associated with considerable errors or unknown ambiguities. The scenario building capabilities, together with the availability of reference data, make VLS-4D a promising data generation tool for the ever-growing pool of deep learning methods for the analysis of forest point clouds and point cloud time series.We distinguish three concepts of how dynamic scenes can be implemented in LiDAR simulation [7]:a) Few static representations of the forest scene at different epochs, e.g., one before and one after a forest disturbance event.b) Many static snapshots sampled from an animated scene, e.g., daily snapshots to simulate a permanent laser scanning setup.c) Animation within the scene, e.g., vegetation moving in the wind during a single terrestrial laser scan.We will present simulation workflows for each of these concepts using the open-source software HELIOS++ [8], from animated 3D scene generation in Blender to final simulated point clouds and point cloud time series. With these simulation examples, we illustrate the research gaps that can be filled by such virtual experiments, address strategies and challenges in implementing VLS-4D, and discuss future directions. We expect VLS-4D data to play an essential role in the development of innovative methods for forest monitoring, complementing the still limited and typically unlabelled real-world multitemporal datasets.References:[1] Winiwarter, L., et al. (2022): DOI: https://doi.org/10.1016/j.rse.2021.112772[2] Liu, J. et al. (2019): DOI: https://doi.org/10.1016/j.isprsjprs.2019.01.005[3] Schäfer, J. et al. (2023): DOI: https://doi.org/10.1093/forestry/cpad061[4] Esmorís, A. et al. (2024): DOI: https://doi.org/10.1016/j.isprsjprs.2024.06.018[5] Eitel, J.U.H. et al. (2016): DOI: https://doi.org/10.1016/j.rse.2016.08.018[6] Balestra, M. et al. (2024): DOI: https://doi.org/10.1007/s40725-024-00223-7[7] Weiser, H. & Höfle, B. (2024): DOI: https://doi.org/10.31223/X51Q5V[8] HELIOS++: https://github.com/3dgeo-heidelberg/helios
Why it matches plant phenotyping methods動的森林シーンのLiDARシミュレーションと参照データ生成を提案し、植生の移動、樹木健全性、森林成長のモニタリング手法開発を支援することが中心である。
abstractwe propose virtual laser scanning of dynamic scenes (VLS-4D) [7] to develop methods for monitoring vegetation movement, tree health, and forest growth.
PoplarField / plotStem / branchCalibration / preprocessingWater status / transpiration
In ecohydrology, stable water isotopes (δ2H and δ18O) are valuable tools for investigating the water’s movement through the soil-plant-atmosphere continuum. Recent tracer-based studies using stable water isotopes showed that different methods for extracting water from plant tissues can return different isotopic compositions due to the presence of organic contaminants and because these methods extract different plant water domains. While Cryogenic Vacuum Distillation (CVD) is widely recognized as a standard method of plant water extraction for isotopic analysis, its indiscriminate water extraction has proven problematic. Various other techniques have been developed and tested for plant water extraction, such as direct vapour equilibration, mechanical squeezing and centrifugation. However, there remains a necessity to develop a cost and time efficient method to discriminately extract xylem water, which better represents the source waters used by plants for transpiration.In this work, we evaluated the viability of Vacuum Extraction (VAC) - a method previously used in ecophysiology for chemical analysis - for the extraction of plant water for isotopic analysis. The specific objectives were to i) assess the likely influence of organic contaminants (glucose, fructose, sucrose, ethanol and methanol) in water samples extracted by VAC, ii) determine whether there is a significant difference in the isotopic signature of plant water extracted by VAC from lignified samples with and without bark, iii) compare the isotopic composition of plant water extracted by VAC and CVD.The comparison tests were carried out in late March and early July 2024 on trees or shrubs of Cornus sanguinea, Carpinus orientalis, Prunus cerasifera, Photinia serratifolia, and Populus canadensis, located in a village close to Padua (Italy). In March, samples were taken from lignified twigs, and we prepared replicates with and without bark for extraction by VAC. In July, twig samples were collected for extraction by VAC and by CVD. Given the negligible presence of organic contaminants in VAC samples, we performed their isotopic analysis by laser spectroscopy. Conversely, CVD samples were analysed by isotope-ratio mass spectrometry. Our results showed no significant differences in the sugar levels of samples with and without bark, and no clear relation between the sugar content and the isotopic composition of plant water extracted by VAC. Additionally, when comparing CVD and VAC, the δ18O values were similar, but there were significant differences in the δ2H between the two methods, with VAC samples plotting significantly closer to the Local Meteoric Water Line compared to CVD samples. These first results indicate that VAC is a promising and effective method for the extraction of plant water for isotopic analysis. However, further tests should be performed for other species and under different environmental conditions. Acknowledgements: This study was carried out within the Agritech National Research Center and received funding from the European Union Next-Generation EU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1032 17/06/2022, CN00000022). This abstract reflects only the authors’ views and opinions, neither the European Union nor the European Commission can be considered responsible for them.
Why it matches plant phenotyping methods植物組織から木部水を抽出し、その同位体組成を測定する手法の開発・比較検証が研究の中心であり、植物の水分状態に関する生理的表現型を取得する方法に該当する。
abstractthere remains a necessity to develop a cost and time efficient method to discriminately extract xylem water
Digital phenotyping is a fast-growing area of hardware and software research and development. Phenotypic studies usually require determining whether there is a difference in some trait between plants with different genotypes or under different conditions. We developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters, ensuring seamless integration with common tasks in phenotypic studies. For maximum versatility across phenotypic methods and platforms, it uses data in the form of a set of spreadsheets (XLSX and CSV files). StatFaRmer is designed to handle measurements that have variation in timestamps between plants and the presence of outliers, which is common in digital phenotyping. Data preparation is automated and well-documented, leading to customizable ANOVA tests that include diagnostics and significance estimation for effects between user-defined groups. Users can download the results from each stage and reproduce their analysis. It was tested and shown to work reliably for large datasets across various experimental designs with a wide range of plants, including bread wheat (Triticum aestivum), durum wheat (Triticum durum), and triticale (× Triticosecale); sugar beet (Beta vulgaris), cocklebur (Xanthium strumarium) and lettuce (Lactuca sativa), corn (Zea mays) and sunflower (Helianthus annuus), and soybean (Glycine max). StatFaRmer is created as an open-source Shiny dashboard, and simple instructions on installation and operation on Windows and Linux are provided.
Why it matches plant phenotyping methods植物フェノタイピングの時系列データ解析を目的とするオープンソースShinyダッシュボードを開発し、データ準備・統計解析・再現可能なワークフローを提供しており、方法・ソフトウェアが中心である。
abstractWe developed StatFaRmer, a user-friendly tool tailored for analyzing time series of plant phenotypic parameters
Reproduction assets foundThe paper's authors publicly release StatFaRmer, an open-source R Shiny dashboard for phenotyping data analysis, via GitHub with installation instructions and a sample phenotypic dataset, and host a live deployment on shinyapps.io.Code · publicThe resulting tool can be accessed at 9 https://github.com/Stathmin/StatFaRmer ), with the instructions on installation and the sample dataset provided.Open asset ↗Stathmin/StatFaRmerlines:521-528Dataset · publicA sample dataset of different plant species (bread wheat ( Triticum aestivum ), durum wheat ( Triticum durum ), and triticale (× Triticosecale )), cultivars (35 variants) and plant genotypes (allelic state of 3 genes), with different treatments (3 variants), and the time series of morphological and spectral parameters of these plants is loaded in this tool as an example and available on GitHub.Open asset ↗lines:340-350Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · Crossref · checked 15 Sept 2026
Abstract Accurate 3D reconstruction is essential for high-throughput plant phenotyping, particularly for studying complex structures such as root systems. While photogrammetry and Structure from Motion (SfM) techniques have become widely used for 3D root imaging, the camera settings used are often underreported in studies, and the impact of camera calibration on model accuracy remains largely underexplored in plant science. In this study, we systematically evaluate the effects of focus, aperture, exposure time, and gain settings on the quality of 3D root models made with a multi-camera scanning system. We show through a series of experiments that calibration significantly improves model quality, with focus misalignment and shallow depth of field (DoF) being the most important factors affecting reconstruction accuracy. Our results further show that proper calibration has a greater effect on reducing noise than filtering it during post-processing, emphasizing the importance of optimizing image acquisition rather than relying solely on computational corrections. This work improves the repeatability and accuracy of 3D root phenotyping by giving useful calibration guidelines. This leads to better trait quantification for use in crop research and plant breeding.
Why it matches plant phenotyping methods3D根フェノタイピングにおけるカメラ校正と画像取得条件の影響を体系的に検証し、再現性・精度向上の指針を提示する方法研究である。
abstractThis work improves the repeatability and accuracy of 3D root phenotyping by giving useful calibration guidelines.
Abstract Potato disease management is crucial to reducing significant crop losses in agriculture. The timely identification and classification of potato leaf diseases are necessary but time-consuming and labor-intensive. Therefore, an automated model capable of accurate and timely recognition and classification is key to resolving these challenges. In this research, a novel Normalized Long- Short Convoluted Temporal Attention Network (NLSCTAN) with Internet of Things (IOT) is developed to classify infected potato leaves and provide fertilizer suggestions to combat diseases. This work introduces a diverse dataset collected from IOT sensors by merging similar classes from three distinct potato leaf datasets: the potato leaf disease dataset, potato leaf diseases, and the plant village dataset. Preprocessing is performed using adaptive Contrast-Limited Adaptive Histogram Equalization (CLAHE) with double gamma correction to enhance the image quality. Feature extraction involves extracting different texture features using ternary patterns and discrete wavelet transform. Subsequently, dimensionality reduction is achieved through an auto encoder. Finally, the NLSCTAN model combines segmentation and classification processes to extract infected regions, determine their identity, and suggest appropriate fertilizers. Modified walrus optimization further enhances accuracy by minimizing the loss function of the neural network. The proposed procedure outperforms existing models, achieving a mean accuracy of 99.2% across various potato disease types. Experimental findings validate its competitiveness and effectiveness.
Why it matches plant phenotyping methodsジャガイモ葉の感染領域抽出と病害分類を行う画像解析モデルを開発・検証しており、植物病害状態のフェノタイピング手法が中心である。
abstractan automated model capable of accurate and timely recognition and classification is key to resolving these challenges
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 8 Sept 2026
Introduction: Applying 3D reconstruction techniques to individual plants has enhanced high-throughput phenotyping and provided accurate data support for developing "digital twins" in the agricultural domain. High costs, slow processing times, intricate workflows, and limited automation often constrain the application of existing 3D reconstruction platforms. Methods: We develop a 3D reconstruction platform for complex plants to overcome these issues. Initially, a video acquisition system is built based on "camera to plant" mode. Then, we extract the keyframes in the videos. After that, Zhang Zhengyou's calibration method and Structure from Motion(SfM)are utilized to estimate the camera parameters. Next, Camera poses estimated from SfM were automatically calibrated using camera imaging trajectories as prior knowledge. Finally, Object-Based NeRF we proposed is utilized for the fine-scale reconstruction of plants. The OB-NeRF algorithm introduced a new ray sampling strategy that improved the efficiency and quality of target plant reconstruction without segmenting the background of images. Furthermore, the precision of the reconstruction was enhanced by optimizing camera poses. An exposure adjustment phase was integrated to improve the algorithm's robustness in uneven lighting conditions. The training process was significantly accelerated through the use of shallow MLP and multi-resolution hash encoding. Lastly, the camera imaging trajectories contributed to the automatic localization of target plants within the scene, enabling the automated extraction of Mesh. Results and discussion: Our pipeline reconstructed high-quality neural radiance fields of the target plant from captured videos in just 250 seconds, enabling the synthesis of novel viewpoint images and the extraction of Mesh. OB-NeRF surpasses NeRF in PSNR evaluation and reduces the reconstruction time from over 10 hours to just 30 Seconds. Compared to Instant-NGP, NeRFacto, and NeuS, OB-NeRF achieves higher reconstruction quality in a shorter reconstruction time. Moreover, Our reconstructed 3D model demonstrated superior texture and geometric fidelity compared to those generated by COLMAP and Kinect-based reconstruction methods. The $R^2$ was 0.9933,0.9881 and 0.9883 for plant height, leaf length, and leaf width, respectively. The MAE was 2.0947, 0.1898, and 0.1199 cm. The 3D reconstruction platform introduced in this study provides a robust foundation for high-throughput phenotyping and the creation of agricultural "digital twins".
Why it matches plant phenotyping methods植物の3D再構成と、そこからの草丈・葉長・葉幅抽出を中心に開発・比較検証した高スループット表現型解析プラットフォームであり、方法が明確に中心的です。
abstractWe develop a 3D reconstruction platform for complex plants to overcome these issues.
The vast volumes of data are needed to train Deep Learning Models from scratch to identify illnesses in soybean leaves. However, there is still a lack of sufficient high-quality samples. To overcome this problem, we have developed the real-life SoyLeaf dataset and used the pre-trained Deep Learning Models to identify leaf diseases. In this paper, we have initially developed the real-life SoyLeaf dataset collected from the ICAR-Indian Institute of Soybean Research (IISR) Center, Indore field. This SoyLeaf dataset contains 9786 high-quality soybean leaf images, including healthy and diseased leaves. Following this, we have adapted data preprocessing techniques to enhance the quality of images. In addition, we have utilized several Deep Learning Models, i.e., fourteen Keras Transfer Learning Models, to determine which model best fits the dataset on SoyLeaf diseases. The accuracies of the proposed fine-tuned models using the Adam optimizer are as follows: ResNet50V2 achieves 99.79%, ResNet101V2 achieves 99.89%, ResNet152V2 achieves 99.59%, InceptionV3 achieves 99.83%, InceptionResNetV2 achieves 99.79%, MobileNet achieves 99.82%, MobileNetV2 achieves 99.89%, DenseNet121 achieves 99.87%, and DenseNet169 achieves 99.87%. Similarly, the accuracies of the proposed fine-tuned models using the RMSprop optimizer are as follows: ResNet50V2 achieves 99.49%, ResNet101V2 achieves 99.45%, ResNet152V2 achieves 99.45%, InceptionV3 achieves 99.58%, InceptionResNetV2 achieves 99.88%, MobileNet achieves 99.73%, MobileNetV2 achieves 99.83%, DenseNet121 achieves 99.89%, and DenseNet169 achieves 99.77%. The experimental results of the proposed fine-tuned models show that only ResNet50V2, ResNet101V2, InceptionV3, InceptionResNetV2, MobileNet, MobileNetV2, DenseNet121, and DenseNet169 have performed better in terms of training, validation, and testing accuracies than other state-of-the-art models.
Why it matches plant phenotyping methods大豆葉画像から病害状態を推定するデータセットと前処理・深層学習ワークフローを開発・評価しており、植物フェノタイピング手法が中心である。
abstractwe have developed the real-life SoyLeaf dataset and used the pre-trained Deep Learning Models to identify leaf diseases.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Vegetation biochemical and biophysical variables, especially chlorophyll content, are pivotal indicators for assessing drought’s impact on plants. Chlorophyll, crucial for photosynthesis, ultimately influences crop productivity. This study evaluates the mean squared Euclidean distance (MSD) method, traditionally applied in soil analysis, for estimating chlorophyll content in five diverse leaf types across various months using visible/near-infrared (vis/NIR) spectral reflectance. The MSD method serves as a tool for selecting a representative calibration dataset. By integrating MSD with partial least squares regression (PLSR) and the Cubist model, we aim to accurately predict chlorophyll content, focusing on key spectral bands within the ranges of 500–640 nm and 740–1100 nm. In the validation dataset, PLSR achieved a high determination coefficient (R2) of 0.70 and a low mean bias error (MBE) of 0.04 mg g−1. The Cubist model performed even better, demonstrating an R2 of 0.77 and an exceptionally low MBE of 0.01 mg g−1. These results indicate that the MSD method serves as a tool for selecting a representative calibration dataset in leaves, and vis/NIR spectrometry combined with the MSD method is a promising alternative to traditional methods for quantifying chlorophyll content in various leaf types over various months. The technique is non-destructive, rapid, and consistent, making it an invaluable tool for assessing drought impacts on plant health and productivity.
