Plant phenotyping methods.

Visual-SLAM for the detection of hidden tomatoes in greenhouses by Hierarchical Localization and GLOMAP for robotized harvesting ↗

Advanced crop monitoring inside greenhouses is becoming one of the primary objectives of research centers. High-performance sensors, such as LiDAR or stereo cameras, have traditionally been employed for this purpose, though these often have a high cost. This work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse. Tests were carried out on a real tomato bunch, located in the Agroconnect experimental greenhouse. A ROS 2 Humble node was developed to run on the robot in order to capture images of these crops, which were then stored for offline processing. To generate a 3D mapped model for the crop in the greenhouse, the GLOMAP mapper, based on Structure-From-Motion, was integrated with the Hierarchical Localization toolbox. This initial mapping is a foundation for future, more advanced algorithms to analyze growth patterns, and optimize agricultural management. The system leverages a hierarchical localization paradigm based on a coarse-to-fine strategy: it first performs global retrieval to generate location hypotheses, then combines local features within the identified candidate regions. The results show a correct identification of the tomato cluster, correctly characterising the tomato that is occluded and inaccessible by classical vision technologies. The reconstructed 3D model was further validated against manual ground-truth measurements of fruit size, centroid position, and orientation, confirming the geometric accuracy of the proposed low-cost monocular pipeline.

Why it matches plant phenotyping methods単なる収穫対象の位置検出ではなく、単眼Visual-SLAMと3D再構成を開発し、果実サイズ・重心位置・向きを実測値で検証しているため、植物器官形質の取得手法が中心である。

abstractThis work proposes a Visual-SLAM system using a monocular camera, which is significantly more cost-effective and specifically tailored for agricultural applications, such as mapping tomato crops in a greenhouse.

Cost-Effective Smartphone-Based Computer Vision Pipeline for Actinidia Phenological Stage Mapping ↗

Abstract Dioecious crops face significant pollination challenges due to the asynchrony in flowering between male and female plants. This asynchrony varies spatially across orchards, requiring targeted interventions in zones where synchrony is lacking. Assisted pollination addresses this deficiency, albeit at a substantial operational cost that could be optimised through spatial phenological mapping. Manual assessment proves economically infeasible at commercial scales, while existing computer vision systems are unable to classify phenological stages and integrate geospatial information. This study presents the Mobile Phenological Mapping (MPM) Framework, which integrates GNSS-synchronised smartphone video with automated phenological detection to generate plant-level phenological distribution maps validated in commercial kiwifruit (Actinidia chinensis) orchards. This modular framework comprises (i) training and validation data acquisition, (ii) model optimisation, (iii) operational pipeline, and (iv) performance evaluation. MPM employs hierarchical deep learning across three stages: structure detection, gender classification, and phenological stage classification. Video frames are georeferenced through timestamp matching with GNSS metadata, enabling spatial phenological mapping. Operational validation across four commercial orchard zones demonstrated mean absolute percentage errors of 17.2% for structure detection and 20.1% for gender classification. The framework reduces monitoring time from 113 to 1.6 hours per hectare, decreasing labour costs from €2 060 (113 hours × 18.20 € per hour) to €29 (1.6 hours × 18.20 € per hour) per hectare based on the Portuguese hourly labour cost for minimum wage workers. When integrated with routine orchard operations, video acquisition incurs negligible additional cost. MPM provides growers with precision phenological maps for targeted pollination interventions. While validated in a kiwifruit orchard, the modular architecture can be adapted to other crops by replacing the training data.

Why it matches plant phenotyping methodsスマートフォン動画とGNSSを用いて植物体レベルの性別・生育段階を自動検出し、フェノロジー分布を作成する手法と、その性能評価・商業園での検証が研究の中心である。

abstractThis study presents the Mobile Phenological Mapping (MPM) Framework, which integrates GNSS-synchronised smartphone video with automated phenological detection to generate plant-level phenological distribution maps validated in commercial kiwifruit (Actinidia chinensis) orchards.

Subcellular Localization of Iron in Rhizophora mangle Leaves Revealed by Integrated Perls Reaction, TEM, STEM-HAADF, and EDS Analyses. ↗

Mangrove ecosystems are frequently exposed to high concentrations of iron (Fe) in sediments, resulting in Fe accumulation in plant tissues. Although Fe is an essential micronutrient involved in several metabolic processes, its excess requires efficient mechanisms of compartmentalization and storage to maintain cellular homeostasis. Histochemical detection using the Perls reaction has usually been applied to identify ferric iron (Fe 3+ ) in biological tissues; however, the combination of this technique with ultrastructural and elemental analyses remains relatively unexplored in plant cells. In this study, we investigated Fe localization in leaf tissues of Rhizophora mangle L. (Rhizophoraceae), a dominant mangrove species, by combining complementary approaches, including Perls cytochemical reaction, transmission electron microscopy (TEM), scanning transmission electron microscopy coupled with high-angle annular dark-field imaging (STEM-HAADF), and energy-dispersive X-ray spectroscopy (EDS). Perls-positive electron-dense deposits were visualized at the ultrastructural level, and their elemental composition was further characterized by EDS analyses. Fe-containing deposits were detected in the epidermis, mesophyll parenchyma, mucilage cells, and vascular tissues, as well as in multiple cellular compartments, including plastids, mitochondria, vacuoles, cell walls, intercellular spaces, and plasmodesmata, whereas sclerenchyma cells showed no detectable Fe-containing deposits. The combination of Perls reaction with TEM, STEM-HAADF, and EDS provides a complementary approach for high-resolution visualization and elemental characterization of Fe-containing deposits at the subcellular level. This integrated methodology may facilitate the investigation of Fe distribution and compartmentalization in plant tissues under contrasting conditions of Fe availability.

Why it matches plant phenotyping methods植物葉の鉄分布・細胞内区画化という生理状態を対象に、複数の顕微鏡・元素分析法を統合した可視化および特性評価手法が研究の中心であるため。

abstractThe combination of Perls reaction with TEM, STEM-HAADF, and EDS provides a complementary approach for high-resolution visualization and elemental characterization of Fe-containing deposits at the subcellular level.

Pinus pinaster Seedling Detection in Coastal Dune Plantations Using a UAS Multispectral Point Cloud and Point Transformer V3 ↗

Early detection of tree-seedling establishment is essential for monitoring regeneration success in coastal-dune plantations, where conventional field assessments remain labour-intensive and spatially limited. This study presents a deep-learning workflow for detecting early-stage Pinus pinaster seedlings using multispectral UAS-derived point clouds. Field surveys in the Quiaios National Forest, Portugal, mapped approximately 1500 seedlings using RTK GNSS positioning, biometric measurements, and field photographs. Multispectral imagery acquired with a DJI Mavic 3 Multispectral platform was processed through Structure-from-Motion to generate calibrated orthomosaics, terrain products, and dense point clouds. Training-data preparation combined pine-centred buffers, spectral conditioning, manual refinement and point-cloud class assignment. Point Transformer V3 models were trained in ArcGIS Pro and evaluated using field-mapped buffers withheld from model training within plantation-line areas. The Baseline high-recall model achieved 88% object-level recall at the operational threshold of at least three classified Pine-Seedling points per buffer. The refined hard-negative model retained 84% recall while reducing off-buffer detections from 243 to 41. False-negative analysis showed that omissions were associated with reduced crown diameter and limited branch development under the adopted buffer-based retrieval framework. These results support transformer-based multispectral point-cloud classification for scalable monitoring of early-stage pine regeneration in heterogeneous coastal environments.

Why it matches plant phenotyping methodsUASマルチスペクトル点群とPoint Transformer V3により、マツ幼苗の存在・定着状態を植物個体レベルで推定する手法を開発・評価しており、検出性能も検証しているため、植物フェノタイピング手法が中心である。

abstractThis study presents a deep-learning workflow for detecting early-stage Pinus pinaster seedlings using multispectral UAS-derived point clouds.

Artificial Intelligence-Based Smart Farming with Internet of Things and Drone Technologies for Integrated Crop and Aquatic Health Monitoring ↗

Agriculture is being revolutionized through the integration of cutting-edge technologies that support efficiency, productivity, and sustainability. Recent trends in transforming traditional agriculture into smart agricultural systems through the application of Artificial Intelligence (AI), IoT, and drone technology are gaining popularity worldwide. In this context, the current study developed an integrated artificial intelligence-based farming system incorporating Internet of Things sensors, drones, deep learning, and web/mobile applications for timely monitoring of crop and aquatic health. Data were collected using IoT sensors and drones equipped with high-resolution cameras to monitor soil and water parameters, including temperature, humidity, and pH, under various climatic conditions. A dataset of 70,000 images was used for system training, validation, and testing with an 80:10:10 split, together with three months of IoT sensor data. Models including YOLOv8, CNN, Faster R-CNN, ResNet50, MobileNet, EfficientNet, DenseNet, LSTM, and Random Forest were used for pest and disease detection, fish classification, fish disease detection, shrimp disease detection, and monitoring of climatic factors. Data pre-processing included denoising, normalization, missing-value handling, and feature extraction. The designed model showed reliable performance across the tasks, with 88.4% accuracy for shrimp detection, 92.1% for pest detection using YOLOv8, and 93.4% accuracy for plant disease detection using the CNN model. Overall performance was recorded at 97% accuracy, with high precision, F1-score, and mAP, and an RMSE of 1.2 for sensor-based prediction and validation. The current findings indicate the potential of using AI, IoT, and drone technologies to detect biotic and abiotic stresses during farming and support a sustainable agricultural system.

Why it matches plant phenotyping methodsAI・IoT・ドローンを統合した作物健康モニタリングシステムを開発し、植物病害検出モデルを訓練・検証しており、植物状態の取得方法が中心的である。

abstractthe current study developed an integrated artificial intelligence-based farming system incorporating Internet of Things sensors, drones, deep learning, and web/mobile applications for timely monitoring of crop and aquatic health

Deep spatial-spectral fusion of UAV RGB and hyperspectral imagery for potato plant disease detection ↗

Accurate plant disease detection remains challenging when using single-modality data, which fails to capture comprehensive disease-related features. However, many existing studies rely on pixel-level classification or prior plant segmentation and lack explicit modeling of cross-modal interactions, limiting their ability to distinguish between healthy and diseased plants. This study presents a spatial–spectral fusion deep learning framework (S2-PDD) that fuses uncrewed aerial vehicles (UAV)-based RGB imagery and hyperspectral-derived feature representations for plant-level detection of potato diseases (blackleg and Potato virus Y (PVY)), employing early fusion (modalities combined at the input stage) and middle fusion (features integrated at intermediate stages within the model backbone) strategies. The multimodal fusion models were compared against single-modal models and an existing S2ADet model. Model performance, assessed through five-fold cross-validation, demonstrated that multimodal models integrating RGB and vegetation index features achieved the highest mAPs of 86.65 ± 1.71 (%; E-RV model) and 85.74 ± 1.96 (M-RV), respectively. These mAPs were higher than those of all single-modal models, including the RGB-only (83.21 ± 1.46) and hyperspectral-only models (PCA features: 79.71 ± 1.45; vegetation index features: 85.31 ± 2.36). They also exceeded mAPs of multimodal models combining RGB with PCA features (early fusion: 83.00 ± 2.81; middle fusion: 83.11 ± 2.46; S2ADet: 84.04 ± 2.73), regardless of the fusion strategy. The superior performance highlights that vegetation index features provide strong class separability compared to other hyperspectral representations. The proposed models achieved strong plant-level detection performance, with AP of 78.65 ± 4.14 (E-RV) and 77.40 ± 3.46 (M-RV) for blackleg disease, as well as 84.82 ± 3.59 (E-RV) and 83.28 ± 3.86 (M-RV) for PVY. These results demonstrate the potential of UAV-based multimodal sensing for disease monitoring in cropping systems. A potato plant disease detection dataset was constructed and made publicly available, containing paired RGB and hyperspectral image tiles with bounding box annotations. The code is available at https://github.com/Tim-Agro/S2-PDD.

Why it matches plant phenotyping methodsUAV RGB・ハイパースペクトル画像からジャガイモ個体の病害状態を推定する融合モデルを開発・比較検証し、公開データセットも構築しており、病害表現型の取得・抽出手法が中心である。

abstractThis study presents a spatial–spectral fusion deep learning framework (S2-PDD) that fuses uncrewed aerial vehicles (UAV)-based RGB imagery and hyperspectral-derived feature representations for plant-level detection of potato diseases (blackleg and Potato virus Y (PVY))

REAL-TIME AND PRECISE DETECTION OF FIELD SOYBEAN RUST AND BACTERIAL SPOT BASED ON IMPROVED YOLOV11N ↗

To overcome YOLOv11’s limitations in complex field environments, this paper proposed SDD-YOLOv11n, a lightweight real-time detector for soybean diseases. The model reconstructed the backbone using GhostConv to minimize redundancy and integrates a C3k2_Star module to enhance small lesion detection against background noise. Additionally, a Detect Efficient (DE) head further compressed the architecture. Experimental results verified the model's efficiency, achieving a parameter count of 1.88 M and a weight size of 3.9 MB—reductions of 27.3% and 25% compared to YOLOv11n, respectively. Furthermore, the model maintained high detection performance with a Mean Average Precision (mAP50) of 75.6% and an F1-score of 69.3%, demonstrating its effectiveness in balancing architectural efficiency and accuracy in complex field environments.

Why it matches plant phenotyping methods圃場のダイズ病害病斑を画像から検出する軽量YOLOモデルの開発と性能評価が研究の中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。

titleREAL-TIME AND PRECISE DETECTION OF FIELD SOYBEAN RUST AND BACTERIAL SPOT BASED ON IMPROVED YOLOV11N

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

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

Why it matches plant phenotyping methodsイネいもち病の病徴を画像から検出・分割し、病斑面積比に基づく重症度を定量化するフェノタイピング手法を開発・評価しており、方法が研究の中心である。

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

Optimizing deep learning models for plant leaf disease classification using nature-inspired algorithms ↗

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.

Integrating UAV-derived enhanced disease detection index and texture features for monitoring southern corn rust severity ↗

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

Why it matches plant phenotyping methodsUAV画像から病害重症度を推定する検出指数・テクスチャ特徴の統合が題名上の中心であり、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します。

titleIntegrating UAV-derived enhanced disease detection index and texture features for monitoring southern corn rust severity

DualSlim-YOLO: A Lightweight Detection Model Based on Unmanned Aerial Vehicle Imagery for Cauliflower Seedling Identification and Growth Assessment ↗

Cauliflower emergence rate and seedling growth are key indicators of field conditions and varietal potential. Traditional manual surveys are unsuitable for continuous monitoring across multiple varieties. This study integrates UAV RGB imagery with the DualSlim-YOLO model to estimate cauliflower emergence rates and monitor seedling growth. Built on YOLOv11, the model incorporates a lightweight feature extraction structure and an optimized detection-scale configuration. It reduces computational complexity while maintaining detection accuracy, thereby improving the efficiency of cauliflower seedling detection. DualSlim-YOLO achieved P, R, F1-score, mAP@0.5, and mAP@0.5:0.95 of 95.35%, 96.75%, 96.05%, 98.55%, and 86.65%, respectively. The number of parameters was reduced by 38.61%, while the inference speed increased by 22.16%, demonstrating good lightweight performance. Based on this model, UAV images of 171 cauliflower varieties acquired at 7, 21, and 28 d after transplanting were used for seedling detection and emergence rate estimation. In addition, 18 time-series seedling phenotypic traits were extracted, enabling a comprehensive quantitative evaluation of emergence dynamics and early-growth performance across multiple cauliflower varieties. This method effectively screens cauliflower varieties for high emergence rates, rapid emergence, and excellent seedling growth performance. It provides technical support for high-throughput, nondestructive seedling phenotyping and early germplasm screening under field conditions.

Why it matches plant phenotyping methodsUAV画像と軽量YOLOモデルを用いて、カリフラワー苗の検出、出芽率推定、18種類の時系列表現型形質抽出を行う手法が中心であり、モデル性能も検証している。

abstractThis study integrates UAV RGB imagery with the DualSlim-YOLO model to estimate cauliflower emergence rates and monitor seedling growth.

Non‐Destructive and High‐Fidelity Sensing of Plant Water Content Enabled by Near‐Infrared Luminescent Metal Halides ↗

ABSTRACT Real‐time, accurate water monitoring is a crucial technical foundation for industrial, environmental, and biological research. However, traditional detection methods typically include destructive processes and suffer from response delays. Near‐infrared (NIR) luminescent metal halides offer a novel solution to this challenge, but they still face issues such as ultraviolet excitation and low photoelectric conversion efficiency. Herein, a luminescent material was synthesized based on the blue‐light‐excited lead‐free perovskite Cs 2 HfCl 6 :Te 4+ /Mo 4+ , in which energy transfer (ET) from Te 4+ to Mo 4+ enables highly efficient NIR luminescence in the 800–1200 nm wavelength range. Upon encapsulation with a commercial blue light chip, the fabricated NIR light‐emitting diode device achieved a photoelectric conversion efficiency of up to 16.1%. By utilizing the absorption characteristics of water molecules in the NIR spectrum and receiving signals via a sensor, an interactive learning process based on a neural network machine learning algorithm was employed, achieving an estimation accuracy of up to 98.6% for plant water content. This non‐destructive and precise NIR detection module provides a new solution for the real‐time monitoring of crop physiological status and holds broad application prospects in the fields of precision agriculture and plant science.

Why it matches plant phenotyping methods植物の含水量を非破壊・リアルタイムに推定するNIRセンシングモジュールと機械学習手法が研究の中心であり、植物生理状態の測定法を開発・検証している。

abstractBy utilizing the absorption characteristics of water molecules in the NIR spectrum and receiving signals via a sensor, an interactive learning process based on a neural network machine learning algorithm was employed, achieving an estimation accuracy of up to 98.6% for plant water content.

CITRUS FLOWER, FRUIT, AND SHOOT RECOGNITION BASED ON IMPROVED YOLOv10 ↗

In the process of agricultural intelligence, precise detection of plant organs serves as the foundation for core tasks such as crop phenotyping analysis and yield prediction. However, in complex field environments, small targets such as citrus flowers and shoots face challenges including scale variation, background interference, and dense occlusion, which severely impact detection accuracy. This study improves the YOLOv10 model by introducing the BAM (Bottleneck Attention Module) attention mechanism and GIoU (Generalized Intersection over Union) loss function, constructing a YOLOv10s-BAM-GIoU model suitable for citrus flower, fruit, and shoot recognition. The BAM attention mechanism enhances the model's feature extraction capability for small target organs under complex backgrounds through parallel channel and spatial attention branches; the GIoU loss function improves the localization accuracy of densely occluded targets by optimizing the geometric alignment between predicted and ground-truth boxes. Validation experiments were conducted on a self-constructed dataset. The experimental results show that the improved YOLOv10s achieves significant advantages in comprehensive detection accuracy, with an mAP50 of 89.1%, representing an improvement of 2.9%~9.5% over the original YOLOv10s and other comparative models. In fine-grained category detection, the model achieves mAP50 of 91.2%, 83.6%, and 92.5% for shoots, flowers, and fruits, respectively. Furthermore, while maintaining high detection accuracy, the model achieves a detection speed of 23.6 ms per frame, meeting real-time detection requirements. The research results demonstrate that the improved YOLOv10s model integrating the BAM attention mechanism and GIoU loss function achieves an optimal balance between accuracy and speed in citrus organ detection tasks, providing a preferred solution for field real-time detection systems.

Why it matches plant phenotyping methods柑橘の花・果実・シュートという植物器官を画像から検出する改良モデルを開発し、データセットで精度と速度を検証しており、表現型取得手法が中心である。

abstractThis study improves the YOLOv10 model by introducing the BAM (Bottleneck Attention Module) attention mechanism and GIoU (Generalized Intersection over Union) loss function, constructing a YOLOv10s-BAM-GIoU model suitable for citrus flower, fruit, and shoot recognition.

OccPepSeg-YOLO for Instance Segmentation of Occluded Peppers in Field Images ↗

Agricultural operations such as pepper harvesting, fruit counting, and field phenotyping rely on accurate visual recognition and instance segmentation algorithms. However, pepper fruits in complex field environments often exhibit slender and curved shapes, partial occlusion, ambiguous boundaries, and adhesion between adjacent instances. Existing object detection and instance segmentation methods therefore struggle to obtain complete fruit masks, which adversely affects subsequent fruit counting, contour measurement, and picking-point localization. To improve the instance segmentation accuracy of occluded peppers in complex field scenes, this study proposes OccPepSeg-YOLO, an improved model based on YOLO11n-seg. First, a P2FreqFusion module is introduced to fuse shallow, high-resolution detail features with deep semantic features, thereby enhancing the representation of fruit edges and tip regions. Second, an ASC module is designed to model the directional and scale-related morphological characteristics of pepper fruits, while a BoundaryGate module strengthens responses at occlusion interfaces and boundaries between adjacent instances. Finally, an OccPepSegment multi-scale prototype segmentation head is constructed, and a BDoU loss function is introduced to improve the boundary consistency of instance masks. Experiments on a self-constructed field-pepper instance segmentation dataset showed that OccPepSeg-YOLO achieved M-P, M-R, M-mAP50, and M-mAP50–95 values of 93.87%, 92.09%, 97.17%, and 82.31%, respectively, representing improvements of 5.59, 3.18, 3.83, and 9.52 percentage points over YOLO11n-seg. Further comparisons with representative YOLO-based instance segmentation models, including YOLOv8n-seg, YOLOv9c-seg, YOLO12n-seg, and YOLOv26n-seg, demonstrated that OccPepSeg-YOLO achieved the best overall segmentation performance. In particular, its M-mAP50–95 exceeded the best competing result obtained by YOLOv9c-seg by 8.35 percentage points. Under a unified repeated-inference protocol on an RTX 3090 GPU using FP32 precision, a batch size of 1, and 640 × 640 inputs, OccPepSeg-YOLO achieved a mean inference latency of 15.801 ± 1.238 ms, a P95 latency of 17.323 ms, and a throughput of 63.29 FPS. These results demonstrate that the proposed model can produce more complete pepper instance masks under leaf occlusion, fruit overlap, and complex background conditions, providing technical support for field-pepper recognition, fruit counting, and visual perception by agricultural robots.

Why it matches plant phenotyping methods圃場画像からピーマン果実のインスタンスマスクを抽出する手法を開発・比較検証しており、果実カウントや輪郭計測に利用可能な植物形質取得が中心である。

abstractAgricultural operations such as pepper harvesting, fruit counting, and field phenotyping rely on accurate visual recognition and instance segmentation algorithms.

Plant Cell-on-Chip (PCOC): Exploring the Electrical Modulation Capability of Plant Cells ↗

The intrinsic properties of plants offer numerous opportunities for scientific and technological advancement. Considerable efforts have been directed toward developing plant-on-chip platforms to investigate cellular responses to external stimuli, including chemical, mechanical, and electrical cues. In this study, we present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection. Various experimental conditions were examined, including ionic and pH stimulation, as well as membrane dimensions, with the onion inner membrane treated as a black-box system. The measurement results are presented as Nyquist plots, and a resistance model incorporating multifactorial influences is proposed. Impedance variations in plant cells serve as a basis for electrical modulation. To explore these properties, we converted acoustic signals into electrical inputs and recorded the outputs after being modulated by onion inner epidermal cells. A transfer function analysis was subsequently performed. Our results indicate that the plant cell-on-chip (PCOC) platform holds promise for further investigations into plant cell properties. The impedance results suggest that plant cells can respond to different external stimuli, enabling modulation of the electrical properties. These findings lay the groundwork for future studies on cellular electrical characteristics and the development of preliminary bioelectrical circuits.

Why it matches plant phenotyping methods植物細胞の電気的生理状態を測定・解析するEISベースのオンチップ基盤を開発しており、植物状態の取得方法が研究の中心である。

abstractwe present a fluidic platform using polydimethylsiloxane (PDMS) and a printed circuit board (PCB), integrated with electrochemical impedance spectroscopy (EIS) detection

YOLOv11-MPD: A Multi-Part Maize Disease Detection Algorithm for Complex Field Environments ↗

Maize diseases affecting leaves, stalks, and ears can substantially reduce yield and quality; therefore, rapid and accurate recognition in complex field environments is important for intelligent agricultural monitoring. To address the large-scale variation, weak fine-grained texture, and strong background interference associated with multi-part maize diseases, this study proposes YOLOv11-MPD (YOLOv11 for Maize Multi-Part Disease Detection), a maize disease detection algorithm based on YOLOv11n. The method jointly improves spatial position awareness, shallow detail preservation, local-context modeling, key semantic-region enhancement, and lightweight detection-head reconstruction. RFCAConv, C3k2_RFCAConv, and Detect_LSDECD are introduced into the baseline network to strengthen directional texture modeling, multi-scale feature aggregation, and detection-head feature representation. FG-RFCAConv, HGD-C3k2, LCA-C3k2, and GRN-BiAttn are further designed for high-frequency differential gated detail compensation, P3 high-resolution detail enhancement, local-context fusion, and global-response-normalized attention regulation, respectively. Experimental results show that YOLOv11-MPD achieves Precision, Recall, mAP50, and mAP50-95 of 72.3%, 72.8%, 79.5%, and 50.2%, improving YOLOv11n by 2.4, 2.5, 2.9, and 2.4 percentage points, respectively, while reducing parameters from 2.6 M to 2.4 M. These results indicate that, within the scope of the dataset used in this study, YOLOv11-MPD improves multi-part maize disease detection under complex field conditions. However, the current conclusions are limited to the constructed dataset, and further validation using larger multi-region, multi-season, and multi-device datasets is required to evaluate its broader generalization ability.

Why it matches plant phenotyping methodsトウモロコシの葉・茎・穂における病害状態を画像から検出するアルゴリズムを開発し、性能比較・検証しており、植物表現型取得が研究の中心です。

abstractthis study proposes YOLOv11-MPD (YOLOv11 for Maize Multi-Part Disease Detection), a maize disease detection algorithm based on YOLOv11n.

Explainable Deep Learning for Multi-Class Plant Disease Classification Using ResNet and EfficientNet with Grad-CAM Analysis ↗

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.

SolanAPP: An Offline-First Mobile Framework for Segmentation-Based Diagnosis of Solanaceae Crop Diseases ↗

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.

FitoView: A Decision Support Application Integrating Weather Forecasts and CNNs for Plant Disease Classification and Severity Assessment - Case Study on Cercospora Leaf Spot in Chili Pepper ↗

Abstract Plant diseases represent major constraints on agricultural productivity, often resulting in significant yield losses. This study presents FitoView, a cloud-based mobile decision support system that integrates deep learning with real-time weather forecasting for sustainable plant disease management. Demonstrated through a case study on Cercospora leaf spot in chili pepper, the system employs custom YOLOv8 models for automated disease detection, classification, and pixel-level severity quantification, combined with meteorological data from the OpenMeteo API. The core innovation lies in an integrated decision matrix that considers three dimensions: AI-assessed disease severity, 48-hour climatic risk forecasts, and optimal spraying conditions, generating four contextualized management scenarios with tailored re-evaluation periods (3-10 days). OpenMeteo API validation across four cities in Sergipe demonstrated very strong correlations, with Pearson coefficients (r) of 0.90-0.97 for temperature, 0.81-0.95 for humidity, and 0.92-0.95 for solar radiation, corresponding to R² values of 0.65-0.94. The YOLOv8 object detection model achieved perfect precision (100%) and macro-averaged recall of 89% across all disease classes, with Cercospora leaf spot detection reaching perfect metrics (100% precision, recall, and F1-score). Field validation in Lagarto, Sergipe, confirmed the system’s practical use: it accurately detected Cercospora leaf spot, estimated severity, and, combined with climatic risk, generated recommendations for alternative treatment and short-term re-evaluation. The Progressive Web Application architecture, deployed on a Cloud Platform, ensures accessibility without installation requirements, while the modular design enables scalability to additional crops and diseases, representing a significant advancement toward democratizing AI-powered precision agriculture tools for smallholder farmers in Brazil.

Why it matches plant phenotyping methods植物病害の検出・分類と病斑のピクセルレベル重症度推定をYOLOv8で実装・検証したシステムであり、植物状態の取得・定量化が中心的な技術貢献です。

abstractThe core innovation lies in an integrated decision matrix that considers three dimensions: AI-assessed disease severity, 48-hour climatic risk forecasts, and optimal spraying conditions

Time course sensor‐based phenotyping can predict Ascochyta blight disease severity in Cicer species ↗

Abstract Ascochyta blight is a widely occurring chickpea fungal disease that can cause severe yield loss. Breeding for crop resistance benefits from high‐throughput evaluation of plant–pathogen interactions in genotypes which can serve as sources of resistance. Current practice for the evaluation is human visual scoring of disease symptoms, which is limited in throughput and precision. Here, we developed open‐source sensor‐based phenotyping methods using red, green, blue (RGB) and multispectral imaging to measure resistance components and predict disease severity classes in chickpea and wild relatives grown outdoors over three seasons. Pots were imaged at multiple time points with a ground‐based platform, providing 86,792 RGB and 8199 multispectral images. Lesion count was estimated with YOLOv5 (You Only Look Once version 5) object detection (F1 score = 0.27–0.30), fractional green canopy cover was estimated from RGB images, and vegetation indices were extracted from multispectral images. A model trained on growth rates of fractional green canopy cover normalized to control genotypes could predict disease severity classes with an accuracy of 65% –81 % ( 0.43–0.59) on unseen data from three different seasons. The developed methods provide a pathway to predict visual disease severity scores and support the breeding of crops for disease resistance. They may also be used to characterize disease progression, to find underlying resistance mechanisms, and for early disease detection.

Why it matches plant phenotyping methodsRGB・マルチスペクトル画像と地上センサープラットフォームを開発し、病斑数、緑色キャノピー被覆率、病害重症度を推定・予測する手法が研究の中心であるため。

abstractHere, we developed open‐source sensor‐based phenotyping methods using red, green, blue (RGB) and multispectral imaging to measure resistance components and predict disease severity classes in chickpea and wild relatives grown outdoors over three seasons.

Application of Remote Sensing Technologies in Crop Health Monitoring and Disease Surveillance ↗

The rapid detection and continuous monitoring of crop health and disease outbreaks are critical components of modern precision agriculture, essential for maintaining global food security. Traditional field-based scouting methods, while accurate, are often labor-intensive, time-consuming, and limited by spatial coverage, making them inadequate for large-scale agricultural operations. Remote sensing (RS) technologies—spanning satellite imagery, drone-based aerial platforms, and proximal sensors—offer a powerful, non-destructive, and scalable alternative for capturing high-resolution spectral and temporal data. This paper provides a comprehensive evaluation of current remote sensing applications in crop health monitoring and disease surveillance. We analyze how vegetation indices derived from multispectral and hyperspectral data, such as NDVI and red-edge parameters, serve as sensitive indicators of physiological stress and pathogen infection, often manifesting before visible symptoms appear. Furthermore, we explore the integration of machine learning and artificial intelligence algorithms in automating disease identification and severity mapping. By synthesizing recent advancements in sensor technology and data analytics, this paper demonstrates that remote sensing is indispensable for proactive, site-specific management. The findings emphasize that a multi-scale RS approach—integrating broad-scale satellite monitoring with high-resolution drone sorties—enables farmers to optimize input efficiency, minimize yield losses, and enhance the overall resilience of agro-ecosystems against biotic and abiotic stressors.

Why it matches plant phenotyping methods作物の健康・病害を対象に、リモートセンシング、センサー、植生指数、機械学習による状態・重症度推定を包括的に評価するレビューであり、フェノタイピング手法が中心です。

abstractThis paper provides a comprehensive evaluation of current remote sensing applications in crop health monitoring and disease surveillance.

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

Plant disease and plant stress early warning systems have advanced through deep learning, remote sensing, digital phenotyping, disease forecasting, and sensor networks. Detection accuracy, precision, recall, F1-score, and area under the curve remain indispensable, but they are insufficient for judging whether a warning can support timely and proportionate phytoprotection under field variability. This Mini Review argues that intelligent plant health warning systems should be evaluated not only as prediction models, but also as safety-relevant decision-support systems embedded in biological, agronomic, and operational contexts. We first relate AI-based detection to established plant disease forecasting and decision-support traditions, including weather-based models, epidemiological forecasting, and integrated disease management. We then adapt selected safety-assurance concepts, including risk assessment, failure mode and effects analysis, Bow-tie reasoning, warning-threshold governance, reliability analysis, resilience thinking, and response closure, to host-pathogen-environment warning chains. The proposed framework links AI or sensor outputs with pathogen biology, host susceptibility, environmental conduciveness, inoculum pressure, uncertainty assessment, risk classification, threshold decisions, human or automated verification, intervention, and feedback learning. Illustrative crop-pathogen scenarios, including wheat rust, rice blast, potato late blight, grapevine downy mildew, and citrus greening, show how safety assurance can complement existing forecasting and decision-support systems rather than replace them. The framework remains conceptual, and whether these added assurance functions improve existing warning systems requires comparative evaluation under field conditions. Future systems should be evaluated through detection performance and response-oriented indicators such as lead time, calibration, false-alert burden, missed-warning rate, response completion, disease suppression, economic value, and learning after field action.

Why it matches plant phenotyping methods植物病害・ストレスの検出を含む知的警戒システムについて、AI・リモートセンシング・デジタルフェノタイピング・センサーネットワークの評価枠組みを体系的に論じる方法論レビューであり、方法論が中心です。

abstractPlant disease and plant stress early warning systems have advanced through deep learning, remote sensing, digital phenotyping, disease forecasting, and sensor networks.

Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases ↗

Plant diseases destroy 20–40% of global food production annually, posing a critical threat to food security for a projected population of 9.7 billion by 2050. Conventional diagnostic approaches relying on expert visual assessment are slow, costly, and unsuitable for modern agricultural scales. While deep convolutional neural networks demonstrated early promise, single-modality, image-centric systems consistently fail under real-world field conditions characterized by variable lighting, co-occurring infections, and cultivar diversity. This review synthesizes a decade of progress across four interconnected frontiers: the evolution of deep learning architectures for plant disease detection; the adaptation of foundation models including CLIP, SAM, and DINOv2 to agricultural contexts; the development of multimodal fusion frameworks integrating imagery, environmental, genomic, and hyperspectral data; and the transition from static disease diagnosis to descriptive comparison of reported metrics, which suggested that multimodal approaches frequently reported improved diagnostic performance relative to corresponding single-modality baselines, although direct cross-study comparison was limited by methodological heterogeneity. A systematic review following PRISMA guidelines identifies eligible comparative studies. Descriptive comparison of reported performance metrics across these studies indicated that multimodal approaches generally achieved higher accuracy and sensitivity than single-modality models, particularly for pre-symptomatic disease detection. Eight critical research gaps are identified, including the absence of a unified agricultural foundation model and limited climate-aware forecasting under non-stationary climate projections. A structured research agenda is proposed to accelerate translation from laboratory performance to globally equitable, field-deployable crop protection systems.

Why it matches plant phenotyping methods植物病害の画像ベース検出・予測手法を対象とする系統的レビューであり、植物の病徴・病害状態を観測から推定するフェノタイピング手法のレビューとして中心的です。

titleMultimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases

Towards Intelligent Disease Phenotyping in Peach: A Deep Feature Extraction Framework for Leaf Disease Detection Under Real Field Conditions ↗

Abstract In modern agriculture, it is essential to identify the early symptoms of plant diseases and to accurately maintain the productivity of the crop and reduce economic losses. Foliar diseases are a special concern in peach because they can cause yield as well as quality if not timely detected. Artificial intelligence, machine learning and deep learning are some of the advanced technologies that are gaining great importance in today's agriculture, especially with the image analysis applications. In this study, a deep learning method for automated detection and classification of three peach leaf diseases and healthy class was presented based on image data. The dataset were taken at different phenological and disease stages under temperate conditions in Kashmir with four classes Healthy, Leaf Curl, Shot hole and Rust. Three convolutional neural networks (CNNs) architectures were applied, VGG-16, ResNet 50 and Xception were trained using transfer learning and Inception-V4 was trained from scratch for a comparative study of the learning strategies. Data augmentation techniques were applied to improve generalization. Results show that all models were able to learn disease specific features well. The result of Inception-V4 was found to be highest with 97.50%, followed by ResNet-50 with 94.49%, VGG-16 with 92.04% and Xception with 75.95%. The results of transfer learning-based architectures were also good and competitive but the best results obtained from the Inception-V4 architecture reveal its capability in modelling complex visual patterns. The results highlight the potential of deep learning techniques for early detection of diseases in peach, supporting precision agriculture and better disease management.

Why it matches plant phenotyping methodsモモ葉の画像から病害症状を自動検出・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いるため。

abstracta deep learning method for automated detection and classification of three peach leaf diseases and healthy class was presented based on image data.

