Accurate point-cloud segmentation of cotton organs is essential for precise phenotypic characterization. However, reliable instance segmentation of cotton bolls in field-derived point clouds remains challenging because foliage occlusion, contact between adjacent bolls and incomplete reconstruction obscure instance boundaries. Here we present RQ-PointNeXt, an end-to-end framework that directly maps input point clouds to boll instance masks within a unified trainable network. Built on PointNeXt, it incorporates relative-elevation geometric channel attention in the shallow encoder to fuse global channel context with elevation and surface-normal cues. Its Query–mask branch integrates semantic guidance, center-seeded queries and a center-aware mask prior to suppress background responses, localize instances and constrain mask extent. Hungarian matching and multitask optimization establish one-to-one query–instance assignments, whereas query-based decoding produces instance masks without external geometric clustering. We evaluated the framework on 226 field-grown cotton plants containing 720 annotated boll instances reconstructed from UAV multi-view imagery using neural radiance fields. On the held-out test set, overall accuracy, mean class accuracy and mean intersection over union reached 0.8942, 0.8980 and 0.8076, respectively. AP25, AP50 and AP75 were 0.7359, 0.5585 and 0.2777, yielding an mAP25/50/75 of 0.5240. The framework provides instance-level outputs for boll counting and spatial analysis in high-throughput field phenotyping.
Why it matches plant phenotyping methods綿花ボールの点群インスタンス分割を開発・評価し、計数や空間解析に利用可能な植物器官表現型を抽出する方法が中心である。
abstractHere we present RQ-PointNeXt, an end-to-end framework that directly maps input point clouds to boll instance masks within a unified trainable network.
Reproduction assets foundThe paper's own field cotton point-cloud dataset (226 plants, 720 annotated bolls) is only available upon request. However, the authors directly used the public UGA-BSAIL Cotton Plants with Foliage point-cloud dataset (with their added boll instance annotations) as an evaluation benchmark for RQ-PointNeXt, and it is公开发Dataset · publict to the pointwise overlap between
predicted and ground-truth instances.
To further evaluate the proposed method under conditions of relatively high point-
cloud completeness, experiments were conducted using the public UGA-BSAIL Cot-
ton Plants with Foliage dataset. The point-cloud data are publicly available through
Figshare (https://figshare.com/projects/Cotton_plant_with_foliage/258065, accessed on
8 September 2026), while the associated code and documentation are hosted on GitHub
(https://github.com/UGA-BSAIL/Cotton_plants_with_foliage, accessed on 8 September
2026). The dataset contains relatively complete cotton plant point clouds, surface-normal
attributes, and semantic labels distinOpen asset ↗Figshare · Cotton_plant_with_foliage/258065pdf-raw-page:16 lines:1-52Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published11 Sept 2026Journal of visualized experiments : JoVE
Volatile organic compounds (VOCs) released by plant leaves play key roles in stress signaling, plant-atmosphere interactions, and plant defense. Among these, wound-induced VOCs (wVOCs) are emitted within seconds of mechanical damage, herbivory, or environmental disturbance. Their emission dynamics depend strongly on the timing, severity, and method of tissue disruption. Yet, accurate quantification remains challenging due to mechanical artifacts, variable exposure conditions, and delays between injury and measurement. This study presents a standardized within-chamber leaf excision protocol for real-time monitoring of wVOCs and gas exchange. A surgical-grade cutter was integrated into a portable gas-exchange chamber to enable clean, controlled cuts within a sealed chamber under stable light, humidity, CO2, and temperature conditions. A proton-transfer-reaction time-of-flight mass spectrometer (PTR-TOF-MS) continuously measured volatile emissions at the chamber outlet, minimizing delay and signal distortion. This setup resolves emission onset, peak timing, maximum rise rate, and total release with high temporal fidelity. Application of the method to Quercus rubra, Acer platanoides, and Gossypium hirsutum demonstrated its ability to resolve distinct wound-induced emission patterns across contrasting leaf types. By eliminating delays associated with conventional sampling, this method resolves the full kinetic trajectory of wound-induced emissions and overcomes major limitations of previous approaches. It provides a robust framework for studying rapid stress responses in plant physiology, ecological biochemistry, and plant-atmosphere interactions.
Why it matches plant phenotyping methods植物葉の創傷誘導揮発性物質とガス交換をリアルタイム定量する測定系を開発しており、植物のストレス生理状態の取得が研究の中心である。
abstractThis study presents a standardized within-chamber leaf excision protocol for real-time monitoring of wVOCs and gas exchange.