Why it matches plant phenotyping methods葉のクロロフィル含量という植物形質を、Vis/NIR分光とケモメトリクスで非破壊推定する手法を開発・検証しており、表現型取得が中心である。
abstractThis study evaluates the mean squared Euclidean distance (MSD) method, traditionally applied in soil analysis, for estimating chlorophyll content in five diverse leaf types across various months using visible/near-infrared (vis/NIR) spectral reflectance.
Multispectral imaging plays a key role in crop monitoring. A major challenge, however, is spectral band misalignment, which can hinder accurate plant health assessment by distorting the calculation of vegetation indices. This study presents a novel approach for short-range calibration of a multispectral camera, utilizing stereo vision for precise geometric correction of acquired images. By using multispectral camera lenses as binocular pairs, the sensor acquisition distance was estimated, and an alignment model was developed for distances ranging from 500 mm to 1500 mm. The approach relied on selecting the red band image as a reference, while the remaining bands were treated as moving images. The stereo camera calibration algorithm estimated the target distance, enabling the correction of band misalignment through previously developed models. The alignment models were applied to assess the health status of baby leaf crops (Lactuca sativa cv. Maverik) by analyzing spectral indices correlated with chlorophyll content. The results showed that the stereo vision approach used for distance estimation achieved high accuracy, with average reprojection errors of approximately 0.013 pixels (4.485 × 10−5 mm). Additionally, the proposed linear model was able to explain reasonably the effect of distance on alignment offsets. The overall performance of the proposed experimental alignment models was satisfactory, with offset errors on the bands less than 3 pixels. Despite the results being not yet sufficiently robust for a fully predictive model of chlorophyll content in plants, the analysis of vegetation indices demonstrated a clear distinction between healthy and unhealthy plants.
Why it matches plant phenotyping methods植物の健康状態・クロロフィル関連形質を推定するマルチスペクトル画像の幾何補正・校正法を開発し、精度検証と作物への適用を行っており、フェノタイピング手法が中心である。
abstractThis study presents a novel approach for short-range calibration of a multispectral camera, utilizing stereo vision for precise geometric correction of acquired images.
Premise Leaf venation network architecture can provide insights into plant evolution, ecology, and physiology. Venation networks are typically assessed through histological methods, but existing protocols provide limited guidance on processing large or challenging leaves. Methods and results We present an illustrated protocol for visualizing whole leaf venation networks, including sample preparation, clearing, staining, mounting, imaging, and archiving steps. The protocol also includes supply lists, troubleshooting procedures, safety considerations, and examples of successful and unsuccessful outcomes. The protocol is suitable for a wide range of leaf sizes and morphologies and has been used with all major plant groups. Conclusion We provide a workflow for obtaining high-quality mounts and images of venation networks of a wide range of species, using readily available materials.
Why it matches plant phenotyping methods葉脈ネットワークを可視化・画像取得するための調製、染色、マウント、撮像、保存ワークフローを中心とした実用的プロトコルであり、植物形態形質の取得法として中心的です。
abstractWe present an illustrated protocol for visualizing whole leaf venation networks, including sample preparation, clearing, staining, mounting, imaging, and archiving steps.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Advances in deep learning have greatly enhanced disease diagnostic techniques in agriculture. Accurate identification of rice plant diseases is crucial to preventing the severe consequences these diseases can have on crop yield. Current methods often struggle with reliably diagnosing conditions and detecting issues in leaf images. Previously, leaf segmentation posed challenges, and while analyzing complex disease stages can be effective, it is computationally intensive. Therefore, segmentation methods need to be more accurate, cost-effective, and reliable. To address these challenges, we propose a hybrid bio-inspired algorithm, named the Hybrid WOA_APSO algorithm, which merges Adaptive Particle Swarm Optimization (APSO) with the Whale Optimization Algorithm (WOA). For disease classification in rice crops, we utilize a Convolutional Neural Network (CNN). Multiple experiments are conducted to evaluate the performance of the proposed model using benchmark datasets (Plantvillage), with a focus on feature extraction, segmentation, and preprocessing. Optimizing feature selection is a critical factor in enhancing the classification algorithm’s accuracy. We compare the accuracy, sensitivity, and specificity of our model against industry-standard techniques such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and conventional CNN models. The experimental results indicate that the proposed hybrid approach achieves an impressive accuracy of 97.5% (Refer Table 8), which could inspire further research in this field.
Why it matches plant phenotyping methodsイネ葉画像から病害状態を推定するCNN・特徴選択・セグメンテーション手法を提案し、ベンチマークデータセットと既存手法で性能比較しており、表現型取得・推定法が研究の中心である。
abstractFor disease classification in rice crops, we utilize a Convolutional Neural Network (CNN).
Leaf and canopy temperature have long been recognized as important indicators of plant water status because leaves cool when water is transpired and warm up when leaf stomata close and transpiration is reduced. Unmanned aerial vehicles (UAVs) open up the possibility to capture high resolution thermal images of forest canopies at the leaf scale. However, a careful calibration procedure is required to convert the thermal images to absolute temperatures, in addition, at high spatial resolution, the complexity of forest canopies leads to challenges in stitching overlapping thermal images into an orthomosaic of the forest site. In this study, we present a novel flight planning approach in which the locations of ground temperature references are directly integrated in the flight plan. Six UAV flight campaigns were conducted over a tropical dry forest in Costa Rica. For each flight five different calibration methods were tested. The most accurate calibration was used to analyze the tree canopy temperature distributions of five tree species. From the distribution we correlated its mean, variance, 5th and 95th percentile against individual tree transpiration estimates derived from sapflow measurements. Our results show that the commonly applied calibration provided by the cameras manufacturer (factory calibration) and empirical line calibration were less accurate than the novel repeated empirical line calibration and the factory calibration including drift correction (MAE 3.5°C vs. MAE 1.5°C). We show that the orthomosaic is computable by directly estimating the thermal image orientation from the visible images during the structure from motion step. We found the 5th percentile of the canopy temperature distribution, corresponding to the shaded leaves within the canopy, to be a better predictor of tree transpiration than the mean canopy temperature (R 2 0.85 vs. R 2 0.60). Although these shaded leaves are not representative of the whole canopy, they may be the main transpiration site in the heat of the day. Spatially high-resolution, validated temperature data of forest canopies at the leaf scale have many applications for ecohydrological questions, e.g., the estimation of transpiration, for comparing plant traits and modeling of carbon and water fluxes by considering the entire canopy temperature distribution in mixed-species forests.
Why it matches plant phenotyping methodsUAV熱画像の飛行計画、温度校正、オルソモザイク生成を開発・比較検証し、樹冠温度という植物生理形質を推定しているため、フェノタイピング手法が中心である。
abstractwe present a novel flight planning approach in which the locations of ground temperature references are directly integrated in the flight plan.
Light Detection and Ranging (LiDAR) technology can be used to assess canopy height in cotton (Gossypium hirsutum L.), but standardized data acquisition and processing guidelines are lacking. Accurate canopy height estimation is crucial in cotton for optimizing growth regulator application and maximizing yield. The main goal of this study was to determine the optimal unmanned aerial vehicle flight settings—altitude and speed—and to assess specific processing parameters impact on data accuracy, processing time, and file size. Nine flight settings comprising three altitudes (12.2 m, 24.4 m, and 48.8 m) and three speeds (4.8 km/h, 9.6 km/h, and 14.8 km/h) were tested. LiDAR data were processed using DJI Terra software, where two user-defined processing steps were examined: point-cloud thinning via grid size sub-sampling (0, 10, 20, 30, 40, and 50 cm) and slope classification (flat, gentle, and steep). The optimal flight altitude was 24.4 m, with no effect of flight speed. Grid sub-sampling up to 20 cm produced balanced accuracy, processing time, and file size. The choice of slope category had no significant effect on LiDAR-derived canopy height. These findings contribute to the development of standardized LiDAR data acquisition and processing guidelines for cotton to support crop management decision.
Why it matches plant phenotyping methodsUAV LiDARによるワタ群落高推定を対象に、飛行条件と点群処理条件を比較・最適化しており、植物形質取得法の技術開発・検証が中心である。
abstractstandardized data acquisition and processing guidelines are lacking
Plant phenotyping, which involves measuring and analysing plant traits, has seen significant advances in recent years by integrating autonomous platforms and sophisticated sensor systems. In contrast to traditional methods, modern unmanned ground vehicles (UGVs) provide robust and accurate phenotyping capabilities by enabling close, detailed and continuous monitoring of crops under different environmental conditions. This study presents the configuration and validation of a multi-sensor platform (MSP) integrated with a UGV to improve plant phenotyping through advanced data fusion and co-registration techniques. The platform incorporates red, green, and blue channel (RGB), hyperspectral from visible light (VIS) and near-infrared light (NIR) spectrum, thermal sensors, and a three-dimensional (3D) light detection and ranging (LiDAR), all subjected to extensive calibration to ensure precise temporal and spatial alignment. Intrinsic calibration was applied, including correcting the spectral signatures of VIS and NIR. Additionally, timestamps were synchronised using the VIS sensor as the primary reference due to its central position and higher data acquisition frequency. Homography matrices were computed using checkerboard patterns for geometric alignment across sensors, and motion corrections accounted for UGV movement and ground sample distance. LiDAR point clouds were transformed into depth-maps (DMs) using radial basis function interpolation, enriching the spatial data for further analysis. The co-registered and synchronised MSP was tested for detecting Cercospora leaf spot (CLS) in sugar beet plants during a field experiment. Two models were implemented: (1) a soil and plant segmentation model based on the DeepLabV3+ architecture, achieving an F1-score of 0.85 and an accuracy of 0.95, and (2) a CLS severity scoring model using a custom convolutional neural network (CNN). The severity model, leveraging NIR and DM channels, achieved an F1-score of 0.7066, accuracy of 0.7104, and recall of 0.7167, with NIR wavelengths between 814 and 851 contributing significantly to performance. These results highlight the importance of accurate data fusion and synchronisation in multi-sensor systems for plant phenotyping. Moreover, the study demonstrates the potential of integrating multiple sensors on a UGV for precision agriculture, thereby enhancing MSP effectiveness in crop monitoring and disease detection. • Multi-sensor platform supports detailed plant phenotyping using data fusion. • Effective synchronization ensured accurate temporal alignment across sensors. • RGB, hyperspectral, thermal, and LiDAR sensors calibrated for accurate alignment. • Soil-plant and segmentation Cercospora leaf spot disease severity estimated using neural network. • NIR and depth map sensor fusion enhance plant phenotyping accuracy for Cercospora leaf spot disease severity.
Why it matches plant phenotyping methodsマルチセンサーUGVプラットフォームの構成、校正、同期、データ融合を開発・検証し、植物病害の重症度という表現型を推定しているため、方法が研究の中心である。
abstractThis study presents the configuration and validation of a multi-sensor platform (MSP) integrated with a UGV to improve plant phenotyping through advanced data fusion and co-registration techniques.
In agricultural practices, plant phenotyping using object detection models is gaining attention, plant phenotyping is a technology that accurately measures the quality and condition of cultivated crops from images, contributing to the improvement of crop yield and quality, as well as reducing environmental impact. However, collecting the training data necessary to create generic and high-precision models is extremely challenging due difficulties associated with annotations and the diversity of domains. Such difficulties arise from the unique shapes and backgrounds of plants, as well as the significant changes in appearance due to environmental conditions and growth stages. Furthermore, it is difficult to transfer training data across different crops, and although machine learning models effective for specific environments, conditions, and crops have been developed, they cannot be widely applied in real-world conditions. Therefore, in this study, we propose a generative artificial intelligence data augmentation method (D4) and investigated its application towards a shoot detection task in a vineyard. D4 uses a pre-trained text-guided diffusion model based on a large number of original images culled from video data collected by unmanned ground vehicles or other means, and a small number of annotated datasets. The proposed method generates new annotated images with background information adapted to the target domain while retaining annotation information necessary for object detection. In addition, D4 overcomes the lack of training data in agriculture, including the difficulty of annotation and diversity of domains. We confirmed that this generative data augmentation method improved the mean average precision by up to 28.65% for the BBox detection task and the average precision by up to 13.73% for the keypoint detection task for vineyard shoot detection. D4 generative data augmentation is expected to simultaneously solve the cost and domain diversity issues of training data generation for agricultural applications and improve the generalization performance of detection models.
Why it matches plant phenotyping methodsブドウのシュート検出を対象に、テキスト誘導拡散モデルによるドメイン適応型データ拡張手法を開発・評価しており、画像から植物器官を抽出する方法が研究の中心である。
abstractTherefore, in this study, we propose a generative artificial intelligence data augmentation method (D4) and investigated its application towards a shoot detection task in a vineyard.
Quantitative wood anatomy (QWA), which involves measuring wood cell anatomical characteristics commonly on dated tree rings, is becoming increasingly important within plant sciences and ecology. This approach is particularly valuable for studies that require processing a large number of samples, such as those aimed at millennial-long climatic reconstructions. However, the field faces significant challenges, including the absence of a publicly available comprehensive protocol for efficiently and uniformly producing high-quality wood thin sections for QWA along dated tree-ring series. This issue is especially critical for more brittle subfossil wood, in addition to fresh material from living trees. Our manuscript addresses these challenges by providing a detailed protocol for producing thin anatomical sections of wood and digital images, specifically tailored for long chronologies of tree-ring anatomy with an emphasis on conifer wood. The protocol includes step-by-step procedures for sample preparation, sectioning, and imaging, ensuring consistent and high-quality results. By offering this well-tried-and-tested protocol, we aim to facilitate reproducibility and accuracy in wood anatomical studies, ultimately advancing research in this field. It aims to serve as a reference for researchers and laboratories engaged in similar work, promoting standardized practices and enhancing the reliability of QWA data.