Automated Drought-Stress Assessment in Lettuce: A Detection-Guided Segmentation Approach for Multi-Plant RGB Imagery ↗

Accurate and non-destructive assessment of drought stress is important for improving lettuce production and supporting timely crop management. This study presents a detection-guided deep learning framework for plant-level drought-stress assessment in hydroponically grown lettuce using bird’s-eye-view RGB images. The study further investigates whether canopy segmentation can improve classification performance by reducing irrelevant background information. The framework was evaluated using 2190 images collected across three independent cultivation cycles in which drought stress was induced by isolating the plant root zones from the nutrient solution. In the first stage, YOLO-based object detection was used to localize individual plants, with YOLO26m achieving the highest detection performance of 99.4% mAP@0.5. The detected regions were subsequently used as spatial prompts for zero-shot canopy segmentation using the Segment Anything Model (SAM), with SAM ViT-B achieving a mean IoU of 0.9864. Six convolutional, transformer-based, and hybrid classification architectures were then evaluated independently using YOLO-cropped and SAM-segmented plant images. Segmented inputs consistently improved classification performance, with MaxViT-S achieving the highest binary test accuracy of 96.3%. The framework further distinguished time-defined pre-stress, early-stress, and late-stress periods with an accuracy of 92.4%. Plant-level generalization was further assessed using six-fold leave-one-plant-out cross-validation, resulting in a mean test accuracy of 90.25 ± 1.78% on unseen plants. These findings demonstrate that RGB-based plant-level analysis can support non-destructive drought-stress assessment and that canopy segmentation improves classification by reducing background influence.

Why it matches plant phenotyping methodsRGB画像からレタス個体の乾燥ストレス状態を推定する検出・セグメンテーション・分類フレームワークを開発し、複数サイクル、未見個体、性能指標で検証しており、表現型取得手法が中心である。

abstractThis study presents a detection-guided deep learning framework for plant-level drought-stress assessment in hydroponically grown lettuce using bird’s-eye-view RGB images.

YOLO-Based Deep Learning for Citrus Fruit Detection, Counting, and Yield Estimation in Complex Orchard Environments: A Systematic Review ↗

Abstract A systematic review based on 174 Scopus-records of studies using YOLO-type one-stage detectors for detecting citrus fruits, their count, and yield estimation followed PRISMA guidelines 2020. The title/abstract-screening process, done in duplicate (κ=0.920) yielded 90 included study-records, followed by two further post-hoc exclusions. Each study in the 80 reporting on fruit-level detection showed an average precision of 89.6%, recall of 85.9%, and mAP@0.5 of 91.0%. However, coverage for any individual metric rarely exceeded half of the studies, and only 13% were able to report the more stringent mAP@0.5:0.95. Both YOLOv8 and YOLOv5 were each utilized as the backbone architecture by approximately 22.2% of the studies. From 2025, YOLOv11 has also been emerging. Half of all studies modified architectural components including attention modules, lightweight architectures, and variants of IoU loss functions. Original contributions are generally concentrated in downstream tracking, sensor fusion, and yield modeling rather than the detector itself. A custom-made seven-domain risk of bias tool was developed and utilized by two reviewers who arbitrated discrepancies (91.5%). Results showed that all but one of the reviewed studies had a high level of risk due to almost universal lack of statistical validation and limited dataset diversity; a sensitivity analysis excluding the most risky studies left the performance profiles nearly identical. We conclude that the field has converged around a common technical toolkit but continues to lack standardized benchmarks, multispectral data, and rigorous field-deployment validation.

Why it matches plant phenotyping methods柑橘果実の検出・計数・収量推定に用いる画像解析手法を体系的にレビューし、性能評価、リスク・オブ・バイアス、標準化やベンチマーク不足を検討しており、植物フェノタイピング手法が中心である。

abstractA systematic review based on 174 Scopus-records of studies using YOLO-type one-stage detectors for detecting citrus fruits, their count, and yield estimation followed PRISMA guidelines 2020.

A triple fluorescent marker for live imaging of plant cell morphogenesis ↗

Live imaging of plant subcellular structures is key to deciphering the spatiotemporal bases of cellular processes, and their functional impact on growth and morphogenesis at various biological scales. Live imaging of plant cells essentially relies on expression of fluorescent markers labeling cells or subcellular structures of interest. Simultaneous multi-channel imaging of several markers is still not routine practice in plant cell biology, owing to issues linked to genetic or spectral compatibility of markers, differences in expression levels, silencing, toxicity, etc. Here we designed a three-color marker in Arabidopsis thaliana and Capsella rubella , enabling high-resolution live imaging of plant morphogenesis, including labeling of the cell membrane, the nucleus and the microtubule cytoskeleton. Detection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells. The three- color marker allows visualization of the three-dimensional organization and dynamics of plant microtubules within the intracellular space with unprecedented precision, in various organs including the root and shoot meristems, the leaf, anther, and gynoecium. Our results demonstrate the potential of such single-construct strategy for cell biology studies in plants.

Why it matches plant phenotyping methods植物細胞の形態形成を可視化する三色ライブイメージング法と、植物細胞用に最適化した微小管マーカーの開発が研究の中心である。

abstractDetection of MT arrays involved the development of a MAP4-MBD-based microtubule marker optimized for plant cells.

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

To address the challenges posed by large lesion-scale variations, weak boundary cues, and high inter-class similarity in rice leaf disease detection, this study proposes an improved RT-DETRv2-R50-based rice disease detection model, termed CED-RTDETR. First, a Lightweight Contour-Guided Aggregation Backbone (LCGA-Backbone) is constructed. In the PResNet residual blocks, an InceptionDWConv2d-based direction-aware depthwise separable spatial mixing strategy is introduced to capture local, horizontal, and vertical lesion texture patterns with low computational overhead. Meanwhile, a Contour-guided Efficient Global Aggregation Block (CEGA Block) is embedded after the outputs of the C3, C4, and C5 stages. Through contour-difference enhancement, channel shuffle, group-wise efficient global aggregation, and bottleneck channel mixing, the proposed block strengthens the representation of the boundaries of small lesions, weak textures, and contextual semantics. Second, a Multi-scale Evidence Interaction Fusion Neck (MEIF-Neck) is used to perform multi-scale evidence interaction and salient-region competition after cross-scale feature concatenation. A lightweight feature reconstruction process is further implemented using Spatial-Evidence RepNCSPELAN (SE-RepNCSPELAN), which is developed from a YOLO-style feature fusion structure. Finally, a Direction-Amplitude Decoupled Deformable Attention (DAD-DA) mechanism is introduced to decompose sampling offsets into direction rotation residuals and radius gains while incorporating an aspect-ratio-aware geometric compression-restoration strategy, thereby improving the geometric stability of sampling locations during the decoding stage. Experimental results on the constructed rice leaf disease dataset show that CED-RTDETR achieves AP, AP50, and AP75 values of 29.5%, 75.2%, and 17.6%, respectively, outperforming RT-DETRv2-R50 by 4.1, 4.9, and 3.8 percentage points. To further evaluate the model on an additional public benchmark dataset, experiments were also conducted on the public Rice Disease Dataset. On this dataset, CED-RTDETR achieves AP, AP50, and AP75 values of 34.1%, 77.1%, and 23.2%, respectively, improving upon RT-DETRv2-R50 by 4.3, 5.6, and 3.4 percentage points. These results indicate that the proposed method achieves consistent overall performance improvements on both the constructed dataset and the public benchmark dataset.

Why it matches plant phenotyping methodsイネ葉の病斑・病害を画像から検出する深層学習モデルを開発し、構築データセットと公開ベンチマークで性能検証しているため、植物病害状態のフェノタイピング手法が中心です。

abstractTo address the challenges posed by large lesion-scale variations, weak boundary cues, and high inter-class similarity in rice leaf disease detection, this study proposes an improved RT-DETRv2-R50-based rice disease detection model, termed CED-RTDETR.

Deep Learning for Plant Disease Detection: A Systematic Review ↗

Plant diseases remain a threat to global agricultural productivity, food security and livelihoods, especially in developing countries where the availability of experts in agriculture is still limited. The recent progress in AI, particularly deep learning and computer vision, has ushered in new possibilities for automated plant disease diagnosis, especially for plant image-based systems. This paper provides a systematic review of the deep learning methods employed for plant disease diagnosis, highlighting CNN-based methods, the application of transfer learning, explainable AI (XAI) methods and deployment issues. In the framework of PRISMA 2020, the relevant peer reviewed literature from 2016 to 2025 was systematically identified, screened and analysed on the most important academic databases. The review compared some of the most popular architectures such as GoogLeNet, DenseNet-121, MobileNetV2, EfficientNet, Attention-CNNs and Vision Transformers. Results showed very high classification accuracy in controlled lab conditions with DenseNet-121 achieving ~99.75% accuracy with good computational efficiency. But it also revealed a big gap between the lab and the field, mainly due to environmental variations, domain shifts, and dependence on datasets. Some innovative and emerging technologies like explainable AI, hyperspectral imaging, few-shot learning, and lightweight mobile architectures showed promise of enhancing the interpretability, early detection of disease, and the use of smart phones in low-resource agricultural settings. In conclusion, the study suggests that in order to be implementable in the field, future intelligent agricultural diagnosis systems must be able to balance predictive accuracy, explainability, computational efficiency and field adaptability. The results enrich the existing knowledge on precision agriculture and serve as useful information for researchers, agricultural technologists, and policymakers working on the creation of AI-based systems for crop protection.

Why it matches plant phenotyping methods植物病害を画像から診断する深層学習手法を体系的に比較・レビューしており、植物の病徴・病害状態の推定方法が中心である。

abstractThis paper provides a systematic review of the deep learning methods employed for plant disease diagnosis

A Hex-View Perspective on Plant Disease Detection Using Remote Sensing ↗

Plant diseases lead to substantial yield losses and pose a persistent threat to global food security, creating an urgent demand for high-throughput, accurate, scalable, and non-destructive disease-monitoring approaches. Remote sensing has emerged as a powerful tool, yet progress in plant disease detection remains fragmented across various disciplines, tasks, sensing methods, and data modalities. This review introduces a hex-view perspective to synthesise remote-sensing-based plant disease detection within a cohesive conceptual framework. Instead of treating sensing technologies, algorithms, and datasets independently, the hex-view incorporates six interconnected dimensions that jointly capture how biological processes, the measurement scale, and data characteristics constrain disease detectability, including when detection is possible and how reliably it can be achieved. The hex-view framework comprises six interconnected dimensions and forms an integrated framework called BTSCAD: (1) Biology (B): plant–pathogen interactions constituting the biological foundation of disease development and expression. (2) Task (T): the diverse disease-detection tasks and their corresponding research objectives. (3) Sensor (S): the sensing modalities that define the data acquisition type and richness of captured information. (4) Condition (C): the environmental conditions, sensing platforms, and spatial scales that shape disease observations and bridge controlled experiments and real-world deployment across leaf, canopy, plot, and regional scales. (5) Algorithm (A): the classical and state-of-the-art data-analysis algorithms used to extract disease-related information from sensor data. (6) Dataset (D): the data sources that underpin model development, evaluation, and generalisability. The hex-view perspective provides a clear framework for interpreting previous research and identifying future research directions. This review lays a structured foundation for developing robust, interpretable, and transferable disease-detection systems, supporting advancements in precision agriculture, high-throughput phenotyping, and sustainable crop production.

Why it matches plant phenotyping methods植物病害を対象としたリモートセンシングによる病徴・病害状態の検出方法を、センサー、条件、アルゴリズム、データセットの観点から体系化する方法論レビューであり、植物フェノタイピング手法が中心です。

abstractThis review introduces a hex-view perspective to synthesise remote-sensing-based plant disease detection within a cohesive conceptual framework.

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

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

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

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

UAV-Hyp: UAV Dataset for Remote Sensing-Based Phenotyping of Hypericum perforatum ↗

Quantitative flowering phenotypes are needed to support breeding and harvest management in Hypericum perforatum L. (St. John’s wort), but manual flower assessment is slow and difficult to standardize under field conditions. UAV-Hyp is a multi-temporal UAV RGB dataset containing 12,653 high-resolution images acquired at 26 measurement dates across the complete flowering period of 15 H. perforatum accessions. The images represent variable illumination, soil moisture, weed pressure, and developmental stages. The dataset provides 59,163 plant bounding boxes and 107,054 flower bounding boxes. As an application example, cascaded YOLOv8 plant and flower detectors achieved mAP@0.50:0.95 values of 0.977 and 0.950, respectively. UAV-Hyp supports scalable flower quantification and the development of time-series phenotyping methods for genotype comparison and quality-oriented medicinal-plant breeding.

Why it matches plant phenotyping methods植物の開花形質を定量化するUAV画像データセットを提供し、検出性能も評価しているため、フェノタイピング用データセット・解析手法が中心です。

abstractUAV-Hyp is a multi-temporal UAV RGB dataset containing 12,653 high-resolution images acquired at 26 measurement dates across the complete flowering period of 15 H. perforatum accessions.

Computer Vision from Tea Cultivation to Quality Evaluation. ↗

Existing reviews on AI in tea production are either agriculture-generic or limited to isolated tasks. This review thoroughly compares vision technologies (RGB, hyperspectral, near-infrared, thermal, Light Detection and Ranging (LiDAR), Unmanned Aerial Vehicle (UAV)) and establishes a task-oriented algorithm selection framework for the tea industry. For small-sample or near-linear problems, traditional machine learning (ML) (support vector machine (SVM); partial least squares regression (PLSR)) remains effective. For unstructured field tasks, deep learning achieves superior performance: pest detection accuracy exceeds 97%, tea bud detection reaches 96.8% with RGB images, and hyperspectral imaging predicts nitrogen content with R 2 > 0.90 and tea polyphenols with R 2 up to 0.925. Algorithm choice further differentiates by task granularity: lightweight convolutional neural networks (CNNs) balance speed and accuracy for edge deployment at 16 fps; You Only Look Once (YOLO) series detectors enable real-time localization on mobile platforms at 93.1% accuracy, 24 ms per target. No single algorithm dominates all tea tasks; selection is a trade-off among accuracy, speed, data availability, and computational constraints. These findings outline a structured analysis of the challenges and pathways for transitioning computer vision (CV) from laboratory research toward field-deployable tools.

Why it matches plant phenotyping methods茶作物の画像センシング技術と解析アルゴリズムを体系的に比較し、害虫検出や窒素含量予測など植物状態・形質の推定方法を扱う方法論レビューである。

abstractThis review thoroughly compares vision technologies (RGB, hyperspectral, near-infrared, thermal, Light Detection and Ranging (LiDAR), Unmanned Aerial Vehicle (UAV)) and establishes a task-oriented algorithm selection framework for the tea industry.

Detection of Candidate Wuyi Rock-Tea Shoots in Field RGB Images Using YOLO11s-CSNG ↗

Accurate field detection of candidate tea shoots could support plantation monitoring, yield estimation, fresh-leaf assessment, and future selective-harvesting research. Wuyi rock-tea shoots are small and slender, have weak visual boundaries, and are easily confused with branches, petioles, and complex canopy backgrounds. Here, we developed YOLO11s-CSNG for candidate shoot detection in natural plantation scenes. The model combines a channel-spatial feature enhancement bottleneck, a normalized Wasserstein distance constraint for bounding-box regression, and ghost convolution layers in the detection head. We evaluated the model through detector comparisons, module ablations, and repeated training with five matched random seeds on a natural-scene dataset containing four Wuyi rock-tea cultivars. Across the five matched seeds, the mean mAP@0.5 increased from 67.37 ± 0.91% to 67.97 ± 0.90% on the validation set and from 61.29 ± 0.40% to 61.88 ± 0.66% on the internal test set. Neither paired difference was statistically significant: The 95% confidence intervals included zero, and the exact two-sided paired-permutation p values were 0.375 and 0.250, respectively. The mean mAP@0.5:0.95 did not improve. YOLO11s-CSNG retained a model size and model-only edge-inference time comparable to YOLO11s, providing a compact design for candidate shoot-region detection under the sampled field conditions.

Why it matches plant phenotyping methods茶芽という植物器官の画像検出手法を開発し、比較・アブレーション・反復検証を行っているため、植物フェノタイピング手法が中心である。

abstractHere, we developed YOLO11s-CSNG for candidate shoot detection in natural plantation scenes.

SporaScan: cost-effective, high-precision leaf-disc disease severity assessment for grapevine downy mildew. ↗

Background Accurate assessment of disease severity is essential for evaluating fungicide performance and breeding disease-resistant crop varieties. Manual scoring of infection on individual leaf discs is labor-intensive and variable, while traditional computer vision methods require manual parameter tuning and lack robustness. Existing deep learning approaches often struggle to simultaneously localize leaf discs and accurately segment disease symptoms, limiting their practical application. Results We developed SporaScan, an automated pipeline combining YOLO v8n for leaf disc localization, Mobile SAM for background removal, and UNet for sporulation segmentation. It achieved high accuracy (mAP@50 >99%, mIoU@50 >96%), with background removal reducing misclassification (0.21% for sporulation and 2.75% for leaf discs). Severity estimates showed strong agreement with manual annotations (R 2 = 0.99). In a blind test, technicians selected SporaScan as superior in 37.2% of cases, manual annotation in 26.2%, and equal performance in 36.6% (P Conclusion These results demonstrate that SporaScan provides an efficient and practical approach for automated assessment of downy mildew severity, supporting applications in disease evaluation, breeding, and fungicide assessment (http://116.10.197.212:9060/segment/#/). © 2026 Society of Chemical Industry.

Why it matches plant phenotyping methodsブドウ葉ディスク上のべと病症状を画像から自動分割し、病害重症度を推定する手法を開発・検証しており、植物表現型取得が中心である。

abstractWe developed SporaScan, an automated pipeline combining YOLO v8n for leaf disc localization, Mobile SAM for background removal, and UNet for sporulation segmentation.

Efficient Ripeness Monitoring in Open-Facility Environments Using a Quadruped Robot and Panoramic AI Recognition ↗

Efficient facility-scale tomato ripeness monitoring remains difficult in greenhouses where uneven terrain limits conventional wheeled and rail-guided platforms and planar cameras provide restricted coverage. This study developed a wheel-legged quadruped monitoring system integrating LiDAR, a depth camera, and a panoramic camera. An adaptive gait-switching strategy supported navigation across heterogeneous terrain. Panoramic images were projected into six perspective views, and the left and right views were processed using a YOLOv8-based ripeness recognition model. Time-synchronized detections and robot poses were fused to map ripeness observations into three-dimensional greenhouse coordinates. Five field experiments in a commercial tomato facility demonstrated autonomous row traversal, inter-row transition, and avoidance of pedestrians, obstacles, and cultivation boundaries. The recognition pipeline continuously identified multiple ripeness stages under variable illumination, foliage occlusion, and robot motion, while the spatial fusion procedure produced a facility-scale three-dimensional ripeness distribution. The integration of terrain-adaptive quadruped mobility, panoramic perception, and spatial mapping provides a practical framework for continuous ripeness monitoring and can support targeted harvesting, yield forecasting, and crop management.

Why it matches plant phenotyping methodsトマト果実の成熟度を画像認識で取得するロボット型フェノタイピングシステムを開発し、実環境で評価しており、表現型取得法が研究の中心である。

abstractThis study developed a wheel-legged quadruped monitoring system integrating LiDAR, a depth camera, and a panoramic camera.

A Method for Measuring Plant Spacing of Maize Seedlings Based on Improved YOLOv8 ↗

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

Deep Learning Techniques for Crop Health Monitoring and Disease Detection ↗

This paper explores how deep learning methods can be used to monitor the health of crops and identify diseases, particularly for the apple crop. As the need for food security and sustainable farming methods increases, there is a strong demand for early detection of crop diseases. We have used a Convolutional Neural Network (CNN), based on the model of VGG16 architecture, since the model is known to be effective in image classification. The dataset contained 7771 training images for to enhance machines deep learning regarding plant diseases. Following that, validation image collection of 1747 images divided into four health conditions of the apple crops. In addition, the model was evaluated using 196 new images as a final test. To enhance the model capacity for recognizing diseases of real leaves rather than just memorize exact training pictures, data augmentation was used with ImageDataGenerator of TensorFlow. This means the training images were zoomed, rotated, and shifted to enable the machine detects more variations. Ten epochs of training were performed to measure the model accuracy. The results indicated that the model obtained significant improvement in training and validation accuracy from 56.43% to 78.12% and 92.94 to 96.93%, respectively. Most impressively, the final test dataset, which contained completely new images, scored an accuracy rate of 98%. The results indicate that in the architecture field the application of deep learning methodologies is effective, suggesting that automated detection of diseases by using sophisticated image analysis manages crop diseases identification efficiently. Combination of these methods successfully creates avenues for novel research to built real-time systems of crop disease monitoring, which help farmers increase their productions and farm their lands sustainably.

Why it matches plant phenotyping methodsリンゴ葉の画像から病害・健全状態をCNNで推定する画像ベース植物フェノタイピング手法であり、学習・検証・未知画像で性能評価を行っているため含める。

abstractWe have used a Convolutional Neural Network (CNN), based on the model of VGG16 architecture, since the model is known to be effective in image classification.

Comparison of UAV Image-Based Detection Accuracy of Pine Wilt Disease-Affected Trees Using Different Dataset Compositions and Attention Modules ↗

This study evaluated the effects of input-channel composition and the incorporation of attention modules on the detection accuracy of visually suspected pine wilt disease (PWD)-affected trees using unmanned aerial vehicle (UAV) RGB orthomosaic imagery and the YOLO26-Large (YOLO26-L) deep learning model.Four datasets were constructed: Dataset A, consisting of three RGB channels; Dataset B, combining RGB with elevation and aspect (5 channels); Dataset C, combining RGB with three graylevel co-occurrence matrix (GLCM) texture features, namely Angular Second Mement (ASM), Entropy, and Homogeneity (6 channels); and Dataset D, combining RGB with the GLCM texture features and topographic information, namely elevation and aspect (8 channels).The Squeeze-and-Excitation (SE) block and Convolutional Block Attention Module (CBAM) were independently integrated into the YOLO26-L baseline model, and the detection performance of the 12 combinations was analyzed.In the accuracy assessment, the YOLO26-L baseline model trained with Dataset B achieved the highest mean Average Precision (mAP)@50 of 0.63 among the 12 combinations.However, the differences among the 12 combinations were marginal and did not indicate the superiority of a specific combination.Dataset C and Dataset D, which incorporated GLCM texture features, achieved accuracy levels similar to those of Dataset A, which used RGB alone, indicating that GLCM texture features did not substantially improve the detection of PWD-affected trees in 5-cm-resolution imagery.The application of attention modules also did not lead to a consistent improvement in accuracy, and the detection rates of all 12 combinations remained around 60% for objects smaller than 25 m².These results suggest that simply adding texture features or attention modules did not reliably improve detection accuracy.This study extends RGB-based UAV detection of PWD-affected trees by progressively integrating elevation, aspect, and GLCM texture features into four datasets and evaluating their interactions with SE and CBAM attention modules across a 12-combination experimental matrix.The results can inform the selection of input channels and model architectures in future forest disease detection studies.

Why it matches plant phenotyping methodsUAV画像と深層学習モデルを用いてマツ材線虫病罹病木の検出精度、入力特徴、注意機構を比較評価しており、植物の病害状態を推定する方法の技術的検証が中心である。

abstractThis study evaluated the effects of input-channel composition and the incorporation of attention modules on the detection accuracy of visually suspected pine wilt disease (PWD)-affected trees using unmanned aerial vehicle (UAV) RGB orthomosaic imagery and the YOLO26-Large (YOLO26-L) deep learning model.

Precision Greenhouse Rose Phenotyping from UAV Imagery Using a Multi-Source Dataset and Lightweight BloomRoseNet ↗

Accurate detection of blooming roses and flower buds is essential for greenhouse phenotyping, cultivation scheduling, harvest planning, and yield management. However, UAV-derived greenhouse imagery presents major challenges because rose targets are often small, densely distributed, partially occluded, and visually similar to complex backgrounds. This study proposes a lightweight rose detection framework that combines multi-source dataset construction with an improved YOLOv12n-based detector, termed BloomRoseNet. A GreenHouse Rose dataset was constructed by integrating self-collected UAV overhead images, screened RoseTracker images, and supplementary multi-view rose images to increase diversity in scale, growth stage, viewpoint, and background complexity. BloomRoseNet introduces task-oriented improvements for fine-grained feature extraction, adaptive feature fusion, and attention-enhanced detection. The supplementary multi-view data improved precision, recall, and mAP@50 from 85.2%, 82.8%, and 89.2% to 86.1%, 85.3%, and 90.5%, respectively. Compared with the baseline YOLOv12n, BloomRoseNet increased precision, recall, mAP@50, and mAP@50:95 by 3.2, 3.3, 3.6, and 1.6 percentage points, respectively, while reducing parameters from 2.55 M to 2.08 M and model size from 5.5 MB to 4.5 MB. The model also maintained real-time inference capability and stronger robustness under blur, occlusion, and illumination disturbances. The proposed framework provides an effective and practical solution for UAV-based greenhouse rose monitoring and supports precision cultivation management.

Why it matches plant phenotyping methodsバラの開花・蕾を対象としたUAV画像フェノタイピング手法を開発し、データセット構築、検出モデル改良、性能評価を中心に扱っているため。

abstractThis study proposes a lightweight rose detection framework that combines multi-source dataset construction with an improved YOLOv12n-based detector, termed BloomRoseNet.

Advances in Binocular Stereo Vision-Driven 3D Perception and Intelligent Analysis Methods for Agriculture ↗

Binocular stereo vision is a low-cost and scalable 3D perception technology that shows strong potential in agricultural phenotyping and smart agriculture. By estimating depth from multi-view RGB images, it enables non-contact, high-precision sensing of crop structure, canopy morphology, growth dynamics, and livestock traits, providing essential support for digital and intelligent agricultural production. With recent advances in deep learning-based stereo matching, multimodal sensor fusion, and 3D reconstruction, its robustness and accuracy in complex field environments have been significantly improved. This paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques, and emerging supervision strategies such as 3D Gaussian splatting. It further summarizes key applications, including high-throughput phenotyping, fruit localization and robotic harvesting, weed detection and precision spraying, autonomous navigation, and livestock body condition assessment, highlighting its role in multi-task agricultural perception systems. Finally, the paper discusses major challenges, including low-texture matching difficulty, occlusions in complex environments, cross-domain generalization, real-time lightweight deployment, and limited dataset availability. Future directions are outlined in foundation model-based visual perception, self- and weakly supervised learning, multimodal fusion, and edge-efficient model design, aiming to support large-scale deployment in smart agriculture.

Why it matches plant phenotyping methods農業における双眼ステレオビジョンのシステム、ステレオマッチング、3D再構成を体系的にレビューし、作物構造・群落形態・生育動態の非接触計測とハイスループット表現型解析を主要対象としているため。

abstractThis paper systematically reviews recent progress in agricultural applications of binocular stereo vision, covering system architectures, traditional and deep learning-based stereo matching methods, point cloud reconstruction techniques

A Comparative Study of Convolutional Neural Network based Transfer Learning Models for Plant Disease Detection ↗

Background: Plant diseases significantly threaten global food security, reducing potential harvests and sometimes causing total crop failure. Traditional detection methods, which rely on manual inspection and laboratory testing, are time-consuming, costly and prone to human error. Methods: To address these challenges, this study applies transfer learning techniques using deep convolutional neural networks for accurate and efficient plant disease detection. A comparative analysis of nine pretrained models, VGG16, VGG19, ResNet50, ResNet101V2, MobileNetV2, InceptionV3, DenseNet121, InceptionResNetV2 and Xception was conducted on the PlantVillage dataset, focusing on apple, potato and peach leaf images. Result: Results show that DenseNet121 and ResNet101V2 achieved the highest accuracy, particularly for potato leaves with 98.5%, while MobileNetV2 also performed well with up to 99% accuracy for apple and peach leaves. The study demonstrates that transfer learning effectively enhances plant disease classification, enabling faster, more reliable and resource efficient detection for precision agriculture.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習手法を比較評価しており、病害表現型の取得・分類が研究の中心である。

abstractthis study applies transfer learning techniques using deep convolutional neural networks for accurate and efficient plant disease detection.

Dual polarity-viscosity responsive fluorescent probes for lipid droplet imaging and smartphone-based on-site crop oil screening ↗

Herein, we rationally designed and synthesized two types of D-π-A red-emitting fluorescent probes with intrinsic intramolecular charge transfer (ICT) and twisted intramolecular charge transfer (TICT) characteristics, for traditional methods for lipid droplet detection in food crops suffer from reliance on bulky instruments, tedious sample pretreatment and the impossibility of on-site quantification. To modulate the molecular skeleton, methoxy groups were introduced, and highly specific recognition of lipid droplet microenvironments was realized through the probes' synergistic spectral response to polarity and viscosity. Typical turn-on fluorescence responses and favorable lipophilicity were displayed by both probes, thus enabling accurate localization of the hydrophobic domains of lipid droplets. Possessing ratiometric fluorescence performance, the modified probe OCH 3 -Phe-FCN efficiently alleviated signal interference from complex matrices, and toward oleic acid was a lower limit of detection presented. By combining smartphone-based RGB colorimetry and image grayscale analysis was a portable detection platform constructed, with fluorescence signals employed to accomplish visual analysis of lipid droplets in food crops. Satisfactory biocompatibility and targeting capability were validated for the as-prepared probes via cell imaging and cytotoxicity evaluations, and far superior imaging quality and signal-to-noise ratio were demonstrated by the modified probe. With the aid of density functional theory (DFT) calculations, systematically elucidated was the spectral mechanism of excited-state transitions synergistically modulated by the two microenvironmental factors. Not only does this work deepen the insight into the response mechanism of dual-responsive fluorescent probes, but also a promising technical strategy is offered for in situ spectral detection of lipid droplets in food crops.

Why it matches plant phenotyping methods食品作物の脂質滴を蛍光プローブとスマートフォン画像解析で可視化・定量する検出法および携帯型プラットフォームの開発が中心であり、植物の細胞状態を測定する技術的貢献が明確です。

abstractBy combining smartphone-based RGB colorimetry and image grayscale analysis was a portable detection platform constructed, with fluorescence signals employed to accomplish visual analysis of lipid droplets in food crops.

MIRA: an open source and user-friendly software to automate counting and sizing of fungal spores ↗

Background The quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standardization and being used as a proxy for disease severity. Historically, spore quantification has relied on manual hemocytometry, which remains the most precise counting process to date, where chambers such as the Malassez slide are used to count a subsample of the inoculum. However, this method applied manually is highly labor-intensive, time-consuming, and can be prone to operator-dependent variability. To overcome these limitations, we introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms. Featuring a user-friendly graphical interface, MIRA is adaptable to multiple camera systems and supports advanced object detection models, including YOLOv11 and YOLOv26. Results We demonstrate that MIRA can be used to accurately detect and count spores from several phytopathogenic fungi, automatically measure spore surface area, and to differentiate spores across different genera. In an exhaustive comparative analysis using Pyricularia oryzae spores as an example, MIRA was benchmarked against manual gold-standard counting slides (Malassez and Kova) and indirect spectrophotometric methods (SPARK). The P. oryzae model loaded via MIRA achieved a strong correlation (R = 0.96) with manual gold standards while reducing processing time by over 90% for high-concentration samples (10⁶ spores/mL). Beyond this benchmark, we also successfully tested specific YOLO models designed to recognize macro- and microconidia of Fusarium oxysporum f. sp. cubense , a model for Pseudocercospora fijiensis , and a single multiclass model capable of identifying six different rice pathogenic fungi. We provide comprehensive tutorials for operating the software and training custom detection models for free using Roboflow and Google Colab. MIRA is available both as open-source Python code and as standalone executables for Windows and Linux. Conclusions MIRA provides a rapid, accurate, and highly reproducible alternative to manual spore counting, effectively removing a major bottleneck in phytopathology workflows. By combining advanced YOLO-based deep learning with an accessible interface and comprehensive training resources, MIRA makes accessible automated image analysis for researchers without programming expertise. Moreover, MIRA drastically improves the efficiency of high-throughput disease phenotyping and can be adapted for a wide range of microscopic quantification tasks across various biological disciplines.

Why it matches plant phenotyping methods植物病害に関わる胞子の画像検出・計数・サイズ測定ソフトウェアを開発し、手動計数法とのベンチマーク検証も行っている。病害フェノタイピングのための画像解析手法が中心である。

abstractwe introduce MIRA (Microscopy Image Recognition & Analysis), a novel open-source software integrating You Only Look Once (YOLO) deep learning algorithms.

ALSDet: a global context-enhanced network for detecting small-target diseases on apple leaves. ↗

Accurate detection of small-target diseases on apple leaves is of great importance for optimizing orchard management and facilitating precision agriculture. Small-target disease detection remains challenging due to insufficient features and complex backgrounds. This work proposes an effective detector for apple leaf small-target diseases called ALSDet. The global context module is integrated into Stage2 to Stage4 of the ResNet-50 backbone, yielding a refinement of the feature-extraction architecture. In the bottleneck blocks of the Stage2 and Stage3, dilated convolution is used in place of the normal 3×3 convolution to enlarge the receptive field for small targets and strengthen feature extraction. During the model training phase, a multi-scale training strategy combined with the online hard example mining method is adopted to focus on learning hard samples and enhance adaptability for various scale targets. According to the experimental results, ALSDet obtains a mean average precision (mAP) of 65.6% and an average recall (AR) of 71.2% on the dataset. The proposed model achieves the highest levels in both mAP and AR when compared to the popular object detection models, such as Cascade R-CNN, Faster R-CNN, GFL, Grid R-CNN, Libra R-CNN, FCOS, VFNet, RetinaNet, SSD, YOLOv7, and YOLOv8. For small-target diseases like rust and frog eye leaf spot, the average precision surpasses 87% with an intersection over union (IoU) threshold of 0.5. These results confirm that ALSDet achieves stable performance against existing methods, demonstrating its potential as a practical tool for intelligent orchard disease management.

Why it matches plant phenotyping methodsリンゴ葉の病斑を画像から検出する深層学習モデルを開発・比較評価しており、植物の病害状態の取得・推定が研究の中心である。

abstractThis work proposes an effective detector for apple leaf small-target diseases called ALSDet.

ShuffleNetV2-hSimKD: A Lightweight Network for Plant Disease Detection ↗

Early and accurate plant disease detection is essential for reducing crop losses and supporting sustainable agricultural management. Although deep learning-based approaches have achieved strong performance in plant disease analysis, many existing models require substantial computational resources, which limits their use in resource-constrained agricultural environments. This study proposes ShuffleNetV2-hSimKD, a lightweight integration framework for plant disease detection. It adopts ShuffleNetV2 as the backbone and incorporates the parameter-free SimAM attention mechanism to enhance disease-related feature representation without introducing additional learnable parameters. In addition, the standard ReLU activation function is replaced with h-swish to improve nonlinear feature extraction and preserve informative feature responses. A hybrid knowledge distillation strategy is further employed to transfer both output-level and feature-level knowledge from a high-capacity teacher model to the lightweight student network during training. Unlike previous studies that apply these techniques in isolation, ShuffleNetV2-hSimKD synergistically integrates parameter-free SimAM, h-swish optimization, and hybrid KD to overcome the representation limitations of lightweight backbones in subtle disease symptom detection. The proposed framework was evaluated on a balanced subset of the PlantVillage dataset, in which leaf images were categorized as healthy or diseased. ShuffleNetV2-hSimKD achieved an accuracy of 90.41% with only 1.4M parameters and 151M FLOPs. Compared with representative lightweight Convolutional Neural Networks (CNNs), the proposed model achieved improved accuracy and recall while maintaining low computational complexity. These results demonstrate that ShuffleNetV2-hSimKD provides an effective balance between detection performance and computational efficiency, highlighting its potential as a lightweight candidate for plant disease detection in resource-constrained agricultural scenarios.

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軽量深層学習モデルを開発し、精度と計算効率を評価しており、病害表現型の取得・推定法が中心である。

abstractThis study proposes ShuffleNetV2-hSimKD, a lightweight integration framework for plant disease detection.

Early identification of glufosinate-resistant soybeans using plant image analysis ↗

The use of glufosinate-resistant GM soybean has expanded, raising concerns about resistant weed development and unintended transgene flow. To support monitoring for timely management, we propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment. We evaluated the potential of spectral imaging, using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors, for early detection of glufosinate resistance in soybean. In the dose-response test, the key spectral indices including NDI, temperature difference, F v /F m , and NPQ distinguished between resistant and susceptible soybeans within 4 to 24 hours after treatment (HAT). IR thermal and CF imaging showed higher sensitivity in identifying resistance than RGB imaging by detecting spectral responses associated with physiological changes before visual symptoms appeared. Validation test with a single dose treatment of glufosinate reconfirmed that image analysis by both the naked eye and machine learning (ML) can discriminate between resistant and susceptible soybeans in a single day after glufosinate treatment. ML-based classification using IR thermal index achieved 100% accuracy as early as 6 HAT and the classification by the naked eye using IR thermal images showed 96.6% accuracy at 24 HAT. These results suggest that plant imaging enables early and non-destructive identification of herbicide-resistant individuals by detecting early spectral changes to herbicide treatment. These findings support its use as a potential alternative to conventional diagnostic methods for detecting individuals containing transgenes in herbicide-resistant GM soybean cultivation for future applications in herbicide-resistant weed monitoring.