Abstract Plant diseases substantially reduce global crop yields, and cotton production is particularly vulnerable to field-acquired variability in symptom appearance, background clutter, and illumination changes that limit the reliability and scalability of expert visual inspection. This study aimed to develop an accurate, computationally efficient, and explainable framework for real-time cotton leaf disease recognition that is suitable for deployment on resource-constrained edge devices. Using the SAR-CLD-2024 dataset (322 RGB images captured under natural agricultural conditions across seven categories, including healthy and diseased leaves), images were preprocessed via resizing and normalization and augmented online in the training set (random rotations, flips, brightness/contrast adjustments, and random cropping). An EfficientNet-B3 backbone initialized with ImageNet-pretrained weights was fine-tuned using categorical cross-entropy loss and Adam optimization, with early stopping, checkpointing, regularization, and a fixed-seed 70/15/15 train–validation–test partition to enhance reproducibility and reduce leakage. Performance was evaluated on an independent test set using accuracy, precision, recall, F1-score, MCC, balanced accuracy, Cohen’s kappa, confusion matrix, multi-class ROC/AUC, and precision–recall analysis, alongside computational benchmarking (parameters, FLOPs, memory, and inference latency) and comparative experiments against contemporary CNN, lightweight, and transformer-based models. The model showed stable convergence over 30 epochs with a small training–validation gap, predominantly correct predictions with limited confusion among visually similar classes, consistently high precision–recall behavior under moderate class imbalance, and stable performance across repeated runs with low variability and a tight confidence interval. Grad-CAM heatmaps localized necrotic lesions, discoloration, and infected tissues while largely ignoring background, and failure cases were associated with early-stage symptoms, occlusion, shadows, and inter-class similarity. Overall, the framework provides a reproducible, interpretable, and efficient solution for cotton leaf disease classification with practical implications for trustworthy, low-latency, on-device decision support in precision agriculture.
Why it matches plant phenotyping methods綿葉の病徴を画像から分類する深層学習手法の開発が中心で、独立テスト、比較評価、計算性能評価、Grad-CAMによる病徴局在化を実施しているため、植物病害フェノタイピング手法に該当する。
abstractThis study aimed to develop an accurate, computationally efficient, and explainable framework for real-time cotton leaf disease recognition
Reproduction assets foundThe paper's Data Availability statement explicitly names the SAR-CLD-2024 cotton leaf dataset used for all experiments as publicly available on Kaggle with a direct URL. No author analysis code or trained model checkpoint is deposited.Dataset · publicntribute to the development of fully automated, scalable, and real-time smart agriculture
systems.
Declaration
Funding
Datta Meghe Institute of Higher Education and Research Wardha, Maharashtra, India
Data Availability: The SAR-CLD-2024 cotton leaf dataset used in this study is publicly
available through the Kaggle platform at: https://www.kaggle.com/datasets/pantho12/sar-cld-2024-dataset-for-cotton
This dataset includes annotated images of various cotton leaf diseases collected under diverse
environmental conditions. All data utilized in this work are freely accessible, and the data
processing methodology has been described in detail to facilitate reproducibility.
Conflict of interest The aOpen asset ↗Kaggle · SAR-CLD-2024pdf-raw-page:32 lines:1-38Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published1 Sept 2026Computers and Electronics in Agriculture
Aboveground biomass density (AGBD) is a key indicator in agricultural systems, directly reflecting crop carbon storage potential and yield levels. The World Food Studies (WOFOST) model is widely used for crop growth simulation due to its process-based interpretability. However, its application is limited by complex calibration demands and struggles with spatial heterogeneity. To address these limitations, this paper proposes an assimilation system that integrates Synthetic Aperture Radar (SAR) data into a modified WOFOST model, which requires simple calibration. WOFOST is run in potential production mode, which assumes ideal conditions to reduce input data requirements. Before assimilation, phenology-related temperature sums are aligned with phenology and meteorological data to match local growth stages. Two quantities are then estimated and updated in the model through data assimilation by minimizing the difference between SAR-derived and simulated AGBD. These quantities are the proposed yield reduction factor, which represents the proportional decrease in potential CO 2 assimilation under stresses, and the initial total dry weight at sowing. Validation experiments were conducted using multi-year cotton datasets from two farms in Georgia, USA, differing in whether irrigation was applied. Compared to WOFOST simulations and evaluated against in situ AGBD measurements, the assimilation results improve agreement and reduce error (approximately 43% RMSE reduction at the rainfed site and 15% at the irrigated site). It also delivers spatial maps of biomass, together with model-derived yield and harvest-index diagnostics, and remains operational under frequent cloud cover where optical observations are sparse. This SAR-based assimilation strategy reduces calibration demands, providing a novel and practical pathway to extend WOFOST applications to diverse agricultural scenarios.