Why it matches plant phenotyping methods樹木年輪の木材解剖学的形質を定量化するための試料調製・薄切・デジタル画像化プロトコルが論文の中心であり、植物形質取得の再現性と標準化を目的としている。
abstractproviding a detailed protocol for producing thin anatomical sections of wood and digital images, specifically tailored for long chronologies of tree-ring anatomy
In the acquisition of Magnetic Resonance (MR) images shorter scan times lead to higher image noise. Therefore, automatic image denoising using deep learning methods is of high interest. In this work, we concentrate on image denoising of MR images containing line-like structures such as roots or vessels. In particular, we investigate if the special characteristics of these datasets (connectivity, sparsity) benefit from the use of special loss functions for network training. We hereby translate the Perceptual Loss to 3D data by comparing feature maps of untrained networks in the loss function. We tested the performance of untrained Perceptual Loss (uPL) on 3D image denoising of MR images displaying brain vessels (MR angiograms - MRA) and images of plant roots in soil. In this study, 536 MR images of plant roots in soil and 450 MRA images are included. The plant root dataset is split to 380, 80, and 76 images for training, validation, and testing. The MRA dataset is split to 300, 50, and 100 images for training, validation, and testing. We investigate the impact of various uPL characteristics such as weight initialization, network depth, kernel size, and pooling operations on the results. We tested the performance of the uPL loss on four Rician noise levels (1%, 5%, 10%, and 20%) using evaluation metrics such as the Structural Similarity Index Metric (SSIM). Our results are compared with the frequently used L1 loss for different network architectures. We observe, that our uPL outperforms conventional loss functions such as the L1 loss or a loss based on the Structural Similarity Index Metric (SSIM). For MRA images the uPL leads to SSIM values of 0.93 while L1 and SSIM loss led to SSIM values of 0.81 and 0.88, respectively. The uPL network's initialization is not important (e.g. for MR root images SSIM differences of 0.01 occur across initializations, while network depth and pooling operations impact denoising performance slightly more (SSIM of 0.83 for 5 convolutional layers and kernel size 3 vs. 0.86 for 5 convolutional layers and kernel size 5 for the root dataset). We also find that small uPL networks led to better or comparable results than using large networks such as VGG (e.g. SSIM values of 0.93 and 0.90 for a small and a VGG19 uPL network in the MRA dataset). In summary, we demonstrate superior performance of our loss for both datasets, all noise levels, and three network architectures. In conclusion, for images containing line-like structures, uPL is an alternative to other loss functions for 3D image denoising. We observe that small uPL networks have better or equal performance than very large network architectures while requiring lower computational costs and should therefore be preferred.
Why it matches plant phenotyping methods植物根のMR画像を対象に、3D画像デノイジング用の損失関数を開発・比較検証しており、根画像からの表現型取得を支える画像解析手法が中心である。
abstractWe tested the performance of untrained Perceptual Loss (uPL) on 3D image denoising of MR images displaying brain vessels (MR angiograms - MRA) and images of plant roots in soil.
Spectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), based on the Red Edge band of MSI/Sentinel-2 (B05, B06, B07 images), are essential tools in coffee monitoring. These indices require resampling the Red Edge band (20 m resolution) to match the NIR (10 m resolution) using methods such as nearest neighbor, bilinear, cubic, and Lanczos. In this technical note, we evaluated these resampling methods using two original B05 images, selected on November 24, 2023, and September 21, 2023, with reference points from the farms "Ouro Verde" (15 hectares) in Barra do Choça (BA) and "Canto do Rio" (45 hectares) in Luís Eduardo Magalhães (BA), respectively. A total of 500 random points were generated and analyzed using PSF, linear models, and cross-validation with metrics such as R², MAE, and RMSE. The PSF analysis indicated the integrity of the data for further analysis. The cubic method showed the best performance (R² = 0.996, MAE = 20.87, RMSE = 32.67). The validation results of the resampling methods suggest that this procedure is crucial for accurate digital processing in remote sensing for coffee cultivation and should be aligned with the study objectives.
Why it matches plant phenotyping methodsコーヒー栽培における植物キャノピーのスペクトル指標算出を対象に、Sentinel-2赤縁バンドのリサンプリング手法を比較・検証しており、植物状態推定の技術的手法が中心である。
abstractSpectral indices such as NDRE (Normalized Difference Red Edge Index), CCCI (Canopy Chlorophyll Content Index), and IRECI (Inverted Red Edge Chlorophyll Index), based on the Red Edge band of MSI/Sentinel-2 (B05, B06, B07 images), are essential tools in coffee monitoring.
Monitoring sesame plant health and detecting disease early are essential to reducing disease spread and facilitate effective management practices. In this research, we developed an image classification model to detect bacterial blight-infected, phyllody-infected, and healthy sesame crops. Since images were necessary to carry out this study, we collected 2300 images at the Gondar and Humera Agriculture Research Centers and directly from the field in Metema. Since the collected images were limited, to increase the number of images in the dataset, we used image augmentation with different variations. In the image preprocessing step, we used a median filter for noise filtering, and contrast stretching techniques were used for image contrast and brightness enhancement. SegNet semantic segmentation, which is deep convolution neural network-based architecture, was used to segment the leaf part of the image from the background. In the feature extraction and classification steps, a deep convolutional neural network was used. Finally, we evaluated the proposed model and compared it with two recent deep convolution neural network models, namely, Xception and InceptionV3. The proposed model for the classification of sesame diseases achieved better accuracy, with 96.67% testing accuracy, 97.78% validation accuracy, and 98% training accuracy.
Why it matches plant phenotyping methodsゴマ葉の画像から病害状態をセグメンテーション・分類する手法を開発し、複数モデルとの比較検証を行っており、植物病害表現型の取得が中心である。
abstractwe developed an image classification model to detect bacterial blight-infected, phyllody-infected, and healthy sesame crops
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
BACKGROUND: Shapefiles are a geospatial vector data format used to indicate geographic features in geographic information systems (GIS) software. Shapefiles are used in high-throughput phenotyping plant breeding and agronomic studies to identify plots from aerial imagery and extract remote sensing data. However, the process of manually creating shapefiles is tedious and error prone. Current options that assist in shapefile generation suffer from issues such as installation processes that require a degree of programming knowledge or inefficient methods for incorporating plot-level information from field books. In this study, we have developed a program called 'SHP Buddy', a QGIS plugin that provides accessible and intuitive functions that quickly generate shapefiles for common experimental layouts used in agricultural research. RESULTS: SHP Buddy is a free and open source QGIS plugin that is easily downloaded directly from the QGIS plugin repository. It provides options for generating serpentine replicated and unreplicated experimental layouts. Further, SHP Buddy is the first of its type to provide an intuitive method for removing non-experimental plots, such as non-experimental "fill" plots at the end of experiments or plots in irrigation wheel tracks. Plot information is easily incorporated by uploading a field book CSV file that contains a column of matching plot numbers. Lastly, plot dimensions can be modified to produce more precise regions of interest. CONCLUSIONS: SHP Buddy substantially reduces the time and increases the accuracy of shapefile generation. This results in reliable shapefiles that improve record keeping and the quality of high-throughput phenotyping data extracted. By working natively in QGIS, SHP Buddy provides an efficient solution to shapefile generation while maintaining a low learning curve.
Why it matches plant phenotyping methods植物育種・農業実験の航空画像からプロット単位のリモートセンシングデータを抽出するためのQGISプラグインを開発しており、フェノタイピング用データ取得ワークフローが中心です。
abstractIn this study, we have developed a program called 'SHP Buddy', a QGIS plugin that provides accessible and intuitive functions that quickly generate shapefiles for common experimental layouts used in agricultural research.
• Method to assess high-resolution point cloud location/reconstruction errors in full 3D context. • The methodology provides values for both reconstruction and location error. • The methodology avoids the need for manual and usually less accurate measurements. • Minimized point pair picking error between assessed and reference GT point clouds. The aim of this paper is to address the lack of standard methodologies for the assessment of 3D point clouds. We present a methodology to realistically assess the accuracy of 3D point clouds, enabling the evaluation in a full 3D context rather than based on isolated points. Additionally, it introduces three significant innovations: a) it bridges the gap related to the unknown error of the reference ground-truth point cloud; b) it provides separate metrics for location error and reconstruction error; and c) it introduces a procedure to compute the location error that eliminates the bias in the selection of point-pair picking between the DGT points and their corresponding pairs in the point cloud being assessed. The geometry and structure of trees are related to the vegetative parameters and productivity in fruit orchards. In consequence, obtaining a precise and accurate geometric characterization of canopies is of interest for implementing site-specific management strategies that optimize input rates and minimize the costs and environmental risks of agricultural operations. Among the different sensing technologies, sensors based on the principle of light detection and ranging (LiDAR) have emerged as the primary choice for accurate geometric characterization of orchards. However, to make informed orchard management decisions based on LiDAR-derived geometric and structural data, it is essential to assess the accuracy of LiDAR-based scanning systems. Unfortunately, there is currently a lack of standard methodologies to evaluate the accuracy of LiDAR-based systems in agricultural environments. This research paper presents a novel methodology to assess the location error and the reconstruction error of 3D point clouds in full 3D context. The methodology involves comparing LiDAR-derived point clouds to an accurate high-resolution 3D digital ground truth (DGT) obtained using digital photogrammetric techniques. One of the main difficulties when using a reference point cloud to assess point cloud errors is the selection of the points to be compared so that they can be considered as corresponding point pairs. When developing the methodology, four procedures of point pair selection and distance calculation were compared. The best performing procedure was selected and proposed as a standard for accuracy assessment of 3D point clouds. The proposed procedure minimizes the error attributed to the selection of the corresponding point pairs between the assessed point cloud and the reference DGT point cloud. Subsequently, the proposed methodology was tested and validated by assessing the accuracy of 46 different point clouds. The conclusions regarding the accuracy, applicability, and practical utility of the proposed methodology are supported by the determination of reconstruction errors and location errors in 46 point clouds obtained with the 3 different MTLS systems operated with different settings. The proposed methodology will be very useful for scanning system manufacturers, researchers, advisors and eventually advanced farmers to quantify the errors committed when characterizing tree canopies. This is crucial to enable accurate management operations in the framework of Precision Agriculture based on canopy variability. Furthermore, the methodology is expected to facilitate the design of new applications requiring high accuracy to be implemented in the near future.
Why it matches plant phenotyping methods果樹キャノピーの3D形状・構造を対象に、LiDAR点群の位置誤差と再構成誤差を評価する手法を開発し、46点群で検証しており、植物形質取得の技術評価が中心である。
abstractWe present a methodology to realistically assess the accuracy of 3D point clouds, enabling the evaluation in a full 3D context rather than based on isolated points.
Tiller number is a key agronomic trait of the crop population, reflecting the adaptability of crop to the environment and the status of plant growth, as well as grain yield. As a state-of-the-art form of active remote sensing that penetrates the vegetation canopy and provides a detailed representation of 3D structures, terrestrial laser scanning (TLS) is beginning to show great potential in precisely counting the tiller number. However, current research of TLS-derived wheat canopy tiller number is commonly affected by mutual occlusion among plants and noise problem. In this study, we proposed a novel Mean Shift Clustering algorithm based on Voxel Interpolation (MSCVI) to effectively mitigate those effects by removing excess noise and interpolating unsampled voxels. The findings demonstrated that there was a strong exponential relationship between the gap fraction (Pgₐₚ) and point cloud density for wheat plots. In addition, MSCVI was effective to count the tiller number of wheat under different field treatments (R² = 0.69, RMSE = 79 tilllers/m²), producing better results than previous adaptive layering and hierarchical clustering (ALHC) algorithm. MSCVI could obtain more precise and detailed wheat canopy information by denoising and compensating the point cloud data, which greatly improved the accuracy of detecting tiller numbers under the condition of high plant density, planophile plant type and tiller stage data. This study provides new insights into effectively mitigating noise and occlusion between plants and within dense canopies, and has potential for accurate calculation of the tiller number in the assessment of crop yield phenotype.
Why it matches plant phenotyping methodsLiDAR点群のノイズ・遮蔽を補正し、コムギの分げつ数を推定する新規アルゴリズムを開発・比較検証しており、植物表現型取得手法が中心である。
abstractwe proposed a novel Mean Shift Clustering algorithm based on Voxel Interpolation (MSCVI) to effectively mitigate those effects by removing excess noise and interpolating unsampled voxels.
Low-cost, minimally invasive microscopy for tracking cellular dynamics in living plants within their natural ecosystems is crucial for addressing fundamental questions in plant ecology and biology. However, existing solutions are constrained by coarse resolution, limited field-of-view (FoV), and poor deployability in natural settings. Here, we utilize a compact, portable microscope ("miniscope") for label-free (autofluorescence) imaging in living poplar wood. We systematically implement and evaluate multiple computational methods to enhance resolution and FoV. Our optimal computational pipeline, comprising maximal intensity projection, deconvolution, and flat-field correction, increases resolution by up to 39% on-axis and up to 49% at the field edges, resolving features of 2.87 μm, averaged over a FoV of ∼1 mm (diameter), compared with a 4.34 μm baseline. We demonstrate microscopy within the tissue of a living poplar plant in our greenhouse, observing the embolism of vessel elements, wound response, and tissue deformation from moisture evaporation.
Why it matches plant phenotyping methods生体ポプラ組織の細胞動態・木部塞栓・創傷応答を観察する携帯型顕微鏡と画像処理パイプラインを開発・評価しており、植物状態の取得手法が中心である。
abstractWe systematically implement and evaluate multiple computational methods to enhance resolution and FoV.
This study introduces a reliable, non-coding software named qREAD-Raman, written in the JavaScript® language, for analyzing and interpreting Raman spectral information. It is designed with a focus on the early detection of diseases in tomato plants (S. lycopersicum) during the asymptomatic stage. The platform integrates a set of machine learning algorithms necessary for the preprocessing consisting of outlier removal, baseline correction, fluorescence removal, smoothing, and normalization. For classification, we applied a Consensus of five different classifiers: Multilayer Perceptron (MLP), Partial Least Squares-Discriminant Analysis (PLS-DA), Linear Discriminant Analysis (LDA), Long Short-Term Memory (LSTM), and K-nearest neighbors (kNN). The experiments were conducted on two bacterial diseases: bacterial canker of tomato induced by Clavibacter michiganesis subsp. michiganensis (Cmm), and the tomato vein-greening associated with Candidatus Liberibacter solanacearum (CLso), a non-culturable bacteria transmitted by Bactericera cockerelli insect. Binary models (Cmm-Healthy and CLso-Healthy) demonstrated excellent classification ability. Asymptomatic Cmm-infected plants were distinguished with an accuracy of 88–95 %, while CLso-infected plants showed an accuracy of 68–77 %. The three-class model (CLso-Cmm-Healthy) exhibited acceptable performance in differentiating between Cmm and CLso, with accuracy rates of 71–83% and 58–67%, respectively. The model's performance highlights differences in the relevant spectral regions associated with the biochemical changes induced by each studied disease. The qREAD-Raman software, implemented for the purpose of this research, was found to be a valuable and comprehensive tool that effectively differentiate diseased tomato plants during their asymptomatic stage.
Why it matches plant phenotyping methodsトマトの無症状病害状態をラマン分光で検出・分類するソフトウェアを開発し、前処理、機械学習分類、精度評価を中心に扱っているため、植物フェノタイピング手法として適格。
abstractThis study introduces a reliable, non-coding software named qREAD-Raman, written in the JavaScript® language, for analyzing and interpreting Raman spectral information.
The extraction of features and textures holds a crucial significance in the realm of image processing and machine vision systems. Even though artificial intelligence (AI) techniques are superior in attaining the best results in image processing, several challenges remain open for further research in computation complexity, memory, and power requirements. In this context, robust preprocessing techniques are required to address such shortcomings and reduce the computational cost of predictive tasks. This paper employed two feature extraction levels to extract the best possible features from images of the cinnamon plant. Local directional positional pattern (LDPP) extracts global image features, while local triangular coded pattern (LTCP) extracts local features. It helps to provide detailed and more relevant information about the image texture. Once features are extracted, identifying and categorizing diverse textures within an image relies on recognizing their unique features. Typically, descriptors serve as the means for representing images in our work. Afterwards, we used ensemble learning to attain better classification results with the help of weak classifiers. Extracted features are provided to machine learning (ML) models like support vector machines (SVM), random forest (RF), and k-nearest neighbors (KNN) for better classification of the cinnamon category.
Why it matches plant phenotyping methodsシナモン植物画像から特徴を抽出し、病害を分類する画像解析手法が研究の中心であり、植物の病害状態を推定するフェノタイピング手法に該当する。
titleSwift and efficient cinnamon plant disease classification using robust feature extraction and machine learning techniques
Abstract has not been obtained from indexed metadata or an accessible article page.