Why it matches plant phenotyping methodsスペクトル画像（RGB、熱赤外、クロロフィル蛍光）と機械学習を用いて、薬剤処理後の植物の生理応答から耐性を早期識別する方法を開発・検証しており、植物表現型の取得が中心である。

abstractwe propose an early, non-destructive identification method using spectral images acquired from whole soybean plants after glufosinate treatment.

AI-enabled simultaneous phenotyping of leaf vein and stomatal traits uncovers independent genetic control in maize ↗

Abstract Background Leaves maintain hydraulic homeostasis during photosynthesis through the coordinated action of stomata, which regulate gas exchange and transpiration, and veins, which supply water to the leaf lamina. While functional links between stomatal and vascular traits are known in dicots, their potential genetic coordination in C4 crops remains poorly understood. We investigated the genetic architecture of these traits in maize using a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis Results We phenotyped 285 recombinant inbred lines and the MAGIC founder lines, generating 8,072 images from 2,026 leaf samples taken from seedlings grown in controlled conditions. A YOLOv8-based model automatically detected stomata, while a custom and efficient image-processing pipeline quantified vein traits and stomatal spatial distribution patterns along cell bundles. This enabled simultaneous characterization of stomatal density, size, and distribution together with vein density, thickness, and bundle-associated spatial patterning. Substantial phenotypic variation was observed among genotypes, with strong correlations between abaxial and adaxial traits but no significant correlations between stomatal and vein traits. QTL mapping identified 37 genomic regions associated with stomatal and vein traits, including loci containing known developmental regulators such as stomatal density and distribution1 and stomagen1 , as well as novel loci controlling stomatal spatial patterns, divergence between leaf surfaces and veins traits. Conclusions These results support independent genetic control of stomata and veins and decoupled contribution to water-use efficiency, providing a novel genetic framework to independently optimize leaf hydraulic capacity and gas exchange in target environments.

Why it matches plant phenotyping methods葉の気孔・葉脈形質を自動画像解析で同時定量する高スループット表現型解析プラットフォームが研究の中心であり、形質抽出手法も具体的に記述されている。

abstractusing a Multi-parent Advanced Generation Inter-Cross (MAGIC) population and a low-cost, high-throughput phenotyping platform integrating leaf clearing, digital microscopy, artificial intelligence, and image analysis

An intelligent edge AI framework for real-time plant disease detection using deep learning under complex field environments ↗

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.

Deep Learning Techniques for Crop Health Monitoring and Disease Detection ↗

This paper explores how deep learning methods can be used to monitor the health of crops and identify diseases, particularly for the apple crop. As the need for food security and sustainable farming methods increases, there is a strong demand for early detection of crop diseases. We have used a Convolutional Neural Network (CNN), based on the model of VGG16 architecture, since the model is known to be effective in image classification. The dataset contained 7771 training images for to enhance machines deep learning regarding plant diseases. Following that, validation image collection of 1747 images divided into four health conditions of the apple crops. In addition, the model was evaluated using 196 new images as a final test. To enhance the model capacity for recognizing diseases of real leaves rather than just memorize exact training pictures, data augmentation was used with ImageDataGenerator of TensorFlow. This means the training images were zoomed, rotated, and shifted to enable the machine detects more variations. Ten epochs of training were performed to measure the model accuracy. The results indicated that the model obtained significant improvement in training and validation accuracy from 56.43% to 78.12% and 92.94 to 96.93%, respectively. Most impressively, the final test dataset, which contained completely new images, scored an accuracy rate of 98%. The results indicate that in the architecture field the application of deep learning methodologies is effective, suggesting that automated detection of diseases by using sophisticated image analysis manages crop diseases identification efficiently. Combination of these methods successfully creates avenues for novel research to built real-time systems of crop disease monitoring, which help farmers increase their productions and farm their lands sustainably.

Why it matches plant phenotyping methodsリンゴ葉画像から健康状態・病害を推定するCNN画像解析手法が研究の中心であり、学習・検証・新規画像での評価も実施しているため、植物病害表現型の手法研究として含める。

abstractThis paper explores how deep learning methods can be used to monitor the health of crops and identify diseases, particularly for the apple crop.

Nanosensors and Geospatial Technologies for Early Crop-stress Detection in Precision Agriculture: A Critical Multiscale Synthesis ↗

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.

A data-driven approach to automate embolism detection in leaves ↗

1 Summary Embolism, the formation of air bubbles in the plant water transport system, is a mechanistic driver of plant death. The Optical Vulnerability Technique (OVT) is an imaging method for non-invasive quantification of embolism (including P50, a common metric for drought vulnerability), which can also provide detailed spatial and temporal information. Its major cost lies in the post-processing of thousands of images. Here we designed, tested, trained, and make publicly available a neural network model to automate post-processing of OVT images. Using a dataset of 65 leaves from Senecio pterophorous , we compared our model predictions to results obtained via traditional post-processing by an expert. Our model resolved P50 to within 0.027 MPa of the expert-processed data with training taking 30 minutes to 2.5 hours and model-runtime in the order of seconds to minutes, demonstrating its promise for increasing the efficiency and throughput of P50 calculation. The model’s performance in replicating the pixels that constitute embolism events was lower (mean event-frame IoU of 0.38). We invite the community to utilise our model but emphasise that it does not replace the expert-processing pipeline and that care must be taken when considering applying this and similar approaches to OVT data.

Why it matches plant phenotyping methods葉の塞栓を画像から定量化するOVTの後処理を自動化するニューラルネットワークを開発・検証しており、植物生理状態の表現型取得が研究の中心である。

abstractHere we designed, tested, trained, and make publicly available a neural network model to automate post-processing of OVT images.

Deep learning enables edge deployment for citrus leaf disease recognition in natural orchard scenes. ↗

Background Accurate and efficient detection of citrus leaf diseases is important for orchard monitoring, early intervention, and intelligent disease management. However, practical application in natural orchard environments remains challenging because of complex backgrounds, large variation in symptom scale, strong interclass similarity, and limited edge computing resources. Methods In this study, a lightweight object detection model, SKM-YOLOv11, was developed based on YOLOv11n for citrus leaf disease recognition in natural scenes. A self-built dataset was constructed from public images and field images, containing 6414 images across six classes. StarNet-S050 was introduced as the backbone, C3k2-Star was designed to enhance feature fusion across scales, and a lightweight shared detection head, MNS-Head, was constructed to reduce prediction redundancy. Results Compared with YOLOv11n, SKM-YOLOv11 increased Precision, Recall, and mAP @0.5 by 2.92, 1.47, and 0.95 percentage points, respectively. Meanwhile, FLOPs, parameter count, and model size were reduced by 31.7%, 32.7%, and 34.55%, respectively. Edge deployment on Jetson Orin NX Super achieved an inference speed of 72.45 frames per second. Conclusion The proposed model has strong potential for real-time citrus disease screening on resource-constrained devices and provides a feasible solution for edge-based intelligent disease management in natural orchards.

Why it matches plant phenotyping methods柑橘葉の病害状態を画像から認識する軽量物体検出モデルを開発し、データセット、精度比較、エッジ実装性能まで評価しており、植物表現型（病害状態）の取得・推定手法が中心である。

abstracta lightweight object detection model, SKM-YOLOv11, was developed based on YOLOv11n for citrus leaf disease recognition in natural scenes.

Cotton seedling detection based on super-resolution reconstruction from UAV remote sensing imagery ↗

Accurate and efficient acquisition of seedling density and growth information is of great significance for guiding modern agricultural field management. Although drone imagery has been widely used in seedling monitoring, the inherent trade-off between operational efficiency and image resolution limits the effectiveness of remote sensing-based seedling detection. To address this challenge, this study proposes an integrated analytical method combining super-resolution reconstruction and object detection. The approach first employs the Real-ESRGAN model to enhance low-resolution image quality, then utilizes the YOLOv12 model to accurately localize cotton seedlings, and finally generates visualizations of seedling density and growth uniformity. Experimental results demonstrate that super-resolution reconstruction enhances the detection algorithm's capability for small targets, increasing the object detection precision by 3.3%. With the incorporation of super-resolution reconstruction, the seedling counting accuracy reaches 92.08%, representing a 46.15% improvement over the method without super-resolution, thereby effectively enhancing the algorithm's counting capability. Furthermore, this method achieves image detail equivalent to that obtained at 7.5 meters flight altitude while operating at 30 meters, reducing data acquisition time to 1/16 of the original requirement. In practical applications, the visualized results of seedling density and growth uniformity provide precise decision-making support for thinning, replanting, and differentiated field management. The proposed method is not only applicable to cotton but can also be extended to staple crops such as corn, wheat, and rice, with additional potential applications in forestry and ecological monitoring.

Why it matches plant phenotyping methodsUAV画像の超解像化と物体検出により、ワタ苗の密度・生育均一性を定量化する手法が研究の中心であり、性能評価も行っているため。

abstractThe approach first employs the Real-ESRGAN model to enhance low-resolution image quality, then utilizes the YOLOv12 model to accurately localize cotton seedlings, and finally generates visualizations of seedling density and growth uniformity.

Early detection of plant pathogens in the asymptomatic phase: A scoping review of hyperspectral imaging combined with machine learning ↗

• First PRISMA-ScR mapping of 79 HSI-ML asymptomatic detection studies (42 species, 74 pathogens) • Controlled-to-field accuracy gap quantified: 91.4% vs. 86.3% (5.1 pp, p = 0.0163 ) • 56.8% of studies omit temporal sampling documentation (CV = 139%) • SWIR underutilization (11.8%) reflects economic, not scientific, barriers • DBVS proposed as standardized temporal metric for cross-study comparability Plant disease management requires non-invasive detection methods capable of identifying infections before visible symptom manifestation, thereby enabling timely intervention. Hyperspectral imaging combined with machine learning and deep learning (HSI-ML) achieves 90.2% classification accuracy in controlled environments for asymptomatic plant detection; however, systematic characterization of methodological practices across this rapidly expanding field remains absent. This PRISMA-ScR compliant scoping review mapped 79 peer-reviewed studies (2010–2025) encompassing 42 plant species and 74 pathogenic agents using a Population-Concept-Context framework. Visible-near-infrared (VNIR) systems dominated deployment (61.8%, n = 49 ), while short-wave infrared (SWIR) systems remained substantially underutilized (11.8%, n = 9 ) due primarily to economic rather than scientific constraints. Among 67 unique algorithms identified, machine learning methods accounted for 30.7% (SVM, random forests, and PLS-DA predominant), whereas deep learning represented 28.4% (2D-CNN, 3D-CNN, and hybrid architectures). Critical methodological gaps emerged: 56.8% of studies omitted temporal sampling documentation (detection latency range: 1–56 days post-inoculation; coefficient of variation = 139%). Platform-stratified analysis revealed controlled environments achieved 91.4% ± 6.6% classification accuracy ( n = 48 ) versus 86.3% ± 9.1% for field/UAV deployments ( n = 26 ), representing a significant 5.1 percentage-point performance decrease ( p = 0.0163 ). Detection accuracy exhibited a weak negative correlation with detection timing ( ρ = − 0.33 , p = 0.067 ), though this association did not reach conventional statistical significance. Methodological heterogeneity—rather than algorithmic limitations—constitutes the primary barrier to field operationalization. Adoption of Days Before Visible Symptoms (DBVS) as a standardized temporal metric could resolve an estimated 40–50% of cross-study variance currently attributed to inconsistent asymptomatic-phase definitions.

Why it matches plant phenotyping methods植物病害の無症状感染をHSIと機械学習で検出する手法群を対象に、79研究の方法、精度、時間指標、標準化課題を体系的に評価したレビューであり、フェノタイピング手法が中心です。

titleEarly detection of plant pathogens in the asymptomatic phase: A scoping review of hyperspectral imaging combined with machine learning

Methodological framework of automatic field-based plot extraction and maize emergence counting using UAV imagery and deep learning ↗

Accurate plant population estimation is critical for crop monitoring, yield prediction, and field management in precision agriculture. However, challenges such as complex backgrounds, overlapping plants, and the need for large-scale coverage make seedling counting from UAV imagery difficult. In this study, we propose a novel automated pipeline for maize seedling counting based on high-resolution UAV orthomosaic images. The pipeline begins with an automatic field-based plot extraction method that isolates individual planting regions without the need for manual intervention. A lightweight object detection model, YOLOv8n-CA, is then employed, incorporating Coordinate Attention to enhance feature localization while maintaining fast inference. To further accelerate the process, we introduce the ‘In-Range Sliding’ strategy, which limits inference to only planting regions, reducing computational overhead. Extensive experiments demonstrate the effectiveness of our approach, achieving a mean absolute error (MAE) of 0.587 and a coefficient of determination ( R 2 ) of 94.19 % at the plot level. Additionally, our system enables spatial feature extraction such as seedling spacing, supporting more refined agronomic analysis. This work provides an efficient and scalable solution for plant counting, offering valuable insights for large-scale, rapid post-processing agricultural monitoring.

Why it matches plant phenotyping methodsUAV画像と深層学習によるトウモロコシ幼苗の計数・圃場区画抽出パイプラインを開発し、計数精度を検証しているため、植物表現型取得法が中心である。

abstractwe propose a novel automated pipeline for maize seedling counting based on high-resolution UAV orthomosaic images.

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

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

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

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

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

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

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

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

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

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

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

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

Linking plant water status dynamics to yield and fruit cracking in citrus orchards using UAV multi-sensor data and machine learning ↗

Citrus fruit cracking causes substantial yield and economic losses, yet its relationship with plant water status (PWS) and irrigation management remains insufficiently characterized. Unlike previous UAV-based irrigation studies that focused on water-stress detection or yield estimation, this study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale. UAV-based multispectral, thermal, and LiDAR data, combined with field physiological measurements and machine-learning models, were evaluated in an irrigation management experiment in an ‘Ori’ mandarin orchard (Israel) across three contrasting growing seasons (2023–2025). Several irrigation treatments with different irrigation timings and water inputs were applied during the growing season to evaluate their effects on temporal PWS dynamics and fruit cracking. Trunk growth (TG), stem water potential (SWP), stomatal conductance (SC), and plant area index (PAI) were measured throughout the two seasons and estimated using Random Forest models (R 2 > 0.783). These indicators were subsequently used to predict yield and fruit cracking with high accuracy (yield: R² = 0.896; cracking: R² = 0.845). Cracking was lowest in 2023 (∼3%), with ∼25% lower irrigation, suggesting reduced irrigation may reduce cracking risk. Higher cracking in 2024 (∼14%, vs ∼8% in 2025) coincided with intense heat events. Mid-season SWP and SC were strongly associated with yield formation and cracking patterns. These findings demonstrate that monitoring temporal PWS dynamics can support precision irrigation management by identifying high-risk zones and enabling irrigation strategies that stabilize PWS, reduce the incidence of cracking, and improve yield under variable climatic conditions.

Why it matches plant phenotyping methodsUAVマルチセンサーと機械学習により、樹体水分状態などの植物形質を推定し、収量・果実裂果を予測する技術的枠組みが研究の中心である。

abstractthis study introduces a dynamic, physiology-based framework that links temporal PWS trajectories during key phenological stages to fruit-cracking risk at the individual-tree scale.

Identification of Unsound Soybean Seeds Based on Hyperspectral Imaging and a Dual‐Channel Residual‐Squeeze‐and‐Excitation Network With Gramian Angular Field Fusion ↗

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).

Machine learning-based framework for plant disease identification and nutrient deficiency severity assessment using leaf images ↗

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.

Explainable AI-based CNN Optimization Model for Plant Leaf Disease Detection ↗

Plant leaf disease is a grave risk to crop suitability and agricultural sustainability, and therefore, the ability to make early and accurate diagnosis is a mandatory need in the contemporary precision farming system.In recent years, deep learning has gained significant attention for image-based plant disease detection.Despite its effectiveness, model performance can be influenced by factors such as redundant feature representations and sensitivity to hyperparameter selection.To address these challenges, this study proposes a nested hybrid optimization framework that combines the Cuckoo Search Algorithm (CSA) for channel selection with the Beluga Whale Optimization Mechanism (BWOM) for tuning the hyperparameters of a Convolutional Neural Network (CNN).The proposed approach is independently evaluated on bean and grape leaf datasets under consistent experimental conditions to assess its disease classification performance.In addition to strong predictive performance, the framework incorporates explainable AI (XAI) techniques, namely Gradient-weighted Class Activation Mapping (Grad-CAM) and Gradientweighted Class Activation Mapping Plus Plus (Grad-CAM++), to enhance model interpretability.These approaches highlight the most significant visual features influencing predictions, thereby providing valuable insights for agronomists and fostering trust in AI-based systems.Experimental results show that the proposed CSA-BWOM optimized CNN achieves classification accuracies of 99.61% and 99.38% on the bean and grape datasets, respectively, outperforming baseline CNN models and exhibiting competitive performance when compared to a few existing approaches.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類するCNN最適化・説明可能AI手法が研究の中心であり、植物病害状態の表現型推定に該当する。

titleExplainable AI-based CNN Optimization Model for Plant Leaf Disease Detection

Detect Plant Disease and Recommend Fertilizer and Supplement Using CNN & Mobile Net Algorithm ↗

Agriculture plays a crucial role in the Indian economy. Early detection of plant diseases is very much essential to prevent crop loss and further spread of diseases. Most plants such as apple, tomato, cherry, grapes show visible symptoms of the disease on the leaf. These visible patterns can be identified to correctly predict the disease and take early actions to prevent it. This can be overcome by the use of machine learning and deep learning algorithms. Hence, we are proposing a method that which is detecting the disease of a tomato plant from their leaf images. Here the process is performed with the deep learning algorithms Convolutional Neural Network (CNN), and MobileNet which is a one of the transfer learning method of CNN. Once after training the dataset with the algorithms, the accuracy of algorithms is compared and the images are classified. And the precautions are also provided for the classified plant.

Why it matches plant phenotyping methodsトマト葉画像から病害状態をCNN/MobileNetで推定・分類する手法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。

abstractwe are proposing a method that which is detecting the disease of a tomato plant from their leaf images

A Deep Learning Methodology for the Early Identification of Chili Plant Diseases Utilizing SSD-ResNet-50 Architecture ↗

The prompt identification and precise categorization of chili plant diseases are crucial for promoting sustainable agriculture and reducing crop losses due to pests and pathogens. This research presents an automated detection method using the Single Shot Detector (SSD) framework, with ResNet-50 as the backbone and a Feature Pyramid Network (FPN) to manage multi-scale feature representations. Dataset including 14,248 images of chili plants, categorized into six classes like Leaf Spot, Rotten Fruit, Healthy Fruit, Healthy Leaf, Mosaic Curl, and Yellowing, underwent preprocessing involving segmentation, resizing, and augmentation, followed by a division into 90% training data and 10% testing data. Transfer learning was implemented using a COCO-pretrained SSD ResNet-50 FPN model, enhanced with cosine-decay learning-rate scheduling and momentum optimization. The assessment results indicated an overall accuracy of 92.7%, with the highest F1-scores achieved for Healthy Fruit (0.965) and Rotten Fruit (0.967). Under the COCO evaluation protocol, the model achieved an mAP@0.5 of 91.5% and mAP@[0.5:0.95] of 65.2%. Model ran at approximately 30 FPS on an NVIDIA T4 GPU. Reduced precision values were noted for Leaf Spot (0.866) and Mosaic Curl (0.850), suggesting a propensity for misclassification due to visual similarities among disease symptoms. Nonetheless, all classes attained F1-scores exceeding 0.86, illustrating the robustness of the proposed model. Importantly, the SSD-ResNet-50 approach offers both efficiency and accuracy within a single pipeline, enabling rapid inference practical for real-world applications. These findings emphasize the potential of deep learning-based solutions to strengthen plant disease monitoring systems. In conclusion, SSD with ResNet-50 FPN provides an effective and scalable methodology for the automated identification of chili plant diseases, contributing directly to sustainable agriculture and improved crop management practices.

Why it matches plant phenotyping methods植物の病徴・病害状態を画像から推定する深層学習手法の開発と評価が中心であり、植物フェノタイピング手法として適格。

abstractThis research presents an automated detection method using the Single Shot Detector (SSD) framework, with ResNet-50 as the backbone and a Feature Pyramid Network (FPN) to manage multi-scale feature representations.

Can Synthetic Data Overcome the Generalization Limits of AI-Based Flower and Pod Detection Across Cowpea Breeding Genotypes and Environments? ↗

High-throughput phenotyping requires AI-enabled computer vision models that generalize across genotypes, locations, and growing seasons, yet such models often lose accuracy under new conditions. Annotating real imagery for every genotype-by-environment (G x E) combination a breeding program encounters is prohibitively expensive. We quantify how G x E shifts affect AI-based detection of cowpea flowers and pods across two California locations and two growing seasons. Flower detection mAP@50 fell from 76.3% to as low as 50.6% under unseen shifts, and pod detection was more sensitive. Feature-space and image-quality diagnostics confirmed these losses track measurable distributional shifts. Because closing this gap with real data alone is not practical, we test whether synthetic imagery, rendered from a procedural 3D cowpea model, can substitute for that annotation burden. Synthetic supervision alone improved over pretraining but remained limited by a domain gap driven by camera image formation, not scene content. A domain-gap-aware camera-realism augmentation strategy, optimized against measured real-image statistics via Wasserstein distance, narrowed this gap, and a linear HDR representation converted a smaller measured gap into a larger detection gain than an 8-bit representation. Optimized HDR synthetic data combined with as few as five real images matched or exceeded the real-data baseline for spatial generalization, and pod detection benefited most at the lowest shot counts, with more modest gains under temporal shift. These results show that synthetic data can overcome the generalization limits of AI-based flower and pod detection, but only when the domain gap is measured and optimized rather than assumed away.

Why it matches plant phenotyping methods花・莢という植物器官の画像検出を対象に、異なる遺伝型・環境への一般化、合成画像、カメラリアリズム拡張、HDR表現を技術的に評価しており、植物表現型取得手法が研究の中心である。

abstractHigh-throughput phenotyping requires AI-enabled computer vision models that generalize across genotypes, locations, and growing seasons

Crop Prediction and Leaf Disease Detection System Using Web-Based ↗

Agriculture continues to be one of the principal contributors to the economy and food security of developing nations, yet farmers regularly face difficulties such as unpredictable weather, variable soil conditions, and crop diseases that reduce quality and income. This paper presents a Crop Prediction and Plant Disease Detection System that integrates machine learning and deep learning techniques within a single web-based platform. The system accepts agricultural parameters — nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall — and applies a Random Forest regression model to estimate the expected crop. In parallel, it allows farmers to upload images of crop leaves, which are pre-processed and classified by a Convolutional Neural Network (CNN) to identify plant diseases and recommend suitable treatment. The application is built using ASP.NET Core for the user-facing interface, authentication, and dashboard, while a Python-based REST API hosts the machine learning and deep learning models; Microsoft SQL Server is used for persistent storage of user data, predictions, and disease records. The proposed system combines two traditionally separate functions — crop estimation and disease diagnosis — together with treatment recommendations and prediction history, into a single decision-support tool for precision agriculture, and was validated through unit, integration, and system-level testing

Why it matches plant phenotyping methods葉画像からCNNで植物病害を推定する機能が意思決定支援システムの主要構成要素であり、植物の病害状態を画像ベースで評価しているため含める。

abstractit allows farmers to upload images of crop leaves, which are pre-processed and classified by a Convolutional Neural Network (CNN) to identify plant diseases and recommend suitable treatment

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

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

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

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

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

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

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

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

Deep Transformer-Based Visual Framework for Early Detection of Plant Leaf Pathologies ↗

Accurate and quick detection of plant leaf diseases is essential for precision agriculture to intervene promptly and boost crop yields. A new deep learning model called ResVNet has been introduced in this study. It combines the powerful local feature detection of ResNet152 with the global attention capabilities of Vision Transformer (ViT) and utilises Low-Rank Adaptation (LoRA) to accelerate fine-tuning. The PlantVillage dataset, which contains both healthy and diseased tomato samples, was used to train and test ResVNet. Experimental evaluation on the PlantVillage tomato dataset using stratified 5-fold cross-validation demonstrates that the proposed ResVNet model achieves a mean classification accuracy of 97.45%, along with superior macro-precision, macro-recall, and macro-F1 scores compared to existing deep learning architectures. The results of the confusion matrix and the ROC analysis validate its discriminatory power. The results highlight the potential of architectures strengthened with transformers in agricultural diagnostics. For real-time disease detection in the field, ResVNet is perfect for edge device deployment on drones and smartphones thanks to its high accuracy and adaptability. The application of Explainable AI (XAI) technologies for interpretability, integration with the Internet of Things (IoT), and multi-crop classification will all be explored in future studies. We will also look into model compression approaches so we can deploy efficiently in low-resource settings without sacrificing performance.

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開発し、PlantVillageで交差検証して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。

abstractA new deep learning model called ResVNet has been introduced in this study.

An Efficient Attention-Enhanced MobileNetV2 Framework for Plant Disease Detection on Resource-Constrained Devices ↗

Leaf disease diagnosis needs models that are accurate enough for agronomic use yet small enough for constrained computing settings. This study examines a late-attention MobileNetV2 design in which one Convolutional Block Attention Module (CBAM) is inserted between the last MobileNetV2 convolutional map and global average pooling. The experiments use 54,306 controlled-background PlantVillage images spanning 38 classes. Under a uniform saved-model re-evaluation, MobileNetV2 + CBAM obtained 97.17% accuracy and 97.15% weighted F1-score, whereas MobileNetV2 obtained 96.78% and 96.73%. On the converted models, paired testing gave a 0.64-percentage-point accuracy advantage for the CBAM variant (95% CI: 0.31–0.96; exact McNemar p

Why it matches plant phenotyping methods植物葉の病徴を画像から検出する軽量深層学習モデルを開発・比較しており、植物病害状態の画像ベース表現型取得が中心である。

titleAn Efficient Attention-Enhanced MobileNetV2 Framework for Plant Disease Detection on Resource-Constrained Devices

Explainable Deep Learning for Plant Leaf Diseases: A Comparative Study of Grad CAM ↗

Plant disease detection is critical for sustainable agriculture and food security. While deep learning models achieve high accuracy in leaf disease classification, their black box nature poses limitations for trust and adoption among agricultural practitioners. This study presents a comparative evaluation of three convolutional neural network architectures (ConvNeXt-Tiny, MobileNetV2, and VGG16) for classifying potato, maize, and pepper leaf diseases, with emphasis on explainability through Gradient-weighted Class Activation Mapping (Grad-CAM). The experimental results demonstrate that ConvNeXt-Tiny achieves 99-100% accuracy across all plant species, MobileNetV2 attains 97-100% accuracy with lower computational requirements, and VGG16 yields 97-99.5% accuracy. Grad-CAM visualizations reveal that modern architectures precisely focus on lesion regions, whereas older models occasionally attend to irrelevant features such as leaf veins and edges. Misclassification analysis identifies shadows and natural leaf patterns as primary error sources. This research demonstrates that explainable artificial intelligence is not merely complementary but essential for developing trustworthy agricultural decision support systems.

Why it matches plant phenotyping methods植物葉の病変領域を画像から分類・可視化する手法を比較評価しており、病害状態の表現型抽出が研究の中心です。

abstractThis study presents a comparative evaluation of three convolutional neural network architectures (ConvNeXt-Tiny, MobileNetV2, and VGG16) for classifying potato, maize, and pepper leaf diseases, with emphasis on explainability through Gradient-weighted Class Activation Mapping (Grad-CAM).

Widespread Increase in Global Plant Water Stress Obscured by Greening ↗

Abstract Understanding the vulnerability of plants to more severe and frequent drought events and developing adaptive management strategies requires robust methods for quantifying long‐term changes in plant water stress (PWS). Most data‐driven explorations of long‐term trends in PWS have focused on alterations in canopy structure (e.g., leaf area index) or canopy structure‐dependent variables (e.g., gross primary productivity and evapotranspiration). This is largely because long‐term trends in canopy structure are relatively easy to detect from satellite observations. However, a focus on structural responses limits our ability to detect physiological stress due to challenges in isolating it from the effects of structural greening. Consequently, this difficulty hampers a comprehensive examination of long‐term PWS in the context of global greening trends. To address this gap, we developed a new process‐based metric for PWS to isolate physiological responses from structural greening, which we then used to detect global PWS trends over the past four decades. Combining site‐level and satellite observations at the half‐degree resolution across the globe, we found that accounting for greening‐related changes substantially alters the sign of long‐term PWS trends inferred from traditional approaches. Specifically, our study reveals a significant increase in PWS that is only detectable when accounting for structural greening trends. When greening trends are not accounted for, global PWS appears to have decreased over time. Overall, our results highlight the need to integrate structural dynamics and greening into PWS detection. Such an integration of observations and land models will improve our understanding of plant‐water‐energy interactions.

Why it matches plant phenotyping methods植物の生理的な水ストレスを定量化する新しいプロセスベース指標を開発し、衛星・地上観測で検証・適用しており、表現型測定法が研究の中心である。

abstractTo address this gap, we developed a new process‐based metric for PWS to isolate physiological responses from structural greening, which we then used to detect global PWS trends over the past four decades.

A comprehensive review of artificial intelligence and Internet of Things integration based plant disease detection for sustainable agriculture ↗

Abstract Detecting crop diseases early and responding promptly is vital for protecting agricultural productivity. It also helps maintain the quality and quantity of yields and reduces the risk of disease transmission to humans and livestock. Effective disease management is therefore critical to ensuring both global and local food security. However, traditional methods often based on visual inspection and delayed human judgment, are typically insufficient for identifying diseases at an early stage. Recent developments in Artificial Intelligence (AI) and the Internet of Things (IoT) offer new opportunities to address these challenges. By integrating IoT sensor networks with AI techniques such as machine learning and deep learning, it becomes possible to monitor plant health in real time and detect diseases with greater accuracy. This review explores the strengths and limitations of current AI-enabled IoT solutions in agriculture. It highlights how these systems leverage large-scale data and advanced image processing to outperform conventional methods in terms of speed, precision, and efficiency. Such improvements can significantly reduce crop losses and support more sustainable agricultural practices. Finally, the paper reviews key research trends, identifies current challenges, and outlines future directions in the field. It emphasizes the transformative potential of smart agriculture in advancing plant disease management and promoting environmentally responsible food production. This systematic review was conducted in strict accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, synthesizing a final selection of 152 peer-reviewed papers. The overarching aim is to critically evaluate and map literature published between 2015 and 2026 using a systematic approach that addresses the integration of the IoT, AI, Machine Learning (ML), Deep Learning (DL), Convolutional Neural Networks (CNN), sensor technologies, and sustainable agricultural practices in the context of plant disease detection.

Why it matches plant phenotyping methods植物病害の症状・健康状態をAI、画像処理、IoTセンサーで検出する手法を主題とした系統的レビューであり、植物フェノタイピング手法のレビューに該当する。

titleA comprehensive review of artificial intelligence and Internet of Things integration based plant disease detection for sustainable agriculture

Intelligent Plant Disease Identification Using Deep Learning and Computer Vision ↗

Plant diseases are generally caused by pest, insects, pathogens and decrease the productivity to large scale if not controlled within time. Agriculturists are facing lose due to various crop diseases. It becomes tedious to the cultivators to monitor the crops regularly when the cultivated area is huge that is in acres. The proposed system provides the solution for regularly monitoring the cultivated area and provides the automated disease detection using remote sensing images. The proposed system intimates the agriculturist about the crop diseases to take further actions. The objective of the proposed system is to early detection of diseases as soon as it starts spreading on the outer layer of the leaves. The proposed system works in two phases: the first phase deals with training data sets. This includes, training both healthy and as well as diseased data sets. The second phase deals with monitoring the crop and identifying the disease using Canny?s edge detection algorithm.

Why it matches plant phenotyping methods植物葉の画像から病害状態を自動検出するコンピュータビジョン手法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ため。

abstractThe proposed system provides the solution for regularly monitoring the cultivated area and provides the automated disease detection using remote sensing images.

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

Introduction Climate extremes increasingly threaten agricultural production, yet many artificial intelligence systems in agriculture remain local, reactive and narrowly trained for one crop, region or sensing modality. Methods We present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions. AgriFM was pretrained using self-supervised objectives on 2.4 million field-season sequences and evaluated on a curated benchmark spanning maize, wheat, soybean, rice and sorghum across five agroclimatic regions. Results In held-out geography and time-split evaluations, AgriFM improved early stress detection and yield-failure prediction over statistical, crop-model and deep-learning baselines. The largest gains occurred during compound drought and heat events, for which AgriFM produced alerts 18 to 24 days earlier than the satellite-only baseline while maintaining improved calibration. Phenology-conditioned fusion improved transfer across planting calendars, and uncertainty calibration reduced false alerts at fixed recall. Discussion Because the study is based on retrospective datasets, these findings establish cross-region retrospective performance rather than prospective field efficacy. The results support further field-based evaluation of multimodal foundation models for climate-resilient crop monitoring.

Why it matches plant phenotyping methods作物ストレス状態と収量失敗リスクを衛星・レーダー・熱画像等から推定する基盤モデルを開発し、複数作物・地域のベンチマークで評価しており、植物状態の取得・推定手法が中心である。

abstractWe present AgriFM, a multimodal geospatial foundation model that combines satellite image time series, radar, thermal observations, weather trajectories, soil properties, topography and sparse management variables to estimate crop-stress and yield-failure risk across crops and regions.

Early Multi-Class Disease Detection in Chili Plant Leaves Using Convolutional Neural Networks: A Comparative Study ↗

Abstract Chili is an important economic and nutritional crop with a relatively limited availability of different disease-resistant varieties. Leaf diseases, including those caused by fungi, bacteria, viruses, pests, or nutritional deficiencies, significantly compromise production and crop quality. Early detection of these diseases is key to reducing yield loss; however, traditional visual examinations are limited by time constraints, human subjectivity, and low detection sensitivity at early stages of infection. To overcome these challenges, this study proposes a deep learning–based framework for early multi-class detection of chili leaf diseases using convolutional neural networks (CNNs). A real-field dataset comprising 16,392 high-resolution images of chili leaves across six disease and healthy classes was collected from multiple regions of Bangladesh. The dataset was preprocessed, augmented, and split into training and validation sets using an 80:20 ratio. Five pre-trained CNN architectures—DenseNet121, EfficientNetB3, MobileNetV2, ResNet50, and InceptionV3 were evaluated using a transfer learning strategy. Experimental results demonstrate that MobileNetV2 achieved the best performance, attaining an overall classification accuracy of 96%. The results indicate that the proposed system demonstrates strong generalization capability and effectively discriminates visually similar chili leaf diseases. This work can be considered a valuable application in precision agriculture, providing an efficient, automated, and practical approach for in situ early diagnosis of chili leaf diseases through smart farm management.

Why it matches plant phenotyping methods唐辛子葉の病害状態を画像から分類するCNN手法の開発・比較評価とデータセット構築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。

abstractA real-field dataset comprising 16,392 high-resolution images of chili leaves across six disease and healthy classes was collected from multiple regions of Bangladesh.

Development of a Vision-Guided Autonomous Variable-Rate Spraying System for Site- Specific Potato Disease Management Using YOLOv26 ↗

Abstract Uneven occurrence of potato leaf diseases necessitates site-specific treatment rather than conventional uniform spraying. This study presents a vision-guided autonomous variable-rate spraying system integrating real-time deep learning-based disease detection, temporal disease severity estimation, and PWM-controlled precision spray actuation for targeted crop protection. A composite dataset comprising 1,561 field images containing 14,166 annotated leaf instances was developed under dense canopy conditions. Comparative evaluation of object detection architectures showed that YOLOv26 achieved the highest detection performance with a peak mAP@0.5 of 0.961, outperforming YOLOv8 (0.924) by 3.7% and YOLOv12 (0.938) by 2.3%. The lightweight YOLOv26n variant was selected for embedded deployment. When deployed on a Raspberry Pi 5, the optimized ONNX model achieved an inference speed of approximately 10 FPS, corresponding to a spatial sampling interval of 0.018 m at an operating speed of 0.18 m/s. A 2 s temporal sliding window generated a stable canopy-level disease severity index, which was mapped to PWM duty cycles for variable-rate pesticide application. Field experiments involving 598 spatial spray decision instances validated the proposed perception-driven spray control framework. Compared with conventional constant-rate spraying, the system reduced agrochemical consumption by 32.75% (from 458 to 308 L/ha). These results demonstrate the feasibility of integrating embedded deep learning and adaptive spray control into a field-deployable autonomous spraying platform for precision potato disease management.

Why it matches plant phenotyping methodsジャガイモ葉の病徴検出とキャノピー病害重症度推定を中核とする視覚センシング・深層学習・噴霧制御プラットフォームの開発および実地検証であり、植物状態の定量的推定方法が中心である。

abstractThis study presents a vision-guided autonomous variable-rate spraying system integrating real-time deep learning-based disease detection, temporal disease severity estimation, and PWM-controlled precision spray actuation for targeted crop protection.