Why it matches plant phenotyping methods植物病害をRaman分光で検出するための前処理・スペクトル解析ソフトウェアが中心であり、植物の病害状態を測定するフェノタイピング手法に該当する。
titleEarly plant disease detection by Raman spectroscopy: An open-source software designed for the automation of preprocessing and analysis of spectral dataset
There has been a recent surge in the number of studies that aim to model crop yield using data-driven approaches. This has largely come about due to the increasing amounts of remote sensing (e.g. satellite imagery) and precision agriculture data available (e.g. high-resolution crop yield monitor data), as well as the abundance of machine learning modelling approaches. However, there are several common issues in published studies in the field of precision agriculture (PA) that must be addressed. This includes the terminology used in relation to crop yield modelling, predicting, forecasting, and interpolating, as well as the way that models are calibrated and validated. As a typical example, many studies will take a crop yield map or several plots within a field from a single season, build a model with satellite or Unmanned Aerial Vehicle (UAV) imagery, validate using data-splitting or some kind of cross-validation (e.g. k-fold), and say that it is a ‘prediction’ or ‘forecast’ of crop yield. However, this poses a problem as the approach is not testing the forecasting ability of the model, as it is built on the same season that it is then validating with, thus giving a substantial overestimation of the value for decision-making, such as an application of fertiliser in-season. This is an all-too-common flaw in the logic construct of many published studies. Moving forward, it is essential that clear definitions and guidelines for data-driven yield modelling and validation are outlined so that there is a greater connection between the goal of the study, and the actual study outputs/outcomes. To demonstrate this, the current study uses a case study dataset from a collection of large neighbouring farms in New South Wales, Australia. The dataset includes 160 yield maps of winter wheat (Triticum aestivum) covering 26,400 hectares over a 10-year period (2014–2023). Machine learning crop yield models are built at 30 m spatial resolution with a suite of predictor data layers that relate to crop yield. This includes datasets that represent soil variation, terrain, weather, and satellite imagery of the crop. Predictions are made at both the within-field (30 m), and field resolution. Crop yield predictions are useful for an array of applications, so four different experiments were set up to reflect different scenarios. This included Experiment 1: forecasting yield mid-season (e.g. for mid-season fertilisation), Experiment 2: forecasting yield late-season (e.g. for late-season logistics/forward selling), Experiment 3: predicting yield in a previous season for a field with no yield data in a season, and Experiment 4: predicting yield in a previous season for a field with some yield data (e.g. two combine harvesters, but only one was fitted with a yield monitor). This study showcases how different model calibration and validation approaches clearly impact prediction quality, and therefore how they should be interpreted in data-driven crop yield modelling studies. This is key for ensuring that the wealth of data-driven crop yield modelling studies not only contribute to the science, but also deliver actual value to growers, industry, and governments.
Why it matches plant phenotyping methods作物収量という植物形質を機械学習で推定し、複数の予測・検証シナリオでモデル校正と検証手法の影響を評価しているため、計算型フェノタイピング手法の検証・応用が中心である。
abstractMachine learning crop yield models are built at 30 m spatial resolution with a suite of predictor data layers that relate to crop yield.
Phragmoplasts are plant-specific intracellular structures composed of microtubules, actin microfilaments (AFs), membranes, and associated proteins. Importantly, they are involved in the formation and expansion of cell plates that partition daughter cells during cell division. While previous studies have revealed the important role of cytoskeletal dynamics in the proper functioning of the phragmoplast, the localization and role of AFs in the initial phase of cell plate formation remain controversial. Here, we used deep learning-based image restoration to achieve high-resolution 4D imaging with minimal laser-induced damage, enabling us to investigate the dynamics of AFs during the initial phase of cell plate formation in transgenic tobacco BY-2 cells labeled with Lifeact-RFP or RFP-ABD2 (actin binding domain 2). This computational approach overcame the limitation of conventional imaging, namely laser-induced photobleaching and phototoxicity. The restored images indicated that RFP-ABD2 labeled AFs were predominantly localized near the daughter nucleus, whereas Lifeact-RFP labeled AFs were found not only near the daughter nucleus but also around the initial cell plate. These findings, validated by imaging with a long exposure time, highlight distinct localization patterns between the two AF probes and suggest that Lifeact-RFP labeled AFs play a role in initiating cell plate formation.
Why it matches plant phenotyping methods深層学習による画像復元を開発・検証し、植物細胞内のアクチン局在と動態を高解像度4D画像から取得しているため、植物表現型取得法が中心である。
abstractwe used deep learning-based image restoration to achieve high-resolution 4D imaging with minimal laser-induced damage
Diseases are a major drawback to crop production, productivity, and food security in nations affected by plant diseases. This work proposes an efficient framework for the automated recognition and diagnosis of diseases within plants using a convolutional neural network architecture known as EfficientNet. For this study, a large dataset containing sharp images of impaired and hale plant organs belonging to various species was collected. Common data preprocessing steps such as Resizing and Augmentation were used to reduce overfitting and increase the model’s ability to generalize. Finally, EfficientNet was trained for multi-class disease segmentation with the validation accuracy of 95%. The model showed high value of accuracy and recall and solved problems of the differentiation of visually similar diseases among the different categories. It is so from the following view: These results show the possibility of this approach as the practical tool for early disease detection and management in agriculture on large scale. Further studies are going to be conducted enlarging the data set, enhancing the transferability of the developed model, and examining how the app is best to be disseminated, for instance, via mobile applications or Internet of Things (IoT) devices for constant farming inspection.
Why it matches plant phenotyping methods植物器官画像から病害状態を推定する深層学習モデルの開発・評価が研究の中心であり、植物病害表現型の画像ベース推定に該当する。
abstractThis work proposes an efficient framework for the automated recognition and diagnosis of diseases within plants using a convolutional neural network architecture known as EfficientNet.
Calibration / preprocessingGrowth / time-series analysisGrowth / development / phenology
● Phenological shifts to global climate change impact ecosystem functions. There are various data sources from which spatiotemporal, and taxonomic phenological data may be obtained: mobilized herbaria, community-science initiatives, observatory networks, and remote-sensing. However, analyses conducted to date have generally relied on single sources of data, thus treating alternative data sources as isolated silos. ● Siloed treatment of data in analyses may be due to the lack of harmonization across different data sources, that offer partially non-overlapping information and are often complementary. Such treatment precludes a deeper understanding of phenological responses at macroecological scales. Here, we describe data harmonization as the direct integration of disparate sources of phenological data using a common schema. ● We highlight existing methods for data harmonization that can be applied to phenological data: data-design patterns, metadata standards, and ontologies. We describe how harmonized data from multiple sources can be integrated into analyses using existing methods and discuss the use of automated extraction techniques. ● Data harmonization is not a new concept in ecology but the harmonization of phenological data is long overdue. We aim to highlight the need for better data harmonization providing a roadmap for how harmonized phenological data may fill data gaps while simultaneously integrated into analyses
Why it matches plant phenotyping methods植物の季節現象データを異種ソース間で調和・統合する方法と自動抽出を論じるレビューであり、植物フェノロジーのデータ基盤・解析ワークフローが中心です。
abstractHere, we describe data harmonization as the direct integration of disparate sources of phenological data using a common schema.
Common beanLeafMorphology / geometry measurementCalibration / preprocessingSegmentationLeaf traits
Leaf dimensioning is relevant for analyzing plant responses to several conditions such as soil fertility, availability of light, agricultural pesticide effect, and access to water in the soil or periods of drought. In this paper, we present a dataset composed of 6981 images of 612 common bean leaves ( Phaseolus vulgaris ). We captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width). We provide annotations concerning image segmentation, known area uniformly distributed over the leaf region, real area of the marker region, marker pose, capture conditions, and camera calibration. This dataset can be useful for developing deep learning algorithms for leaf dimensioning and related problems. Therefore, there is a potential to contribute to computer vision and plant physiology researchers and specialists.
Why it matches plant phenotyping methods葉面積・周長・長さ・幅の画像ベース計測用データセットを提供し、セグメンテーション、マーカー姿勢、カメラ校正も含むため、植物表現型取得手法の基盤として中心的です。
abstractWe captured the images of each leaf accompanied by a fiducial marker and annotated the known leaf dimensions (area, perimeter, length, and width).
Reproduction assets foundThe paper is itself a data descriptor for the LSID-Beans bean leaf image dataset (6981 images, 612 leaves, with leaf dimension annotations, segmentation masks, area maps, and camera calibration). The dataset is publicly deposited on Mendeley Data (DOI 10.17632/f42hwwrpgn.2), and the authors' data-processing scripts areDataset · publicstakes and improved the data quality.
Data source location
The images were collected in the city of Ouro Branco, Minas Gerais, Latitude −20.535912, Longitude −43.711031, Brazil.
Data accessibility
Repository name: Leaf on Stem Image Dataset Beans (LSID-Beans)
Data identification number: 10.17632/f42hwwrpgn.2
Direct URL to data: https://data.mendeley.com/datasets/f42hwwrpgn/2
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Value of the Data
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The dataset images are useful for developing deep learning methods for non-destructive leaf dimension estimation. We provide each leaf's known area, perimeter, width, and length, which can be used to train supervised machine learning algorithms.
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Methods developed using the dataset can help to moniOpen asset ↗10.17632/f42hwwrpgn.2lines:1-50Code · publicfor that split. Section Cross-validation protocol definition details our proposed cross-validation protocol.
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Experimental Design, Materials and Methods
Fig. 3 shows the steps performed to build our dataset. We describe each step in the next sections. The source codes used to process the data are available in this repository: https://github.com/gcg-ufjf/LSID-Beans-Scripts . Fig. 3
Steps of the dataset construction.
Fig 3
4.1
Plant cultivation
We selected black bean seeds and carried out planting in April 2022. On average, 3 seeds were sown in each pit, made with the aid of a hoe, along 9 rows of 30 plants. The soil used had never been cultivated and had rejects of construction material on tOpen asset ↗githublines:66-146Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published24 Jan 2025Journal of Information Systems Engineering and ManagementCited by 1 · OpenAlex ↗
The agricultural sector is vital to ensuring global food security, yet plant diseases caused by various environmental factors lead to significant reductions in crop yields. Early detection of such diseases is critical, as crop health directly affects both yield and quality. This research introduces a novel pre-processing algorithm designed to enhance the accuracy of crop disease detection from leaf images. The proposed algorithm is compared against three widely used filtering techniques: Median filtering, Wiener filtering, and Gaussian filtering. Before implementing the proposed approach, a detailed assessment of these conventional methods is conducted.The algorithm integrates Laplacian filtering and average filtering to optimize image pre-processing. The effectiveness of this approach is evaluated using performance metrics such as Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE), and Mutual Information (MI). Results indicate that the proposed method achieves a 0.34% increase in PSNR, a 0.92% reduction in MSE, and a 0.48% improvement in MI compared to existing techniques. These improvements underscore the algorithm's ability to enhance image quality and outperform traditional methods.The findings suggest that incorporating Laplacian and average filtering into the pre-processing pipeline introduces a more efficient methodology for image enhancement. This approach holds potential for advancing image analysis and interpretation in agriculture and other fields, providing a foundation for further innovation in image processing technologies.
Why it matches plant phenotyping methods葉画像から植物病害を検出するための画像前処理アルゴリズムを開発・比較評価しており、植物の病害状態の取得に関わる方法が中心である。
abstractThis research introduces a novel pre-processing algorithm designed to enhance the accuracy of crop disease detection from leaf images.
Introduction Advancements in machine learning (ML) algorithms that make predictions from data without being explicitly programmed and the increased computational speeds of graphics processing units (GPUs) over the last decade have led to remarkable progress in the capabilities of ML. In many fields, including agriculture, this progress has outpaced the availability of sufficiently diverse and high-quality datasets, which now serve as a limiting factor. While many agricultural use cases appear feasible with current compute resources and ML algorithms, the lack of reusable hardware and software components, referred to as cyberinfrastructure (CI), for collecting, transmitting, cleaning, labeling, and training datasets is a major hindrance toward developing solutions to address agricultural use cases. This study focuses on addressing these challenges by exploring the collection, processing, and training of ML models using a multimodal dataset and providing a vision for agriculture-focused CI to accelerate innovation in the field. Methods Data were collected during the 2023 growing season from three agricultural research locations across Ohio. The dataset includes 1 terabyte (TB) of multimodal data, comprising Unmanned Aerial System (UAS) imagery (RGB and multispectral), as well as soil and weather sensor data. The two primary crops studied were corn and soybean, which are the state's most widely cultivated crops. The data collected and processed from this study were used to train ML models to make predictions of crop growth stage, soil moisture, and final yield. Results The exercise of processing this dataset resulted in four CI components that can be used to provide higher accuracy predictions in the agricultural domain. These components included (1) a UAS imagery pipeline that reduced processing time and improved image quality over standard methods, (2) a tabular data pipeline that aggregated data from multiple sources and temporal resolutions and aligned it with a common temporal resolution, (3) an approach to adapting the model architecture for a vision transformer (ViT) that incorporates agricultural domain expertise, and (4) a data visualization prototype that was used to identify outliers and improve trust in the data. Discussion Further work will be aimed at maturing the CI components and implementing them on high performance computing (HPC). There are open questions as to how CI components like these can best be leveraged to serve the needs of the agricultural community to accelerate the development of ML applications in agriculture.
Why it matches plant phenotyping methods農業向けサイバーインフラの開発が中心で、UAS画像処理パイプラインとMLモデルにより作物の生育ステージおよび収量を推定しており、植物形質の取得・抽出方法が実質的に扱われている。
abstractThis study focuses on addressing these challenges by exploring the collection, processing, and training of ML models using a multimodal dataset and providing a vision for agriculture-focused CI to accelerate innovation in the field.
Phenotypic analysis of mature soybeans is a critical aspect of soybean breeding. However, manually obtaining phenotypic parameters not only is time-consuming and labor intensive but also lacks objectivity. Therefore, there is an urgent need for a rapid, accurate, and efficient method to collect the phenotypic parameters of soybeans. This study develops a novel pipeline for acquiring the phenotypic traits of mature soybeans based on three-dimensional (3D) point clouds. First, soybean point clouds are obtained using a multi-view stereo 3D reconstruction method, followed by preprocessing to construct a dataset. Second, a deep learning-based network, PVSegNet (Point Voxel Segmentation Network), is proposed specifically for segmenting soybean pods and stems. This network enhances feature extraction capabilities through the integration of point cloud and voxel convolution, as well as an orientation-encoding (OE) module. Finally, phenotypic parameters such as stem diameter, pod length, and pod width are extracted and validated against manual measurements. Experimental results demonstrate that the average Intersection over Union (IoU) for semantic segmentation is 92.10%, with a precision of 96.38%, recall of 95.41%, and F1-score of 95.87%. For instance segmentation, the network achieves an average precision (AP@50) of 83.47% and an average recall (AR@50) of 87.07%. These results indicate the feasibility of the network for the instance segmentation of pods and stems. In the extraction of plant parameters, the predicted values of pod width, pod length, and stem diameter obtained through the phenotypic extraction method exhibit coefficients of determination (R2) of 0.9489, 0.9182, and 0.9209, respectively, with manual measurements. This demonstrates that our method can significantly improve efficiency and accuracy, contributing to the application of automated 3D point cloud analysis technology in soybean breeding.
Why it matches plant phenotyping methods成熟ダイズの3D点群取得・分割・形質抽出パイプラインを開発し、手動測定と検証しており、植物フェノタイピング手法が研究の中心である。
abstractThis study develops a novel pipeline for acquiring the phenotypic traits of mature soybeans based on three-dimensional (3D) point clouds.
Crop diseases significantly threaten agricultural productivity, leading to unstable food supply and economic losses. The current approaches to automated crop disease recognition face challenges such as limited datasets, restricted coverage of disease types, and inefficient feature extraction, which hinder their generalization across diverse crops and disease patterns. To address these challenges, we propose an efficient data augmentation method to enhance the performance of deep learning models for crop disease recognition. By constructing a new large-scale dataset comprising 24 different classes, including both fruit and leaf samples, we intend to handle a variety of disease patterns and improve model generalization capabilities. Geometric transformations and color space augmentation techniques are applied to validate the efficiency of deep learning models, specifically convolution and transformer models, in recognizing multiple crop diseases. The experimental results show that these augmentation techniques improve classification accuracy, achieving F1 scores exceeding 98%. Feature map analysis further confirms that the models effectively capture key disease characteristics. This study underscores the importance of data augmentation in developing automated, energy-efficient, and environmentally sustainable crop disease detection solutions, contributing to more sustainable agricultural practices.
Why it matches plant phenotyping methods作物の葉・果実画像から病害状態を認識するためのデータ拡張法と大規模データセットを中心に開発・検証しており、植物病害フェノタイピング手法に該当する。
abstractwe propose an efficient data augmentation method to enhance the performance of deep learning models for crop disease recognition.
BACKGROUND: The reconstruction of 3-dimensional (3D) plant models can offer advantages over traditional 2-dimensional approaches by more accurately capturing the complex structure and characteristics of different crops. Conventional 3D reconstruction techniques often produce sparse or noisy representations of plants using software or are expensive to capture in hardware. Recently, view synthesis models have been developed that can generate detailed 3D scenes, and even 3D models, from only RGB images and camera poses. These models offer unparalleled accuracy but are currently data hungry, requiring large numbers of views with very accurate camera calibration. RESULTS: In this study, we present a view synthesis dataset comprising 20 individual wheat plants captured across 6 different time frames over a 15-week growth period. We develop a camera capture system using 2 robotic arms combined with a turntable, controlled by a re-deployable and flexible image capture framework. We trained each plant instance using two recent view synthesis models: 3D Gaussian splatting (3DGS) and neural radiance fields (NeRF). Our results show that both 3DGS and NeRF produce high-fidelity reconstructed images of a plant subject from views not captured in the initial training sets. We also show that these approaches can be used to generate accurate 3D representations of these plants as point clouds, with 0.74-mm and 1.43-mm average accuracy compared with a handheld scanner for 3DGS and NeRF, respectively. CONCLUSION: We believe that these new methods will be transformative in the field of 3D plant phenotyping, plant reconstruction, and active vision. To further this cause, we release all robot configuration and control software, alongside our extensive multiview dataset. We also release all scripts necessary to train both 3DGS and NeRF, all trained models data, and final 3D point cloud representations. Our dataset can be accessed via https://plantimages.nottingham.ac.uk/ or https://https://doi.org/10.5524/102661. Our software can be accessed via https://github.com/Lewis-Stuart-11/3D-Plant-View-Synthesis.
Why it matches plant phenotyping methods3D植物表現型取得のための撮影システム、再構成手法、データセットを開発し、スキャナとの精度比較で検証しているため、方法が中心的である。
abstractWe develop a camera capture system using 2 robotic arms combined with a turntable, controlled by a re-deployable and flexible image capture framework.
Reproduction assets foundThe paper releases its wheat plant multiview image dataset (via plantimages.nottingham.ac.uk and GigaDB DOI 10.5524/102661), its authors' analysis/capture codebase on GitHub (3D-Plant-View-Synthesis), a Software Heritage archive of that code, and a DOME-ML registry annotation. All are paper-specific, public, and have作者Code · publicruction output across all plants. We hope that our study will provide opportunities for researchers exploring new and improved 3D phenotyping algorithms, 3D reconstruction and view synthesis research, and active vision systems.
Availability of Source Code and Requirements
Project name: 3D Plant View Synthesis:
Project homepage: https://github.com/Lewis-Stuart-11/3D-Plant-View-Synthesis [ 13 ]
Operating system(s): Windows, Ubuntu
Programming language: Python (>=3.8)
License: Apache 2.0
Any restrictions to use by nonacademics: None
Our code has also been archived in Software Heritage [ 66 ]. Functionality, such as Robotic View Capturing, 3DGS to Point Cloud, and our UR5 Configs files, are storOpen asset ↗GitHub · Lewis-Stuart-11/3D-Plant-View-Synthesislines:663-695Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Published1 Jan 2025IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingCited by 1 · OpenAlex ↗
Over the past few decades, advances in proximal sensing uncrewed ground vehicles (UGVs) have provided researchers with vast opportunities to harness high-fidelity phenotyping information in seed breeding trials. These platforms feature a variety of sensors that are often directly georeferenced using a global navigation satellite system/inertial navigation system (GNSS/INS) for high absolute accuracy. However, one of the biggest challenges with using GNSS is the limited availability of satellite signals under plant canopy. The poor georeferencing accuracy arising from GNSS signal outage together with sensor noise leads to discrepancies within single/multi-mission data, hindering the high-precision aspect of the high-throughput (HT) phenotyping. This paper presents a UGV mapping system for proximal scanning of agricultural crops and proposes a technique to enhance the georeferencing accuracy degraded by under-canopy data acquisitions. This platform is equipped with a light detection and ranging (LiDAR) sensor and a fisheye camera that are georeferenced using an integrated GNSS/INS unit. The proposed technique begins by identifying geometric primitives pertaining to mechanized fields popular for seed breeding trials. These primitives originate from ground patches, plant rows, and individual plant stalks, extracted from one or more multi-track UGV datasets. The extracted features are then incorporated in an optimization framework that improves the precision of UGV point clouds. This data enhancing strategy simultaneously yields individual stalk information, which can be valuable for deriving plant's inner attributes such as leaf count and angle. The proposed workflow is demonstrated using two maize field datasets separated by a period of several weeks. The enhancement results in a reduction of feature fitting error for linear features from as much as 44 cm to 2 cm. Furthermore, the approach also improves the absolute accuracy of the resulting point clouds after the inclusion of a reference UAV point cloud. The improvement is shown through the reduction of the overall feature fitting error to within the sensor noise range of under 3 cm for the combined multi-platform dataset.
Why it matches plant phenotyping methodsLiDAR搭載UGVによる作物の近接スキャンと、作物下で劣化する測位精度を改善する点群処理ワークフローが中心であり、植物形質抽出に再利用可能なフェノタイピング手法を開発・実証している。
abstractThis paper presents a UGV mapping system for proximal scanning of agricultural crops and proposes a technique to enhance the georeferencing accuracy degraded by under-canopy data acquisitions.
Vegetation indices have become an indispensable tool in remote sensing‐based agricultural research. A recent area of advancement in agricultural remote sensing research is in high‐throughput phenotyping, often conducted on a plot by plot basis. FIELDimageR is a tool used extensively in high‐throughput phenotyping that estimates zonal statistics of vegetation indices per plot. However, being written in R language, FIELDimageR requires high computing time. As a high‐resolution image over a large area means a large number of pixels, FIELDimageR is incapable of using high‐resolution orthomosaicked images without reducing image resolution by aggregating digital numbers of several pixels and treating them as one pixel. This research tool implements FIELDimageR in the Python language as FIELDimagePy. FIELDimagePy follows similar workflows as FIELDimageR and generates equivalent results for zonal statistics of vegetation indices per plot. FIELDimagePy is significantly and substantially faster than FIELDimageR. Computing time by FIELDimagePy are three to four times lower than computing times by FIELDimageR, even when using raw images with 16 times denser pixels. Moreover, FIELDimagePy is useful beyond plot by plot research in agriculture and capable of estimating zonal statistics of any raster bounded by any polygons. With slight modifications, FIELDimagePy can be useful for other disciplines of science, such as geophysics, geography, economics, medical sciences, among others. FIELDimagePy can be accessed from the GitHub repository: https://github.com/SumantraChatterjee/FIELDimagePy.
Why it matches plant phenotyping methods植物画像から区画ごとの植生指数のゾーン統計を推定する高スループット表現型解析ツールの開発・性能比較が中心であり、単なる生物学的測定ではない。
abstractThis research tool implements FIELDimageR in the Python language as FIELDimagePy.
Cotton and soybeans are important crops for the country's economic growth. Due to the rapid spread of disease, plants are susceptible to bacterial and viral diseases. Early identification and classification using machine or deep learning models aid farmers in reducing potential losses. Model‐based detection necessitates a large number of training samples and high‐quality images. Thus, this study generates new datasets to diagnose soybean and cotton plant diseases. The images are collected with the help of the Central Institute for Cotton Research (CICR) in Nagpur, Maharashtra, to create a clean and comprehensive dataset for research purposes. The dataset contains 5200 images, including both diseased and healthy images. The collected images are labelled using the Robo flow tool, masked with the Photoshop tool and stored in the dataset. The generated dataset is examined through pre‐processing and classification using the novel proposed algorithms. Initially, the Gabor filter is used for pre‐processing to eliminate unwanted noise from the collected images. Afterwards, the Position attention‐based capsule network (PA‐CapNet) model is proposed to perform multidisease classification for the soybean and cotton datasets. Finally, the performances are assessed by evaluating varied metrics. The result analysis shows that the proposed method obtains better results than the other existing models. The proposed method obtains an accuracy of 98% for the soybean dataset and 96.89% for the cotton dataset.
Why it matches plant phenotyping methods植物葉の画像から病害状態を分類するデータセットの生成・前処理・評価が中心であり、植物病害フェノタイピング手法および再利用可能なデータセットに該当する。
abstractThus, this study generates new datasets to diagnose soybean and cotton plant diseases.
Selective harvesting robots for broccoli face significant challenges in field operations, where occlusions by leaves and stems, varying maturity stages and lighting interferences greatly affect performance. Addressing the need for a robust network capable of maturity recognition and localisation under various occlusion conditions for spherical crops, OccluInst-a single-stage instance segmentation network based on RGB-D and CNN-Transformer architecture was proposed. The solution is to make full use of visible information and crop characteristics. This model builds a dual-branch cross-modal calibration framework to generate instance-aware kernels and segmentation mask features. The proposed Attention Weight Interactive Fusion Module (AWIF) enhances the fusion efficiency of multi-scale RGB and depth features in complex scenarios, while the designed Adaptive Fusion Ratio Module (AFR) filters out noisy depth data and extracts valuable information to achieve feature alignment. Additionally, the developed Material Awareness Module (MA) highlights critical areas, improving feature extraction for irregular, multi-scale targets. The improved circular boundary anchor box accurately localises broccoli under various levels of occlusion. Ablation studies confirm the effectiveness of each module. OccluInst can swiftly and accurately identify the maturity categories and coordinates of broccoli under different occlusion levels. It achieves a mAP₅₀ of 86.2% and mAR of 83.5%, with an average centre point deviation of 3.68 pixels on images with a resolution of 848×480, and a detection speed of 51.4 frames per second, providing a robust visual foundation for selective harvesting robots.
Why it matches plant phenotyping methodsRGB-D画像からブロッコリーの成熟カテゴリーという植物状態を推定するセグメンテーション手法を開発し、遮蔽条件下で性能評価している。単なる収穫対象の位置検出にとどまらず、成熟度推定が中心的である。
abstractOccluInst can swiftly and accurately identify the maturity categories and coordinates of broccoli under different occlusion levels.
Abstract The rice plant is one of the most significant crops in the world, and it suffers from various diseases. The traditional methods for rice disease detection are complex and time-consuming, mainly depending on the expert’s experience. The explosive growth in image processing, computer vision, and deep learning techniques provides effective and innovative agriculture solutions for automatically detecting and classifying these diseases. Moreover, more information can be extracted from the input images due to different feature extraction techniques. This paper proposes a new system for detecting and classifying rice plant leaf diseases by fusing different features, including color texture with Local Binary Pattern (LBP) and color features with Color Correlogram (CC). The proposed system consists of five stages. First, input images acquire RGB images of rice plants. Second, image preprocessing applies data augmentation to solve imbalanced problems, and logarithmic transformation enhancement to handle illumination problems has been applied. Third, the features extraction stage is responsible for extracting color features using CC and color texture features using multi-level multi-channel local binary pattern (MCLBP). Fourth, the feature fusion stage provides complementary and discriminative information by concatenating the two types of features. Finally, the rice image classification stage has been applied using a one-against-all support vector machine (SVM). The proposed system has been evaluated on three benchmark datasets with six classes: Blast (BL), Bacterial Leaf Blight (BLB), Brown Spot (BS), Tungro (TU), Sheath Blight (SB), and Leaf Smut (LS) have been used. Rice Leaf Diseases First Dataset, Second Dataset, and Third Dataset achieved maximum accuracy of 99.53%, 99.4%, and 99.14%, respectively, with processing time from $$100(\pm 17)ms$$ . Hence, the proposed system has achieved promising results compared to other state-of-the-art approaches.
Why it matches plant phenotyping methodsイネ葉の病徴を画像から検出・分類する特徴抽出および分類システムが研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法に該当する。
abstractThis paper proposes a new system for detecting and classifying rice plant leaf diseases by fusing different features, including color texture with Local Binary Pattern (LBP) and color features with Color Correlogram (CC).
Reproduction assets foundThe paper evaluates its rice leaf disease classification system on three publicly available image datasets, each with explicit public URLs in the Data Availability statement. No author analysis code or trained models are shared.Dataset · publichors have read and agreed to the published version of the manuscript.
Funding
Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
Data availibility
This research study was tested using three datasets which are publicly available in: https://data.mendeley.com/datasets/fwcj7stb8r/1 . https://www.kaggle.com/datasets/rajeshbhattacharjee/rice-diseases-using-cnn-and-svm . https://data.mendeley.com/datasets/dwtn3c6w6p/1 .
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Te authors declare no competing interests.
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Springer Nature remains neutral with regard to jurisdictional claims in publishedOpen asset ↗fwcj7stb8rlines:2657-2679Dataset · publicof the manuscript.
Funding
Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
Data availibility
This research study was tested using three datasets which are publicly available in: https://data.mendeley.com/datasets/fwcj7stb8r/1 . https://www.kaggle.com/datasets/rajeshbhattacharjee/rice-diseases-using-cnn-and-svm . https://data.mendeley.com/datasets/dwtn3c6w6p/1 .
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Te authors declare no competing interests.
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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1.Open asset ↗Kaggle · rajeshbhattacharjee/rice-diseases-using-cnn-and-svmlines:2657-2679Dataset · publicInnovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB).
Data availibility
This research study was tested using three datasets which are publicly available in: https://data.mendeley.com/datasets/fwcj7stb8r/1 . https://www.kaggle.com/datasets/rajeshbhattacharjee/rice-diseases-using-cnn-and-svm . https://data.mendeley.com/datasets/dwtn3c6w6p/1 .
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Te authors declare no competing interests.