Early Detection of Sesame Leaf Diseases Using Convolutional Neural Networks ↗

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

LeafTip-RN: Generative AI-powered temporal interpolation for continuous phenotypic analysis of seedling establishment traits in wheat. ↗

Seedling establishment represents a critical phase in early crop growth and development, directly influencing biomass accumulation and yield potential. To characterise early growth dynamics under field conditions, both growth rate and uniformity of emergence need to be assessed continuously; however, manual quantification of these dynamic traits in large-scale trials remains impractical. Here, we present LeafTip-RN, an open-source and deep learning (DL)-powered pipeline for dynamically measuring wheat (Triticum aestivum L.) early establishment in the field. To enable flexible and scalable data collection, ultralow-altitude drone phenotyping was employed, followed by the development of an optimised DL model to automate leaf-tip-related feature extraction from complex backgrounds. Notably, to address data sparsity arising from eight phenotyping timepoints, we integrated an image-to-video generative AI (GenAI) module into the pipeline to interpolate keyframes between early and late seedling stages (i.e. 18-40 days after sowing), resulting in a training library comprising 353,019 labelled leaf tips. Using the pipeline, we successfully quantified multiple agronomically important establishment-related traits (e.g. plot-level leaf tips and seedling spatial uniformity), followed by deriving their growth curves for 51 wheat varieties across two growing seasons (2024-2026). After validating these LeafTip-RN-derived traits, we further computed varietal relative growth rates and uniformity indices, based on which the 51 varieties were classified into high-, medium-, and low-performance groups, revealing discrepancies between LeafTip-RN-derived classification (18-40 DAS) and manual assessment at 40 DAS when dynamic early performance was considered. Finally, to facilitate broad adoption by the plant research community, we developed an openly accessible graphical user interface (GUI) for non-expert users to visualise and analyse rapid seedling developmental changes. Taken together, our study provides a scalable GenAI-powered solution for evaluating seedling establishment in wheat, offering valuable tools for breeders and researchers to identify varieties with enhanced early growth vigour and emergence dynamics that are extensible to other cereal crops.

Why it matches plant phenotyping methodsLeafTip-RNは、ドローン画像と深層学習・生成AIによって小麦の葉先や出芽均一性などの形質を自動抽出・連続推定する手法およびGUIを開発し、導出形質を検証しているため、植物フェノタイピング手法が研究の中心である。

abstractHere, we present LeafTip-RN, an open-source and deep learning (DL)-powered pipeline for dynamically measuring wheat (Triticum aestivum L.) early establishment in the field.

Image-Based Estimation of Blueberry Yield Incorporating External Validation and Canopy Architecture Under Field Conditions ↗

Abstract Quantifying blueberry fruit yield and maturity is important for evaluating yield potential in breeding trials, but manual measurement remains slow, labor-intensive, and costly. Object detection and classification networks offer a high-throughput solution, yet few studies validate image-based counts against hand-harvested ground truth while explicitly accounting for canopy occlusion. Hence, this study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts across 32 diverse southern highbush blueberry genotypes. Among the models evaluated, YOLOv8x achieved the highest detection performance, with an mAP50 of 0.82 and an mAP50–95 of 0.66. External validation produced F1 scores ranging from 0.74 to 0.91 for berry maturity classes. However, image-based detections systematically underestimated hand-harvested fruit counts, with R² values ranging from 0.40 to 0.61. Fruit occlusion varied widely among genotypes, from 42% to 90%, indicating that canopy structure strongly affects berry visibility. Incorporating image-derived canopy architecture, color, and texture features improved predictions of berry counts and maturity. Partial Least Squares regression provided the best performance, increasing R² values of hand-harvested fruit counts, ranging from 0.52 to 0.74. These results show that accounting for canopy occlusion improves image-based estimation of blueberry yield and supports more accurate high-throughput phenotyping.

Why it matches plant phenotyping methodsブルーベリーの収量・成熟度を画像から推定する検出パイプラインを開発し、手収穫値との外部検証および樹冠遮蔽・構造を考慮した改良を行っており、植物表現型取得法が中心である。

abstractthis study developed a multi-class berry detection pipeline for immature and mature berries and validated image-based estimates against hand-harvest counts

Remote Sensing Applications in Sugar Beet Production: From Crop Monitoring to Precision Management ↗

Remote sensing has become an important tool for crop monitoring and precision agriculture, yet its applications in sugar beet production remain fragmented across sensing platforms, target traits and modelling strategies. This review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management. A structured search was conducted in Scopus and the Web of Science Core Collection for publications from 2003 to 2025, and 181 relevant peer-reviewed articles were retained for thematic analysis. The literature shows a clear increase in sugar beet remote sensing studies, particularly after 2015, coinciding with the availability of Sentinel-2 imagery and, from 2016 onwards, the growing use of unmanned aerial vehicle-based sensing. It also indicates a gradual shift from crop mapping and canopy monitoring towards disease detection, weed mapping, yield prediction and management-oriented applications. Current studies demonstrate the value of satellite, unmanned aerial vehicle and proximal sensing for retrieving canopy traits, assessing biotic stresses, estimating root yield and supporting field-scale management. However, sugar beet presents specific challenges because its economic value depends not only on canopy development or root biomass, but also on sucrose concentration, recoverable sugar yield, and processing quality. These quality-related traits remain less studied and are difficult to infer directly from canopy observations. Modelling approaches have evolved from vegetation-index-based empirical models towards machine learning, deep learning, multi-temporal analysis, data fusion and crop model assimilation, but issues of model transferability, ground-truth availability and operational decision support remain unresolved. Future research should strengthen multi-source observations, external validation, quality-oriented prediction and decision-support workflows to promote robust, scalable and economically meaningful remote sensing applications in sugar beet production.

Why it matches plant phenotyping methodsサトウダイコンのリモートセンシングによるキャノピー形質、ストレス、根収量などの推定手法を体系的にレビューしており、センシング基盤とモデル化・検証課題が中心的に扱われている。

abstractThis review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management.

Improving Out-of-Distribution Robustness for Wheat Head Detection: A Lightweight Modified YOLOv13 Approach ↗

Wheat head detection is a critical component in high-throughput phenotyping, holding significant application value for wheat yield estimation and breeding analysis. With the continuous advancement of general object detection models, state-of-the-art detectors achieve high accuracy in same-distribution wheat head detection scenarios. However, when applied to cross-distribution environments, their performance often degrades significantly. To investigate this issue, this paper adopts the official division of the GWHD2021 public dataset as the cross-distribution evaluation setting. Using the recently state-of-the-art object detection model YOLOv13 as the baseline, we systematically explore effective approaches to enhance cross-distribution generalization performance in wheat head detection. Specifically, we propose two lightweight modifications to YOLOv13’s Full-PAD architecture and HyperACE’s core modules, analyzing their potential mechanisms: (1) Replacing scalar gating in Full-PAD with channel-level gating enables finer-grained branch injection control. Concurrently, channel-level gating introduces equivalent stronger weight penalties during training, generating additional regularization effects. Through exploratory controlled experiments aligning weight-penalty strengths, we find that this gain depends on both channel decoupling and the accompanying implicit regularization rather than on decoupling alone. (2) Removing Batch Normalization from HyperACE’s core C3AH modules and adopting normalization strategies independent of batch statistics—such as Identity, Group Normalization, or Instance Normalization. Preliminary experiments show that while this results in a small, directionally positive change, the change remains within run-to-run variance; we therefore do not claim BN removal as a reliable standalone improvement. Furthermore, combining channel gating with BN removal does not yield further additive improvements compared to channel gating alone, indicating an interaction effect. To address this, we analyze relevant statistics between channel gating and HyperACE branches, providing an exploratory explanation for this non-additive phenomenon. In summary, this paper delivers empirical evidence and preliminary insights for enhancing generalization performance in the cross-distribution wheat head detection task of the advanced general-purpose detection model YOLOv13.

Why it matches plant phenotyping methods小麦穂の画像検出を対象に、YOLOv13の改良と異分布ロバスト性をGWHD2021で系統的に評価しており、植物表現型取得・解析手法が中心である。

abstractWheat head detection is a critical component in high-throughput phenotyping

Temporal germination of lettuce under salinity and nano-silicon: A high-throughput phenomics framework with deep learning. ↗

Accurate high-throughput evaluation of seed germination under abiotic stress is often hindered by subjective manual scoring and insufficient temporal resolution. This study introduces an integrated phenomics framework leveraging an explainable deep learning model for real-time monitoring of lettuce (Lactuca sativa L.) germination dynamics under salinity stress and nano-silicon priming. Utilizing a custom X-Y motorized imaging system, we captured continuous time-lapse data across 16 treatment combinations (0-60 mM NaCl × 0-300 mg L⁻¹ nano-SiO₂). We developed an ultra-lightweight architecture, YOLO26n-Ghost-EMA, which integrates Ghost convolutions and Efficient Multi-scale Attention. This model achieved 99.46% mAP@50 with a 4.5 ms inference time, providing a high detection accuracy while maintaining a lightweight architecture and favorable accuracy-efficiency trade-off compared with standard YOLO variants. while reducing computational demand by 35-50%. To ensure biological validity, Explainable AI (XAI) via Grad-CAM confirmed that the model precisely targets radicle protrusion zones, eliminating 'black-box' opacity. Response Surface Methodology (RSM) quantified the potent ameliorative effect of nano-SiO₂, identifying 100 mg L⁻¹ as the optimal concentration to recover germination from 58.57% to 84.28% under severe salinity (60 mM NaCl). By bridging real-time computer vision and plant stress physiology, this framework provides a scalable, high-resolution solution for precision seed biology and rapid assessment of abiotic stress.

Why it matches plant phenotyping methods深層学習とカスタム撮像システムによる発芽動態の高スループット・リアルタイム定量が研究の中心であり、植物状態（発芽・幼根突出）を画像から抽出する手法を開発・検証している。

abstractThis study introduces an integrated phenomics framework leveraging an explainable deep learning model for real-time monitoring of lettuce (Lactuca sativa L.) germination dynamics under salinity stress and nano-silicon priming.

Comprehensive plant disease classification and severity estimation for sustainable farming via automatic segmentation and multi-scale feature fusion ↗

Abstract Detecting plant leaf diseases at an early stage is one of the most important requirements for sustainable agriculture, increasing crop productivity, and achieving the global Sustainable Development Goals (SDGs). However, accurately recognizing them in real-world farm fields can still be difficult due to factors such as background complexity, changes in light conditions, and very similar looking classes from a visual standpoint. In order to solve these problems, the authors here present a new Multi-Scale Feature Fusion (MSFF) model that can offer robust and highly accurate performance in identifying plant leaf diseases. Firstly, the brand new hybrid method starts with a U-Net segmentation designed exclusively to separate the diseased parts and thus allow the classification to be more robust. Next, Rank Order Fuzzy (ROF) is implemented to get rid of the background while still maintaining the edges, and the additional data is used for the network to generalize better. The color distribution is then analyzed to determine the variations in color brought about by the infection. In terms of features, EfficientNet is paired with an Attention-based Autoencoder to produce both spatially detailed global features and compact latent representations. The two sets of features are then combined through Canonical Correlation Analysis (CCA) which not only identifies the dependencies between the features but also enhances the discriminative strength. The final fused feature set is fed to module based on YOLO for detection and classification in order to obtain the final result of the plant disease identification system which is both accurate and fast. Among other datasets, the model has been tested on different apple leaf datasets such as FGVC7, AppleLeafSet, PlantVillage Apple, Kaggle Apple Leaves, and ATLDSD, which contain five disease classes. The results of the experiments indicate a classification accuracy of 99.95%, thus the model is superior to several state-of-the-art deep learning and classical machine learning methods. Statistical methods like five-fold cross-validation, paired t-tests ( p

Why it matches plant phenotyping methods植物葉の病変部位を画像から自動抽出し、病害の識別・分類を行う画像ベースのフェノタイピング手法を開発・評価しており、手法が研究の中心である。

abstractthe authors here present a new Multi-Scale Feature Fusion (MSFF) model that can offer robust and highly accurate performance in identifying plant leaf diseases

Advancements in Plant Leaf Disease Recognition: YOLO-Based Deep Learning Approaches ↗

Plant leaf diseases cause severe losses in crop yields and qualities, and account for considerable volume of losses to the agricultural output globally. Recognition of plant disease early and rightly is crucial to disease treatment and to reduce loss to the crop and to maintain agricultural sustainability. Plant disease that occurs on the leaves has been traditionally detected by farmers and experts with naked eyes by checking its symptoms like discoloration, spots and lesions. However, the process requires time, labour, expertise and is subjective, which renders it unusable for large-scale implemented agriculture. Recent years have seen the promising use of Artificial Intelligence (AI) as a tool for automated plant disease identification. The extraction of manually-crafted features from photographs of plant leaves, such as colour, texture, and form, is at the heart of many Machine Learning (ML) approaches used for disease classification. While these ML models have shown acceptable performance, they require significant manual feature engineering and can be poor at operating in real-world settings and with voluminous data. To address these issues, Deep Learning (DL) algorithms have found extensive usage in the identification and categorisation of plant leaf diseases. The You Only Look Once (YOLO) family of detection of objects models is making waves in the DL object detection space thanks to its impressive dual-tasking capabilities: object identification and multiple illness categorisation in a single pass, all at lightning speed and with pinpoint accuracy. For real-time disease identification in precision agriculture, YOLO stands out as an end-to-end feature learning and object recognition method, set apart from typical ML approaches. Understanding the DL models suggested for plant leaf disease detection and classification using the YOLO principle is the primary goal of this survey. It also provides a comparative and performance analysis of these models by examining their techniques, merits, demerits, datasets used, and evaluation metrics.

Why it matches plant phenotyping methods植物葉の病徴を画像から認識・分類するYOLO系手法を主題とした比較・性能分析レビューであり、植物の病害状態を抽出するフェノタイピング手法が中心です。

abstractUnderstanding the DL models suggested for plant leaf disease detection and classification using the YOLO principle is the primary goal of this survey.

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

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

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

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

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

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

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

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

BeeMonitor: Automated IoT video surveillance and an AI-powered video processing system for monitoring the foraging and nesting behavior of cavity-nesting solitary bees ↗

1. Solitary bee species that use artificial trap nests are important for agricultural crop production and as indicators of habitat quality. Quantifying cavity-nesting solitary bee foraging and nesting behavior is essential for real-time analysis of population numbers and pollination activity, as well as understanding how environmental conditions shape reproductive success and population dynamics. However, manual observation is labor-intensive, prone to observer bias, and unable to deliver continuous data. Existing automated systems either require individual bee marking or detect presence without resolving nest-tube-level entry and exit events. 2. We developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video, using Osmia cornifrons (the horn-faced mason bee) as a model system. A low-cost Raspberry Pi handles solar-powered field recording, while the software combines object detection (YOLOv26), a custom multiple-object tracker (BeeTrack), and a Random Forest classifier trained on trajectory-derived features to distinguish genuine events from incidental detections. 3. Over a 29-day deployment, hardware reliability averaged 97.5% recording coverage. The pipeline achieved 91.3% precision and 87.3% recall (F1 = 0.893), generalizing robustly under leave-one-video-out cross-validation (mean F1 = 0.904 ± 0.045). Detected foraging trips correlated strongly with brood cell counts (R² = 0.849, p < 0.001, n = 19), and a Random Forest model (AUC = 0.820 ± 0.062) identified solar radiation as the dominant driver of foraging activity, followed by temperature. 4. BeeMonitor demonstrates that automated computer vision can reliably extract ecologically relevant behavioral data from continuous video, enabling real-time analysis of pollinator behavior and abundance at a temporal and spatial resolution unattainable through manual observation. Its modular design supports adaptation to other species and monitoring contexts.

Why it matches plant phenotyping methods映像からハチの巣穴への入退出や採餌行動を自動抽出するハードウェア・コンピュータビジョン基盤を開発し、精度検証も行っているため、動物対象ではあるが植物フェノタイピングの範囲外です。

abstractWe developed BeeMonitor, an integrated hardware and computer-vision pipeline that detects nest entry and exit events in cavity-nesting solitary bees from continuous video

A Field-Calibrated UAV LiDAR Workflow-Level Case Study for Individual-Tree Inventory in Jilin Larch Plantations Using PCS, MCRG, and RHCSA ↗

Sustainable forest management requires inventory workflows that provide spatially explicit structural information while retaining field-based calibration and uncertainty control. This case study evaluated a field-calibrated UAV LiDAR workflow for individual-tree inventory in middle-aged and near-mature larch plantations in Chuanying District, Jilin City, China. UAV laser scanning point-clouds were integrated with six 30 m × 30 m field plots to assess three individual-tree extraction algorithms: point-cloud segmentation (PCS), marker-controlled region growing (MCRG), and region-based hierarchical cross-section analysis (RHCSA). Algorithm performance was evaluated using plot-level recall, precision, F-score, localization RMSE, tree-height and crown-width accuracy, bootstrap confidence intervals, exploratory Wilcoxon signed-rank comparisons, and leave-one-plot-out stability checks. MCRG provided the most balanced numerical performance under the tested configuration, with a mean F-score of 0.845, compared with 0.808 for PCS and 0.827 for RHCSA. However, the MCRG-RHCSA paired difference was not robust across the six plots, and the analysis should be interpreted as a dataset-specific workflow comparison rather than a universal algorithm ranking. Tree height was estimated with comparatively high accuracy, whereas crown-width estimation remained weak, indicating that vertical canopy structure was more reliable than lateral crown delineation. After calibration assessment, the workflow was applied to 157.47 ha of UAV LiDAR survey areas and generated 219,996 algorithm-based detections. These outputs are best interpreted as a spatial decision-support layer for compartment updating, density screening, and field-inspection prioritization, not as an independently verified wall-to-wall stem census.

Why it matches plant phenotyping methodsUAV LiDARによる個体樹の抽出・樹高・樹冠幅推定を中心に、複数アルゴリズムの精度比較、校正、安定性検証を行うワークフロー研究であり、植物形質取得手法が中核である。

abstractThis case study evaluated a field-calibrated UAV LiDAR workflow for individual-tree inventory

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

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

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

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

Apple Crop Health Detection Based on Vegetation Indices at Shalimar, Kashmir: North-Western Himalayas ↗

Apple orchard health monitoring is important for supporting timely crop management under the temperate conditions of Kashmir Valley. The present study evaluated the seasonal behaviour of four Sentinel-2-derived vegetation indices, namely the Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index, Modified Soil Adjusted Vegetation Index and Enhanced Vegetation Index, in a 2 ha apple orchard at SKUAST-K, Shalimar, Kashmir, during the 2020 growing season. Sentinel-2 imagery acquired from April to September was processed using SNAP and QGIS, and mean vegetation index values were extracted for the orchard area. Monthly weather data, including average temperature and rainfall, were obtained from the on-campus meteorological observatory. Ground observations at 20 georeferenced points were used to support the interpretation of canopy development, phenological stage and visible plant health condition. All four vegetation indices showed a seasonal increase from April to July-August, followed by a decline during September-October, corresponding to canopy development, fruit maturation, harvest and senescence. Based on the monthly values presented in the study, temperature showed positive associations with the vegetation indices, with the strongest relationship observed for MSAVI, followed by NDVI and SAVI. Rainfall showed weak and non-significant associations with the indices during the study period. The results indicate that Sentinel-2-derived vegetation indices can reflect seasonal canopy dynamics in apple orchards under the studied conditions. MSAVI appeared particularly useful for representing canopy development, while field observations remained necessary for interpreting pest, disease and phenological effects.

Why it matches plant phenotyping methodsSentinel-2画像から植生指数を算出し、リンゴ樹冠の季節動態・健康状態を評価する測定ワークフローが研究の中心であり、植物状態の推定に直接用いられている。

abstractThe present study evaluated the seasonal behaviour of four Sentinel-2-derived vegetation indices

A novel deep-learning approach for robust identification of plant diseases ↗

Rising temperatures and changing weather conditions are accelerating the spread of plant diseases and increasing the threat to global food security. Reliable detection of leaf diseases is therefore essential to protect crop yields and ensure food quality. Deep learning has proven to be a powerful tool for classifying leaf diseases across various crops. Due to the natural variability of plants, plant diseases often appear in irregular structures. Surface unevenness, folds, or dirt particles are common in field images and can be mistakenly identified as important features by convolutional neural networks (CNNs). This is a challenge that has not been sufficiently addressed in previous studies. This study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure, such as surface irregularities or prominent leaf veins, which may mislead the model. Using stratified five-fold cross-validation on a peer-reviewed dataset, which comprises 2,801 images of radish leaves across five classes (healthy, three disease classes: mosaic virus, black leaf spot, and downy mildew, and one pest-affected class: flea beetle), the proposed method achieved an average and balanced accuracy of 99.86%, establishing a new dataset-level benchmark in the field and demonstrating its effectiveness. The results indicate that the proposed approach may provide a promising basis for future applications in agricultural field monitoring, automated sorting and post-harvest quality control, offering potential to reduce both food waste and associated costs.

Why it matches plant phenotyping methods植物の葉画像から病害状態を分類する深層学習手法の開発・交差検証が研究の中心であり、植物表現型（病害状態）の取得・推定に該当する。

abstractThis study proposes a novel deep learning approach that takes into account both the specific visual characteristics of plant diseases and potential disturbances in the microstructure

A Hex-View Perspective on Plant Disease Detection Using Remote Sensing ↗

Plant diseases lead to substantial yield losses and pose a persistent threat to global food security, creating an urgent demand for high-throughput, accurate, scalable, and non-destructive disease monitoring approaches. Remote sensing has emerged as a powerful tool, yet progress in plant disease detection remains fragmented across various disciplines, tasks, sensing methods, and data modalities. This review introduces a hex- view perspective to synthesise remote sensing–based plant disease detection within a cohesive conceptual framework. Instead of treating sensing technologies, algorithms, and datasets independently, the hex-view incorporates six interconnected dimensions that jointly capture how biological processes, measurement scale, and data characteristics constrain disease detectability, including when detection is possible and how reliably it can be achieved. The hex-view framework comprises six interconnected dimensions and forms an integrated framework called BTSCAD: (1) Biology (B): Plant-pathogen interactions constituting the biological foundation of disease development and expression. (2) Task (T): The diverse disease detection tasks and their corresponding research objectives. (3) Sensor (S): The sensing modalities that define the data acquisition type and richness of captured information. (4) Condition (C): The environmental conditions, sensing platforms, and spatial scales that shape disease observations and bridge controlled experiments and real-world deployment across leaf, canopy, plot and regional scales. (5) Algorithm (A): The classical and state-of-the-art data analysis algorithms used to extract disease-related information from sensor data. (6) Dataset (D): The data sources that underpin model development, evaluation, and generalisability. The hex-view perspective provides a clear framework for interpreting previous research and identifying future research directions. This review lays a structured foundation for developing robust, interpretable, and transferable disease detection systems, supporting advancements in precision agriculture, high-throughput phenotyping, and sustainable crop production.

Why it matches plant phenotyping methods植物病害を対象としたリモートセンシングによる観察・検出法を、センサー、条件、アルゴリズム、データセットの観点から体系化する方法論レビューであり、植物状態の推定手法が中心である。

abstractThis review introduces a hex- view perspective to synthesise remote sensing–based plant disease detection within a cohesive conceptual framework.

Plant Leaf Disease Detection Using Machine Learning and Deep Learning: A Review and Experimental Study ↗

India’s economy is primarily based on agriculture. Agriculture has significant contribution in nation’s GDP. Food security and employment significantly influenced by agriculture. However factors like uncertain weather conditions, poor quality of seeds and plant diseases impact on agriculture productivity. Computer vision and DL algorithms are most crucial components of precision agriculture. Early detection can improve decision making, maximize pesticide use, and preserve harvests. Using CNN architectures, segmentation-based approaches, handcrafted feature-based methods, and hybrid approaches incorporating Machine Learning and Deep Learning this study seek to provide review of recent publications from 2020 to 2026. The review was carried out using a variety of publications with different datasets, methodologies, and outcomes. The findings show that DL, especially CNN and transfer learning models, performed better than machine learning techniques. It points out several significant problems, such as dataset imbalance, insufficient generalization, computing inefficiency, and a dearth of real-world data. Future research topics are also suggested which includes IoT-driven real-time solutions, lightweight architecture, domain adaption, and multimodal imaging. This review aims to develop plant disease detection technologies that are more dependable, scalable, and field deployable.

Why it matches plant phenotyping methods植物葉の病徴を画像から検出する機械学習・深層学習手法をレビューおよび実験的に扱っており、植物フェノタイピング手法が中心である。

titlePlant Leaf Disease Detection Using Machine Learning and Deep Learning: A Review and Experimental Study

A Review on Artificial Intelligence Methods for Plant Disease and Pest Detection ↗

Artificial intelligence (AI) has emerged as a transformative tool for plant health monitoring, offering new opportunities for scalable, timely, and data-driven pest and disease management in agriculture. This review provides a comprehensive synthesis of AI-based methods for pest and plant disease detection, systematically organizing existing literature across sensing modalities, learning paradigms, and deployment scales. We distinguish between population-level pest monitoring, plant-centric visual inspection, and field-scale surveillance, as well as between post-symptomatic disease recognition and pre-symptomatic detection enabled by spectral imaging technologies. Beyond summarizing recent advances, this work places strong emphasis on critical analysis, discussing fundamental limitations related to data scarcity, domain shift, generalization under field conditions, and the challenge of disentangling biotic from abiotic stress factors. The review further examines the distinction between correlation-driven AI predictions and causal disease understanding, positioning AI as a complementary decision-support tool alongside established diagnostic methods. Building on these insights, we outline key future research directions, including multimodal sensor fusion, explainable and trustworthy AI, edge-based deployment for real-time monitoring, and the development of foundation models for unified agricultural intelligence. This review aims to serve as both an accessible entry point and a critical reference for advancing AI-driven plant health management.

Why it matches plant phenotyping methods植物の病害・害虫状態を画像・スペクトルなどで検出するAI手法を対象としたレビューであり、植物状態の取得・推定手法が中心である。

titleA Review on Artificial Intelligence Methods for Plant Disease and Pest Detection

Medium-adaptive wideband near-field antenna for microwave detection of internal cavities in plant stems ↗

Abstract This paper presents a novel medium-adaptive wideband near-field antenna for early detection of internal cavities in plant stems, including branches and small trunks. Unlike conventional antennas designed for free-space operation, the proposed antenna is explicitly engineered to operate in close proximity to a lossy, anisotropic, and dispersive cylindrical medium representing wood tissues. A physics-based electromagnetic model of the stem is incorporated into the design process, enabling accurate optimization under realistic dielectric loading conditions. The antenna consists of a compact quasi-planar dipole with blended arms integrated with a medium-adaptive dual-ring balun that ensures balanced current excitation and stable impedance matching under strong near-field loading. Both simulation and experimental measurements demonstrate wideband impedance matching to a 50 Ω source over the 2.0–3.0 GHz frequency range. Surface current distribution and specific absorption rate (SAR) analyses confirm efficient electromagnetic coupling into the stem tissues with minimal radiation leakage. To evaluate the sensing capability of the proposed design, a conceptual two-element antenna system is introduced as a feasibility study for cavity detection. The detection performance is assessed through a sensitivity-driven framework based on variations in both self- and mutual-scattering parameters. A comprehensive sensitivity analysis is conducted to quantify the response of the system to changes in cavity diameter, radial position, and angular location. The results demonstrate that while the reflection coefficient is primarily sensitive to near-surface inhomogeneities, the mutual coupling between antenna elements provides strong and reliable sensitivity to internal cavity characteristics. Based on the sensitivity analysis, an optimal operating frequency band centered at 2.76 GHz and an appropriate antenna clearance are identified to maximize detection performance. The proposed antenna and sensing methodology are further validated through experimental measurements, confirming the consistency with numerical results. Simulation results demonstrate cavity-detection sensitivity, while experimental measurements validate the antenna impedance matching and mutual-coupling characteristics. The compact geometry of the antenna enables scalable multi-element configurations, establishing a practical framework for non-destructive, microwave-based monitoring of internal tree degradation in agricultural and forestry applications.

Why it matches plant phenotyping methods植物茎内の空洞という状態をマイクロ波で非破壊検出するアンテナとセンシング手法を開発し、シミュレーションおよび実験で検証しており、植物フェノタイピング手法が中心である。

abstractThis paper presents a novel medium-adaptive wideband near-field antenna for early detection of internal cavities in plant stems, including branches and small trunks.

Deep Learning for Early Detection of Crop Pathogens: A Multimodal Fusion Framework Leveraging Hyperspectral Imaging and Climate Data in Precision Agriculture ↗

This research introduces a multimodal deep learning framework for early detection of plant pathogens to capture pre-symptomatic biochemical changes in plants while simultaneously modeling the environmental drivers of disease development. A hybrid fusion architecture combines 3D convolutional neural networks for spatial-spectral feature extraction from HSI cubes with transformer-primarily based temporal modeling of climate sequences. Cross-modal attention mechanisms dynamically weight discriminative features, which includes chlorophyll degradation bands and humidity thresholds, to permit joint representation learning. The framework achieved 94.5% accuracy in pathogen detection, outperforming unimodal HSI (84.1%) and climate- only (76.5%) baselines by 10-18 percentage points. Moreover, it detected fungal infections 5-7 days before visual symptom onset and had a 12.3% higher F1-rating compared to the current methods. Field simulations showed that precision application resulted in 41% reduction in fungicide use. By connecting proximal sensing with climatic analytics, this research contributes to precision agriculture by providing timely and eco-friendly pest control of diseases. The multimodal fusion framework is introduced to overcome the limitations of unimodal approaches. It integrates the most appropriate data sources, thus allowing the earliest and most accurate detection of plant pathogens.

Why it matches plant phenotyping methods植物の病害状態をハイパースペクトル画像から抽出するマルチモーダル手法の開発・評価が中心であり、単なる病原体診断や農薬施用試験ではない。

abstractThis research introduces a multimodal deep learning framework for early detection of plant pathogens to capture pre-symptomatic biochemical changes in plants

PolliCrop: A high-throughput computer vision pipeline for pollinator monitoring in agroecosystems ↗

Abstract Flower-visiting insect populations are declining since the 1990s, especially because of the decrease of floral resources in agricultural settings. Mass flowering crops can help increase resource availability, and plant breeding can be directed towards selecting varieties attracting more flower-visiting insects. This requires the implementation of an automated high-throughput phenotyping tool for assessing the attractiveness of plant genotypes to flower-visiting insects. In this study, ( i ) we present a procedure to take standardized images of sunflower heads with camera traps continuously at day and night in the field; ( ii ) we trained two versions of a deep learning model, named PolliCrop, to automatically detect and identify the three insect classes visiting the most sunflower (non- Bombus bees, bumble bees, lepidopterans); ( iii ) we assessed and validated the ability of PolliCrop to correctly predict the true visitation frequencies of the insect classes on three sunflower genotypes; ( iv ) we presented two statistical approaches to compare the insect visitation frequencies between plant genotypes, one including weather variables, and the other one without. One PolliCrop version yielded satisfying performance to correctly detect the three insect classes. In particular, it correctly predicted the insect visitation frequencies on two sunflower genotypes in a range of ±10%. The other PolliCrop version can be useful in certain contexts of images and objectives. PolliCrop can be extended in the future to other crop species by training PolliCrop on new images captured in these crops. The field experimental design to set up for comparing the attractiveness between genotypes is also discussed.

Why it matches plant phenotyping methods植物遺伝型の花への訪花昆虫誘引性を推定する画像取得・深層学習パイプラインを開発し、訪花頻度の予測性能も検証しており、表現型取得法が中心である。

abstractwe present a procedure to take standardized images of sunflower heads with camera traps continuously at day and night in the field

Efficient Attention Guided Fusion Model for Plant Disease Classification ↗

Early detection of diseases in plants has been identified as a critical factor for ensuring the maintenance of productivity, preventing economic losses, and promoting sustainable agriculture. Traditional manual approaches for diagnosing diseases are time-consuming, subjective, and inappropriate for large-scale and real-time agriculture. In order to overcome the limitations of traditional approaches, the CNN–CBAM–MobileViTNet has been proposed, an efficient attention-guided network by the fusion of Convolutional Neural Networks (CNNs), Convolutional Block Attention Module (CBAM), and Mobile Vision Transformer (MobileViT) for plant diseases recognition.The CNN component is effective in capturing local visual patterns like lesions, discoloration, and texture. The CBAM component is effective in refining the feature representations by focusing on disease-related spatial areas and useful channels. The MobileViTNet branch is useful in capturing contextual relationships from the leaf areas through lightweight transformer blocks. The CNN–CBAM—MobileViTNet is tested on an enhanced dataset with 38 classes of plant diseases and health conditions, splitting data into 70% training, 15% validation, and 15% testing. Significantly, extensive experimental analysis reveals that the test accuracy is 99%, with high precision, recall, and [Formula: see text]1-score values. Training-validation curves show that the model converges stably with little overfitting, while ROC analysis shows high classwise discrimination ability of the model. Hence, the CNN–CBAM–MobileViTNet model is reliable and may be used for real-world applications in smart agriculture and automatic crop disease monitoring systems.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類するCNN・注意機構・Transformer融合モデルを開発し、38クラスの病害・健全状態で性能評価しており、病害状態の表現型取得・推定が中心である。

abstractthe CNN–CBAM–MobileViTNet has been proposed, an efficient attention-guided network by the fusion of Convolutional Neural Networks (CNNs), Convolutional Block Attention Module (CBAM), and Mobile Vision Transformer (MobileViT) for plant diseases recognition.

Plant area index estimation from UAV LiDAR time-series over cherry orchards ↗

Purpose In recent years, there has been a growing use of unmanned aerial vehicle (UAV) based light detection and ranging (LiDAR) data for mapping plant area index (PAI) in orchards. However, using LiDAR time-series collected throughout the growing season to assess PAI variations in response to phenology, represents an understudied area of investigation. Furthermore, establishing the optimal spatial resolution for mapping biophysical variables of tree crops from LiDAR point cloud data remains poorly defined. Here, we assess the capability of a UAV-based LiDAR system to characterize cherry trees throughout the growing season, with a focus on monitoring PAI and the vertical structure of individual trees. Methods A time-series of 14 point cloud acquisitions with a density of 3300 points/m2 was collected between February and December 2022, covering all phenological stages of a cherry orchard in southern France. A voxel-based method was applied to create a three-dimensional grid within which PAI was estimated for each voxel. PAI was mapped by accumulating the individual voxel-based PAI values within each vertical voxel column. Results The results demonstrate that a voxel size of at least 0.7 m is required to retrieve reliable PAI estimates (RMSE = 0.58 m2.m−2, MAE = 0.48 m2.m−2, bias = 0.19 m2.m−2, rRMSE = 23%, and R2 = 0.51), while a voxel size of 1 m produced the most accurate PAI estimates (RMSE = 0.5 m2.m−2, MAE = 0.41 m2.m−2, bias = 0.07 m2.m−2, R2 = 0.59), when assessed against field-based PAI measurements obtained with a LAI-2200 Plant Canopy Analyzer. The temporal variation of canopy PAI illustrated the progression of key phenological stages, including flowering, leaf development, ripening and senescence, as well as the response of the canopy to drought stress (reduction in PAI due to leaf rolling) during the summer. The maps of PAI successfully described the variations in leaf canopy density for different cherry varieties and allowed assessment of the vertical PAI profile at the individual tree level, which provides valuable insight into tree condition. Conclusion This study confirms that seasonal UAV-LiDAR monitoring is a viable, informative approach for capturing orchard canopy dynamics at the individual tree and sub-canopy level, linking canopy structure to phenology, varietal differences, and stress responses across the growing season.

Why it matches plant phenotyping methodsUAV-LiDARとボクセル法による樹冠PAI・垂直構造の推定手法を開発・検証し、時系列および個体レベルで評価しているため、植物フェノタイピング手法が中心である。

abstractHere, we assess the capability of a UAV-based LiDAR system to characterize cherry trees throughout the growing season, with a focus on monitoring PAI and the vertical structure of individual trees.

HSGAN-based near-infrared hyperspectral reconstruction from characteristic wavelengths images for apple bruise detection. ↗

Hyperspectral images contain richer spectral and spatial information than multispectral images, yet traditional equipment suffers from limitations such as bulky size and complex data processing. This study constructs a task-oriented near-infrared (NIR) hyperspectral reconstruction and detection framework for the precise detection of early apple bruises. First, apple samples were collected using a hyperspectral imaging system. The Weight Extremum Method was employed to screen seven characteristic wavelengths (976.4 nm, 1064.8 nm, 1175.8 nm, 1192.5 nm, 1295.4 nm, 1449 nm, and 1631.6 nm), which were further reduced to three key wavelengths (1064.8 nm, 1175.8 nm, and 1449 nm). Based on the datasets constructed from these bands, the HSGAN framework was used as the reconstruction backbone to reconstruct hyperspectral images ranging from 866 nm to 1701 nm. Results demonstrated that reconstruction performance was optimal with seven input bands (PSNR = 37.81, SSIM = 0.973) and remained favorable with three bands (PSNR = 34.70, SSIM = 0.950). Finally, YOLOv11n was used to detect bruises on both original and reconstructed images. Detection accuracy using reconstructed spectra from the 7-band input approached that of the original images (mAP50 = 0.994), while the 3-band input also maintained high precision (mAP50 = 0.992, Recall = 0.993). These results demonstrate that reconstructing 254 NIR bands from just three characteristic wavelengths is feasible. This framework significantly reduces data acquisition costs while enabling high-precision early bruise detection, offering a practical solution for agricultural quality control.

Why it matches plant phenotyping methodsリンゴ果実の打撲（植物器官の状態）を対象に、少数波長画像からNIRハイパースペクトル画像を再構成し、検出性能を評価する画像・計算フェノタイピング手法が研究の中心である。

abstractThis study constructs a task-oriented near-infrared (NIR) hyperspectral reconstruction and detection framework for the precise detection of early apple bruises.

SLR-YOLO: An Improved YOLO-Based Method for Accurate Detection of Potato Leaf Diseases in Complex Field Images ↗

Potato leaf diseases directly reduce yield and quality, and accurate field detection is important for precision plant protection. However, potato disease lesions are often weak in deep semantic representation, easily disturbed by complex field backgrounds, and variable in multi-scale lesion texture. To address these challenges, this study proposes an improved YOLO-based potato leaf disease detection model. The proposed model enhances the detector through three task-oriented modules. Deep Symptom Enhancement is used to strengthen deep disease feature extraction. Lesion Selection Attention based on large separable kernel attention improves the spatial selection of lesion regions. Multi-Scale Refinement Adapter uses a Mona-based C2PSA structure with two stacked Mona adapters to refine multi-scale texture and lesion-boundary information. Experiments were conducted on a potato leaf disease image dataset using mAP50, average recall (AR), parameters, GFLOPs, and FPS as evaluation metrics. The baseline YOLO26s achieved 81.31% mAP50 and 77.85% AR. The proposed SLR-YOLO model achieved 88.92% mAP50 and 83.51% AR, improving mAP50 and AR by 7.61 and 5.66 percentage points, respectively, while maintaining 118.6 FPS. The results show that the proposed framework improves detection accuracy for potato leaf disease images while retaining practical real-time performance.

Why it matches plant phenotyping methodsジャガイモ葉の病斑・病害状態を画像から推定するYOLOベース手法を開発し、データセット上で精度とリアルタイム性能を評価しており、植物表現型取得手法が中心である。

abstractthis study proposes an improved YOLO-based potato leaf disease detection model

An automated dual-module AI-based solution for early detection and classification of crop diseases and stress conditions ↗

Abstract One of the most important challenges faced by smallholder farmers in the agricultural industry is the lack of accurate, timely knowledge to predict and detect crop health issues. Crop productivity is often threatened not only by diseases but also by environmental and physiological stresses, which contribute to significant yield losses and negatively impact the national economy. Traditional detection methods are time-consuming, costly, and require expert knowledge, creating a need for automated and intelligent systems. This work proposes a dual-functional framework that combines crop disease and stress detection using advanced machine learning and deep learning techniques to accurately classify healthy and diseased leaves. This integrated system ensures early detection, reduces crop loss, improves productivity, and provides a scalable, farmer-friendly solution for sustainable agriculture.

Why it matches plant phenotyping methods葉画像から健康・病害状態を機械学習で分類する手法が研究の中心であり、植物の病害・ストレス状態を直接推定するため、植物フェノタイピング手法として採用。

abstractThis work proposes a dual-functional framework that combines crop disease and stress detection using advanced machine learning and deep learning techniques to accurately classify healthy and diseased leaves.

A Resource-Efficient Framework for Plant Disease Classification Using Classical Image Features ↗

Plants can suffer a number of diseases that impact agricultural productivity and food security, particularly in developing farming communities. Although deep learning is capable of classification of diseases with outstanding results, its use is limited due to the difficulty of obtaining large labeled databases and the high requirement of computation. To address these challenges, this study proposes a new method for plant disease classification based on traditional image processing and machine learning algorithms with lightweight and low computation requirements. This one uses several handcrafted descriptors such as color histograms, Haralick texture features and Hu moments to retrieve the information relevant to a disease from the segmented leaf images. Performance of top five classifiers, namely Random Forest, Support Vector Machine, K-Nearest Neighbors, Logistic Regression and Naïve Bayes classifiers are evaluated from the dataset of healthy plant leaves and diseased plant leaves images on 10-fold cross validation. Based on the results of the research work, the best classification model was the Random Forest Classifier model with the accuracy value is 98.12%, 0.98 precision, 0.98 recall, and 0.98 F1 value. The proposed solution was also found to be uncomputation complex and low memory consuming and can be made realtime inference. Therefore, this solution can be implemented onto agricultural systems, considering edge computing and IoT. The results of the research also demonstrated that feature-based machine learning approaches afford interpretable and reliable plant disease detection at a low computation cost, further contributing to sustainable, and precision agriculture.

Why it matches plant phenotyping methods葉画像から病徴を抽出・分類する画像処理および機械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するため、植物フェノタイピング手法として含める。

abstractthis study proposes a new method for plant disease classification based on traditional image processing and machine learning algorithms

AI-Based Mango Plant Disease Detection System ↗

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.

From Benchmark Accuracy to Field Performance: Hybrid Deep Learning-Based Plant Disease Classification with IoT-Enabled Environmental Monitoring ↗

Accurate detection of plant leaf diseases is essential for enhancing crop productivity and supporting global food security. In addition to disease classification, understanding how environmental and soil conditions affect model performance is important for developing robust real-world agricultural monitoring systems. Although deep learning (DL) models achieve high accuracy on benchmark datasets, their performance in real-world settings is often limited by variations in illumination, background complexity, and environmental conditions. This study proposes a smart DL framework for detecting and classifying multiple leaf diseases in tomato, potato, and pepper plants. The framework combines U2-Net-based leaf segmentation with a Convolutional Neural Network–Bidirectional Gated Recurrent Unit (CNN–Bi-GRU) architecture. MobileNetV2 is employed as the feature extraction backbone to capture spatial characteristics, while Bi-GRU layers model sequential feature dependencies, forming a spatio-temporal network whose architectural design prioritizes parameter efficiency through depthwise separable convolutions and reduced gating complexity. The model was trained and validated using the PlantVillage benchmark dataset and achieved a classification accuracy of 99.8% with a macro-averaged F1-score of 94%, outperforming several state-of-the-art architectures. To assess robustness under real-world conditions, the trained model was further tested on leaf images collected from open-field environments near Eluru, South India. The field evaluation revealed a reduction in classification accuracy to 61.97%, indicating the impact of domain shift and environmental variability. To investigate potential contributing factors, soil parameters, including pH, temperature, moisture, and NPK levels, were monitored using an IoT-based Arduino sensing system over ten consecutive days. Rather than serving as direct inputs to the disease classification model, these environmental measurements were analyzed to assess their potential influence on disease symptom expression and the observed reduction in model performance under field conditions. The results suggest that environmental conditions may influence disease symptom expression and model transferability. This study highlights the importance of integrating DL-based disease recognition with environmental monitoring for reliable field-level agricultural applications. Nevertheless, computational complexity metrics, including inference latency and memory footprint, were not evaluated in the present work and are identified as a priority for future edge deployment studies.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類するセグメンテーション・深層学習手法を開発し、ベンチマークと圃場画像で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。

abstractThis study proposes a smart DL framework for detecting and classifying multiple leaf diseases in tomato, potato, and pepper plants.

GCP-YOLO: high-precision detection of tiny chili flowers in complex greenhouse scenes. ↗

Precise identification of chili flowers and buds is essential for enabling intelligent robotic pollination, continuous crop monitoring, and early yield prediction in protected horticulture. Reliable visual sensing remains challenging because chili targets are extremely small, densely distributed, and frequently occluded by foliage, while greenhouse environments introduce strong illumination variations and background reflections. These factors often lead to insufficient feature extraction and unstable detection accuracy in existing models, limiting their practical deployment in automated monitoring systems. To address these challenges, this study proposes GCP-YOLO, a lightweight yet high-performance detection framework built upon the YOLOv11n architecture. The model enhances small-target perception through three key improvements. First, a redesigned Generalized Feature Pyramid Network (GFPN) strengthens cross-scale feature interaction, improving the fusion of fine-grained texture cues and deep semantic information. Second, a C2CGA context-guided attention module is introduced to emphasize floral structural features while suppressing background noise caused by reflections and canopy clutter. Third, extended multi-scale detection heads (P2-P6) incorporate broader contextual information to reduce missed detections and false positives in dense planting scenarios. Experimental results on a custom chili flower dataset show that the proposed method achieves 92.8% precision, 83.7% recall, 90.8% mAP50, and 72.7% mAP50-95, improving upon the YOLOv11n baseline by 2.1, 1.3, 3.9, and 6.6 percentage points, respectively. Deployment on an NVIDIA Jetson AGX Orin edge platform demonstrates real-time inference at 97.9 FPS, confirming its suitability for on-device phenotyping. Overall, the proposed approach significantly improves detection robustness under complex greenhouse conditions, providing an effective visual sensing methodology for automated crop monitoring and data-driven yield estimation.

Why it matches plant phenotyping methodsチリの花・蕾という植物器官の画像検出手法を中心に、モデル改良、専用データセットでの性能評価、エッジ実装まで行っており、植物表現型取得の技術的貢献が明確である。

titleGCP-YOLO: high-precision detection of tiny chili flowers in complex greenhouse scenes.

Plant−Plant Communication for Systemic Acquired Resistance under Biotic Stress Spatiotemporally Tracked by an In Situ Surface-Enhanced Raman Spectroscopy Aerosol Spraying Analyzer ↗

Abstract This study pioneers a surface-enhanced Raman spectroscopy (SERS) analyzer leveraging engineered Au core/Ag shell nanocubes (Au@AgNCs) to bridge in planta pathogen tracking with airborne defense signal monitoring, enabling unprecedented decoding of plant–plant communication (PPC) kinetics. Within a Pseudomonas aeruginosa (P. aeruginosa)-infected plant biotic stress model, the analyzer achieved: (1) spatiotemporal mapping of virulence kinetics through sensitive detection of P. aeruginosa-specific virulence factor pyocyanin, establishing infection progression timelines and tissue-specific dissemination gradients. (2) Quantification of stress-responsive signaling via dual-functionalized Au@AgNCs, revealing methyl salicylate (MeSA) release kinetics and establishing a direct correlation between pathogen invasion severity and airborne alarm signal—a calibrated defense response heretofore unquantified. (3) Real-time in situ monitoring of MeSA-mediated PPC revealed fundamental plant physiological breakthroughs: First, receiver-specific signaling reprogramming occurs where healthy plants exhibit delayed yet amplified defense hormone kinetics, contrasting sharply with the immediate response of infected emitters. Second, evolutionarily constrained coordination emerges through cross-species signaling divergence, where phylogenetic adaptations in phytohormone perception circuits drive distinct defense strategies−exemplified by Solanaceae amplification versus Poaceae suppression. (4) Validation of systemic acquired resistance (SAR) in PPC-primed plants showing 63.5% reduced infection severity and two days delayed susceptibility. This analyzer integrates molecular-scale pathogen kinetics with ecosystem-level signaling networks, advancing precision agriculture through field-deployable plant immunity diagnostics.

Why it matches plant phenotyping methodsSERSセンサーアナライザーの開発・検証が研究の中心で、植物感染進行、ストレス応答、空中防御シグナル、感染重症度を時空間的に測定するため、植物フェノタイピング手法に該当する。

abstractThis study pioneers a surface-enhanced Raman spectroscopy (SERS) analyzer

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

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

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

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

FewShotCropNet: Real-Time Detection of Emerging Crop Diseases with Limited Labels Using Spectral-Temporal Attention Prototypical Networks ↗

The acquisition of labelled data for new or emerging plant diseases is difficult due to the high cost and logistical complexity of ground-truth collection, challenges that are further compounded by the limited infrastructure available to smallholder farmers in sub-Saharan Africa. This study presents FewShotCropNet, a few-shot learning model based on Spectral-Temporal Attention Mechanisms and Prototypical Networks that utilises multispectral time-series data from Sentinel-2 to classify crop diseases. Two principal innovations are incorporated in the proposed model: (1) a spectral attention mechanism based on the Squeeze-and-Excitation approach to learn disease-relevant spectral band weights; and (2) a temporal attention pooling mechanism to identify the most discriminative growth stages for disease classification. The model employs a two-phase training strategy comprising supervised pre-training followed by episodic meta-learning, enabling the generation of optimal feature representations under extreme label scarcity. Crop disease detection experiments were conducted in Edo State, Nigeria on cassava and maize using monthly Sentinel-2 composites from 2024 (10 spectral bands and five vegetation indices across twelve temporal steps). Under a 4-way 5-shot classification scenario with 100 GPS-validated labelled samples (25 per-class), FewShotCropNet achieved a mean accuracy of 98.15% with a 95% confidence interval of ±0.58%. An equitable comparison was enabled by introducing a Pre-trained Simple Prototypical Network, a variant sharing the same two-phase training strategy as FewShotCropNet but without the attention modules—which achieved 97.75% (±0.63%). FewShotCropNet exceeded the Pre-trained Simple ProtoNet by +0.40 percentage points (t = 1.52, p = 0.13), with the attention module contribution verified as positive though not statistically significant on the current dataset. Statistically significant improvements over models trained without pre-training were observed: FewShotCropNet outperformed the Relation Network (94.40%), Matching Network (94.40%), and the Optimised Baseline Convolutional neural networks (CNN) (86.65%, pre-trained backbone with 5-shot linear probe), with p

Why it matches plant phenotyping methods植物病害状態を対象に、Sentinel-2時系列データから病害を分類するFewShotCropNetを開発し、比較評価しているため、病害フェノタイピング手法が中心である。

abstractThis study presents FewShotCropNet, a few-shot learning model based on Spectral-Temporal Attention Mechanisms and Prototypical Networks that utilises multispectral time-series data from Sentinel-2 to classify crop diseases.

Frequency-domain attention enhanced YOLOv11-EfficientFormerV2 for Tiny lesion detection in complex field plant images. ↗

Introduction To address the challenges of low detection precision, severe background interference, and high model complexity in tiny crop disease lesion detection (defined as lesions occupying 8×8 to 32×32 pixels at 640×640 input resolution) under complex field environments, this study proposes a lightweight detection model named FDA-YOLO by integrating frequency-domain attention and improved YOLOv11. Methods The model employs EfficientFormerV2 as the backbone to extract multi-scale features with low computational cost, and introduces a frequency domain attention module to enhance high-frequency tiny disease lesion details and suppress background noise. Results Comprehensive experiments on the PlantDoc dataset demonstrate that the proposed model achieves 96.3% mAP@0.5, 96.8% precision, and 36.4 FPS with only 28.5M parameters, outperforming the selected baseline detectors under the adopted experimental setting. Discussion The model realizes an optimal balance between accuracy, efficiency, and lightweight performance, providing a reliable and practical solution for real-time tiny lesion detection inprecision agriculture and edge device deployment.

Why it matches plant phenotyping methods植物病斑という植物の病態を画像から検出するモデルを開発し、PlantDocデータセットで精度・速度・計算量を比較検証しているため、植物フェノタイピング手法が中心である。

abstractthis study proposes a lightweight detection model named FDA-YOLO

Integrating Kolmogorov-Arnold networks and sparse attention for robust visual plant disease symptom identification across diverse agricultural crops ↗

Introduction Accurate and rapid diagnosis of plant leaf disease symptoms is critical for sustainable agricultural crop production, yet traditional methods often lack efficiency and robustness under field conditions. Methods Here, we propose a deep learning framework based on an improved Vision Transformer architecture that integrates a dynamic sparse attention mechanism, termed KBTNet, for targeted feature extraction in symptom-affected regions of leaf images. The model incorporates a learnable nonlinear enhancement module to capture subtle visual disease symptom variations such as lesions, discoloration patterns, and spot distributions, and a lightweight Transformer design to reduce computational cost. Results Evaluated on a multisource dataset containing soybean and tomato leaf images representing diverse disease symptom patterns, our approach achieved 93.19% classification accuracy, outperforming current state-of-the-art models. Additional evaluations on public plant disease datasets from multiple crops further demonstrate the model's ability to recognize disease symptom patterns across diverse crop species. Discussion The proposed framework achieves stable performance across diverse crop and disease symptom categories, maintains high efficiency under reduced parameter complexity, and exhibits strong potential for realtime field diagnostics on edge devices. This work provides a scalable and efficient tool for plant disease symptom detection and classification and supports the integration of visionbased intelligence into crop disease monitoring and management systems.

Why it matches plant phenotyping methods植物葉画像から病徴（病斑、変色、斑点分布）を抽出・分類する深層学習手法を開発しており、植物病害状態の表現型取得が研究の中心である。

abstractwe propose a deep learning framework based on an improved Vision Transformer architecture that integrates a dynamic sparse attention mechanism, termed KBTNet, for targeted feature extraction in symptom-affected regions of leaf images.

Additively Manufactured in planta Integrated Microneedle–Microfluidic Sensing: Nondestructive Electrochemical Tracking of Glucose and Water Stress in Agricultural Crop Plants ↗

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

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

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

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

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

A multiregional image–text dataset and benchmark for vision-language modeling of plant diseases ↗

Plant diseases remain a major challenge to global food production, and timely, accurate, and scalable detection of plant stress is critical to reducing these losses. Recent advances in digital imaging and artificial intelligence offer unprecedented opportunities for precision crop disease detection and management. Yet, existing plant disease datasets remain often fragmented across crop and disease systems, and are largely dominated by controlled-environment imagery. The lack of standardized, interoperable, and representative datasets limits reproducibility, transferability, and scalability of AI systems, thereby constraining their deployment in operational agricultural applications. Here we present LeafMD, an integrated multimodal plant disease dataset and benchmark resource that includes LeafNet 2.0, a large-scale multimodal digital image dataset comprising 255,855 image–text pairs across 37 crop species, 197 crop–disease classes, and 9 geographic regions spanning tropical, subtropical, and temperate agricultural systems. Unlike conventional datasets, LeafNet 2.0 integrates biologically grounded symptom descriptions with image-level annotations of early and late disease stages, enabling symptom-aware analysis of disease progression under realistic field conditions. We further introduce LeafBench 2.0 as part of LeafMD, a visual-question answering benchmark covering nine fine-grained plant pathology tasks, including pathogen classification, lesion characterization, symptom interpretation, and disease severity assessment. Evaluation across 16 vision–language models revealed substantial performance gaps between coarse disease recognition and fine-grained pathological reasoning, while agriculture-adapted models consistently outperformed several larger general-domain architectures on symptom-oriented tasks. Together, LeafNet 2.0 and LeafBench 2.0 establish LeafMD as a multimodal resource for developing disease-aware agricultural foundation models and studying fine-grained pathological reasoning in real-world environments.

Why it matches plant phenotyping methods植物病害の画像・症状記述データセットとベンチマークを構築し、病徴解釈・病変特徴・病害重症度評価を対象にモデル性能を評価しており、植物状態の取得・評価手法が中心である。

abstractHere we present LeafMD, an integrated multimodal plant disease dataset and benchmark resource

Using UAV Multispectral Imagery to Predict Leaf SPAD Dynamics During Maize Growth Under Different Plant Densities ↗

Chlorophyll content represents a key growth indicator for maize. The traditional SPAD (Soil and Plant Analyzer Development) method, though easy to operate, is inefficient, destructive, and unsuitable for high-throughput field monitoring. UAV (Unmanned Aerial Vehicle) remote sensing technology is highly efficient and detects abundant indicators, enabling large-scale SPAD measurement. In this study, 18 vegetation indices and eight texture features were selected as the indicator system by combining prior knowledge and experimental analysis. In a two-year maize density experiment, multispectral images were collected in the growth period. The correlations among SPAD values, multispectral indices and texture features were analyzed using Pearson correlation coefficients. Then the detection accuracies of three algorithms, i.e., RF (Random Forest), PLSR (Partial Least Squares Regression), and SVR (Support Vector Regression), were compared under this indicator system. Compared with models constructed using single vegetation indices or single texture features, the estimation accuracy of the indicator system at the jointing stage was improved by 0.13 and 0.22, respectively. The results showed that SVR achieved the highest estimation accuracy among the three algorithms, with determination coefficients (R2) of 0.73, 0.77and 0.70 at the jointing, silking, and grain-filling stages, respectively. This study established a non-destructive monitoring framework for chlorophyll content during the entire maize growth stage based on UAV data.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像と回帰モデルにより、トウモロコシの葉緑素量（SPAD）を非破壊推定する手法を構築し、複数アルゴリズムの精度比較も行っており、表現型取得手法が中心である。

abstractThis study established a non-destructive monitoring framework for chlorophyll content during the entire maize growth stage based on UAV data.

Concept and rule guided neural network for early crop leaf nutrient deficiency diagnosis ↗

Crop nutrition deficiency poses a major challenge to achieving optimal yield, particularly in smallholder farming systems where timely expert diagnosis is limited. Early detection is crucial to minimize losses and reduce unnecessary fertilizer or pesticide usage. While deep learning offers potential for automated visual diagnosis, most existing approaches operate as black boxes and lack interpretability, explainability, or actionable recommendations. In this work, we present a neurosymbolic framework for early nutrient deficiency detection in ash gourd leaves using the EarlyNSD dataset. Our approach integrates a ResNet-50 backbone with a dual-head design: a classification head for deficiency prediction and a concept-prediction head that quantifies physiologically meaningful visual patterns such as yellowing, edge discoloration, spots, and vein greenness. These concept scores are combined with predefined domain rules to guide the learning of the neural component and to generate transparent, human-aligned explanations for each diagnosis. Building on the model outputs, we incorporate a Retrieval Augmented Generation (RAG)-based pipeline along with an agricultural knowledge base to generate targeted recommendations. This approach overcomes key shortcomings of pure neural models by incorporating domain knowledge in the form of differentiable fuzzy logic rules. The study demonstrates that the proposed framework improves both classification performance and interpretability compared to standard ResNet baselines. Grad-CAM analysis demonstrates that concept-guided attention aligns with symptom-specific regions, such as yellowed areas for Nitrogen deficiency or marginal discoloration for Potassium deficiency, providing visual validation of the reasoning process. Since EarlyNSD is limited in scale and visual diversity, the results are not directly comparable to large open-field datasets. Overall, our results establish a proof of concept for integrating neural detection with symbolic reasoning, enabling interpretable, actionable, and domain-informed nutrient management for practical applications.

Why it matches plant phenotyping methods葉画像から栄養欠乏状態と症状形質を推定する解釈可能な画像解析手法が研究の中心であり、植物表現型の取得・抽出に該当する。

abstractwe present a neurosymbolic framework for early nutrient deficiency detection in ash gourd leaves using the EarlyNSD dataset.

Pollen Germination as a High-Throughput Phenotyping Tool for Assessing Heat Tolerance in Soybean ↗

Abstract Background High temperatures during the reproductive stage of soybean severely disrupt reproductive processes and reduce yield. Heat stress causes ultrastructural damage in pollen grains, leading to reduced pollen germination, pollen size and shortened pollen tube length, ultimately lowering seed set and yield. This experiment aimed to evaluate pollen germination as a reliable, scalable phenotyping tool for assessing male gametophytic tolerance to high temperature stress in soybean. Sixteen soybean breeding lines (genotypes) were grown under controlled environments at optimal (28/18°C; day/night) and high temperature (38/28°C; day/night) regimes during flowering. In vitro pollen germination was quantified using a deep learning–based object detection tool to reduce the manual labor and improve accuracy. Several advanced object detection models belonging to the YOLO (You Only Look Once) family, specifically, YOLOv7–YOLOv12, were evaluated to identify the most reliable model. Results Comparative evaluations of different object detection models indicated that YOLOv9 model achieved superior performance in evaluating pollen germination relative to other YOLO models, especially for detecting germinated and non-germinated pollen in complex images. High temperature significantly reduced mean pollen germination from an average of 40% under optimal conditions to an average of 21% under heat stress (P

Why it matches plant phenotyping methodsダイズの耐暑性評価のため、花粉発芽を対象とした画像ベースの深層学習測定法を開発・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 experiment aimed to evaluate pollen germination as a reliable, scalable phenotyping tool for assessing male gametophytic tolerance to high temperature stress in soybean.

Genetic markers of stomatal cluster development in Begoniaceae revealed through trait analysis assisted by interactive deep-learning. ↗

Stomata of plants track the immediate demand for carbon dioxide for photosynthesis while limiting transpirational water loss. Solitary stomatal patterns are common, yet some land plants develop noncontiguous stomatal clustering, where 2 or more stomata occur in groups and overlay a single air cavity. Clustering improves stomatal efficiency, reduces plant water use, and increases resilience to environment stress. How cluster development and physiology interact and integrate with the environment are open questions. Here we used TESSERA, a deep-learning platform for stomatal detection with an interactive interface for data review. Tracking Begonia stomatal clustering patterns across various Begonias, we have uncovered correlations for stomatal clustering traits. The stomatal parameter data were applied to identify genetic loci involved in Begonia stomatal development using quantitative trait locus analysis. Combined with differential gene expression to refine the candidate list, our analysis reveals known and potential new Begonia candidates in stomatal development. As a test of this knowledge, we cloned Begonia SPEECHLESS (BegSPCH), a loci identified in this screen and an established development-related gene in Arabidopsis. Unexpectedly, Arabidopsis spch-3 mutants transformed to express BegSPCH developed stomatal clusters unlike the mutant plants expressing AtSPCH. Thus, various molecular and environmental factors likely overlay transcriptional regulation in stomatal development.

Why it matches plant phenotyping methodsTESSERAによる気孔検出プラットフォームを用いて気孔クラスタリング形質を抽出し、複数のBegoniaで解析しているため、植物表現型取得・解析手法が研究の中心的要素です。

abstractHere we used TESSERA, a deep-learning platform for stomatal detection with an interactive interface for data review.

Enhancing Vision Transformers with Kolmogorov–Arnold Networks for Plant Leaf Disease Classification ↗

Abstract Early and accurate detection of plant diseases is vital for global food security and sustainable agriculture. While deep learning offers promising solutions, there is a continuous need for architectures that enhance learning capacity and efficiency. This study introduces ViT-KAN, an innovative hybrid model merging the powerful feature extraction of Vision Transformers (ViT) with the flexible, learnable activation functions of Kolmogorov-Arnold Networks (KAN). By replacing the standard Multilayer Perceptron (MLP) classification head of ViT with a KAN module, the proposed architecture aims to better capture nonlinear patterns in agricultural images. Evaluated on the PlantVillage dataset for potato and maize leaf diseases using standard fivefold cross-validation, with final results reported as mean ± standard deviation across the five folds, the model was trained entirely from scratch. ViT-KAN achieved 99.49 ± 0.13% accuracy on the maize dataset and 98.28 ± 0.51% on the potato dataset, compared with 98.92 ± 0.40% and 97.77 ± 0.88%, respectively, for the standard ViT model. Beyond mean accuracy, ViT-KAN showed lower standard deviation across folds, while representative fold curves suggested smoother early training trajectories under the shared training configuration. These findings suggest that ViT-KAN is a promising alternative to conventional ViT-based classification models for plant disease diagnosis.

Why it matches plant phenotyping methods植物葉画像から病害状態を分類する新規ViT-KANモデルを開発・交差検証しており、病害表現型の取得・推定手法が研究の中心である。

abstractThis study introduces ViT-KAN, an innovative hybrid model merging the powerful feature extraction of Vision Transformers (ViT) with the flexible, learnable activation functions of Kolmogorov-Arnold Networks (KAN).

AI-Driven Crop Detection and Plant Disease Prediction ↗

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.

Early detection and firmness prediction of apple fruit infested by Bactrocera dorsalis using hyperspectral imaging combined with 1D convolutional neural network ↗

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

Why it matches plant phenotyping methodsリンゴ果実の硬度という植物器官形質と食害状態を、ハイパースペクトル画像および1D CNNで推定する手法が題名上の中心である。

titleEarly detection and firmness prediction of apple fruit infested by Bactrocera dorsalis using hyperspectral imaging combined with 1D convolutional neural network

Artificial Intelligence Adoption in Smart Agriculture: A Review of Convolutional Neural Networks for Plant Disease Detection and Agribusiness Sustainability ↗

Plant diseases remain one of the most persistent and economically damaging threats to global food security, with yield losses across major staple crops running into the tens of billions of dollars each year. The rise of artificial intelligence, and convolutional neural networks (CNNs) in particular, has opened genuinely new possibilities for detecting plant disease early, accurately, and at scale. This review critically examines the state of CNN-based plant disease detection within the wider context of smart agriculture and agribusiness sustainability. Drawing on peer-reviewed literature published between January 2016 to February 2026, the paper traces the evolution of CNN architectures, training methods, and benchmark performance across a wide range of crops and disease categories. Particular attention is given to transfer learning, data augmentation, and lightweight architecture design as responses to the recurring problem of limited annotated training data. The paper also considers how CNNs are being combined with complementary technologies, including the Internet of Things, unmanned aerial vehicles, and edge computing, and what this means for deployment in real farming conditions. Economic and sustainability dimensions are explored throughout, with attention to whether the gains from AI adoption are likely to reach smallholder farmers or remain concentrated among larger, better-resourced agribusinesses. Despite genuinely impressive results under controlled benchmark conditions, several barriers to field deployment persist: dataset bias, poor generalisation in complex agricultural environments, computational constraints, and a continuing shortfall in model interpretability. The review closes by identifying priority research directions, including cross-domain transfer learning, explainable AI, the development of field-representative datasets, and participatory approaches to tool design. Taken together, the evidence suggests that CNN-based disease detection holds real promise for agribusiness sustainability, but realising that promise will depend on sustained interdisciplinary collaboration and deployment strategies that are sensitive to local context rather than assuming one-size-fits-all solutions.

Why it matches plant phenotyping methods植物病害を画像から検出・評価するCNN手法を中心に、モデル、学習法、ベンチマーク、汎化性、データセット、実運用上の課題をレビューしており、植物状態の画像ベース推定に関する方法論的レビューである。

abstractThis review critically examines the state of CNN-based plant disease detection within the wider context of smart agriculture and agribusiness sustainability.

Drone-Based Crop Health Analysis and Precision Agriculture System ↗

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

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

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

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

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

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

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

PLANT DISEASE IDENTIFICATION AND PESTICIDES RECOMMENDATION SYSTEM USING CONVOLUTIONAL NEURAL NETWORK FOR PROTECTION ↗

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.

A Transfer Learning based Plant Leaf Disease Detection Method ↗

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

Why it matches plant phenotyping methods植物葉の病害を画像から検出する計算手法が題名で明示されており、病害状態という植物表現型の推定が中心です。

titleA Transfer Learning based Plant Leaf Disease Detection Method

Field Validation of Chlorophyll Fluorescence Imaging for Early Detection of Wheat Powdery Mildew: Identifying QYmax as a Key Physiological Indicator ↗

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

Why it matches plant phenotyping methodsコムギうどんこ病の早期検出に向け、クロロフィル蛍光画像法を圃場で検証し、QYmaxという植物生理形質を指標化する研究であり、フェノタイピング手法が中心です。

titleField Validation of Chlorophyll Fluorescence Imaging for Early Detection of Wheat Powdery Mildew: Identifying QYmax as a Key Physiological Indicator

A data-centric deep learning framework for Esca disease detection on visible grapevine leaf surfaces under cross-dataset field conditions ↗

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

Why it matches plant phenotyping methodsブドウ葉の可視表面からEsca病を検出するデータ中心型深層学習フレームワークが主題であり、植物の病害状態を画像から推定する方法開発に該当する。

titleA data-centric deep learning framework for Esca disease detection on visible grapevine leaf surfaces under cross-dataset field conditions

A Hybrid Deep Learning Approach for Plant leaf Disease Detection and Classification using YOLO and Transformer-based CNN ↗

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

Why it matches plant phenotyping methods植物葉の病害を画像から検出・分類する深層学習手法が題名上の中心であり、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。

titleA Hybrid Deep Learning Approach for Plant leaf Disease Detection and Classification using YOLO and Transformer-based CNN

Crop leaf disease detection based on improved YOLOv8 ↗

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

Why it matches plant phenotyping methods改良YOLOv8による作物葉の病害検出を主題としており、植物の病害状態を画像から推定する計算・画像ベースのフェノタイピング手法が中心と判断できる。

titleCrop leaf disease detection based on improved YOLOv8

Field Dataset Construction and Real-Time Object Detection for Strawberry Leaf Disease Monitoring in Drone-Based Precision Spraying ↗

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

Why it matches plant phenotyping methodsイチゴ葉の病害状態を画像から検出するデータセット構築とリアルタイム手法が題名上の中心であり、植物病害フェノタイピングおよび評価基盤に該当する。

titleField Dataset Construction and Real-Time Object Detection for Strawberry Leaf Disease Monitoring in Drone-Based Precision Spraying

Attention-Gated Multimodal Fusion for Sustainable Crop Disease Detection Using Simulated Spectral Indices and IoT Sensor Data ↗

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

Why it matches plant phenotyping methods作物病害を対象に、マルチモーダル融合、スペクトル指標、IoTセンサーデータを用いた検出手法が題名上の中心であり、植物の病害状態を推定するフェノタイピング手法と判断します。抄録がないため詳細な植物観測の妥当性は限定的です。

titleAttention-Gated Multimodal Fusion for Sustainable Crop Disease Detection Using Simulated Spectral Indices and IoT Sensor Data

A review: research progress on intelligent technologies for orchard yield monitoring ↗

Accurate yield estimation and crop load monitoring are essential for precision orchard management, supporting targeted fertilization, pruning, thinning, harvest planning, and marketing decisions. However, reliable in-situ monitoring remains challenging because commercial orchards are characterized by severe canopy occlusion, fruit overlap, heterogeneous tree architecture, variable illumination, and complex backgrounds. This review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions. First, yield-related indicators are summarized, including direct phenotypic traits such as fruit number, size, volume, and spatial distribution, as well as indirect structural and physiological proxies such as canopy volume, vegetation indices, flowering intensity, and spectral maturity attributes. Second, representative sensing devices and carrying platforms are reviewed, including red-green-blue (RGB) cameras, red-green-blue-depth (RGB-D) sensors, light detection and ranging (LiDAR), hyperspectral and multispectral systems, unmanned ground vehicles (UGVs), and unmanned aerial vehicles (UAVs). Third, the evolution of estimation methods is discussed, from traditional image processing and machine learning to object detection, instance segmentation, multi-object tracking, point-cloud analysis, remote-sensing regression, and multi-modal fusion. The review shows that no single sensor or algorithm can satisfy all orchard monitoring requirements. Ground-based vision and depth sensing are more suitable for fine-scale fruit counting and sizing, whereas UAV and spectral sensing provide advantages for regional yield mapping and quality-enhanced assessment. Future research should emphasize occlusion-aware perception, robust cross-environment generalization, lightweight edge deployment, standardized benchmarks, and integrated quantity-quality monitoring frameworks for actionable crop load management.

Why it matches plant phenotyping methods果実数・サイズ・体積・空間分布などの植物形質を対象に、センシング機器と画像解析・深層学習による収量推定法を体系的にレビューしており、フェノタイピング手法が中心である。

abstractThis review synthesizes advances in multi-modal sensing and deep learning for orchard yield estimation, breaking down the paradigm into intermediate fruit-counting or crop-load monitoring steps and supplementary spectral quality-assessment dimensions.

Design and Development of a Deep Learning-Based System for Multi-Fruit Disease Classification and Severity Detection Using VGG-16 and VGG-19 Architectures on an Expert-Verified Indian Fruit Crop Dataset ↗

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

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

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

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

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

Crop Disease Detection Using Machine Learning ↗

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.

XAI-Based SmartAgriGo: An Intelligent Agriculture Framework for Transparent Crop Recommendation and Plant Disease Detection ↗

Agriculture in India is challenged due to inappropriate crop selection, climate change, soil nutrient imbalance, and late identification of plant diseases. To overcome these problems, this paper proposes SmartAgriGo, an Explainable Artificial Intelligence (XAI)-based smart agriculture framework for transparent crop recommendation and automated plant dis-ease identification. The proposed framework combines machine learning, and explainable AI for accurate and interpretable agricultural decision support. Crop recommendation is done based on soil nutrients, pH, temperature, humidity and rainfall, where XLNet-based feature extraction and Support Vector Machine (SVM) classification identify the best-suited crop. Plant disease identification is done based on Convolutional Neural Network (CNN) and Softmax classification of leaf images. To improve interpretability, SHAP values are used for crop recommendation, and LIME values are used for disease identification.The interface designed for farmers shows the prediction results with confidence and explanation. SmartAgriGo fills the gap between state-of-the-art AI approaches and real-world agriculture by providing accurate, interpretable, and data-driven agricultural support.