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Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
1. Viana, C. M., Freire, D., Abrantes, P., Rocha, J. & Pereira, P. Agricultural land syOpen asset ↗dwtn3c6w6plines:2657-2679Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 6 Sept 2026
BACKGROUND: This research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping. Furthermore, a concept for the analysis of quality assured hyperspectral images to investigate plant disease progress is proposed. The quality assurance was applied to a handheld line scanning HSI-system consisting of evaluating spatial and spectral quality parameters as well as the integrated illumination. To test the spatial accuracy at different working distances, the sine-wave-based spatial frequency response (s-SFR) was analysed. The spectral accuracy was assessed by calculating the correlation of calibration-material measurements between the HSI-system and a non-imaging spectrometer. Additionally, different illumination systems were evaluated by analysing the spectral response of sugar beet canopies. As a use case, time series HSI measurements of sugar beet plants infested with Cercospora leaf spot (CLS) were performed to estimate the disease severity using convolutional neural network (CNN) supported data analysis. RESULTS: The measurements of the calibration material were highly correlated with those of the non-imaging spectrometer (r>0.99). The resolution limit was narrowly missed at each of the tested working distances. Slight sharpness differences within individual images could be detected. The use of the integrated LED illumination for HSI can cause a distortion of the spectral response at 677nm and 752nm. The performance for CLS diseased pixel detection of the established CNN was sufficient to estimate a reliable disease severity progression from quality assured hyperspectral measurements with external illumination. CONCLUSION: The quality assurance pipeline was successfully applied to evaluate a handheld HSI-system. The s-SFR analysis is a valuable method for assessing the spatial accuracy of HSI-systems. Comparing measurements between HSI-systems and a non-imaging spectrometer can provide reliable results on the spectral accuracy of the tested system. This research emphasizes the importance of evenly distributed diffuse illumination for HSI. Although the tested system showed shortcomings in image resolution, sharpness, and illumination, the high spectral accuracy of the tested HSI-system, supported by external illumination, enabled the establishment of a neural network-based concept to determine the severity and progression of CLS. The data driven quality assurance pipeline can be easily applied to any other HSI-system to ensure high quality HSI.
Why it matches plant phenotyping methods植物フェノタイピング用ハイパースペクトル撮像システムの品質保証パイプラインを開発・検証し、病害進展の表現型推定にも適用しており、取得・解析手法が中心的である。
abstractThis research proposes an easy to apply quality assurance pipeline for hyperspectral imaging (HSI) systems used for plant phenotyping.
Phyto Pathogen Perception is an advanced Python-based web application designed to revolutionize agriculture by offering early and precise plant disease diagnosis. Developed with the Flask framework, the platform provides a user-friendly interface for farmers, researchers, and agricultural experts to upload plant images and receive instant, detailed diagnostic reports. Leveraging cutting-edge machine learning through PyTorch and TorchVision, the platform ensures high-accuracy disease classification using deep learning models trained on diverse and continuously updated datasets, including rare and emerging diseases, for greater reliability across various plant types and environments.To enhance accessibility and scalability, the platform features cloud-based storage and processing, enabling fast analyses and real-time updates even during peak usage. Multilingual support allows users from different regions to access diagnostic reports and prevention tips in their native languages. Real-time alerts and predictive analytics, driven by environmental factors like temperature and humidity, empower users to manage diseases proactively. Integration with IoT devices such as soil sensors and weather monitors provides comprehensive insights into crop health and environmental conditions influencing disease prevalence.The platform’s robust data handling capabilities, powered by Pandas and NumPy, enable efficient analysis and actionable insights, while image preprocessing tasks such as resizing, cropping, and augmentation are seamlessly managed using the Pillow library to optimize input for machine learning models. Tailored disease management plans, including customized recommendations for pesticide use, organic solutions, and crop rotation strategies, enhance user outcomes.To foster collaboration, a community forum enables farmers and experts to share knowledge and feedback. An AI-powered chatbot provides instant guidance on disease prevention and management, improving user engagement. The integration of e-commerce platforms simplifies access to recommended agricultural products, while crop yield optimization features support better productivity. Gamification elements educate users about plant diseases, prevention techniques, and best farming practices, making the platform both interactive and impactful. Keywords: Crop health, Sustainable agriculture, Disease classification, Image preprocessing, K learning Algorithms, Flask framework.
Why it matches plant phenotyping methods植物画像から病害を分類・診断するAIプラットフォームが研究の中心であり、植物の病害状態を直接推定する画像ベースの表現型計測に該当する。
abstractthe platform provides a user-friendly interface for farmers, researchers, and agricultural experts to upload plant images and receive instant, detailed diagnostic reports.
Plant phenotyping plays a crucial role in crop science and plant breeding. However, traditional methods often involve time-consuming and manual observations. Therefore, it is essential to develop automated, sensor-driven techniques that can provide objective and rapid information. Various methods rely on camera systems, including RGB, multi-spectral, and hyper-spectral cameras, which offer valuable insights into plant physiology. In recent years, 3D sensing systems such as laser scanners have gained popularity due to their ability to capture structural plant parameters that are difficult to obtain using spectral sensors. Unlike images, point clouds are not structured and require pre-processing steps to extract precise information and handle noise or missing points. One approach is to generate mesh-based surface representations using triangulation. A key challenge in the 3D surface reconstruction of plants is the pre-processing of point clouds, which involves removing non-plant noise from the scene, segmenting point clouds from populations to individual plants, and further dividing individual plants into their respective organs. In this study, we will not focus on the segmentation aspect but rather on the other pre-processing steps, like denoising parameters, which depend on the data type. We present an automated pipeline for converting high-resolution point clouds into surface models of plants. The pipeline incorporates additional pre-processing steps such as outlier removal, denoising, and subsampling to ensure the accuracy and quality of the reconstructed surfaces. Data were collected using three different sensors: a handheld scanner, a terrestrial laser scanner (TLS), and a mobile mapping platform, under varying conditions from controlled laboratory environments to complex field settings. The investigation includes five different plant species, each with distinct characteristics, to demonstrate the potential of the pipeline. In a next step, phenotypic traits such as leaf area, leaf area index (LAI), and leaf angle distribution (LAD) were calculated to further illustrate the pipeline’s potential and effectiveness. The pipeline is based on the Open3D framework and is available open source.
Why it matches plant phenotyping methods植物の高解像度点群から表面モデルを構築し、葉面積・LAI・葉角度分布を算出する自動パイプラインの開発が中心であり、植物表現型の取得・推定手法に該当する。
abstractWe present an automated pipeline for converting high-resolution point clouds into surface models of plants.
Purpose Diagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete destruction of the crops. To overcome these challenges, in this work a light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset. Design/methodology/approach The proposed system is divided into four phases: image acquisition and preprocessing, data augmentation, feature extraction and classification. In the first phase, data have been collected using a drone in real time, and preprocessing has been performed to improve the images. In the second phase, four data augmentation techniques have been applied to increase the size of the real-time dataset. In the third phase, feature extraction has been done using two deep convolutional neural network (DCNN)-based models, individually, ResNet49 and ResNet41. In the last phase, four machine learning classifiers random forest (RF), support vector machine (SVM), logistic regression (LR) and eXtreme gradient boosting (XGBoost) have been employed, one by one. Findings These proposed systems have been trained and tested using our own real-time dataset that consists of healthy and unhealthy leaves for six crops such as corn, grapes, okara, mango, plum and lemon. The proposed combination of Resnet49-SVM and ResNet41-SVM has achieved accuracy of 99 and 97%, respectively, for the images that have been collected from the city of Kurukshetra, India. Originality/value The proposed system makes novel contribution by using a newly proposed real time dataset that has been collected with the help of a drone. The collected image data has been augmented using scaling, rotation, flipping and brightness techniques. The work uses a novel combination of machine learning methods based classification with ResNet49 and ResNet41 based feature extraction.
Why it matches plant phenotyping methodsドローン画像から植物の健康状態・病害を推定する画像解析システムの開発が研究の中心であり、特徴抽出、分類、データセット構築と性能評価を含むため。
abstracta light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset.
Plant diseases significantly impact agricultural productivity, leading to economic losses and food insecurity worldwide. Timely and accurate detection of plant diseases is critical to mitigate these effects and ensure sustainable farming practices. This project explores the use of deep learning techniques for detecting plant diseases based on leaf images. The system leverages convolutional neural networks (CNNs), specifically pre-trained models like ResNet and MobileNet, fine-tuned on the publicly available PlantVillage dataset. The dataset consists of thousands of labeled images of healthy and diseased leaves from various crops. To enhance model performance and generalization, data augmentation techniques such as rotation, flipping, and brightness adjustments were applied during preprocessing. The proposed system achieves high classification accuracy, validated using metrics such as precision, recall, and F1-score. Additionally, visualization tools like Grad-CAM are used to interpret model predictions, highlighting regions of the leaf that influence the decision-making process. The model is further optimized for deployment on mobile and web platforms, enabling real-time disease diagnosis. This approach offers an efficient, scalable, and user-friendly solution for farmers and agricultural experts, aiding in early disease detection and contributing to improved crop management and yield. Future work involves expanding the dataset, incorporating more plant species, and integrating the model with IoT devices for field application
Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習手法が研究の中心であり、植物の病害状態を直接推定して性能評価しているため、植物フェノタイピング手法として含める。
abstractThis project explores the use of deep learning techniques for detecting plant diseases based on leaf images.
The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the bio-geophysical Essential Climate Variables assessed through remote sensing observations and distributed globally by space and environmental agencies. Any reliable remote sensing product should be benchmarked against a reference, which is normally determined by means of ground-based measurements. They should generally be aggregated spatially to be compared with remote sensing products at different resolutions. In this work, the effectiveness of various in situ sampling methods proposed to assess FAPAR from flux measurements was evaluated using a three-dimensional radiative transfer framework over eight virtual vegetated landscapes, including dense forests (leaf-on and leaf-off models), open canopies, sparse vegetation, and agricultural fields with a nominal extension of 1 hectare. The reference FAPAR value was determined by summing the absorbed PAR-equivalent photons by either all canopy components, both branches and leaves, or by only the leaves. The incoming and upwelling PAR fluxes were simulated in different illumination conditions and at a high spatial resolution (50 cm). They served to replicate in situ virtual FAPAR measurements, which were carried out using either stationary sensor networks or transects. The focus was on examining the inherent advantages and drawbacks of in situ measurement protocols against GCOS requirements. Consequently, the proficiency of each sampling technique in reflecting the distribution of incident and reflected PAR fluxes—essential for calculating FAPAR—was assessed. This study aims to support activities related to the validation of remote sensing FAPAR products by assessing the potential uncertainty associated with in situ determination of the reference values. Among the sampling schemes considered in our work, the cross shaped sampling schemes showed a particular efficiency in properly representing the pixel scale FAPAR over most of the scenario considered.
Why it matches plant phenotyping methods植物キャノピーのFAPARを測定する現地サンプリングプロトコルを3D放射伝達シミュレーションで評価し、リモートセンシング検証用の基準値測定における不確実性と各手法の性能を比較しているため、測定法の評価が中心である。
abstractIn this work, the effectiveness of various in situ sampling methods proposed to assess FAPAR from flux measurements was evaluated using a three-dimensional radiative transfer framework over eight virtual vegetated landscapes
The assessment of plant health and quality is a critical aspect of plant biology, agriculture, and the food industry. With the global population continuously increasing, the demand for high-quality plant products is expected to surge. Consequently, there is a necessity for the development of automated systems that are user-friendly, accurate, affordable, and capable of rapid evaluation of plant health in a multitude of settings, including fields, farms, and laboratories. Such systems would apply to various applications, including investigating the impact of environmental conditions or developing new potential biostimulants and biopesticides. This paper introduces a novel, low-cost, and innovative multifunctional high-throughput plant phenotyping system that addresses an unmet need in the market. The system was designed to be affordable, scalable, applicable, and reliable to diverse customers. The key components of the system include a robotic arm AR4 (Annin Robotics, USA), a three-dimensional (3D) scanner POP 3 (Revopoint, USA), and a multispectral (MS) visible near-infrared (VNIR) camera FS 3200D 10GE (JAI Ltd., Japan). The paper describes these devices, their calibration, performance evaluation, and final applicability assessment. In particular, the motion characteristics of the AR4 are evaluated by a pose repeatability with obtained values of and without load and with load, respectively. Furthermore, it is shown that the calibrated camera provides comparable NDVI index data with the content of plant pigments ( R 2 > 0.92 ) and also against the reference VNIR hyperspectral (HS) camera SPECIM PFD4K-65-V10E ( R 2 > 0.99 ). In addition, the presented 3D scanner demonstrated superior 3D models with a high degree of fit compared to the more expensive 3D scanners such as Shining 3D EinScan Pro 2X 2020 or Shining 3D EinScan-SP V2. The paper concludes with a discussion of the results, limitations, future improvements, and potential applications of the device in laboratory, educational, and field settings.
Why it matches plant phenotyping methods低コストの植物表現型解析プラットフォームを開発し、ロボット、3Dスキャナー、マルチスペクトルカメラの校正・性能評価・適用性を検証しており、表現型取得手法が研究の中心である。
abstractThis paper introduces a novel, low-cost, and innovative multifunctional high-throughput plant phenotyping system
The three-dimensional reconstruction of fruit trees plays a crucial role in assessing their growth status, analyzing agronomic traits, and categorizing their organs. This is vital for implementing intelligent orchard management. This study aims to develop a cost-effective and efficient method for the three-dimensional reconstruction and skeleton extraction of fruit trees. The proposed method leverages the 3D geometric structure captured by Time-of-Flight (TOF) sensors and addresses common issues such as occlusion and perspective ambiguity. Firstly, the TOF sensor and its supporting components are used to build an acquisition platform to collect the full range point cloud of fruit trees in the key growth period. The noise information is filtered through the point cloud preprocessing operation to obtain the complete target point cloud and extract its structural invariant features. The IWOA-RANSAC-NDT algorithm is introduced for 3D model registration. Secondly, the Delaunay triangulation algorithm and Dijkstra shortest path algorithm are used to calculate the Minimum Spanning Tree. Branch segmentation is expedited using the Kd-tree data structure. The Levenberg Marquardt algorithm and the cylindrical fitting method are used to obtain the full fruit tree skeleton model. Finally, taking walnut tree as the experimental object, a high-precision fruit tree point cloud model is constructed, and the actual verification is carried out based on the measured data. Findings indicate that the proposed methodology can accurately construct both 3D point cloud and skeleton models of fruit trees with accuracy deviations from the measured data remaining within 7 %. The proposed method offers valuable data and technical support for the future development of highly autonomous, practical, and user-oriented fruit tree pruning systems.
Why it matches plant phenotyping methods果樹の3D形態・骨格を取得および抽出するTOFセンサベースの手法と取得プラットフォームを開発し、実測データで検証しているため、植物フェノタイピング手法が中心である。
abstractThis study aims to develop a cost-effective and efficient method for the three-dimensional reconstruction and skeleton extraction of fruit trees.
In recent decades, xylem anatomical traits have become increasingly important in dendrochronological research, as they offer the unique opportunity to assess eco-physiological drivers of tree growth at intra-annual resolution. However, standard protocols for generating such data are still missing, leading to methodological uncertainty, and complicating data exchange among laboratories. Here, we compare protocols for high-quality permanent slide preparation in dendroanatomy and address the effects of paraffin embedding vs. non-embedding approaches. Tests are conducted on both gymnosperm and angiosperm wood types of widely distributed European tree species, considering cell wall thickness (CWT), mean lumen area (MLA), and hydraulic diameter (Dh). Results indicate that non-embedding does not significantly alter the qualitative and quantitative characteristics of permanent slides compared to embedded samples. Whereas the mean chronologies of MLA and Dh and their non-embedded counterparts share substantial high-frequency variance, the CWT chronologies reveal slightly larger discrepancies at inter-annual scale. However, methodological differences do not exceed 11.1 % for any parameter. While these results show high similarity between the two approaches, we recommend adopting the non-embedding procedure, since it saves resources and therefore allows to produce larger datasets. Regardless of the protocol used to build wood anatomical datasets, assembling large-scale networks of wood anatomical data could transform our understanding of forest responses to global changes.