Why it matches plant phenotyping methods葉画像からCNNで植物病害を自動識別する手法が、農業支援フレームワークの主要構成として明示されており、植物の病害状態を画像から推定する中央的な方法貢献がある。

abstractThe proposed framework combines machine learning, and explainable AI for accurate and interpretable agricultural decision support.

TDAVM-UNet: task-driven attention VM-UNet for crop disease detection from UAV imagery ↗

Crop diseases pose a serious threat to agricultural yield and global food security. Accurate detection using Unmanned Aerial Vehicle (UAV) remote sensing imagery is of great significance for precision agriculture. However, this task remains challenging due to complex field backgrounds, diverse spectral-spatial characteristics of diseased leaf regions, irregular lesion boundaries, and variable texture patterns. To address these issues, this paper proposes a Task-Driven Attention VM-UNet (TDAVM-UNet), a novel deep learning model for crop disease detection from UAV imagery. The model integrates two task-driven attention modules: (1) Disease-Aware Dynamic Attention (DADA), which enhances the representation of diseased regions through disease feature enhancement, multi-scale dynamic channel attention, and texture-guided spatial attention; and (2) Channel-Spatial Visual State Space (CSVSS), which enables efficient long-range dependency modeling and local-global feature fusion while maintaining linear computational complexity. A hybrid loss strategy combining binary cross-entropy (BCE) loss, Dice loss, and cross-entropy (CE) loss with optimized coefficients is employed to address class imbalance and boundary delineation challenges. Extensive experiments are conducted on a self-constructed UAV crop disease unified mix dataset, comprising soybean disease images from Maharashtra, India, and rust disease images from wheat, corn, and other crops in Yangling, China, totaling 6,680 raw collected images, which after deduplication yields 5,000 images for experimentation. The results demonstrate that TDAVM-UNet achieves 26.87M parameters and 31.45 GFLOPs for 256×256 inputs, maintaining O(N) linear complexity (80% lower than TransUNet’s 156.78 GFLOPs), with 82.22% mIoU. This work provides a high-accuracy, robust, and computationally efficient method for UAV-based crop disease detection, offering significant technical support for precision agriculture applications.

Why it matches plant phenotyping methodsUAV画像から作物病害の病変領域・病害状態を推定する深層学習モデルを開発し、データセット上で性能評価しており、植物表現型取得・抽出手法が研究の中心である。

abstractthis paper proposes a Task-Driven Attention VM-UNet (TDAVM-UNet), a novel deep learning model for crop disease detection from UAV imagery.

Plant Wearable Sensors: Emerging Technology for Real-Time Plant Monitoring ↗

Plant wearable sensors are emerging as flexible, non-invasive platforms for continuous assessment of plant physiological status and plant–environment interactions. This review examines recent progress in wearable sensing systems for real-time monitoring of water status, growth dynamics, chlorophyll content, volatile organic compounds, humidity, temperature and stress-associated responses. It summarises major sensing approaches, including capacitive, chemical, photodetector-based and piezoresistive sensors, with attention to their materials, fabrication strategies, operating principles and potential applications in plant health monitoring. Advances in flexible substrates, conductive materials, nanostructured sensing layers, biodegradable polymers and wireless communication have improved sensor compatibility with plant surfaces and enhanced the detection of physiological changes under field-relevant conditions. Integration with the Internet of Things, artificial intelligence, machine learning, cloud platforms and data analytics further supports continuous data acquisition and interpretation for precision crop management. These systems may contribute to early detection of biotic and abiotic stresses, enabling timely interventions and improved resource-use efficiency. However, broader adoption remains limited by sensor durability, environmental interference, power requirements, scalability, cost and the complexity of interpreting plant-derived signals. Continued interdisciplinary research is required to develop reliable, affordable, energy-efficient, biodegradable and multifunctional sensing platforms that support sustainable agricultural management under changing environmental conditions.

Why it matches plant phenotyping methods植物の生理状態・成長・クロロフィル・ストレス応答を測定するウェアラブルセンシング手法を中心に扱うレビューであり、植物フェノタイピング手法が中核である。

abstractThis review examines recent progress in wearable sensing systems for real-time monitoring of water status, growth dynamics, chlorophyll content, volatile organic compounds, humidity, temperature and stress-associated responses.

An Edge AI and Mobile Sensing Framework for Real-Time Multi-Crop Disease Detection and Geospatial Surveillance in Smallholder Agricultural Systems ↗

Abstract Maize and cassava are staple crops in Nigeria, but their productivity is limited by viral and fungal diseases. This study created a mobile system based on lightweight CNN for smartphone portable real-time detection of cassava and maize diseases. In the 2025 planting season, 10,800 leaf images were gathered from a 10-acre experimental farm in Araromi Area, Bakatari Farm, Ido Local Government, Ibadan that cut across the seven classes of healthy cassava (1,620, 15%), cassava mosaic disease (1,540, 14.3%), cassava brown streak disease (1,410, 13.1%), healthy maize (1,880, 17.4%), maize leaf blight (1,540, 14.3%), maize rust (1,360, 12.6%) and maize streak virus (1,450, 13.4%). In order to make the dataset more diverse, data augmentation was done in the form of rotation by +/-30°, flipping, brightness by +/-20% and random cropping in the range of 80-100%. Lightweight CNN architectures MobileNetV2, EfficientNet-Lite, ShuffleNet, and custom CNN were trained in an 80:20 ratio for train and test. Out of 11 models tested, EfficientNet-Lite model forecast the highest where it achieved an accuracy of 94.6%, precision of 0.95, recall of 0.94, F1-score of 0.94, and an ROC-AUC of 0.97. As for MobileNetV2, it achieved an accuracy of 93.8% while ShuffleNet was estimated to achieve the fastest mobile inference at 65 ms. As for the class-wise analysis, it can be seen that the maize leaf blight (95.2%) and cassava mosaic disease (94.1%) had the most accurate predictions. The offline prediction from mobile deployment showed that EfficientNet-Lite occupied 92 ms and 135 MB. The findings show that low-cost and practical smartphone-based disease diagnosis requires the use of lightweight CNN models that can deliver accuracy and alertness that can allow farmers to manage the crop and food security on their own. Smallholder farmers in a resource-poor rural environment will be able to benefit from these models.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定するCNNベースのモバイル画像解析手法を開発・比較・実装しており、植物状態の取得と技術性能評価が研究の中心である。

abstractThis study created a mobile system based on lightweight CNN for smartphone portable real-time detection of cassava and maize diseases.

Multimodal Deep Learning for Pest and Disease Recognition and Crop Growth Assessment in Open-Field Agricultural Environments ↗

Against the backdrop of the rapid development of smart agriculture, pest and disease monitoring and crop growth assessment for large-scale farmlands are of substantial importance for precision management and risk early warning. However, traditional unimodal visual methods are highly susceptible to illumination variation, canopy occlusion, scale differences, and background interference in real field environments, and thus fail to make full use of environmental sensing information and spatial priors. To address these issues, a multimodal target perception framework for intelligent farmland inspection is proposed in this study. By jointly integrating UAV imagery, time-series data from ground Internet of Things sensors, and spatial positional information, joint modeling of pest and disease recognition and crop growth assessment is achieved through cross-modal alignment and collaborative encoding, multi-scale target perception, and dynamic multimodal fusion and decision-making. Experimental results demonstrate that, in the pest and disease recognition task, the proposed method achieved a Precision of 91.63%, a Recall of 90.27%, an F1-score of 90.94%, and an mAP of 93.15%, significantly outperforming comparison models such as Faster R-CNN with ResNet50 backbone, YOLOv8-m, Swin Transformer-Tiny, and Multimodal Transformer. In the crop growth assessment task, an Accuracy of 89.96%, a Precision of 89.11%, a Recall of 88.74%, and a Macro-F1 of 88.92% were achieved, again clearly exceeding those of ResNet50, EfficientNet-B3, ViT-B/16, and conventional multimodal fusion models. The ablation study further verified the effectiveness of the cross-modal alignment module, the multi-scale target perception module, and the dynamic fusion module, with the complete model reaching 90.94%, 93.15%, and 88.92% in Pest F1, Pest mAP, and Growth Macro-F1, respectively. Furthermore, the net economic return regression experiment at the unit-area level further demonstrates that the proposed method can effectively connect state information with economic outcomes, showing strong application potential in return prediction, performance evaluation, and resource allocation optimization. These findings indicate that the proposed method can effectively improve perception accuracy and robustness in complex farmland environments, thereby providing reliable technical support for intelligent inspection, pest and disease early warning, and precision management in agricultural scenarios.

Why it matches plant phenotyping methodsUAV画像、IoT時系列データ、空間情報を統合したマルチモーダル手法を開発し、作物生育状態の評価を技術的に検証している。害虫認識単独ではなく、植物の生育評価を含む取得・推定手法が中心である。

abstracta multimodal target perception framework for intelligent farmland inspection is proposed in this study.

Improved Agricultural Monitoring through Automated Detection and Classification of Purple Blotch and Yellow Blotch Diseases on Onion Leaves utilizing Dual Branch Graph Neural Network ↗

Traditional visual inspection is replaced by plant disease detection (PDD), which integrates plant pathology, image processing, remote sensing, agronomy and machine learning. Remote sensing tools and high-resolution drone imagery enhance real-time disease monitoring. However, challenges like data scarcity and algorithm generalization make accurate and automated disease diagnosis more complex. In this manuscript, Improved Agricultural Monitoring through Automated Detection and Classification of Purple Blotch and Yellow Blotch Diseases on Onion Leaves utilizing Dual Branch Graph Neural Network (ADC-PBYBD-OL-DBGNN) is proposed. The onion leaf dataset was first collected in the Karnataka village of Chilwadigi, which has a variety of climates. Then the collected images are preprocessed using Distributed Adaptive Spatial Filtering (DASF) is used for resizing and cropping. The preprocessed images are fed to Onion Leaf Disease Detection utilizing Dual Branch Graph Neural Network (DBGNN) for detecting and classifying as Healthy, Iris Yellow Virus, Purple Blotch and Leaf Blight. DBGNN generally doesn't show any adaptation of optimization techniques for figuring out the best parameters to guarantee precise onion leaf detection. Hence, Superb Fairy-wren Optimization Algorithm (SFOA) is utilized to optimize DGRGNN for precisely classifying the onion leaves. Then the proposed ADC-PBYBD-OL-DBGNN is implemented and the performance metrics like Accuracy, Precision, Recall, F1 score, Computational time are analyzed. Finally, the performance of proposed ADC-PBYBD-OL-DBGNN method provides 26.68%, 25.75%, and 26.16% higher accuracy and 27.49%, 24.75%, and 25.85% higher precision while compared with existing methods such as the onion plant leaf image dataset for classification and detection (OPLM-CD-Xpection), the onion and maize image datasets for creating AI-based classification models for pests and diseases (OMI-PD-CNN) and the meta-learning based neural architecture search for automated generation of deep neural networks for plant disease detection tasks (SAG-PDD-DNN) correspondingly.

Why it matches plant phenotyping methodsタマネギ葉の画像から病害状態を検出・分類する画像ベースの植物フェノタイピング手法を提案し、性能比較まで行っており、方法開発が中心である。

abstractplant disease detection (PDD)

SLIF-Tomato: Inverted Residual Convolutional Block Attention Module for In-field Tomato Leaf Disease Recognition ↗

Abstract Tomato cultivation represents a critical component of nutrition, economic development, and public health, yet it is increasingly compromised by foliar diseases that diminish yield and intensify dependency on hazardous agrochemicals. Although deep learning models have demonstrated strong capabilities for automated disease recognition, existing benchmark datasets exhibit limited real-world utility, primarily due to the absence of in-field imagery and precise annotations of diseased regions. A novel attention mechanism, I nverted R esidual C onvolutional B lock A ttention M odule ( IR-CBAM ), is proposed, combining inverted residual blocks with the CBAM Module, and is specifically tailored to address challenges posed by in-field image variability, such as complex backgrounds and inconsistent lighting. Furthermore, this study introduces SLIF-Tomato , the S ri L ankan I n- F ield Tomato leaf disease dataset, which is the first complete in-field dataset comprising class labels and bounding box annotations collected under diverse real-world conditions. The proposed approach achieved 99.66% and 99.91% accuracy rates on two curated versions of the SLIF-Tomato dataset. Subsequently, the YOLOv12-large model is employed to detect diseased regions, which obtained an average precision score of 88.5%. These contributions advance the development of accurate, efficient and field-adaptable diagnostic systems for tomato leaf disease management in precision agriculture.

Why it matches plant phenotyping methodsトマト葉の病害領域を画像から検出・認識する手法を開発し、実圃場データセットも構築・評価しており、植物病害状態の表現型取得が中心的です。

abstractA novel attention mechanism, I nverted R esidual C onvolutional B lock A ttention M odule ( IR-CBAM ), is proposed, combining inverted residual blocks with the CBAM Module, and is specifically tailored to address challenges posed by in-field image variability, such as complex backgrounds and inconsistent lighting.

Cotton Leaf Spot Detection Based on an Improved YOLOv11n Model. ↗

In cotton disease detection, the complex farmland environment and the varying scales of disease spots, especially the presence of small-target disease spots, limit the detection accuracy of lightweight models. To address this issue, an improved YOLOv11n detection algorithm is proposed. First, the backbone network is reconstructed using the GhostConv (G-conv) module, which generates redundant feature maps through linear operations, thereby reducing computational complexity. Second, an Adaptive Calibration and Feature Fusion Architecture Head (ACFFA) with prior calibration and cross-scale fusion capabilities is constructed in the detection stage to handle the problem of varying disease spot scales. Furthermore, the Adaptive Scale-aware Wise Intersection over Union (AS-WIoU) loss function, improved from WIoUv3, is introduced to enhance the stability of bounding box regression and improve detection accuracy for low-resolution, small-target lesions. Experimental results show that on the cotton disease dataset constructed based on the Mendeley Data database, the proposed model achieves mAP 50 and mAP 50-95 of 90.30% and 73.84%, respectively, with precision and recall of 92.33% and 87.68%, and a parameter count of 3.81 M. The algorithm significantly improves detection accuracy while maintaining efficient inference, making it suitable for real-time monitoring tasks on agricultural embedded terminals.

Why it matches plant phenotyping methods綿花葉の病斑を画像から検出・定量する改良YOLO手法が研究の中心であり、植物の病害状態を直接推定する画像ベースのフェノタイピングに該当する。

abstractan improved YOLOv11n detection algorithm is proposed

Multi-Modal Learning with Explainable Artificial Intelligence for Crop Analysis: A Comprehensive Review ↗

In modern agriculture, artificial intelligence (AI) is doing excellent work in crop monitoring, crop disease detection, crop yield prediction, and crop stress assessment. Various techniques such as deep learning, generative models, vision transformers, explainable AI (XAI), multimodal fusion, etc., have helped in building intelligent crop analysis. This paper provides the comparative crop analysis of various crop species, data modes, and environmental conditions for the review of benchmark studies for the current framework, experimental methodologies, and datasets. The important challenges and open issues identified are limited field datasets, class imbalance, dataset bias, high computational complexity, privacy concerns, etc. Based on these, we suggested future work that can include foundation models, digital twin techniques, federated learning, multimodal frameworks, and interpretability architecture. This review provides a review for creating reliable, scalable, and sustainable AI-driven crop analysis systems. In addition to that, the survey seeks to give researchers and AI practitioners a comprehensive analysis of the current situation.

Why it matches plant phenotyping methods作物の病害・収量・ストレス評価を対象に、AI手法、データモード、ベンチマーク、実験方法、データセットを体系的にレビューしており、植物表現型取得・推定手法のレビューが中心です。

abstractThis paper provides the comparative crop analysis of various crop species, data modes, and environmental conditions for the review of benchmark studies for the current framework, experimental methodologies, and datasets.

DFA-YOLO: an enhanced YOLOv11-OBB and knowledge distillation-based maize stomata detection system. ↗

Introduction Stomata are vital gatekeepers of plants that regulate the fundamental trade-off between carbon gain and water loss. Precise, high-throughput identification of stomatal traits is therefore essential for assessing plant stress tolerance and water-use efficiency. However, conventional bounding box detection struggles to accurately localize densely distributed and arbitrarily oriented stomata. Methods This study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction. Based on a maize stomatal dataset expanded from 1,053 to 3,597 microscopic images, DFA-YOLO integrates three task-specific components: (1) a cross-dataset MGD distillation strategy that transfers structural priors from single-stomata images to dense multi-stomata scenes; (2) a fixed-threshold Focaler-CIoU localization-loss reweighting strategy (u stomata = 0.95, d stomata = 0.00) to emphasize hard positive samples during OBB regression; and (3) a C3k2_AssemFormer feature-aggregation module that combines convolutional local feature extraction with linear attention-based context aggregation. Results DFA-YOLO achieved 94.1% mAP50, 84.8% mAP75, 74.1% mAP50-95, and 90.0% recall, with higher recall, mAP50, mAP75, and mAP50-95 than the YOLOv11-OBB baseline under the same OBB evaluation protocol. When deployed on an automated platform, the system processed images at 44.9 FPS and supported stomatal localization, density estimation, and preliminary orientation-aware size description. Discussion Under the tested maize microscopic imaging workflow, DFA-YOLO enables rapid extraction of detection-oriented stomatal traits and provides a prototype tool for high-throughput maize stomatal phenotyping.

Why it matches plant phenotyping methodsトウモロコシ気孔の画像検出・配向局在化と形質抽出を目的とするYOLOベース手法を開発し、データセット、精度比較、自動化プラットフォームでの性能を評価しているため、植物フェノタイピング手法が中心である。

abstractThis study proposes DFA-YOLO, an enhanced YOLOv11-OBB (Oriented Bounding Box) model for orientation-aware maize stomatal localization and preliminary OBB-derived trait extraction.

A study on plant disease and pest detection and counting based on multi-scale enhancement and cross-scale fusion. ↗

This paper targets the practical needs of plant disease and pest object detection and counting in complex field environments and proposes a lightweight improved framework based on YOLOv10. A MEMBA-F multi-scale feature enhancement module is introduced on the Neck to strengthen representations of small targets and weak-texture lesions, and a CSCAF cross-scale context-aware fusion module is designed to adaptively align high-level semantics with low-level details via cross-scale attention and gated selection, suppress background interference, and improve localization stability. The proposed method is systematically compared with two-stage detectors, YOLO-series models, and Transformer-based detectors on three public datasets, and is further investigated through ablation studies, confusion matrix analysis, and Grad-CAM interpretability analysis. In addition, a density-binned counting evaluation is conducted to validate robustness from sparse to dense scenarios. Experimental results demonstrate that the proposed method achieves superior performance in Precision, Recall, mAP@50, and mAP@50-95, and significantly reduces counting errors in dense scenes under deployable inference cost, providing reliable support for precision plant protection monitoring and decision making.

Why it matches plant phenotyping methods植物病害の病変と害虫を画像から検出・計数するYOLOv10改良法を開発し、複数データセット、比較実験、アブレーション、密度別評価で性能検証しており、植物の病害状態の取得方法が中心である。

abstractThis paper targets the practical needs of plant disease and pest object detection and counting in complex field environments and proposes a lightweight improved framework based on YOLOv10.

Formal Verification and Deep Learning for Smart Farming: A Robust Framework for Cotton Crop Monitoring ↗

Cotton, a critical global cash crop, faces significant challenges in disease detection due to wetland conditions and climate-change inconsistency. This work presents a Cotton Crop Disease Detection Model that integrates an EfficientNet--Convolutional Neural Network (CNN) architecture with the Temporal Logic of Actions (TLA+) for formal verification. The proposed model ensures accurate disease classification while providing formal verification for correctness, reliability, and availability. The EfficientNet--CNN demonstrates robust performance in identifying multiple disease conditions, including aphids, armyworms, and bacterial blight, achieving an overall weighted accuracy of 94%, with macro-average scores of 0.94 for precision, recall, and F1-score. Class-specific performance shows an F1-score of 97% for armyworms and 96% for powdery mildew. The TLA+ formal verification validates the model's compliance with disease-monitoring requirements, ensuring correctness, reliability, and availability in real-world industrial applications. This integrated framework enhances cotton crop disease detection and supports sustainable, technology-driven agricultural practices.

Why it matches plant phenotyping methods綿花の病害状態を画像ベースのCNNで分類する手法を開発・検証しており、植物の状態推定が中心的な技術貢献である。

abstractThis work presents a Cotton Crop Disease Detection Model that integrates an EfficientNet--Convolutional Neural Network (CNN) architecture with the Temporal Logic of Actions (TLA+) for formal verification.

An automated unmanned aerial vehicle framework for non-destructive soybean leaf morphology extraction using a structure-adaptive deep learning model ↗

Soybean leaf morphology is an important breeding trait that requires large-scale phenotyping in commercial breeding programs. Conventional leaf phenotyping still relies on manual destructive measurements, which are labor-intensive and inefficient. Low-altitude unmanned aerial vehicles (UAVs) have emerged as a high-throughput phenotyping platform, but the retrieval of leaf morphology in densely occluded canopies remains challenging due to complex canopy backgrounds, illumination heterogeneity, and leaf overlap under field conditions. To address this issue, this study developed an integrated UAV framework that couples a YOLOv10-based leaf detection module, a novel structure-adaptive segmentation network (DynamicU), and a regression-based trait prediction model for retrieving a set of leaf morphological parameters across 273 soybean genotypes under field conditions. The YOLOv10 detector reliably localized individual leaves under complex canopy conditions, achieving a mean average precision (mAP@50) of 0.84. Subsequently, the DynamicU network, whose architecture was automatically optimized via Emperor Penguin Optimization, achieved a segmentation accuracy of 97.2% and a mean Intersection over Union of 93.8%, substantially outperforming conventional models. Using random forest regression, the framework retrieved relative leaf shape traits, including length-to-width ratio and dissection index, with markedly higher accuracy (R 2 =0.97), compared to absolute morphological traits, including leaf length, width, perimeter, and area (R 2 : 0.76–0.84). Notably, relative leaf shape traits showed positive associations with oil yield per plant and protein yield per plant, supporting their potential as complementary indicators for screening soybean germplasm with differential industrial product output. This end-to-end framework establishes a reliable bridge between UAV remote sensing and leaf-level morphological quantification, advancing high-throughput phenotyping capabilities to support precision breeding in soybean.

Why it matches plant phenotyping methodsUAV画像、葉検出・セグメンテーション・回帰モデルを統合し、圃場でダイズ葉形態を自動定量するフレームワークの開発と性能評価が研究の中心であるため。

abstractthis study developed an integrated UAV framework that couples a YOLOv10-based leaf detection module, a novel structure-adaptive segmentation network (DynamicU), and a regression-based trait prediction model for retrieving a set of leaf morphological parameters across 273 soybean genotypes under field conditions.

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

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

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

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

New computer vision tools help to assess wheat ear diseases ↗

PHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases, with two AI-powered applications now reaching proof-of-concept stage. The first, FUSASEYD, addresses Fusarium Head Blight (FHB), a major fungal disease in winter wheat. Using RGB field images and a deep learning instance segmentation model (YOLOv11), the application detects and quantifies FHB symptoms on wheat ears, offering an automated alternative to time-consuming expert visual scoring. GEVES has developed both a PC interface and a smartphone application to visualise model predictions in the field. Validation in French registration trials is planned for the 2026 campaign. A companion article by V. Cadot et al. is currently under review in the Journal of Experimental Botany special issue on Plant Phenomics & Enviromics Across Scales. The second, COYL (Counting Orange and Yellow Larvae), tackles a practical challenge faced by breeders, and rapidly counting wheat blossom midge larvae, both Sitodiplosis mosellana and Contarinia tritici, to characterise variety susceptibility. Using smartphone RGB images and YOLOv-based object detection, the best-performing model achieved high accuracy and successfully distinguished between the two visually similar species. An online counting application has been developed, currently accessible to Walloon Agricultural Research Centre members. The labelled COYL-1 dataset is publicly available at https://doi.org/10.5281/zenodo.19402333 for community use. A companion article by Antoine Deryck et al. is under submission at Plant Phenomics Journal.

Why it matches plant phenotyping methodsRGB画像と深層学習によるコムギ穂の病徴検出・定量化を開発し、専門家評点の自動化とセンサー／画像法の検証を目的とするため、植物フェノタイピング手法が中心である。

abstractPHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases

New computer vision tools help to assess wheat ear diseases ↗

PHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases, with two AI-powered applications now reaching proof-of-concept stage. The first, FUSASEYD, addresses Fusarium Head Blight (FHB), a major fungal disease in winter wheat. Using RGB field images and a deep learning instance segmentation model (YOLOv11), the application detects and quantifies FHB symptoms on wheat ears, offering an automated alternative to time-consuming expert visual scoring. GEVES has developed both a PC interface and a smartphone application to visualise model predictions in the field. Validation in French registration trials is planned for the 2026 campaign. A companion article by V. Cadot et al. is currently under review in the Journal of Experimental Botany special issue on Plant Phenomics & Enviromics Across Scales. The second, COYL (Counting Orange and Yellow Larvae), tackles a practical challenge faced by breeders, and rapidly counting wheat blossom midge larvae, both Sitodiplosis mosellana and Contarinia tritici, to characterise variety susceptibility. Using smartphone RGB images and YOLOv-based object detection, the best-performing model achieved high accuracy and successfully distinguished between the two visually similar species. An online counting application has been developed, currently accessible to Walloon Agricultural Research Centre members. The labelled COYL-1 dataset is publicly available at https://doi.org/10.5281/zenodo.19402333 for community use. A companion article by Antoine Deryck et al. is under submission at Plant Phenomics Journal.

Why it matches plant phenotyping methodsRGB画像と深層学習によりコムギ穂の病徴を検出・定量する手法の開発とセンサー／画像手法の検証が中心であり、植物病害表現型の取得に該当する。

abstractPHENET's Use Case 1 on plant health is validating sensors and imaging methods for the assessment of wheat ear diseases

Assessing plant water status: Part 2 – Non‐destructive and remote sensing approaches ↗

Precise, real time and non-destructive assessment of plant water status is important for advancing plant physiological understanding, optimizing water usage, improving crop resilience and supporting precision agriculture in the face of increasingly variable climatic conditions. Classical methods for measuring plant water status reviewed in Part 1 of this two-part review have significant limitations for field level applications, providing only discrete, single-point measurements and potentially altering plant physiology through destructive sampling. This second of a two-part review synthesizes recent advances in non-destructive approaches for measuring plant water status, evaluating their principles, applications and limitations. We review techniques such as ZIM-probe, terahertz spectroscopic techniques, microwave remote sensing, infrared transmission sensor, microtensiometers, dendrometers and leaf thickness sensors, light detection and ranging (i.e. LiDAR), imaging spectroscopy, NMR relaxation, spectroscopy based on equivalent water thickness, spectral indices, derivative spectra, post-continuum removal indicators, visible and near-infrared spectroscopy, and infrared thermography. These emerging techniques facilitate high-resolution, real-time monitoring of water status across leaf, canopy and ecosystem scales. This comprehensive comparison provides guidance for selecting most appropriate technique based on experimental objectives, guiding applications ranging from single leaf to canopy scale ecosystem assessment. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Why it matches plant phenotyping methods植物の水分状態を非破壊・遠隔センシングで測定する手法を体系的に比較・評価したレビューであり、植物フェノタイピング手法が中心です。

abstractThis second of a two-part review synthesizes recent advances in non-destructive approaches for measuring plant water status, evaluating their principles, applications and limitations.

Lightweight Real-Time Detection Transformer for Tomato Leaf Disease Recognition in Complex Agricultural Scenarios ↗

Abstract Accurate detection of tomato leaf diseases is essential for sustainable tomato production. To overcome the limitations of existing detection models, such as large parameter sizes, insufficient accuracy, weak robustness, and poor small-target detection performance, this study proposes a lightweight tomato leaf disease detection algorithm based on an improved Real-Time Detection Transformer (RT-DETR), named Tomato Leaf Diseases RT-DETR (TLD-RTDETR). Specifically, a partial convolution-based PConvBlock is introduced into the backbone to enhance feature extraction while reducing model complexity. In addition, a Coordinate Attention-based hierarchical feature pyramid module (CA_HSFPN) is designed to suppress background interference and strengthen small-target feature representation. Furthermore, a learnable positional encoding strategy is integrated into the feature encoding stage to improve the extraction of critical disease features in complex environments. Experimental results show that TLD-RTDETR achieves an mAP of 92.9%, precision of 94.2%, and recall of 87.5% on the tomato leaf disease dataset, outperforming the RT-DETR-R18 baseline by 2.4%, 0.5%, and 3.3%, respectively. Meanwhile, the model size, parameter count, and computational cost are reduced by 38.1%, 38.1%, and 31.2%. Compared with mainstream methods, the proposed model achieves better detection performance with a more lightweight architecture. Additional visualization, anti-interference, and generalization experiments further verify its robustness and cross-scene adaptability, demonstrating its potential for practical deployment in tomato leaf disease detection.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出・認識する軽量Transformer手法を開発し、精度・頑健性・汎化性能を検証しており、植物病害状態のフェノタイピング手法が中心である。

abstractthis study proposes a lightweight tomato leaf disease detection algorithm based on an improved Real-Time Detection Transformer (RT-DETR), named Tomato Leaf Diseases RT-DETR (TLD-RTDETR).

GuavaVision AI: An Explainable Deep Learning Framework for Automated Classification, Lesion Localization, and Segmentation of Guava Diseases ↗

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

Plant Disease Detection Using CNN and GAN ↗

Agriculture is a critical sector for global food security, but plant diseases and nutrient deficiencies remain major challenges that reduce crop yield and economic returns for farmers. Traditional diagnosis methods depend on manual observation and expert intervention, which are often time-consuming, subjective, and inaccessible in remote regions. This paper presents an intelligent crop health analysis system that automates the detection of plant diseases and nutrient deficiencies using a hybrid deep learning framework. The proposed approach integrates Convolutional Neural Networks (CNNs) for feature extraction and classification with Generative Adversarial Networks (GANs) for synthetic image generation and dataset augmentation. The CNN model learns discriminative features such as color variations, texture patterns, and lesion characteristics from leaf images, while the GAN enhances dataset diversity by generating realistic samples, thereby addressing class imbalance and limited training data. The system is trained on a dataset containing more than 55,000 leaf images across 32 classes and is deployed through a Flask-based web application. It supports two operational modes: Basic Mode for disease identification and Advanced Mode for comprehensive crop health assessment through the integration of CNN-based predictions and rule-based nutrient analysis. Experimental results demonstrate improved classification performance, robustness, and scalability under real-world conditions. Additionally, the multilingual user interface enhances accessibility for farmers from diverse linguistic backgrounds. The proposed system provides an effective and practical solution for early crop health monitoring, enabling timely intervention, reducing dependency on agricultural experts, and contributing to increased agricultural productivity and sustainable farming practices.

Why it matches plant phenotyping methods葉画像から植物病害と栄養欠乏を推定するCNN・GAN手法と運用システムが研究の中心であり、植物の病徴・健康状態を直接評価する画像ベース表現型計測に該当する。

abstractThis paper presents an intelligent crop health analysis system that automates the detection of plant diseases and nutrient deficiencies using a hybrid deep learning framework.

Apple Leaf Disease Detection Based on Improved YOLOv11 with DSSA Mechanism. ↗

Visual inspection of apple leaf diseases is inefficient and subjective, limiting large-scale orchard applications. To realize rapid and accurate disease identification, this paper proposes an improved YOLOv11 model integrated with a Dual Sparse Selection Attention (DSSA) module. By embedding the DSSA module into the key layers of the YOLOv11 backbone network, the model enhances fine-grained feature extraction for small and complex lesions while suppressing background interference. A tailored training strategy with an optimized learning rate and optimizer is designed to ensure stable convergence. Experiments are conducted on a dataset consisting of 7594 images covering four categories: black rot, rust, scab, and healthy leaves. The proposed model achieves precision of 0.973, recall of 0.978, mAP50 of 0.991, and 0.949 mAP50-95, outperforming YOLOv8, YOLOv9, YOLOv10, and the vanilla YOLOv11. Furthermore, a Qt-based visualization system is developed for practical orchard deployment. This method provides a reliable solution for intelligent apple leaf disease detection and smart orchard management.

Why it matches plant phenotyping methodsリンゴ葉の病斑・健全状態を画像から推定する検出モデルを開発・比較し、実用システムまで構築しており、植物病害表現型の取得手法が中心である。

abstractVisual inspection of apple leaf diseases is inefficient and subjective, limiting large-scale orchard applications.

A task-specific architecture with multi-scale attention and shape-aware loss for strawberry phenophase recognition in complex fields. ↗

To address the challenges of recognizing small strawberry targets and achieving accurate phenological perception in complex field environments, this paper proposes a novel end-to-end lightweight detection architecture named HCMS-Net. The backbone is a Residual Efficient Layer Aggregation Network (R-ELAN) enhanced with a Multi-Scale Convolutional Attention (MSCA) mechanism, which emphasizes subtle color and texture variations to differentiate key phenological phases. For feature fusion, hypergraph convolution (from HyperC2Net) and a Mixed Aggregation Network (MANet) are incorporated, modeling the clustered morphology of strawberries and strengthening the representation of sparse small fruits. The detection head incorporates a lightweight Conv2Former module to capture long-range dependencies and spatial contextual information across growth stages, thereby enhancing the model's capacity to represent continuous phenological changes. A Shape-Normalized Wasserstein Distance (Shape-NWD) loss is introduced to stabilize optimization against minor pixel deviations. Experimental results demonstrated that HCMS-Net achieved a mean average precision (mAP) of 94.9% and an F1-score of 90.0%. Specifically, the average precision (AP) values for the flowering, young fruit, green fruit, veraison, and mature fruit stages reached 99.3%, 88.3%, 90.9%, 97.0%, and 98.2%, respectively. Heatmaps confirmed HCMS-Net's precise attention focus across all five phenological stages, effectively suppressing irrelevant backgrounds. Compared to ten mainstream detectors, HCMS-Net surpassed alternatives such as RT-DETR and the YOLOv5n to v13n by 3.4-8.0 percentage points in mAP. It even surpassed YOLOv12s by 2.7 percentage points, while containing only 32.86% of its parameters. The model offers high accuracy and efficiency for phenological period detection, supporting selective harvesting and intelligent agricultural management.

Why it matches plant phenotyping methodsイチゴの生育フェノフェーズを画像から認識する新規検出モデルを開発し、複数手法との性能比較・検証を行っているため、植物フェノタイピング手法が中心である。

abstractthis paper proposes a novel end-to-end lightweight detection architecture named HCMS-Net

AgroPulse: A Real-Time Field Intelligence System for Crop Disease Tracking and Notification System ↗

Agriculture faces significant challenges due to plant diseases, which directly affect crop yield, quality, and farmer income. Early detection of agricultural diseases is critical to prevent large-scale crop losses and reduce excessive use of pesticides. Traditional disease detection methods rely on manual inspection by farmers or agricultural experts, which is time-consuming, subjective, and often inaccurate, especially during early stages of infection. With the advancement of Machine Learning (ML) and Internet of Things (IoT) technologies, automated and intelligent solutions for crop disease detection have become feasible. The IoT-Based Crop Disease Recognition and Field Notification System proposes an intelligent system that combines machine learning–based image analysis with IoT-enabled monitoring to detect crop diseases at an early stage. The system uses an ESP32 microcontroller integrated with an ESP-CAM module to capture images of plant leaves. These images are analyzed using trained machine learning models to identify disease patterns and abnormalities. The detection results are communicated through an IoT platform, enabling remote monitoring and real-time alerts. An LCD display provides local status information, while a buzzer generates immediate alerts when a disease is detected. The system is designed to be cost-effective, scalable, and suitable for deployment in real agricultural environments. By enabling early disease identification and timely intervention, the proposed solution helps improve crop productivity, reduce losses, and promote smart and sustainable agricultural practices.

Why it matches plant phenotyping methods植物葉の画像を機械学習で解析し、病徴・異常を検出するシステムが研究の中心であり、植物病害状態の画像ベースフェノタイピングに該当する。

abstractThe system uses an ESP32 microcontroller integrated with an ESP-CAM module to capture images of plant leaves.

A Stage-Aware Cascaded Detection-Segmentation Framework for Leaf Phenotyping and Leaf Dry Biomass Estimation of Pepper Seedlings. ↗

Quantitative phenotyping of pepper seedlings is important for greenhouse plug tray seedling cultivation, but it remains constrained by inefficient manual monitoring, complex greenhouse backgrounds, and growth-stage-dependent discrepancies between two-dimensional image traits and actual leaf biomass. In this study, a cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping. The framework integrated Visual Dynamic Momentum YOLO (VDM-YOLO) for individual seedling localization and growth-stage recognition, Variance Guided Strip Ghost Gated UNet (VSG-UNet) for lightweight, high-resolution leaf segmentation, and a stage-aware correction model for leaf dry biomass estimation. In performance evaluation, VDM-YOLO achieved a mean average precision at an intersection over union threshold of 0.5 (mAP0.5) of 89.27%, improving mAP0.5 by 1.82 percentage points over YOLOv12. VSG-UNet achieved a mean intersection over union (mIoU) of 83.9% and a Dice coefficient of 81.8%, while reducing floating point operations (FLOPs) and parameters by 44.2% and 61.2%, respectively, compared with U-Net. After stage-aware calibration, the coefficient of determination (R2) between segmented area and leaf dry weight increased from 0.764 to 0.813, and the root mean square error (RMSE) decreased from 0.0210 g to 0.0190 g. These results demonstrated that the proposed framework provided a proof of concept approach based on RGB images for the nondestructive assessment of leaf area and leaf dry biomass in pepper seedlings under restricted experimental conditions.