Why it matches plant phenotyping methods木材解剖形質（細胞壁厚、管腔面積、水理直径）を取得する永久切片作製法について、埋包・非埋包プロトコルを比較検証しており、植物形質測定法が研究の中心である。
abstractHere, we compare protocols for high-quality permanent slide preparation in dendroanatomy and address the effects of paraffin embedding vs. non-embedding approaches.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 13 Sept 2026
Plant diseases can spread rapidly, leading to significant crop losses if not detected early. By accurately identifying diseased plants, farmers can target treatment only to the affected areas, reducing the number of pesticides or fungicides needed and minimizing environmental impact. Tomatoes are among the most significant and extensively consumed crops worldwide. The main factor affecting crop yield quantity and quality is leaf disease. Various diseases can affect tomato production, impacting both yield and quality. Automated classification of leaf images allows for the early identification of diseased plants, enabling prompt intervention and control measures. Many creative approaches to diagnosing and categorizing specific illnesses have been widely employed. The manual method is costly and labor-intensive. Without the assistance of an agricultural specialist, disease detection can be facilitated by image processing combined with machine learning algorithms. In this study, the diseases in tomato leaves will be detected using new feature extraction method using conformable polynomials image features for accurate solution and faster detection of plant diseases through a machine learning model. The methodology of this study based on:•Preprocessing, feature extraction, dimension reduction and classification modules.•Conformable polynomials method is used to extract the texture features which is passed classifier.•The proposed texture feature is constructed by two parts the enhanced based term, and the texture detail part for textual analysis.•The tomato leaf samples from the plant village image dataset were used to gather the data for this model. The disease detected are 98.80 % accurate for tomato leaf images using SVM classifier. In addition to lowering financial loss, the suggested feature extraction method can help manage plant diseases effectively, improving crop yield and food security.
Why it matches plant phenotyping methodsトマト葉画像から病害状態を推定する画像特徴抽出・機械学習手法が中心であり、植物病害という表現型状態の取得・分類を技術的に扱っている。
abstractIn this study, the diseases in tomato leaves will be detected using new feature extraction method using conformable polynomials image features for accurate solution and faster detection of plant diseases through a machine learning model.
In this review, we are diving deep into helping farmers keep their crops healthy. We are using software to detect and fight plant leaf diseases automatically. Since the early days of automation, software has been a trusty sidekick, making our lives easier by simplifying complex tasks. Our aim? To catch those pesky diseases before they wreak havoc on crop yields. Agriculture has not fully reaped the benefits of technology like other industries have. Shockingly, nearly half of all crop losses are due to plant leaf diseases. But fear not! We are on a mission to change that. By using fancy image processing tricks like preprocessing, segmentation, and feature extraction we are teaching computers to spot disease symptoms in plant photos. This means faster detection and quicker action to save crops. Ultimately, we are hoping to make farming more sustainable and secure our food supply for the future.
Why it matches plant phenotyping methods植物葉の病徴を画像処理で検出する方法を中心に扱うレビューであり、植物病害状態の画像ベース表現型推定に該当する。
abstractIn this review, we are diving deep into helping farmers keep their crops healthy.
This article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop) and apply the necessary action without human interaction. The system can remotely send a report on the plant conditions to the farmers through IoT, enabling them for tracking the healthiness of plants. Chili plant has been selected to test the proposed AI-powered IoT monitoring and actuating system as it is so sensitive to the soil moisture, weather changes and can be attacked by several types of diseases. The structure of the proposed system is passed through five main stages, namely, AI-powered IoT system design, prototype fabrication, signal and image processing, noise elimination and proposed system testing. The prototype for monitoring is equipped with multiple sensors, namely, soil moisture, carbon dioxide (CO 2 ) detector, temperature, and camera sensors, which are utilized to continuously monitor the conditions of the plant. Several signal and image processing operations have been applied on the acquired sensors data to prepare them for further post-processing stage. In the post processing step, a new AI based noise elimination algorithm has been introduced to eliminate the noise in the images and take the right actions which are performed using actuators such as pumps, fans to make the necessary actions. The experimental results show that the prototype is functioning well with the proposed AI-powered IoT algorithm, where the water pump, exhausted fan and pesticide pump are actuated when the sensors detect a low moisture level, high CO 2 concentration level, and video processing-based pests' detection, respectively. The results also show that the algorithm is capable to detect the pests on the leaves with 75% successful rate.
Why it matches plant phenotyping methods植物状態を監視するAI・IoTシステムを設計し、画像処理とノイズ除去によって葉上の害虫を検出する手法を中心的に開発・試験しているため、植物状態の計測プラットフォームとして収録する。
abstractThis article aims to develop a novel Artificial Intelligence-powered Internet of Things (AI-powered IoT) system that can automatically monitor the conditions of the plant (crop)
Accurate retrieval of forest functional traits from remote sensing data is critical for monitoring forest health and productivity. To achieve sufficient accuracy using inverse methods it is essential to have representative database of simulated or measured spectral properties together with corresponding forest traits. However, existing datasets are often limited in scope, covering specific sites and times with simplified structures. This limitation hinders the development of generalizable machine learning models for trait prediction. To address this issue, we present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests. The dataset includes 3.5 million unique combinations of leaf biochemical and canopy structural characteristics of forest scenes together with a variety of sun geometry. The spectral data cover wavelengths from 450 nm to 2300 nm, with a resolution of 2 nm. The dataset is organised into two files: one capturing the average reflectance of all scene pixels and another focusing solely on sunlit leaf pixels. LUT were generated using the Discrete Anisotropic Radiative Transfer model version 5.10.0. Virtual forest scenes were based on 3D tree representations derived from Terrestrial Laser Scanning of European beech trees, adjusted to various leaf area index values and structural configurations to simulate natural forest variability. The reflectance data were processed using MATLAB and Python scripts, resulting in hyperspectral cubes that were processed to generate the LUT. The dataset can be used to train machine learning models, such as Random Forest and Support Vector Machines, for predicting forest functional traits and assisting in the calibration of remote sensing algorithms. The biggest advantage of the dataset is high spectral and spatial resolution, together with the high number of different trait combinations, which allows for adaptability to different times, locations, and hyper- and multispectral sensors, and can support up-coming hyperspectral satellite missions. ESA Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) and NASA Surface Biology and Geology (SBG) future satellite missions can utilise this dataset to develop their product processors for monitoring forest traits.
Why it matches plant phenotyping methods森林の機能形質を推定するための大規模ハイパースペクトルLUTデータセットを構築しており、形質取得・推定基盤そのものが研究の中心である。
abstractwe present a comprehensive high-resolution dataset of hyperspectral Look-Up Tables (LUT) designed for Central European temperate broadleaf forests.
Reproduction assets foundThe paper is a Data in Brief article describing a public hyperspectral LUT dataset (3.5 million trait/structural combinations) deposited in the Czech National Repository, including the authors' processing codes (merge_images.m, LUT_processing.py) within the deposit. The direct repository URL is given in the text and isDataset · publiceaf pixels.
Data source location
Institutions: Institute of Computer Science, Masaryk University; Global Change Research Institute of the Czech Academy of Sciences
City: Brno
Country: Czech Republic
Data accessibility
Repository name: National Repository
Data identification number: 10.48700/datst.bcnpf-47q73
Direct URL to data: https://data.narodni-repozitar.cz/general/datasets/4y0sy-qh735
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Value of the Data
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Look-Up Tables (LUT) are considered important training datasets for machine learning models to predict leaf traits.
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To date, only a limited number of LUT datasets have been developed for forest sites, particularly for Central European temperate broadleaf forests. Most of them are lOpen asset ↗National Repository · 10.48700/datst.bcnpf-47q73lines:36-69Plant phenotyping relevance match · UnverifiedEurope PMC · bioRxiv · checked 13 Sept 2026
Cassava ( Manihot esculenta Crantz) is a critical food security crop for sub-Saharan Africa. Efforts to improve cassava through breeding have expanded over the past decade. At the same time, crop growth models (CGM) are becoming common place in breeding efforts to expand the inference of evaluations of breeding germplasm to environments that have not been tested and to prepare for breeding for adaptation to future climates. We parameterized a CGM, the CROPGRO-MANIHOT-Cassava model in the DSSAT family of models, using data on 67 clones from the International Institute of Tropical Agriculture cassava breeding program evaluated from 2017 to 2020 and over eight locations in Nigeria using trial and error parameter adjustments and the General Likelihood Uncertainty Estimation method. Our objectives were to assess the feasibility of this large-scale calibration in the context of a cassava breeding program and to identify systematic biases of the model. For each cultivar we calculated the Pearson correlation between model prediction and observation across the environments, as well as root mean squared error and d statistics. As a result of calibration, the correlation coefficient increased from −0.03 to +0.08, the RMSE dropped from 21 t ha -1 to 5 t ha -1 while d increased from 0.23 to 0.44. We found that the model underestimated root yield in dry environments (low precipitation and high temperature) and overestimated root yield in wet environments (high precipitation and low temperature). Our experience suggests both that CGM calibration could become a routine component of the cassava breeding data analysis cycle and that there are opportunities for model improvement.
Why it matches plant phenotyping methodsキャッサバの根収量という植物形質を推定する作物成長モデルを大規模に較正・評価しており、モデル性能の検証が研究の中心である。
abstractWe parameterized a CGM, the CROPGRO-MANIHOT-Cassava model in the DSSAT family of models, using data on 67 clones from the International Institute of Tropical Agriculture cassava breeding program evaluated from 2017 to 2020 and over eight locations in Nigeria using trial and error parameter adjustments and the General Likelihood Uncertainty Estimation method.
Lignin fluorescence in plant cell walls significantly interferes with Raman spectroscopic signals, resulting in compromised analytical accuracy and resolution. To address this issue, a strategy was implemented to both reduce the absolute lignin content in samples and prepare thinner plant tissue sections. This approach involved embedding plant samples in LR White resin, complemented by an ultrathin sectioning technique. Additionally, algorithms were developed to eliminate the impact of resin spectra on the imaging process. These advancements collectively enhanced the performance of Raman spectroscopy by effectively diminishing the disruptive effects of lignin fluorescence. Further analysis with confocal laser scanning microscopy (CLSM) elucidated the presence of aggregation-induced luminescence (AIE) in plant tissues, revealing a direct correlation with lignin concentration. These findings not only offer a new perspective for the application of Raman spectroscopy in plant science, but also pave the way for advancements in tip-enhanced Raman spectroscopy (TERS) detection.
Why it matches plant phenotyping methods植物細胞壁のリグニンを対象とするRaman分光イメージングの干渉低減、薄切片調製、樹脂スペクトル除去アルゴリズムを開発し、植物組織の化学的形質測定法の性能向上を中心に扱っているため。
abstractTo address this issue, a strategy was implemented to both reduce the absolute lignin content in samples and prepare thinner plant tissue sections.
In this project, we have developed a plant disease detection model using a machine learning approach on a dataset named “Plant Disease Expert” which is available on Kaggle. The dataset contains 199,611 images across 58 subdirectories. The splitting of the dataset in training, validation and testing is done in ratio 90:5:5. The data preprocessing included resizing the images, normalizing pixel values and applying augmentation techniques. We have used different libraries throughout the project such as NumPy, Pandas, Operating system, Time, Matplotlib, OpenCV, Shutil, TensorFlow, Keras, Seaborn, etc. EfficientNetB3 is used as the base model. EfficientNetB3 is a convolutional neural network architecture developed by Google Brain researchers [1]. It is known for its efficiency and effectiveness in image classification tasks. Additional custom layers such as max pooling, batch normalization, dense layers, and activation functions are used to optimize the performance. The adamax optimizer and categorical cross-entropy were used during training. The model was trained for 10 epochs with a batch size of 20 and a learning rate of 0.01. For evaluation, classification report, accuracy, precision, recall, F1-score and support were used. The model achieved an accuracy of 98.76% [1].
Why it matches plant phenotyping methods植物画像から病害状態を推定する機械学習モデルの開発・評価が研究の中心であり、植物病害の画像ベース表現型解析に該当します。
abstractwe have developed a plant disease detection model using a machine learning approach
Reproduction assets foundThe paper's plant-phenotyping analysis is built entirely on a public image dataset, 'Plant Disease Expert' by S. S. Mahi, hosted on Kaggle (199,611 leaf images across 58 subdirectories), which the authors explicitly state is publicly available. No author analysis code, trained model checkpoint, or supplement with code/Dataset · publicwe have developed a plant disease detection model using a machine learning approach on a dataset
named “Plant Disease Expert” which is available on KaggleOpen asset ↗Kaggle · Plant Disease Expertpdf-page:2 lines:1-57Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract In spatio-temporal plant monitoring, optical sensing (including hyperspectral imaging), is being deployed to, non-invasively, detect and diagnose plant responses to abiotic and biotic stressors. Early and accurate detection and diagnosis of stressors are key objectives. Level of radiometric repeatability of optical sensing data and ability to accurately detect and diagnose biotic stress are inversely correlated. Accordingly, it may be argued that one of the most significant frontiers and challenges regarding widespread adoption of optical sensing in plant research and crop production hinges on methods to maximize radiometric repeatability. In this study, we acquired hyperspectral optical sensing data at noon and midnight from soybean ( Glycine max ) and coleus wizard velvet red (Solenostemon scutellarioides) plants with/without experimentally infestation of two-spotted spider mites ( Tetranychus urticae ). We addressed three questions related to optimization of radiometric repeatability: (1) are reflectance-based plant responses affected by time of optical sensing? (2) if so, are plant responses to two-spotted spider mite infestations (biotic stressor) more pronounced at midnight versus at noon? (3) Is detection of biotic stress enhanced by spatial binning (smoothing) of hyperspectral imaging data? Results from this study provide insight into calculations of radiometric repeatability. Results strongly support claims that acquisition of optical sensing data to detect and characterize stress responses by plants to detect biotic stressors should be performed at night. Moreover, the combination of midnight imaging and spatial binning increased classification accuracies with 29% and 31% for soybean and coleus, respectively. Practical implications of these findings are discussed. Study results are relevant to virtually all applications of optical sensing to detect and diagnose abiotic and biotic stress responses by plants in both controlled environments and in outdoor crop production systems.
Why it matches plant phenotyping methods夜間ハイパースペクトル撮像と空間ビニングによる植物ストレス検出・放射計測再現性の最適化が研究の中心であり、植物の生物的ストレス状態を画像から推定する手法を検証している。
titleMethods to optimize optical sensing of biotic plant stress – combined effects of hyperspectral imaging at night and spatial binning
The paper describes the theoretical and technical aspects of obtaining information on the condition of cereal crops based on medium resolution space multispectral imagery. Experimental studies have been carried out to create digital index maps for fields with grain crops. The high efficiency of the methodology for studying the state of fields using multispectral satellite images with the use of L1C and higher L2A processing level products is proved, the features and limitations of the methodology are shown. The theoretical and methodological foundations for processing multispectral satellite imagery results have been implemented in many application services and software. They allow, first of all, to generate such important products for explaining the state of vegetation in fields as vegetation index maps. Sentinel-2 L1C and Sentinel-2 L2A space imagery is a valuable and inexpensive source of information on crop condition. During the pilot study, Sentinel- 2 L1C satellite image products were processed to bring them up to the level of Sentinel-2 L2A products. This is a complex step related to the correct selection of the atmospheric correction model and the conversion of pixel values into reflectivity of objects on the Earth's surface. Another important step in this process is the ortho-correction of the images using global terrain models. Such correction can be important for the further generation of task maps for soil and crop management. The creation of a time series of vegetation index maps for a field of winter wheat is a very substantive way to build a field history that fully confirms the theory of remote field monitoring. The information obtained allows us to adapt the processing algorithm to refine the measurement and prediction of quantitative indicators (biomass calculation, yield prediction, etc.). The experiment also revealed some positive features and drawbacks of the method of creating index maps. For example, under certain weather conditions in summer, correcting the images for atmospheric influence does not significantly affect the calculated index values. On the contrary, with the specified acquisition period (5 days), it may turn out that there are no more than 3-5 cloudless days in a month during the spring growing season, which significantly affects the efficiency of mapping. Accordingly, more reliable and accurate, but significantly more expensive, is the multispectral field survey from unmanned aerial vehicles.