Why it matches plant phenotyping methodsRGB画像による葉の検出・セグメンテーションと、葉面積から葉乾燥バイオマスを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。

abstracta cascaded vision framework with stage-specific morphological correction was developed for nondestructive seedling phenotyping.

Editorial: Plant phenotyping for agriculture ↗

Modern agriculture operates at an unprecedented crossroads, it must simultaneously accelerate crop yields to feed an expanding global population and adapt to the severe, fluctuating pressures of climate change, structural soil degradation, abiotic water deficits, and evolving biological threats. Historically, selecting resilient crop varieties and implementing field-scale management strategies relied extensively on destructive, labor-intensive, and fundamentally subjective visual metrics. This manual processing approach has long been recognized as the primary operational bottleneck in agricultural advancement.To bridge the gap between rapidly expanding genomic data and actual field performance, the systematic, non-destructive quantification of structural and functional plant traits, plant phenotyping, has emerged as a transformative frontier. By integrating high-throughput engineering, multi-scale remote sensing, deep learning, and advanced molecular biology, modern phenotyping transitions crop science away from qualitative estimation toward highly reproducible, multidimensional data frameworks. This Research Topic presents new advances in advanced 3D reconstruction and deep semantic segmentation at the seedling stage; amodal fruit segmentation, morphological extraction, and early water-stress diagnostics; high-throughput in-field seedling counting and dynamic density modeling; multimodal foundation models, network pruning, and intelligent phytoprotection; aerial and spaceborne remote sensing for canopy analysis and weed monitoring; plant physiology, functional spectroscopy, and functional genomics under abiotic stress; and automated diagnostics for real-time orchard scouting and vineyard management.Automating the characterization of complex spatial layouts under controlled or greenhouse environments is essential for early variety selection and early-stage structural evaluation. Several contributions within this volume provide key breakthroughs in navigating overlapping tissues, severe occlusions, and low-contrast edge regions. showcases how substituting standard convolutions with deformable convolutions enables deep neural networks to accurately isolate the main stem of mature, high-density crops like soybeans. This architecture overcomes the traditional challenges of color mimicry and severe occlusion by pods and leaves, achieving an outstanding mIoU of 90.58% and providing reliable indices for lodging resistance and structural yield modeling (R 2 = 0.9746).Accurately extracting fruit morphology under commercial greenhouse conditions remains heavily constrained by overlapping crop structures, foliage cover, and variable shadows. Simple semantic masks typically fail when a target fruit is partially blocked, leading to a loss of key volumetric data.To resolve the challenge of hidden boundaries, Li, Yin, et al. (2025) developed CGA-ASNet, a specialized RGB-D amodal segmentation network driven by a Contextual and Global Attention (CGA) module designed to restore occluded tomato regions. Trained on a high-fidelity synthetic greenhouse dataset (Tomato-sim) generated via NVIDIA Isaac Sim's Replicator Composer and optimized with a mean coordinate fusion algorithm for real-world validation, this architecture expands the network's receptive field to predict the complete, hidden circular forms of occluded tomatoes, achieving an F@0.75 score of 94.2 and an amodal mIoU of 82.4%. This proves that simulation-to-real (Sim2Real) domain pathways can successfully decode full physical volumes under dense commercial canopies.Complementing this structural restoration, Yang, Li, et al. (2025) designed an integrated diagnostic framework to identify early water stress dynamics in greenhouse tomatoes. Built upon an optimized YOLOv11n core, their system integrates adaptive kernel convolutions (AKConv) into the network backbone's C3k2 modules and implements a recalibration feature pyramid detection head based on the specialized P2 small-target layer. This combination achieved a 5.4% increase in mAP50-95 for identifying fine phenotypic parts. By applying automated geometric analysis to the extracted bounding boxes, the system extracts plant heights and petiole count with low relative errors, feeding these phenotypic parameters into a Random Forest classification routine that flags water-stressed plants with 98% accuracy to guide targeted, automated drip irrigation.Accurate plant stands during early vegetative stages represent the foundational metric required to establish true field emergence rates, validate seed vigor across diverse breeding blocks, and perform early yield predictions.To solve the challenges of small targets, extreme spatial density, and adjacent leaf overlap, Zang et al. (2025) designed DM_IOC_fpn, a wheat seedling counting framework that balances local and global contextual features. By structuring a point-annotated dataset and embedding a densityenhanced encoder module, their network balances micro-scale spatial limits with macro-scale canopy structures. Optimized through a combined loss function tracking counting, classification, and regression parameters, this architecture achieved low error scores (RMSE = 2.91; MAE = 2.23), outperforming standard object-detection benchmarks in complex field environments.At the same time, scaling up to real-time aerial monitoring required major reductions in model complexity to support resource-constrained edge computers on autonomous aerial platforms. Feng, Nie, and Li (2025) engineered an ultra-lightweight YOLOv8n variant tailored for real-time maize seedling counting from high-speed UAV RGB overflights. By reparametrizing RepConv with HGNetV2, they constructed a lean Rep_HGNetV2 backbone, integrated a Bidirectional Feature Pyramid Network (BiFPN) for multi-scale feature alignment, and implemented a Task Dynamically Aligned Detection Head (TDADH). This architecture compressed total model parameters by 47% and reduced weight sizes to 3.5 MB while maintaining a 96.5% detection accuracy and an ultra-fast processing speed of 146.3 FPS, paving the way for low-cost, real-time field scouting.Automated phytoprotection requires machine-vision architectures capable of generalizing across highly diverse species, complex field conditions, and varying computational boundaries. A significant subset of the published papers addresses these challenges through foundation model adaptation, multi-modal alignment, and efficient network compression.A major paradigm shift presented in this collection involves moving away from task-specific training and toward foundation model adaptation. Chen, Ruan, et al. (2026) introduce a novel architecture integrating the DinoV3 foundation model with a Unet framework to achieve robust leaf lesion segmentation across diverse species (such as coffee and black gram). By incorporating a Spatial Prior Module (SPM), their approach surpassed standard benchmark networks by over 10.5% in IoU while reducing inference times by approximately 93.6%, demonstrating that highparameter foundation models can be highly optimized for resource-constrained edge devices in real-time scouting.To solve the perennial problem of limited training data for rare or emerging crop diseases, Cooper et al. ( 2026) developed an ingenious synthetic data generation pipeline. Combining 3D procedural leaf modeling in Blender with diffusion-based disease synthesis (Stable Diffusion fine-tuned with LoRA and ControlNet), they synthesized highly accurate plant disease images with perfect groundtruth annotation masks. When deployed in low-resource data settings, combining these synthetic pipelines with restricted real-world datasets consistently drives significant improvements in downstream segmentation tasks. To tackle specific, complex pathologies, Xu, Chang, et al. (2025) developed the TSSC deep learning model, which embeds three-neighbor channel attention paired with a complementary squeeze-and-excitation mechanism. This specific architecture minimizes structural degradation risks while pushing classification accuracy to 99.61% for highly complex pea leaf pathologies. Similarly, Feng, Liu, et al. (2025) tackled overlapping leaf occlusions and small lesion footprints in citrus groves with YOLO-Citrus, an optimized framework integrating C3K2-STA, ADown modules, and a Wise-Inner-MPDIoU loss function to strike a balance between edge computational constraints and field deployment.UAVs and high-resolution satellite imagery have expanded the operational scale of phenotyping from individual pots to vast breeding blocks and commercial fields, allowing researchers to capture macro-dynamic parameters over time.In complex canopy systems that defy standard top-down aerial sensing, such as single-staked white Guinea yams, Iseki et al. (2026) demonstrated the distinct advantage of utilizing multi-angle (combined nadir and oblique) UAV imaging configurations. When coupled with support vector regression, this method captures complementary canopy-structure information to model shoot biomass trajectories (R 2 = 0.79) across multiple years and management zones. These nondestructive, time-series datasets enabled the fitting of genotype-specific Richard's growth curves using Bayesian inference, isolating valuable genetic variations in early growth allocation.To capture full-season vertical physiological changes over large scales, Li, Yue, and Luo (2025) developed a hybrid CNN-LSTM-Attention (CLA) model designed to estimate the full-period Leaf Area Index (LAI) in rice using multi-temporal UAV multispectral imagery. By using the CNN layer to extract instantaneous spatial features, the LSTM block to process seasonal time-series intervals, and a self-attention mechanism to weight critical growth transitions, their platform achieved a high coefficient of determination (R 2 = 0.92) and kept relative root mean square errors (RRMSE) below 9%. This network minimized soil background noise during early vegetative stages (LAI values 1-

Why it matches plant phenotyping methods植物フェノタイピングの技術動向を扱うEditorialであり、画像解析、UAVセンシング、深層学習、形質抽出などの方法が中心的に整理されている。

titleEditorial: Plant phenotyping for agriculture

Deep Learning and Computer Vision for Crop Maturity Assessment ↗

Maturity at harvest is a critical determinant of yield, storability, market value, and nutritional quality, making accurate and objective maturity assessment essential for sustainable crop and fruit production. The agricultural sector is under pressure to satisfy rising global food demand while reducing losses and environmental impacts, yet conventional maturity assessment methods remain largely manual, subjective, and labour-intensive. Against this backdrop, computer vision and deep learning have emerged as powerful tools for non-destructive, high-throughput evaluation of maturity traits in the field and along the supply chain.​ This review consolidates recent advances in deep learning-based maturity assessment across a wide range of crops, with a particular emphasis on citrus fruits, where external colour change, internal quality, and heterogeneous orchard conditions pose distinctive challenges. The paper analyzes state of the art architectures for classification, segmentation and detection, associated datasets and imaging modalities, and the metrics used to benchmark performance. By critically examining their advantages and limitations for real-world deployment, the review outlines key research gaps and future directions toward robust, scalable, and sustainable DL-driven maturity assessment systems for both citrus and other major crops.

Why it matches plant phenotyping methods作物の成熟度という植物形質を対象に、画像・深層学習による評価手法、データセット、画像モダリティ、ベンチマーク指標を体系的にレビューしており、フェノタイピング手法が中心です。

abstractThis review consolidates recent advances in deep learning-based maturity assessment across a wide range of crops

Mobile Application for Automated Plant Disease Detection ↗

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.

Wolf Bird Skill Optimization Enabled Deep High Levenberg–Marquardt Based Custom Convolutional Network for Multiclass Plant Disease Detection Using Leaf Images ↗

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

Why it matches plant phenotyping methods葉画像から植物病害を検出するカスタム畳み込みニューラルネットワークの開発・適用が題名の中心であり、植物の病害状態を画像から推定する方法研究に該当します。

titleWolf Bird Skill Optimization Enabled Deep High Levenberg–Marquardt Based Custom Convolutional Network for Multiclass Plant Disease Detection Using Leaf Images

Leaf movements as a quantitative metric for early stress detection ↗

Early, precise, and non-destructive stress detection is essential for maintaining crop productivity, particularly in high-density plant growth systems like controlled environment agriculture (CEA), where manual monitoring is often impractical. Using plant motion as a proxy for growth and plant health, we demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis in lettuce and five other CEA relevant crops. Leaf-movement dynamics under stress were imaged with a low-cost, scalable Raspberry Pi imaging setup and quantified using a repurposed open-source motion estimation algorithm; Tracking Rhythms in Plants (TRiP). Our system detected stress-induced changes in leaf-movement within 1 hour of stress, with the timing dependent on the nature of the stress. Sustained reductions in leaf-movement coincide with decreased biomass accumulation. This approach offers a non-invasive, rapid, scalable, and cost-effective solution for continuous crop monitoring, with potential for application in both terrestrial and space farming CEA systems. Abstract Figure Graphical abstract: Quantification of leaf-movement dynamics as a high-throughput proxy for plant physiological status, enabling early stress detection and timely intervention to mitigate yield penalties in CEA settings (image made with biorender.org).

Why it matches plant phenotyping methods低コスト撮像と既存アルゴリズムを用いて葉の動きを定量化し、植物ストレス・生理状態を早期推定する方法が研究の中心である。

abstractwe demonstrate a method for early, non-invasive stress detection through quantitative leaf-movement analysis

Development of a New Handheld Device for Measuring Photosynthetic Carbon Dioxide Assimilation in Plant Leaves ↗

With increasing constraints on extensive farming—including soil degradation, salinisation and more frequent climatic anomalies—the development of ‘smart’ agriculture requires the integration of affordable, non-invasive methods for monitoring the physiological state of plants. A key indicator for assessing productivity and the early detection of stress is the rate of photosynthetic CO2 assimilation (A); however, widely available commercial gas analysers are characterised by high cost, technical complexity and considerable weight, which limits their use in large-scale field studies. Here, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO2 concentration using simple infrared sensors and without maintaining a constant air flow around the leaf. A comparison was carried out between a prototype of the developed system and a commercial gas analyser when measuring leaf assimilation under irrigation and simulated drought conditions. The results demonstrated the consistency of the readings from the two systems. The developed system is characterised by its compact size, low cost, and the absence of moving parts and consumables. The proposed system has the potential to be effective for large-scale screening tasks and rapid diagnosis of stress-induced changes; it represents a promising, affordable tool for addressing applied tasks in precision agriculture, environmental monitoring and physiological research.

Why it matches plant phenotyping methods植物葉の光合成CO2同化速度を測定する携帯型センサーを開発し、市販ガス分析計との比較検証まで行っており、植物表現型取得法が研究の中心である。

abstractHere, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO2 concentration using simple infrared sensors

Plant Disease Detection Using a Simple Deep Learning Framework ↗

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.

Deep learning for real-time strawberry detection, ripeness classification, and picking point localization: A review of architectures, field studies, and open challenges ↗

Abstract Accurate detection of strawberry fruit, reliable ripeness estimation, and precise localization of the picking point are essential for automated harvesting and yield prediction in smart farming. However, real-world environments introduce significant challenges, including occlusion, illumination variability, and high visual similarity between ripeness stages. Deep learning (DL)-based object detection methods have become the dominant approach to address these issues. This paper presents a systematic review of 60 peer-reviewed studies published between 2023 and 2026, focusing on detecting strawberries and their ripeness using the YOLO family (v5–v11) of DL algorithms. The studies are analyzed with respect to dataset characteristics, preprocessing and augmentation strategies, model architectures, and evaluation protocols. Our results show a clear dominance of YOLOv8, used in 28 (46.7%) of the 60 reviewed works, due to its real-time capability and architectural flexibility. Despite its short history, YOLOv11 has been adopted in 13 studies (21.7%) owing to its balanced precision and computational efficiency. Hybrid CNN–ViT models that integrate Transformer modules or networks into YOLO are gaining attention (8 studies, 13.3%) and show improved performance in complex scenarios, but they still incur higher computational cost. However, we identify critical methodological issues that affect the validity of reported results. In particular, the improper application of data augmentation prior to dataset splitting — a practice observed in precisely one-third of the reviewed studies — poses a significant risk of data leakage and can result in overly optimistic performance estimates. Additional challenges include inconsistent evaluation metrics, limited dataset diversity, and a lack of standardized benchmarks. This review provides a structured overview of current approaches, a critical assessment of existing research practices, and actionable guidance for developing robust, deployment-ready DL solutions for precision agriculture.

Why it matches plant phenotyping methodsイチゴ果実の検出・成熟度推定を対象とする画像ベース手法の系統的レビューであり、データセット、モデル、評価法、データリークやベンチマーク不足を批判的に検討しているため、フェノタイピング手法レビューとして中心的である。

abstractThis paper presents a systematic review of 60 peer-reviewed studies published between 2023 and 2026, focusing on detecting strawberries and their ripeness using the YOLO family (v5–v11) of DL algorithms.

Improved MobileNetV2 Architecture with Modified Lite Attention Model for Detection of Plant Leaf Disease ↗

Global agriculture is seriously threatened by plant diseases, which result in large losses in both productivity and quality. Timely and accurate disease detection is essential for effective crop management and food security. This work presents an improved MobileNetV2 architecture with Modified Lite Attention (MLA) Model for detecting plant leaf disease. Our methodology incorporates pre-processing, feature extraction through attention model, convolution layers, and classifying into diseased or healthy categories. Further, multiclassification of diseases is performed on a dataset comprising 4432 samples including whitefly, leaf spot, leaf curl, yellowish and healthy leaves. The proposed attention model is compared with existing attention models like CBAM (Convolutional Block Attention Model), SE (Squeeze and Excitation), ECA (Efficient Channel Attention) and SDMnet (Spatially Dilated Multi-Scale Network) to validate our hybrid MLA feature extraction technique. Customizing the categorization with fully connected layers and utilisation of a pre-trained MobileNetV2 model allow the system to achieve excellent results. Findings show encouraging accuracy, surpassing 97% compared to existing techniques for multiclass dataset classification. The integration of MobileNetV2 with custom dense layers enables robust detection even with limited datasets, making it ideal for use in mobile or low-resource agricultural environments. Further, the proposed method is tested on the PlantVillage dataset consisting of 10,836 samples using K-Fold cross-validation for K = 5 and K = 4 to obtain an average accuracy of 98.4% and 98.69%, respectively.

Why it matches plant phenotyping methods植物葉の病徴を画像から分類する深層学習手法を開発し、複数モデルおよびデータセットで精度検証しており、病害状態の表現型取得が中心である。

abstractThis work presents an improved MobileNetV2 architecture with Modified Lite Attention (MLA) Model for detecting plant leaf disease.

Beyond the Visual Spectrum: From RGB-Based Learning to Hyperspectral Intelligence for Plant Disease Detection—Challenges and Opportunities ↗

Plant diseases result in the estimated loss of 20–40% of the world’s crop production annually, amounting to more than $220 billion in economic losses and threatening food security for a rapidly expanding world population. While the conventional methods for detecting plant diseases rely on visual inspection of the symptoms, they are resource-consuming. For effective plant disease detection at a pre-mature stage, hyperspectral imaging (HSI) represents a paradigm shift in technology. It can be used to obtain subtle spectral signatures outside the visible spectrum, which enables pre-symptomatic and highly specific plant disease diagnosis. Concurrently, deep learning (DL) has become the prevalent analytical paradigm for decoding the complex and high-dimensional data that HSI produces. This paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026. We first set out the biological and physical principles by which HSI is uniquely suited to detecting plant–pathogen interactions in the absence of visible symptoms. We then present a detailed taxonomy of deep learning architectures for Vision Imaging and HSI data, ranging from basic 1D and 3D convolutional neural networks (CNNs) to hybrid models with attention mechanisms and, most recently, vision transformers, which have achieved greater robustness to real-world conditions. There is currently a major and consistent “lab-to-field” performance gap. A critical analysis of various studies reveals a persistent and significant performance gap between models that perform well on controlled lab datasets (ranging from 95 to 99%) and field-collected data (typically 70–85%). This paper also addresses the practical gap of environmental variability, image noise, and the domain gap between the controlled environment and the real dataset. Finally, this review concludes by providing strategic research recommendations and a roadmap, highlighting that the future of the field is contingent upon not only architectural innovation but also a holistic approach, with robustness, scalability, affordability, and interpretability as the main focus to bring the proven potential of HSI-DL systems from the lab to the field, ultimately contributing to global food security.

Why it matches plant phenotyping methods植物病害の症状・状態をハイパースペクトル画像と深層学習で推定する手法を中心に扱うレビューであり、植物フェノタイピング手法レビューに該当する。

abstractThis paper covers a comprehensive narrative review of the intersection of these two transformative technologies from 2008 to 2026.

YOLO-APLD: A Lightweight Apple Leaf Disease Detection Model Based on Multiscale Feature Fusion. ↗

The precise and timely identification of apple leaf diseases play a key role in targeted pesticide application in orchards. Conventional deep learning techniques encounter issues like the substantial size of model parameters and low detection accuracy across various disease scales in natural environments. To overcome these limitations, this paper presents YOLO-APLD, a lightweight algorithm for detecting apple leaf diseases, utilizing the improved YOLOv8n model. The proposed model incorporates four key improvements to enhance its detection performance. First, an EP-C2f enhancement module is embedded at the output of the backbone to strengthen the representation of local and structural features of damaged area, thereby achieving significant improvements in the recognition of morphologically complex diseases such as rust. Additionally, spatial intersection over union (SIoU) loss and focal loss are combined to form Focal-SIoU loss, which simultaneously optimizes bounding box regression and classification, thus enhancing the detection stability for hard-to-distinguish samples and few-shot categories including mosaic and brown spot. Meanwhile, a bidirectional feature pyramid network is adopted in the neck for efficient multiscale feature fusion, which strengthens the perceptual capability for both large-scale damaged area (powdery mildew and scab) and small-scale damaged area (Alternaria blotch and gray spot). Finally, a Slim-neck structure is employed to simplify the feature fusion architecture, reducing model size and accelerating inference speed. Comprehensive experiments demonstrate that YOLO-APLD achieves excellent performance while maintaining real-time capability, with precision, recall, mean average precision, and F1-score reaching 88.5, 84.3, 88.5, and 86.4%, respectively. Compared with YOLOv8n, these metrics show respective improvements of 1.7, 1.5, 0.8, and 1.6%. Meanwhile, floating point operations, parameter count, and model size are reduced by 22.2, 23.3, and 17.5%, respectively. The detection frame rate on edge computing devices reaches 90.3 f/s, indicating significantly accelerated inference speed. Additionally, testing performance on grape and tomato datasets further validates the generality of the proposed method. In summary, YOLO-APLD exhibits strong detection performance in the field of apple leaf disease detection and can provide practical technical support for precision pesticide application in orchards and on-site disease monitoring.

Why it matches plant phenotyping methodsリンゴ葉の病斑・病害状態を画像から検出するYOLOベース手法を開発・評価しており、植物病害表現型の取得が研究の中心である。

abstractComprehensive experiments demonstrate that YOLO-APLD achieves excellent performance while maintaining real-time capability

LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments ↗

Maize leaf diseases in field environments often exhibit large variations in lesion scale, irregular morphology, blurred boundaries, and complex backgrounds. These factors pose challenges for existing detection models, particularly in detecting small lesions and achieving precise bounding-box localization. To address these issues, this study proposes LSL-YOLO11n, a maize leaf disease detection model based on the YOLO11n framework. The proposed model improves feature representation, localization quality modeling, and bounding-box regression to enhance disease detection performance under complex field conditions. Experiments were conducted on a dataset containing 15,119 images and 29,366 annotated instances across eight categories, including seven maize disease categories and healthy leaves. To evaluate the effectiveness of the proposed model, ablation experiments, comparative experiments with mainstream object detection models, and visual detection analyses were carried out. The ablation results show that the improved components contribute positively to the overall detection performance. LSL-YOLO11n achieves a Precision of 84.4%, Recall of 73.9%, and mean Average Precision (mAP) of 83.3%, which is 3.1 percentage points higher than that of the baseline YOLO11n model. Compared with YOLOv8n, YOLOv9t, YOLOv10n, and YOLOv12n, the proposed model improves mAP by 4.7, 3.3, 5.3, and 10.9 percentage points, respectively. The visual detection results further indicate that LSL-YOLO11n performs more stably in complex backgrounds and small-lesion scenarios. These findings provide technical support for rapid maize disease recognition and intelligent field monitoring.

Why it matches plant phenotyping methodsトウモロコシ葉の病斑・病害状態を画像から検出するYOLOベース手法を開発し、アブレーション比較や既存モデルとの性能評価を行っており、植物病害表現型の取得法が中心である。

abstractTo evaluate the effectiveness of the proposed model, ablation experiments, comparative experiments with mainstream object detection models, and visual detection analyses were carried out.

Edge-Based IoT Plant Leaf Condition Classification Using Classical Digital Image Processing on Raspberry Pi ↗

This study aims to develop an edge-based Internet of Things (IoT) system for automatic plant leaf condition classification using classical digital image processing on a Raspberry Pi. The proposed system classifies leaf conditions into healthy, diseased, and pest-attacked categories while providing real-time remote monitoring through a Telegram Bot. The system employs a Raspberry Pi as the edge computing device and a Raspberry Pi Camera for image acquisition. Images are processed locally using OpenCV through RGB-to-HSV color space conversion, thresholding, edge detection, and contour analysis. System performance was evaluated using 15 test samples for each image acquisition distance (15 cm, 30 cm, 45 cm, and 60 cm). Experimental results achieved detection accuracies of 100% at image acquisition distances of 15 cm and 30 cm, while the accuracy decreased to 87% at 45 cm. At 60 cm, the system failed to detect the target object because insufficient visual information prevented reliable feature extraction. The proposed edge-based IoT system provides an efficient and low-cost solution for real-time plant leaf condition classification. The experimental results indicate that an image acquisition distance of 15-30 cm is optimal for reliable detection under the evaluated experimental conditions. The proposed system integrates Raspberry Pi-based edge computing with lightweight classical digital image processing and Telegram Bot notifications, eliminating the need for computationally intensive deep learning models or cloud-based image processing.

Why it matches plant phenotyping methods植物葉の健全・病害・害虫被害状態を画像から分類する手法と、Raspberry Pi上の実装・性能評価が研究の中心であり、植物状態のフェノタイピング手法に該当する。

abstractThis study aims to develop an edge-based Internet of Things (IoT) system for automatic plant leaf condition classification using classical digital image processing on a Raspberry Pi.

Classification of Banana Leaf and Ornamental Plant Diseases Using Gray Level Co-occurrence Matrix (GLCM) and Hybrid Random Forest–Support Vector Machine (SVM) ↗

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).

Real-time detection of plant leaf diseases based on improved YOLOv13-LM in complex field environments ↗

Plant leaf diseases are a major factor leading to crop yield reduction, seriously threatening food security and sustainable agricultural development. Timely and accurate detection is crucial for scientific prevention and control. Traditional manual detection is time-consuming, labor-intensive, and highly subjective. Existing deep learning detection models still have significant shortcomings in complex field scenarios, struggling to balance detection accuracy, real-time performance, and stability. They are easily affected by background interference, differences in lesion scale, and sample imbalance, and some models are not lightweight enough to meet the needs of real-time field detection. Therefore, this paper uses YOLOv13 as the baseline model and constructs a YOLOv13-LM model through multi-module collaborative optimization. The optimization directions cover the backbone, neck, detection head, and loss function, strengthening lesion feature extraction and multi-scale fusion, reducing task interference, and improving localization accuracy. Model validation was completed in a complex farmland environment. The results show that the model’s mAP@0.5 is improved by 5.4 percentage points to 87.9% compared to the original YOLOv13, the FPS is improved by 21.1% to 46 frames/second, and the number of parameters and computational cost are reduced by 27.3% and 25.7% respectively. The overall performance is better than the mainstream YOLO models of the same scale. However, the lightweight nature of the model is still not as good as that of the ultra-lightweight model, and its generalization and interpretability need to be improved. In the future, we will focus on ultra-lightweight design, expanding the generalization ability of multiple crops and diseases, and studying the interpretability of the model to further adapt to the actual needs of field applications.

Why it matches plant phenotyping methods植物葉の病斑を画像から検出するYOLOモデルを開発・改良し、複雑な圃場環境で精度とリアルタイム性能を検証しており、病害状態の表現型取得が中心である。

abstractTherefore, this paper uses YOLOv13 as the baseline model and constructs a YOLOv13-LM model through multi-module collaborative optimization.

Plant stress early detection through a low-cost multispectral device: Toward safer and more sustainable agricultural practices ↗

While multispectral sensors offer a cost-effective and robust solution for monitoring plant responses to environmental stress, their limited spectral resolution, largely dependent on vegetation indices, can hinder accurate classification of stress severity using machine learning. This paper aims at overcoming these limitations by introducing a multispectral device for plant stress early detection that is 1) affordable for a wide range of end-users, 2) robust to environmental factors, 3) capable of automatically finding the most meaningful features that maximize the stress detection accuracy, and 4) capable of discriminating different plant stress severity. The device integrates a broadband LED and a VIS-NIR multispectral sensor to early predict plant stress through machine learning algorithms (i.e., SelectKBest, kNN, SVM, and LDA). It was trained on spectral measurements acquired from tobacco plants under salinity stress. The results demonstrated its high capability to discriminate with high accuracy different stress severity (average accuracy of 91.0 ± 3.1%).

Why it matches plant phenotyping methods植物ストレスの重症度を推定する低コスト multispectral デバイスと機械学習手法を開発・評価しており、植物状態の取得・判別が研究の中心である。

abstractThis paper aims at overcoming these limitations by introducing a multispectral device for plant stress early detection

Multi Class Support Vector Machine Based Plant Leaf Disease Detection from Color Texture And Shape Pictures ↗

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.

GG-YOLO: A Lightweight Dual-Path Attention Detector with Dynamic Sampling for Dense Wheat Spike Detection ↗

Accurate wheat spike detection is essential for crop phenotyping and yield estimation, but real-world field conditions—such as dense spike overlap, environmental domain shifts, and degradation-induced failures like motion blur—pose significant challenges. Achieving robust perception under these circumstances while maintaining a strict accuracy-efficiency trade-off for edge devices remains a pressing research problem. To overcome these limitations, we propose GG-YOLO, a unified lightweight detection framework specifically tailored for complex agricultural environments. Rather than a simple recombination of existing lightweight modules, GG-YOLO integrates three original structural adaptations: First, a Dual-path Attentive Ghost Mechanism (DAGM) introduces gradient-guided attention modulation to enhance feature discrimination and explicitly resolve feature confusion in dense, overlapping regions. Second, a C3Ghost module combines multi-branch aggregation with linear feature generation, mitigating parameter redundancy in the prediction head by approximately 31% compared to the standard YOLOv8s without sacrificing semantic capacity. Third, DSample, a dynamic upsampling operator featuring an original dual-mode adaptive mechanism, robustly recovers fine-grained spatial details during multi-scale feature pyramid fusion. Extensive cross-dataset experiments on the GlobalWheat2020 and HNKJXYwheat datasets validate the model’s exceptional resilience to domain shifts and varying growth stages. GG-YOLO achieves a precision of 94.35%, a recall of 91.93%, and a state-of-the-art mAP@50 of 96.47%. Furthermore, the model contains only 7.89 M parameters and requires 20.4 GFLOPs, reaching an inference speed of 165 FPS on a desktop GPU and a validated real-time speed of 64 FPS on an NVIDIA Jetson edge computing platform. These results demonstrate that GG-YOLO establishes a superior accuracy-efficiency frontier, making it highly reliable for real-time field deployment in precision agriculture.

Why it matches plant phenotyping methodsコムギ穂の検出を作物フェノタイピングおよび収量推定に用いる画像解析手法を開発し、複数データセットで性能検証しているため、フェノタイピング手法が中心である。

abstractAccurate wheat spike detection is essential for crop phenotyping and yield estimation

Explainable Deep Learning for Intelligent Plant Disease Detection ↗

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

An Interpretable Multimodal Transfer Learning Decision-Support Framework for Multi-Crop, Multi-Disease Detection in Precision Agriculture ↗

Despite the widespread use of image-only convolutional models for plant disease diagnosis to provide global food security, image variability at the field level, visually similar symptoms, and stress due to soil nutrients or moisture conditions, can cause loss of accuracy. This paper compares classical machine learning classification models to custom convolutional neural networks and pretrained transfer-learning models for multi-crop and multi-disease classification and also introduces a late-fusion multimodal decision support model that could integrate data about images, soil, and weather. Experiments were conducted on the PlantVillage dataset which had 54,305 RGB images belonging to 38 different crop-condition classes (43,456 training, 10,849 validation, and 10,849 test images) across 14 different crops. The accuracy, macro-precision, macro-recall and macro-F1 score were computed for the five classical classifiers (KNN, Random Forest, Extra Trees, SGD-linear SVM, and SVC-RBF using PCA-reduced features), three custom CNN variants, and four pretrained models (ResNet50, MobileNetV2, GoogleNet, and EfficientNetB7). Of the classical models, SVC with RBF kernel yielded the highest accuracy (83.58%) and MacroF1 (82.96%). In the case of the custom CNN models, the deeper they became and the more dropout the higher accuracy they gained, with CNN-V3 attaining 95.71% accuracy and 95.66% macro-F1. Transfer learning yielded the best results with MobileNetV2 (99.30% accuracy and macro-F1) outperforming ResNet50 (98.60% accuracy and macro-F1) and GoogleNet (97.83% accuracy and 97.46% macro-F1). The findings serve as the basis for the introduction of a multimodal system for soil forecasting integrating a MobileNetV2 image encoder, a residual MLP for soil features, and two dual LSTMs for 48-hour and 168-hour time-series of the weather. The interpretability, modularity and the sufficient resistance towards loss of sensor information for probability-level late fusion with 0.80, 0.10, 0.05 and 0.05 respectively make the framework appropriate for precision-agriculture advisory systems, until it is tested in the field.

Why it matches plant phenotyping methods植物画像から病害状態を推定する分類手法を複数比較し、マルチモーダル意思決定フレームワークを構築・評価しており、病害表現型の取得・推定が中心である。

abstractThis paper compares classical machine learning classification models to custom convolutional neural networks and pretrained transfer-learning models for multi-crop and multi-disease classification

Detection Of Crop Disease Using MobileNet ↗

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.

OPTIFARM: Benchmarking YOLO Architectures for Location-Robust Potato Quality Detection. ↗

Potato sorting in post-harvest processing relies heavily on manual visual inspection, which is physically demanding, subjective, and insufficiently scalable for modern packing lines. This study investigates the feasibility of a low-cost RGB-based optical inspection system for automated potato quality detection using deep learning-based object detection. A controlled imaging platform was constructed using commodity hardware, and a dataset of 19,805 manually annotated instances across 1361 images was collected from two geographically distinct farm locations in Slovenia. A systematic benchmark of 25 model configurations spanning five YOLO architecture families-YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLO26-was conducted across three practical quality classes (Edible, Feed, Rotten) using a strict cross-location evaluation protocol in which models were trained on one location and tested on a completely unseen second location. All models achieved strong in-distribution performance (F1 ≥ 0.906), but showed considerable variation under cross-location conditions, with external F1 ranging from 0.792 to 0.918. The yolo26_l configuration achieved the best cross-location performance (F1 = 0.918, mAP@0.5:0.95 = 0.816, ΔF1 = 0.029), demonstrating that transferable representations are achievable under a standard supervised training protocol. Per-class analysis identified feed detection as the primary generalization bottleneck. The results confirm that affordable RGB-based sorting systems are technically feasible and highlight cross-location evaluation as an essential protocol for assessing real-world deployment readiness.

Why it matches plant phenotyping methodsジャガイモ塊茎の品質・腐敗状態をRGB画像と物体検出で推定する撮像システムを構築し、複数YOLOモデルの交差地域ベンチマークと外部検証を行っており、表現型取得法が中心である。

abstractThis study investigates the feasibility of a low-cost RGB-based optical inspection system for automated potato quality detection using deep learning-based object detection.

Fluorescence Lifetime Imaging in Plants: Practical guidelines for multiplexing, label-free imaging and data analysis ↗

ABSTRACT Fluorescence Lifetime Imaging Microscopy (FLIM) is becoming a key technique for live-cell multiplexing and label-free detection of endogenous fluorescence in animal systems. Its potential in plant biology, however remains largely unexploited, despite its integration into a number of commercial microscopy setups. Here, we build a systematic, subcellular FLIM reference library for a panel of genetically-encoded fluorophores. Lifetime imaging of different fluorescent reporters targeted to distinct organelles (nucleus, plasma membrane, endoplasmic reticulum, etc.) and subsequent analysis of the decay curves using different modes allowed us to simultaneously discriminate up to four spectrally overlapping fluorophores solely by lifetime differences in specific subcellular compartments. Remarkably, fluorophores with lifetimes differing by as little as 0.1 ns can be reliably discriminated using one of these modes, namely Phasor-based analysis. Moreover, we show that the same fluorophores exhibit compartment-specific lifetime shifts, enabling Phasor separation of identical tags residing in different organelles. Finally, we extended the Phasor approach to label-free imaging of endogenous plant fluorescence. Together, these results establish FLIM-Phasor as a versatile, multiplex-capable tool for plant cell biology, opening new avenues for imaging strategies that yield higher content information at both cellular and tissue-level resolution.

Why it matches plant phenotyping methods植物細胞・組織の蛍光状態を取得・解析するFLIM-Phasor法を体系的に構築・検証し、マルチプレックスおよびラベルフリー植物蛍光イメージングへの応用を示した、方法中心の研究である。

abstractHere, we build a systematic, subcellular FLIM reference library for a panel of genetically-encoded fluorophores.