Why it matches plant phenotyping methods作物の状態を対象に、Sentinel-2マルチスペクトル画像の補正・処理と植生指数マップ生成手法を技術的に検討し、限界や精度に関わる特性を評価しているため、フェノタイピング手法が中心である。
abstractThe paper describes the theoretical and technical aspects of obtaining information on the condition of cereal crops based on medium resolution space multispectral imagery.
Field / plotFlowerFruitLeafWhole plant / canopy / plot / fieldClassificationCalibration / preprocessingTrackingGrowth / development / phenology
Plant phenology plays a fundamental role in shaping ecosystems, and global change-induced shifts in phenology have cascading impacts on species interactions and ecosystem structure and function. Detailed, high-quality observations of when plants undergo seasonal transitions such as leaf-out, flowering, and fruiting are critical for tracking causes and consequences of phenology shifts, but these data are often sparse and biased globally. These data gaps limit broader generalizations and forecasting improvements in the face of continuing disturbance. One solution to closing such gaps is to document phenology on field images taken by public participants. iNaturalist, in particular, provides global scale research-grade data and is expanding rapidly. Here we utilize over 53 million field images of plants and millions of human annotations from iNaturalist – data spanning all angiosperms and drawn from across the globe – to train a computer vision model (PhenoVision) to detect the presence of fruits and flowers. PhenoVision utilizes a vision transformer architecture pretrained with a masked autoencoder to improve classification success, and it achieves high accuracy for flower (98.5%) and fruit presence (95%). Key to producing research-ready phenology data is post-calibration tuning and validation focused on reducing noise inherent in field photographs, and maximizing the true positive rate. We also develop a standardized set of quality metrics and metadata so that results can be used effectively by the community. Finally, we showcase how this effort vastly increases phenology data coverage, including regions of the globe where data have been limited before. Our end products are tuned models, new data resources, and an application streamlining discovery and use of those data for the broader research and management community. We close by discussing next steps, including automating phenology annotations, adding new phenology targets, e.g., leaf phenology, and further integration with other resources to form a global central database integrating all in-situ plant phenology resources.
Why it matches plant phenotyping methods植物の野外画像から開花・結実というフェノロジー形質を推定する画像解析モデルを開発し、校正・検証・品質指標も扱っており、フェノタイピング手法が中心である。
abstractto train a computer vision model (PhenoVision) to detect the presence of fruits and flowers
China is a large agricultural country, and the crop economy holds an important place in the national economy. The identification of crop diseases and pests, as well as the non-destructive classification of crops, has always been a challenge in agricultural development, hindering the rapid growth of the agricultural economy. Hyperspectral imaging technology combines imaging and spectral techniques, using hyperspectral cameras to acquire raw image data of crops. After correcting and preprocessing the raw image data to obtain the required spectral features, it becomes possible to achieve the rapid non-destructive detection of crop diseases and pests, as well as the non-destructive classification and identification of agricultural products. This paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios. Subsequently, it organizes the processing of hyperspectral data for crop disease and pest detection and classification, deriving relevant preprocessing and analysis methods for hyperspectral data. Finally, it conducts a detailed analysis of classic cases using hyperspectral imaging technology for detecting crop diseases and pests and non-destructive classification, while also analyzing and summarizing the future development trends of hyperspectral imaging technology in agricultural production. The non-destructive rapid detection and classification technology of hyperspectral imaging can effectively select qualified crops and classify crops of different qualities, ensuring the quality of agricultural products. In conclusion, hyperspectral imaging technology can effectively serve the agricultural economy, making agricultural production more intelligent and holding significant importance for the development of agriculture in China.
Why it matches plant phenotyping methods作物の病害検出を含むハイパースペクトル画像法について、データ取得、前処理、解析手法、適用事例を体系的に整理したレビューであり、植物状態の取得・抽出法が中心です。
abstractThis paper first provides an overview of the current applications of hyperspectral imaging technology in crops both domestically and internationally. It then summarizes the methods of hyperspectral data acquisition and application scenarios.
PoplarField / plotRootCalibration / preprocessingRoot system architecture
Roots are important in agricultural and natural systems for determining plant productivity and soil carbon inputs. Sometimes, the amount of roots in a sample is too much to fit into a single scanned image, so the sample is divided among several scans, and there is no standard method to aggregate the data. Here, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation. We developed a Python script that identifies which images belong to the same sample and returns a single, larger concatenated image. These concatenated images and the original images were processed with RhizoVision Explorer, a free and open-source software. An R script was developed, which identifies rows of data belonging to the same sample and applies correct statistical methods to return a single data row for each sample. These two methods were compared using example images from switchgrass, poplar, and various tree and ericaceous shrub species from a northern peatland and the Arctic. Most root measurements were nearly identical between the two methods except median diameter, which cannot be accurately computed by statistical aggregation. We believe the availability of these methods will be useful to the root biology community.
Why it matches plant phenotyping methods複数の根スキャン画像から根形質を統合する画像連結・統計集約法を開発し、比較検証した研究であり、根形質取得ワークフローが中心です。
abstractHere, we describe and validate two methods for standardizing measurements across multiple scans: image concatenation and statistical aggregation.
Reproduction assets foundThe paper's Data availability statement deposits the root scan imageset, the Python image-concatenation script, and the R statistical-aggregation/figure code on Zenodo with explicit DOIs, making the paper-specific phenotyping images and analysis code publicly actionable. Since the Zenodo deposit URLs are not among the,Dataset · publicThe imageset is available at doi: 10.5281/zenodo.12667583Open asset ↗Zenodo · 10.5281/zenodo.12667583pdf-raw-page:7 lines:1-85Code · publicthe R code for statistical aggregation along
with the figures and statistics presented here are available at doi:
10.5281/zenodo.12668177Open asset ↗Zenodo · 10.5281/zenodo.12668177pdf-raw-page:7 lines:1-85Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Moderate-Resolution Imaging Spectroradiometer (MODIS) Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) products are being increasingly used for the quantitative remote sensing of vegetation. However, the assumption underlying the MODIS NBAR product’s inversion model—that surface anisotropy remains unchanged over the 16-day retrieval period—may be unreliable, especially since the canopy structure of vegetation undergoes stark changes at the start of season (SOS) and the end of season (EOS). Therefore, to investigate the MODIS NBAR product’s temporal effect on the quantitative remote sensing of crops at different stages of the growing seasons, this study selected typical phenological parameters, namely SOS, EOS, and the intervening stable growth of season (SGOS). The PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product. The Anisotropic Flat Index (AFX) was further employed to explore the influence of vegetation type and mixed pixel distribution characteristics on the BRDF shape under different stages of the growing seasons and different FVC; that was then combined with an NDVI spatial distribution map to assess the feasibility of using the reflectance of other characteristic directions besides NBAR for FVC correction. The results revealed the following: (1) Generally, at the SOSs and EOSs, the differences in PCCs before vs. after the NBAR correction mainly ranged from 0 to 0.1. This implies that the accuracy of FVC derived from MODIS NBAR is lower than that derived from MODIS Surface Reflectance. Conversely, during the SGOSs, the differences in PCCs before vs. after the NBAR correction ranged between –0.2 and 0, suggesting the accuracy of FVC derived from MODIS NBAR surpasses that derived from MODIS Surface Reflectance. (2) As vegetation phenology shifts, the ensuing differences in NDVI patterning and AFX can offer auxiliary information for enhanced vegetation classification and interpretation of mixed pixel distribution characteristics, which, when combined with NDVI at characteristic directional reflectance, could enable the accurate retrieval of FVC. Our results provide data support for the BRDF correction timescale effect of various stages of the growing seasons, highlighting the potential importance of considering how they differentially influence the temporal effect of NBAR corrections prior to monitoring vegetation when using the MODIS NBAR product.
Why it matches plant phenotyping methodsMODIS反射率から作物のFVCを推定するリモートセンシング手法について、NBAR補正の時期効果を比較・検証しており、植物形質推定が研究の中心である。
abstractThe PROBA-V bioGEOphysical product Version 3 (GEOV3) Fractional Vegetation Cover (FVC) served as verification data, and the Pearson correlation coefficient (PCC) was used to compare and analyze the retrieval accuracy of FVC derived from the MODIS NBAR product and MODIS Surface Reflectance product.
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 · publicwhile the GEOV3 FVC data can be downloaded from:
https://land.copernicus.eu/global/products/fcoverOpen asset ↗pdf-page:17 lines:1-59Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 15 Sept 2026
Monitoring the number of cotton flowers can provide important information for breeders to assess the flowering time and the productivity of genotypes because flowering marks the transition from vegetative growth to reproductive growth and impacts the final yield. Traditional manual counting methods are time-consuming and impractical for large-scale fields. To count cotton flowers efficiently and accurately, a multi-view multi-object tracking approach was proposed by using both RGB and depth images collected by three RGB-D cameras fixed on a ground robotic platform. The tracking-by-detection algorithm was employed to track flowers from three views simultaneously and remove duplicated counting from single views. Specifically, an object detection model (YOLOv8) was trained to detect flowers in RGB images and a deep learning-based optical flow model Recurrent All-pairs Field Transforms (RAFT) was used to estimate motion between two adjacent frames. The intersection over union and distance costs were employed to associate flowers in the tracking algorithm. Additionally, tracked flowers were segmented in RGB images and the depth of each flower was obtained from the corresponding depth image. Those flowers tracked with known depth from two side views were then projected onto the middle image coordinate using camera calibration parameters. Finally, a constrained hierarchy clustering algorithm clustered all flowers in the middle image coordinate to remove duplicated counting from three views. The results showed that the mean average precision of trained YOLOv8x was 96.4%. The counting results of the developed method were highly correlated with those counted manually with a coefficient of determination of 0.92. Besides, the mean absolute percentage error of all 25 testing videos was 6.22%. The predicted cumulative flower number of Pima cotton flowers is higher than that of Acala Maxxa, which is consistent with what breeders have observed. Furthermore, the developed method can also obtain the flower number distributions of different genotypes without laborious manual counting in the field. Overall, the three-view approach provides an efficient and effective approach to count cotton flowers from multiple views. By collecting the video data continuously, this method is beneficial for breeders to dissect genetic mechanisms of flowering time with unprecedented spatial and temporal resolution, also providing a means to discern genetic differences in fecundity, the number of flowers that result in harvestable bolls. The code and datasets used in this paper can be accessed on GitHub: https://github.com/UGA-BSAIL/Multi-view_flower_counting.
Why it matches plant phenotyping methodsRGB-Dカメラ、ロボットプラットフォーム、物体追跡、深度投影、重複除去を統合してワタ花数を自動計測する手法を開発・検証しており、植物表現型取得が中心である。
abstractTo count cotton flowers efficiently and accurately, a multi-view multi-object tracking approach was proposed by using both RGB and depth images collected by three RGB-D cameras fixed on a ground robotic platform.
In the field of precision agriculture, crop growth monitoring is integral for acquiring a holistic understanding of plant growth status, facilitating the implementation of precise and timely agronomic interventions. While traditional methods rely on labor-intensive sampling surveys, the advent of remote sensing technologies, characterized by multi- or hyperspectral imaging systems, has provided a leap forward in the non-destructive assessment of crop growth parameters. Restrictions due to technical constraints, however, curtail the widespread application of these spectral imaging modalities for plant growth surveillance. To address this challenge, this study endeavored to conceptualize and contrive a cost-effective, ground-based digital apparatus. This instrument, amalgamating a non-imaging multi-spectral module with bandwidths of 10 nm across six distinct wavelengths (530 nm, 570 nm, 680 nm, 700 nm, 740 nm, and 780 nm), a digital camera module, an operational control unit, and a solar cell assembly, was designed for continuous monitoring of crop growth. This was accomplished through intricate hardware engineering and sophisticated system control programming. Performance of multi-spectral module from the designed device were comprehensively evaluated. Results showed that measurements procured from the multi-spectral module were in strong linear correlation with irradiance observations from a canonical irradiance meter (ST-85) across all spectral bands under diverse indoor lighting scenarios, registering non-linear error (NLE) values below 3.1 %, and return error (RE) indices not exceeding 0.7 %. Continuous monitoring under variable outdoor ambient conditions—sunny and cloudy—underscored the multi-spectral module’s commendable performance. Notably, in sunlit conditions, results were particularly promising: statistical metrics of relative error for relative reflectance of each wavelength, alongside vegetation indices (VIs), were detained beneath the threshold of 5 % and 3 %, respectively. Further, a rigorous cross-radiometric calibration of the multi-spectral module, benchmarked against an ASD Field Spec 4 spectrometer in tandem with paddy rice canopy, yielded precise results. The calibration models were characterized by relative root mean square error (RRMSE) values below 6 % and determination coefficients (R2) approximating 0.8 across all wavebands. Exploiting these calibrated models, Normalized difference red-edge index (ND₇₀₅), deciphered from cross-calibrated reflectance data, delivered the highest accuracy, with an RMSE of 3.76 μg/cm², and a RRMSE of 8.16 %, for LCC estimation in paddy rice among all the selected vegetation indices. Moreover, a lightweight model, based on combination of digital image and non-imaging multi-spectral data, achieved an overall accuracy of 95.12 % for paddy rice pests and diseases classification. These results indicated that the newly-developed device could be used for crop status monitoring.
Why it matches plant phenotyping methods植物の生育状態・葉色素量・病害虫状態を取得するマルチスペクトル／画像センサー装置を開発し、放射計・分光計との校正と性能検証を行っており、フェノタイピング手法が中心です。
abstractThis instrument, amalgamating a non-imaging multi-spectral module with bandwidths of 10 nm across six distinct wavelengths (530 nm, 570 nm, 680 nm, 700 nm, 740 nm, and 780 nm), a digital camera module, an operational control unit, and a solar cell assembly, was designed for continuous monitoring of crop growth.
INTRODUCTION: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking. MATERIALS AND METHODS: This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control. RESULTS: Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls. CONCLUSION: Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.
Why it matches plant phenotyping methods作物ストレスという植物状態を推定するUAVマルチセンサーの取得、校正、前処理、データ融合、品質管理の実践的方法を主題としており、フェノタイピング手法が中心である。
abstractThis paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.
High-throughput imaging enables rapid collection of large datasets and is used widely in many systems. However, this is not often used in plant-based systems due to issues related to the need to mount tissues and autofluorescence of plant metabolites. We therefore developed methodology enabling high-throughput imaging of Arabidopsis roots. In this system, growth media supplemented with India Ink (to block autofluorescence from cotyledons) is poured directly into multi-well coverglass-bottom plates and seedlings grown such that the roots grow down with the gravity vector and along the coverglass, effectively mounting themselves for imaging. This method enables high-throughput imaging of Arabidopsis roots.
Why it matches plant phenotyping methodsアラビドプシス根の高スループット画像取得を可能にする培養・固定・撮像法を開発しており、植物表現型取得手法が研究の中心である。
abstractWe therefore developed methodology enabling high-throughput imaging of Arabidopsis roots.