Tea Pests and Diseases Detection Method Based on Multi Scale Dynamic Routing Network ↗

Abstract To address the challenges of high computational cost and strong background interference in automatic recognition of tea pests and diseases in complex tea garden scenarios, this paper proposes a detection method named MSDR-Net (Multi-Scale Dynamic Routing Network). First, to significantly reduce the model parameter count and computational complexity, a lightweight backbone is constructed using depthwise separable convolutions. Second, a parallel multi-scale feature extraction structure is designed to capture both the contours and details of small pests and large disease spots through differentiated branches. Finally, to suppress background interference and improve feature fusion efficiency, a SimpleRouter dynamic routing mechanism is introduced to enable adaptive filtering and weighted fusion of key features. Experimental results on a self-built real-world tea pest and disease dataset show that the model achieves a mean average precision (mAP@0.5) of 98.0\%, which is 0.3 percentage points higher than the baseline model YOLOv8n. Meanwhile, the parameter count, computational complexity, and model size are reduced to 1.89M, 7.0 GFLOPs, and 3.91 MB, representing reductions of 37.1\%, 13.6\%, and 34.6\%, respectively, compared to the baseline model. Furthermore, an intelligent monitoring system developed based on this model verifies its effectiveness and usability in practical applications.

Why it matches plant phenotyping methods茶の病害スポットを画像から検出するモデルを開発・評価しており、植物の病害状態の取得が中心的な技術貢献です。害虫検出も含みますが、病害スポット検出と実運用システムの検証があるため対象に含めます。

abstractthis paper proposes a detection method named MSDR-Net (Multi-Scale Dynamic Routing Network).

Early Crop Disease Detection using Vision Transformers ↗

Crop diseases pose a significant threat to global food security, often resulting in substantial yield losses and economic instability for farmers. Traditional methods of disease identification, which rely on manual visual inspection, are labor-intensive, subjective, and frequently prone to error. While Convolutional Neural Networks (CNNs) have established a baseline for automated detection, they occasionally struggle with capturing global context within complex leaf patterns. This paper presents a robust image-based classifier utilizing Vision Transformers (ViT) to identify crop diseases from leaf images. Leveraging the self-attention mechanism, the proposed model effectively captures long-range dependencies in image data. The system is trained and validated on the PlantVillage dataset using transfer learning techniques. Experimental results demonstrate that the Vision Transformer architecture achieves a classification accuracy of 98.4%, outperforming traditional CNN architectures such as ResNet50 and VGG16. These findings suggest that transformer-based models offer a promising avenue for precision agriculture, enabling early intervention and reduced pesticide usage.

Why it matches plant phenotyping methods葉画像から作物病害を分類するVision Transformer手法の開発・検証が中心で、植物の病害状態を直接推定しているため。

abstractThis paper presents a robust image-based classifier utilizing Vision Transformers (ViT) to identify crop diseases from leaf images.

Multi-Modal Deep Learning for Early Detection of Human and Plant Diseases using Image and Sensor Data ↗

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

Why it matches plant phenotyping methods植物病害を画像・センサーデータで早期検出するマルチモーダル手法の開発が題名上の中心であり、植物の病害状態を対象とするため採用。

titleMulti-Modal Deep Learning for Early Detection of Human and Plant Diseases using Image and Sensor Data

Early hyperspectral detection of Carlavirus vignae in common bean under field conditions ↗

Abstract Virus-associated diseases are among the biological stresses that affect common bean yield, such as Carlavirus vignae ( Cowpea mild mottle virus , CPMMV), transmitted by the whitefly Bemisia tabaci . CPMMV is present in different continents, and recent outbreaks have concerned Brazilian farmers and researchers. Integrated Pest Management routines for field monitoring of viral spread are laborious and may be limited to visible symptoms. We hypothesized that CPMMV infection can be detected in asymptomatic plants by differences in the plant canopy reflectance. To test this hypothesis, we used a hyperspectral sensor mounted on a drone to capture images of CPMMV-inoculated and non-inoculated field plots in 2022 and 2023, across a tolerant common bean cultivar (BRS FC420 RMD) and a susceptible one (BRS FC401 RMD). Results showed that CPMMV was detected in common bean plants by hyperspectral imaging at early infection stages (~ 6 DAI) before symptom onset and at an advanced infection stage (~ 22 DAI). Reflectance within the visible light spectrum was affected by soil cover on all flights, and in most of these, also in the near-infrared region. The main differences between CPMMV-inoculated and control plants were consistent across two years of experiments, regardless of the common bean phenological stage and genotype. Fit statistics using the sum of squared errors, R 2 and AIC indicated that reflectance from 401 to 425 nm, especially near 415 nm, differed significantly between infected and healthy plants. Such changes are associated with chlorophyll degradation and disruption of the photosynthetic apparatus, and are detectable even before symptom onset. Progress in the disease severity index also differentiated the tolerant cultivar from the susceptible one. CPMMV infection significantly reduced common bean yield by ~ 21% compared with healthy plants. CPMMV detection by hyperspectral imaging enables early scouting to optimize disease management.

Why it matches plant phenotyping methodsハイパースペクトル画像を用いて、症状発現前の感染植物の反射特性と病害状態を検出し、複数年・品種で技術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 hypothesized that CPMMV infection can be detected in asymptomatic plants by differences in the plant canopy reflectance.

Using UAV Multispectral Imagery to Predict Leaf SPAD Dynamics During Maize Growth Under Different Plant Densities ↗

Chlorophyll content represents a key growth indicator for maize. The traditional SPAD method, though easy to operate, is inefficient, destructive, and unsuitable for high throughput field monitoring. Unmanned Aerial Vehicle (UAV) remote sensing technology is highly efficient and detects abundant indicators, enabling large-scale SPAD measurement. In this study, 18 vegetation indices and 8 texture features were selected as the indicator system by combining prior knowledge and experimental analysis. In a two-year maize density experiment, multispectral images were collected in the full growth period. The correlations between SPAD values, multispectral indices and texture features were analyzed using Pearson correlation coefficients. Then the detection accuracies of three algorithms i.e. Random Forest (RF), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), were compared under this indicator system. Compared with models constructed using single vegetation indices or single texture features, the estimation accuracy of the indicator system at the jointing stage was improved by 0.13 and 0.22, respectively. The results showed that SVR achieved the highest estimation accuracy among the three algorithms, with determination coefficients (R²) of 0.73, 0.77and 0.70 at the jointing, silking, and grain-filling stages, respectively. This study established a non-destructive monitoring framework for chlorophyll content during entire maize growth period based on UAV data.

Why it matches plant phenotyping methodsUAVマルチスペクトル画像からトウモロコシ葉のSPAD／クロロフィル含量を推定する指標体系と機械学習モデルを構築・比較しており、表現型取得手法が研究の中心である。

abstractThis study established a non-destructive monitoring framework for chlorophyll content during entire maize growth period based on UAV data.

Efficient plant disease detection using reinforced coati optimization algorithm (RCOA) for Precision Agriculture ↗

Recent advancement in Artificial Intelligence (AI) have greatly improved their application in agriculture, especially in the identification of plant leaf diseases and in facilitating enhanced decision-making. Detection of plant leaf diseases plays important role for placing crop healthy and assisting farmers to take action on time. However, despite these improvements, they are still challenging to use in the real world. The process can be difficult to analyze plant leaf images because they often have complicated background and different structural patterns. Differences in light, texture, and the way leaves naturally change make things even more complicated, making it hard for automated detection systems to be reliable. Therefore, the paper introduces novel method as Reinforced Coati Optimization Algorithm (RCOA) for determining useful selection of features from a huge set of feature set generated from feature extraction methods. The RCOA algorithm is tested on CEC 2017 Benchmark function suite. Further, the model is trained using Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The outcome depicts that proposed algorithm is providing better outcomes on comparative analysis with state-of-the-art methods.

Why it matches plant phenotyping methods植物葉画像から病害を検出する特徴選択・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。

abstractthe paper introduces novel method as Reinforced Coati Optimization Algorithm (RCOA) for determining useful selection of features from a huge set of feature set generated from feature extraction methods.

From Sensing to Action: A Leaf Humidity–Triggered Closed Loop System for Precision Salicylic Acid Delivery to Mitigate Plant Stress ↗

Abstract Real-time detection of plant stress and timely delivery of protective biomolecules are essential for improving crop resilience under adverse environmental conditions. However, conventional plant monitoring systems typically rely on ambient measurements and passive treatment strategies that fail to enable targeted plant recovery based on their localized physiological conditions. As a result, current approaches largely operate as open-loop systems, where sensing and intervention are not directly integrated, limiting the ability to respond dynamically to plant stress. This study presents an integrated plant healthcare platform that bridges this gap by combining leaf-level humidity sensing with stimulus-responsive delivery of the phytohormone salicylic acid (SA) to enable a closed-loop plant care system. The objective of this work was to develop a platform capable of monitoring transpiration driven humidity changes at the leaf surface and enabling controlled hormone delivery based on plant physiological responses. A temperature responsive hydrogel encapsulating SA was synthesized to achieve sustained biomolecule release while minimizing initial burst release. Salicylic acid release kinetics were evaluated using multiple mathematical models, with the Korsmeyer–Peppas model providing the best fit (R² = 0.9978), indicating that SA release was governed primarily by polymer relaxation and degradation mechanisms. Leaf-level relative humidity was continuously monitored on the abaxial surface under different treatment conditions. Plants treated with the hydrogel-based SA delivery system showed improved drought tolerance, with localized relative humidity increasing from approximately 20–30% in stressed plants to 60–70% after treatment, while untreated stressed plants did not show any noticeable recovery. This improvement was further supported by measurements of stomatal aperture, which showed a mean opening of 1.932 micrometers in treated plants, compared to 0.396 micrometers in untreated plants. SA treated seeds also demonstrated accelerated germination within 14 days. These findings demonstrate the potential of integrating plant wearable sensors with stimulus responsive biomaterials to establish closed-loop plant healthcare systems that couple physiological sensing with adaptive intervention.

Why it matches plant phenotyping methods葉面湿度を連続測定して植物の生理状態（蒸散・ストレス回復）を推定するセンサーと、応答型処置を統合した植物フェノタイピング／ケア基盤の開発が中心である。

abstractThis study presents an integrated plant healthcare platform that bridges this gap by combining leaf-level humidity sensing with stimulus-responsive delivery of the phytohormone salicylic acid (SA) to enable a closed-loop plant care system.

Automated phenotyping of soybean stomatal responses to water deficit using YOLOv8 ↗

Artificial intelligence applied to plant phenotyping is crucial for consistent results, as stomata classification under stress impacts physiology, water use efficiency, and productivity. Manual analysis is laborious and error-prone, limiting the efficiency and accuracy of evaluations. In this context, this study developed a soybean-specific dataset from water deficit (WD) and well-watered (WW) plants, training YOLOv8 model for automated detection and classification of open vs. closed stomata. Soybean plants were grown under water deficit and well-watered conditions, generating significant variations in stomatal structural opening and associated gas exchange traits. To capture stomata variations, epidermal printing techniques were employed, with images obtained by microscopy. The dataset was labeled using the intelligent polygon tool of the Roboflow application, with 269 images of the adaxial and abaxial surfaces of leaves annotated in two categories: open and closed stomata. The images underwent geometric transformations to facilitate model training. The results demonstrated that the YOLOV8 neural network achieved precision recall and mAP greater than 90%, highlighting its effectiveness in detecting and classifying stomata. By integrating automated classification of aperture states (open and closed) with a defined physiological stress context in soybean, this work establishes a dataset specifically designed for functional analysis. This approach extends the applicability of deep learning toward stress-oriented plant physiology studies, offering a robust tool for evaluating crop adaptation under climate change scenarios.

Why it matches plant phenotyping methodsヨロウ豆の気孔開閉状態を画像から自動検出・分類するYOLOv8手法とデータセットを開発・評価しており、植物表現型取得が研究の中心である。

abstractthis study developed a soybean-specific dataset from water deficit (WD) and well-watered (WW) plants, training YOLOv8 model for automated detection and classification of open vs. closed stomata.

AgroVision: Bridging Laboratory and Field Data for Enhanced Plant Disease Recognition ↗

Worldwide, crop health still suffers despite constant watch – diseases linger, cutting harvests and weakening quality across regions. Early detection shifts outcomes once outbreaks begin; yet current approaches lean heavily on trained eyes examining symptoms up close – a resource often missing at critical moments. Enter AgroVision: an imaging tool powered by deep learning that scans leaf photos to catch signs of illness quickly. Instead of relying on rigid rules, it leans on Convolutional Neural Networks, uncovering subtle clues linked to specific ailments while learning on its own. What sets it apart? It learns patterns naturally, spotting threats without step-by-step instructions. Every now and then, working outside or under lab lights shows how tricky shifting conditions can be - this slip between environments is the core of what folks call the domain gap. Designed tight and with intent, the model moves fast yet expands smoothly if demands grow. Learning from earlier jobs helps it start faster, still hitting close even on fresh, unfamiliar inputs. A browser tab opens, farm photos go in, answers show up instantly, no lagging behind. Tests back its steady precision, all while staying light on computing load. Most older devices handle it without slowing down. Where connections drop often, that matters more than speed. Smart programming tackles messy farm decisions anywhere. Clarity comes when software respects tough conditions on the ground.

Why it matches plant phenotyping methods葉画像から植物病徴を認識する深層学習画像手法を開発し、実験室・圃場間のドメインギャップと性能を評価しているため、植物フェノタイピング手法が中心である。

abstractan imaging tool powered by deep learning that scans leaf photos to catch signs of illness quickly

Advances in Artificial Intelligence-Enabled Crop Pest and Disease Detection: A Systematic Review ↗

The detection technology of crop diseases and pests is transitioning from single sensor monitoring to intelligent perception and multimodal fusion. This paper follows the PRISMA 2020 standard and systematically reviews the relevant core literature. This paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases. The focus is on the architectural evolution of deep learning models, including lightweight convolutional neural networks (CNNs), vision transformers (ViTs) with long-range dependency modeling capabilities, and the efficient computing state space model Mamba. In addition, the research progress of spatial spectral joint learning, heterogeneous data fusion, and vision-language models (VLMs) in improving system robustness and interpretability are introduced. By synthesizing the integrated applications of UAV remote sensing, Internet of Things (IoT) edge computing and intelligent robots in staple and cash crops, this paper summarizes the implementation of the integrated system of perception, decision-making and execution. To address the issues of insufficient cross-domain generalization ability and uneven allocation of computing resources in existing models, this paper provides perspectives on the future development of agricultural artificial intelligence (AI) towards foundation model-driven, edge-intelligent collaboration, and green sustainable direction, which can provide theoretical reference for engineering applications in the field of intelligent plant protection.

Why it matches plant phenotyping methods作物病害の画像・スペクトル観測による植物の病徴・病害状態推定を中心に、検出技術と計算手法を体系的にレビューしており、植物フェノタイピング手法のレビューに該当する。

abstractThis paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases.

AI CROP DISEASE DETECTION ↗

The rapid growth of technology in agriculture has created new opportunities to improve crop productivity and reduce losses caused by diseases. Crop diseases are one of the major challenges faced by farmers, leading to significant reduction in yield and quality. Early detection and proper diagnosis of these diseases are essential for effective treatment and prevention. This project, titled “AI Crop Disease Detection System”, aims to develop a web-based application that uses Artificial Intelligence to identify crop diseases from images. The system allows users to upload images of infected crop leaves through a simple and user-friendly interface. The uploaded image is processed using an AIbased model, which analyzes the image and predicts the type of disease. The backend of the system is developed using Python Flask, which handles image processing, model prediction, and server-side operations. The frontend is designed using HTML, CSS, and JavaScript to provide an interactive user experience. The system uses MySQL database to store user information, uploaded images, and prediction results.

Why it matches plant phenotyping methods感染葉画像から作物病害を推定するAI画像解析システムの開発が中心であり、植物の病害状態を直接評価するため、植物フェノタイピング手法として収録する。

abstractaims to develop a web-based application that uses Artificial Intelligence to identify crop diseases from images

Smart Plant Monitoring System ↗

The Smart Plant Monitoring System is designed to improve the health, growth, and safety of plants using modern IoT and artificial intelligence technologies. The system provides real-time monitoring of soil moisture, temperature, humidity, and light intensity using sensor hardware, combined with AI-based plant disease detection through image processing. It helps farmers, plant enthusiasts, and agricultural researchers monitor plant conditions continuously and receive intelligent recommendations for better crop management. The system is developed using a Python Flask backend, PyTorch-based deep learning for disease detection, ESP32-CAM hardware, Claude AI integration, SQLite database, and a glassmorphism HTML/CSS/JS frontend with Chart.js visualizations. Overall, it improves plant health management, reduces manual monitoring effort, and provides a reliable intelligent solution for modern precision agriculture.

Why it matches plant phenotyping methods植物の状態をセンサーと画像処理で継続的に取得し、AIによる病害検出を中核機能とする監視プラットフォームであり、単なる生物学的実験のルーチン測定ではない。

abstractThe system provides real-time monitoring of soil moisture, temperature, humidity, and light intensity using sensor hardware, combined with AI-based plant disease detection through image processing.

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

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

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

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

Design and Application of an Intelligent Plant Disease and Pest Recognition System for Landscape Architecture Based on Deep Learning ↗

Manual inspection of landscape pathology and textile fiber defects suffers from inherent subjective bias and suboptimal throughput. To bypass these bottlenecks, we propose the Ghost-Convolution Enlightened Vision Transformer (GeT). We constructed a novel hybrid neural network architecture, the Ghost-Convolution Enlightened Vision Transformer (GeT), which synergistically integrates the lightweight local feature extraction proficiency of Convolutional Neural Networks (CNN) with the global semantic modeling capabilities of Vision Transformers (ViT). Utilizing a newly established standard dataset (GLDP15k) comprising 15, 000 heterogeneous field images, the model was subjected to rigorous hyperparameter optimization and ablation studies. Optimization on the GLDP15k dataset yielded a peak accuracy of 96.8% across 12 target classes, maintaining a Kappa-coefficient of 0.941. Constrained to 1.16 M parameters, the architecture executes at 5.5 ms per image (180 FPS) on edge hardware. A 6-month application at Yuexiu Park demonstrated a 3.2-fold improvement in detection efficiency and a 35% reduction in pesticide usage compared to manual inspections. This study not only elucidates the interpretability of hybrid attention mechanisms in phytopathology but also adapts these vision-based paradigms to the detection of microscopic anomalies in textile weaving patterns, providing a scalable and computationally efficient solution for both precision plant protection and industrial fabric defect inspection.

Why it matches plant phenotyping methods植物病害を画像から認識する深層学習モデルを開発し、専用データセットで検証・応用しており、植物の病害状態の取得方法が中心的な研究貢献である。

abstractUtilizing a newly established standard dataset (GLDP15k) comprising 15, 000 heterogeneous field images, the model was subjected to rigorous hyperparameter optimization and ablation studies.

LiteMS-YOLO: a lightweight framework for small target detection in complex wheat field environments ↗

Wheat spike detection is essential for yield estimation in precision agriculture, yet it remains challenging due to the small size of targets, dense distribution, and complex field environments. In this study, we propose LiteMS-YOLO, a lightweight object detection framework based on YOLO26n. The model integrates a Feature Complementary Mapping (FCM) module to enhance spatial-semantic feature interaction and a Multi-Kernel Perception (MKP) unit to improve multi-scale feature representation. In addition, targeted redundancy reduction strategies are introduced to significantly lower model complexity. Experiments are conducted on a combined dataset comprising the public Global Wheat Head Detection (GWHD) dataset and 100 field images collected by the Tangshan Academy of Agricultural Sciences, with a total of 6,378 high-resolution images and over 44,000 annotated wheat spikes. LiteMS-YOLO achieves a mAP50 of 92.28% and a mAP50–95 of 52.56%, while using only 0.627 million parameters. Compared with YOLO26n and YOLOv8n, the proposed method reduces parameters by approximately 75% and 79%, respectively, while maintaining competitive accuracy. These results demonstrate that LiteMS-YOLO strikes an excellent balance between detection accuracy and efficiency, making it well-suited for real-time deployment in resource-constrained agricultural scenarios.

Why it matches plant phenotyping methods小麦穂の検出による収量推定を目的に、画像ベースの検出モデルを開発し、複数データセットで性能検証している。植物器官の検出・計数に基づく表現型取得が研究の中心である。

abstractWheat spike detection is essential for yield estimation in precision agriculture

CFPR-YOLO: chili flower pose estimation for robotic pollination in unstructured environments. ↗

Agricultural engineering informatics is playing an increasingly important role in enabling intelligent perception, decision-making, and automated operations in modern horticultural production systems. Within this context, accurate visual perception of reproductive structures is essential for agricultural informatization tasks such as flowering-stage monitoring, precision pollination, and information-driven fruit-set management in chili cultivation. However, reliable detection and pose-aware recognition of chili flowers remain challenging because of small target size, dense distribution, foliage occlusion, and illumination variability in natural or semi-controlled environments. To address these challenges, this study proposes a lightweight and robust edge vision framework, termed CFPR-YOLO, for chili flower detection and pose-aware perception under complex agricultural conditions. Built upon an improved YOLOv11n architecture, the proposed framework incorporates EfficientFormerV2 to strengthen global-context feature extraction, a C3k2_EMA module to enhance localization of small and occluded targets, and Poly-Scale Convolution (PSConv) to preserve structural details while reducing computational redundancy. In addition, a lightweight attention mechanism is introduced to improve feature discrimination in cluttered backgrounds. Experimental results on both self-constructed and generalization datasets show that the proposed method achieves a precision of 92.6%, a recall of 86.8%, and an mAP50 of 92.1% with only 7.26 M parameters. The framework also demonstrates strong robustness and generalization across different chili varieties. When deployed on an edge computing platform (NVIDIA Jetson AGX Orin), the model achieves real-time inference at 39.5 FPS. Furthermore, validation experiments under controlled indoor conditions show that the proposed framework can effectively support simulated pollination tasks, achieving a success rate of 90.0% for upwardfacing flowers. These results indicate that CFPR-YOLO provides an effective visual perception solution for agricultural engineering informatics-oriented pollination systems and offers practical potential for precision pollination and intelligent fruit-set management in horticultural production.

Why it matches plant phenotyping methodsチリ花の検出・姿勢推定という植物器官の画像計測手法を開発し、データセット、汎化性能、エッジ実装、実環境に近い条件での検証まで行っており、単なる受粉実験の補助計測ではない。

abstractthis study proposes a lightweight and robust edge vision framework, termed CFPR-YOLO, for chili flower detection and pose-aware perception under complex agricultural conditions.

Research on potatoes defect classification based on hyperspectral imaging and convolutional neural networks ↗

Abstract Potato quality detection is a critical step that determines their market value. However, manual sorting suffers from low efficiency, high cost, and a high misjudgment rate. Therefore, the rapid and accurate classification of defective potatoes is of great economic significance for reducing industrial losses. In this study, a lightweight convolutional neural network (WavebandCNN) was constructed combined with hyperspectral imaging (HSI) technology to achieve rapid and accurate classification of four categories of potatoes: healthy, greening, skin damage, and dry rot. First, hyperspectral images of 400 potato samples were collected and calibrated, and regions of interest (ROI) were extracted to construct a spectral dataset. The performance of the lightweight convolutional neural network was evaluated using raw spectra and five preprocessed spectra, respectively, and compared with three traditional machine learning models: Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM). Meanwhile, the successive projections algorithm (SPA) was employed to select characteristic wavelengths for data dimensionality reduction. The results show that WavebandCNN achieved the highest classification accuracy of 93.11% with raw spectra, significantly outperforming all comparative models. After screening 20 characteristic wavelengths via SPA, the classification accuracy was improved to 95.98%, while the training time and data redundancy were greatly reduced. This study confirms that the combination of hyperspectral imaging technology and the WavebandCNN model enables accurate identification of potato defects, providing a new approach for the online detection and practical application of potato defects.

Why it matches plant phenotyping methodsジャガイモの欠損・病変状態をハイパースペクトル画像とCNNで直接分類する手法を構築・比較・評価しており、植物状態の取得・推定が研究の中心である。

abstracta lightweight convolutional neural network (WavebandCNN) was constructed combined with hyperspectral imaging (HSI) technology to achieve rapid and accurate classification of four categories of potatoes: healthy, greening, skin damage, and dry rot.

Robust multi-target multi-scale tomato leaf disease detection for precision agriculture applications. ↗

The tomato is one of the most important economic crops worldwide; frequent occurrences of foliar diseases can severely affect its quality and yield, resulting in substantial economic losses. However, state-of-the-art methods still struggle with multi-target, multi-scale disease detection in complex scenarios, lacking accuracy and speed for tomato leaf diagnosis. A novel improved YOLO v8s model is proposed in this study to achieve high-precision and fast identification of multi-target and multi-scale tomato leaf diseases. First, a multi-target, multi-scale image dataset encompassing seven typical tomato diseases was developed to effectively enhance the model's robustness under complex practical scenario by integrating multiple public datasets and employing diverse data augmentation techniques. Second, a transfer learning strategy was employed to transfer high-quality features from a pretrained model to the disease detection task, thereby improving convergence speed and generalization ability. Finally, the CBAM (Convolutional Block Attention Module) channel-spatial attention mechanism was introduced into the YOLO v8s network, enabling the model to adaptively focus on critical regions and significantly enhance feature extraction and target localization performance. Experimental results demonstrate that the improved YOLOv8s-CBAM model achieves superior performance in complex scenarios, with a precision of 96.9%, recall of 97.3%, F1 score of 97.0%, and mAP@0.5 of 99.1%, representing improvements of 2.5%, 2.0%, 2.2%, and 1.8%, respectively, over the original YOLO v8s model. Moreover, the model size was reduced to 24.8 MB, a decrease of 11.7 MB compared to the original, achieving an effective balance between accuracy and lightweight design. These results indicate that the proposed method exhibits enhanced feature extraction and localization stability in multi-target, multi-scale disease identification tasks, providing an effective technical solution for automated detection in complex agricultural disease scenarios.

Why it matches plant phenotyping methodsトマト葉の病徴を画像から検出・識別するYOLOv8s-CBAM手法を開発し、データセット構築と性能比較検証を行っており、植物病害状態の画像ベース表現型計測が中心である。

abstractA novel improved YOLO v8s model is proposed in this study to achieve high-precision and fast identification of multi-target and multi-scale tomato leaf diseases.

Integrated UAV-Based Multispectral Scouting and Variable-Rate Aerial Spraying for Enhanced Crop Yield and Input Efficiency: A Field-Validated Study ↗

Precision agriculture demands integrated systems that couple accurate crop stress detection with targeted intervention to mitigate climate volatility and input overuse. Traditional manual scouting and uniform chemical application are spatially imprecise, labour-intensive, and environmentally burdensome. This study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS). A randomized complete block design with four replications was implemented in maize (Zea mays L.) across a 2.4 ha field in Davangere Karnataka, India. Scouting flights at 25 m altitude (1.8 cm ground sampling distance) utilized a MicaSense RedEdge-P and FLIR thermal sensor, with imagery processed through a radiometrically calibrated YOLOv8-Seg pipeline to detect early-stage disease, nutrient deficiency, and water stress. Prescription maps derived from NDRE and CWSI thresholds directly controlled a DJI Agras T40 centrifugal sprayer calibrated to ASABE S572.1 standards. The integrated system achieved an AI detection F1-score of 0.91, reduced agrochemical volume by 34.2%, and improved spray deposition uniformity (coefficient of variation = 18.4%) relative to conventional blanket spraying. Grain yield increased significantly by 11.7% (p

Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像とAI解析により、作物の病害、栄養欠乏、水ストレスを検出する方法を開発・現地検証しており、植物状態の取得が統合システムの中心的要素である。

abstractThis study field-validates a closed-loop unmanned aerial vehicle (UAV) framework integrating AI-driven multispectral scouting with prescription-mapped variable-rate aerial spraying (VRS).

Multi-Scale and Global–Local Feature Enhanced Detection for Tobacco Plants in Complex Field Environments ↗

Accurate detection of tobacco plants in complex field environments is critical for precision agriculture, crop monitoring, and yield estimation. Traditional manual counting methods are time-consuming, labor-intensive, and susceptible to environmental and subjective factors. In this study, we propose an improved YOLO11-based framework for automated tobacco plant detection, specifically designed to address challenges such as scale variation, dense distribution, and background interference. The framework integrates four key modules: the Edge-Enhanced Feature Stem (EEFS) to strengthen low-level feature extraction, the Multi-Scale Kernel Interaction (MSKI) to capture multi-scale contextual information, the Adaptive Weighted Feature Fusion (AWFF) to optimize feature aggregation, and the Global–Local Synergistic Attention (GLSA) to enhance feature discrimination by jointly modeling local details and global context. A comprehensive UAV-based tobacco dataset was constructed, encompassing multiple lighting conditions, collection heights, and observation angles. Experimental results demonstrate that the proposed method significantly outperforms the YOLO11 baseline and achieves superior performance compared to mainstream YOLO variants. Ablation studies and heatmap visualizations confirm the effectiveness of each module. Furthermore, the model exhibits robust performance under multi-dimensional environmental perturbations, including varying illumination, scale, and camera angles. The proposed framework provides a practical and efficient solution for automated tobacco plant counting, offering potential applications in UAV-based precision agriculture and large-scale crop monitoring.

Why it matches plant phenotyping methodsUAV画像からタバコ個体を自動検出・計数する手法を開発し、専用データセット、比較実験、アブレーション、頑健性評価まで行っており、植物個体数の取得方法が中心です。

abstractwe propose an improved YOLO11-based framework for automated tobacco plant detection

Deep Learning-Based Models For Crop Disease Detection Using Leaf Images: A Comprehensive Review ↗

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.

An Efficient Dynamic Deep Learning Methodology for Identification of Plant Disease and it’s classification ↗

Abstract Nowadays, estimation & identification of plant diseases (PD) pointedly shows the high impact on agricultural & food productivity. In this paper, develop the Dynamic Deep Learning Methodology-(DDLM) for Plant Disease Classification-(PDC) using the Residual Neural Network (ResNet) Architecture, enhanced with an intelligent supplement recommendation module. The procedure of present Deep Learning Methodology (ResNet) is gathering the images from the number of input sensors, create large amount of dataset that contains number of sample leaf images (both diseased & healthy) finally applying ResNet model to dataset. The model is trained (80 %) on a large dataset of plant leaf images, including healthy and diseased samples across various species. Pre-processing steps such as (R_N_A) Resizing (R), Normalization (N), and Augmentation (A) are working on development of the model to improve model generalization. Once a disease is detected, Methodology generates output including the disease name (e.g., "Tomato Late Blight") and a Recommended Supplement (e.g., "Apply Copper-Based Fungicide, Ensure Proper Drainage"). The ResNet50 model, fine-tuned using Transfer Learning (TL), achieves a classification accuracy of 97.4%, outperforming traditional CNN models. Early estimation & identification of plant diseases (PD) gives the high increases the yield of the crop. Evaluation metrics such as Confusion Matrix, Precision, Recall & F1-score validate the reliability of the model across multiple classes. By integrating accurate disease detection with actionable supplement guidance, the proposed solution empowers farmers to take immediate and informed actions, enhancing crop health and yield with supplement recommendation. When comparing with resnet50 the other methods had a less accuracy. KEYWORDS— Hybrid Machine Learning Methodology (Dynamic Deep Learning Methodology-(DDL) for Plant Disease Classification-(PDC), Transfer Learning (TL), Residual Neural Network (ResNet), Image Classification, Accuracy, Disease Detection, Precision Agriculture, Smart Farming, Transfer Learning.

Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習手法の開発・評価が中心であり、植物フェノタイピング手法に該当する。

abstractdevelop the Dynamic Deep Learning Methodology-(DDLM) for Plant Disease Classification-(PDC) using the Residual Neural Network (ResNet) Architecture

Construction and validation of a YOLO-Soy Model for field soybean seed counting on a self-propelled phenotyping platform ↗

To address the bottlenecks of low efficiency, poor consistency, and inadequate compatibility with high‑throughput phenotyping pipelines inherent in manual field‑based seed counting during soybean breeding, this study developed and validated an enhanced automatic soybean seed detection and counting model, YOLO‑Soy, tailored for complex field environments. Built on a YOLO11n backbone, the model integrates a Zoom multi‑scale feature fusion module, a C2PSA self‑attention enhancement module, a ScalSeq hierarchical feature sequence aggregation module, and a soybean-specific detection head. These additions systematically enhanced the saliency of tiny-seed features under dense occlusion and complex backgrounds and strengthened the capacity for foreground-background separation. Experiments were conducted using two‑year field imagery (2024–2025) and a year-stratified leave-one-year-out cross-validation strategy for training and validation. Ablation study revealed that the four improved modules are functionally complementary, forming a comprehensive pipeline of interference mitigation, scale adaptation, precise feature fusion, and detection output transformation. A single module exhibited limited effect when acting independently, whereas multi-module synergy produced substantial gains. Test-set results demonstrated that the seed counts predicted by the model were highly consistent with manual ground truth, achieving a coefficient of determination ( R ²) of 0.934, a mean relative error of 2.446%, a mean average precision (mAP@0.5) of 0.737, and an inference speed of 58.78 FPS. These metrics satisfy the requirements for real-time field detection. The findings indicated that YOLO-Soy can accelerate the seed‑counting step in variety selection processes, greatly reducing manual workload and subjective errors.

Why it matches plant phenotyping methods圃場画像からダイズ種子数を自動推定するYOLOモデルを開発し、交差検証・アブレーション・精度評価で検証しており、植物表現型取得法が研究の中心である。

abstractthis study developed and validated an enhanced automatic soybean seed detection and counting model, YOLO‑Soy, tailored for complex field environments

Improved plant leaf disease detection architecture using unmanned aerial vehicle images and hybrid YOLOv11 ↗

Plant diseases affect crop quality and quantity, which significantly reduces crop production. Grapes are a popular fruit, and pumpkin is a widely consumed vegetable in the world. Early detection of grape and pumpkin leaf disease is crucial to avoiding large losses in crop quality and yield. Even though the use of unmanned aerial vehicle (UAV) remote sensing can assist in reaching a broader scale for plant leaf disease identification, the work of detection is significantly hampered by the overlapping leaves, and blurring of UAV images. This paper suggests combining object detection techniques with Real-World Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) for enhancing image quality to detect grape and pumpkin leaf diseases in UAV-captured images. The Atrous Spatial Pyramid Pooling (ASPP) technique is incorporated into YOLOv11 architecture to enhance the network’s feature representation capabilities by efficiently incorporating depth-wise separable convolutions and dilated convolutions with different rates. The proposed approach has shown remarkable effectiveness. In the validation, the ASPP-YOLOv11 outperforms the baseline YOLOv11 architecture in precision achieving 84% with 5.2% increase, 94.7% with 3.3% increase in mAP50, and 74% with 0.9% increase in mAP50-95. Additionally, in the testing set the ASPP architecture significantly enhances mAP50 to 91.6%, attaining 1.6% gain and obtaining 1.6% increase in mAP50-95.

Why it matches plant phenotyping methodsUAV画像から植物葉の病害を推定する画像解析アーキテクチャを開発し、既存モデルと性能比較しており、植物病害状態の取得方法が中心である。

abstractThis paper suggests combining object detection techniques with Real-World Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN) for enhancing image quality to detect grape and pumpkin leaf diseases in UAV-captured images.

PatchNet-Inception: A Hybrid Mask R-CNN and Patch-Based Inception CNN Framework for Overlapped Plant Leaf Disease Detection and Severity Quantification ↗

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

Why it matches plant phenotyping methods植物葉の病害検出と重症度定量化を目的とする画像解析手法の開発であり、植物状態の取得・推定が研究の中心です。

titlePatchNet-Inception: A Hybrid Mask R-CNN and Patch-Based Inception CNN Framework for Overlapped Plant Leaf Disease Detection and Severity Quantification

Plant Leaf Disease Detection and Severity Estimation Using Ghostnet-VIT and Attention U-Net ↗

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

Why it matches plant phenotyping methods植物葉の病害検出と重症度推定を目的とする画像ベース手法であり、植物の病害状態を直接推定する方法が中心と判断できる。

titlePlant Leaf Disease Detection and Severity Estimation Using Ghostnet-VIT and Attention U-Net

EfficientNetV2-Based Classification Approach for Plant Leaf Disease Detection ↗

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

Why it matches plant phenotyping methods植物葉の病害検出にEfficientNetV2分類手法を用いる方法開発が題名上の中心であり、病害状態という植物表現型を画像から推定する研究と判断できる。

titleEfficientNetV2-Based Classification Approach for Plant Leaf Disease Detection

Hybrid Machine Learning and Deep Learning Approach for Automated Crop Disease Detection Using Leaf Images ↗

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

Why it matches plant phenotyping methods葉画像から作物病害を自動検出する画像・機械学習手法が題名上で中心的に示されており、植物の病害状態を推定するフェノタイピング手法に該当します。

titleHybrid Machine Learning and Deep Learning Approach for Automated Crop Disease Detection Using Leaf Images

A Lightweight Real-Time Tomato Leaf Disease Detection System for Edge-Based Smart Agriculture. ↗