Why it matches plant phenotyping methodsSARデータを作物成長モデルに同化して綿の地上部バイオマスを推定する手法を開発し、複数年・複数圃場データで検証しているため、植物形質取得が中心である。
abstractthis paper proposes an assimilation system that integrates Synthetic Aperture Radar (SAR) data into a modified WOFOST model
Abstract This study evaluated the potential of UAV-derived RGB spectral indices to predict fiber quality traits in cotton genotypes. Nineteen genotypes were assessed under a randomized complete block design, and RGB imagery acquired at full flowering was used to calculate GLI, NGRDI, SCId, and SI. Significant genetic variability and moderate-to-high heritability were observed for both fiber traits and spectral indices. GLI was positively associated with the Spinning Consistency Index, whereas NGRDI was associated with fiber length uniformity. Regression models showed moderate predictive ability (R² LOOCV between 32.4–35.4%; and accuracy between 0.57–0.60). GLI and NGRDI demonstrated potential as complementary tools for large-scale phenotyping and preliminary genotype selection, although they do not replace conventional fiber quality analyses. Further studies across additional developmental stages are needed to improve prediction accuracy.
Why it matches plant phenotyping methodsUAV由来RGB画像からスペクトル指標を算出し、ワタの繊維品質形質を予測する手法を評価しており、大規模フェノタイピングへの応用と予測性能の検証が中心である。
abstractThis study evaluated the potential of UAV-derived RGB spectral indices to predict fiber quality traits in cotton genotypes.
Canopy 3D architecture is a critical determinant of light interception, photosynthetic efficiency, and final yield in cotton, yet its rapid and accurate characterisation remains challenging in field conditions. To achieve efficient, non-destructive, and quantitative monitoring of the canopy structure of field-grown cotton, this study proposes a 3D structure-based technology stack for high-efficiency, low-cost, and high-precision phenotyping extraction. This stack directly addresses the technical bottlenecks of traditional 3D data acquisition, namely high cost, long processing time, and low operational efficiency, which have hindered large-scale application. We developed a pipeline that integrates a fast reconstruction algorithm with a scale-recovery mechanism using ground control points (GCPs), enabling the generation of true-scale 3D point clouds from UAV aerial images in a cost- and time-effective manner. Using only 141 UAV images and with a reconstruction time of approximately 20 min, we efficiently reconstructed high-quality, scale-accurate point clouds of two 5.5 m × 5.5 m cotton plots, significantly outperforming SfM-MVS and Instant-NGP in terms of both reconstruction efficiency and point cloud completeness. This method, whose current validation is confined to a single season, one growth stage, and two experimental plots, not only achieves a breakthrough by using fewer input images with high efficiency, but also ensures point cloud accuracy and completeness, showing strong potential for rapid field monitoring and real-time management. Based on the high-quality reconstructed point clouds, we further quantitatively evaluated canopy characteristics at harvest, analyzing the coefficient of variation of canopy height, porosity distribution, and canopy volume fraction. The core shortcomings and optimization strategies for the existing canopy structure were identified, providing scientific data support and practical technical references for precision cultivation management and mechanization-compatible planting in cotton.
Why it matches plant phenotyping methodsUAV画像からの3D点群再構成とスケール復元パイプラインを開発・比較検証し、綿花キャノピー形質を定量化することが研究の中心であるため含める。
abstractthis study proposes a 3D structure-based technology stack for high-efficiency, low-cost, and high-precision phenotyping extraction.
Accurate estimation of the cotton seedling Leaf Area Index (LAI) is essential for yield prediction and precision crop management. Traditional manual methods are limited by low throughput, while two-dimensional remote sensing techniques often struggle with sparse canopy cover and soil background interference. Although three-dimensional LiDAR presents a promising alternative, existing deep learning approaches typically depend on costly fully supervised point-wise annotations. To address this challenge, this study proposes an end-to-end framework that integrates UAV-based LiDAR, weakly supervised segmentation, and physical parameter inversion. A weakly supervised network, termed CogNet, was developed—incorporating self-distillation and structure-aware label propagation—to achieve precise segmentation of cotton plants using only 10% sparse annotations. Following instance segmentation via Density-Based Spatial Clustering of Applications with Noise (DBSCAN), individual plant phenotypic traits were extracted. A nonlinear Extreme Gradient Boosting (XGBoost) model was then constructed to invert LAI by leveraging allometric relationships between 3D structural parameters and leaf area. Experimental results showed that CogNet achieved an Intersection over Union (IoU) of 85.34%, effectively mitigating overfitting to label noise and achieving performance competitive with the fully supervised RandLA-Net (82.13%). Notably, under the specific conditions of this cotton seedling dataset characterized by strong geometric priors, the weakly supervised model demonstrated enhanced robustness against annotation inconsistencies. The framework attained a plant detection rate of 96.2%, and the XGBoost model delivered high estimation accuracy (R² = 0.879, RMSE = 0.138). This study demonstrates that weakly supervised learning can substantially reduce annotation costs while maintaining model performance, providing an efficient and cost-effective solution for field-scale crop phenotyping.
Why it matches plant phenotyping methodsUAV-LiDAR、弱教師ありセグメンテーション、個体形質抽出、LAI推定を統合した作物フェノタイピング手法の開発・評価が研究の中心である。
abstractthis study proposes an end-to-end framework that integrates UAV-based LiDAR, weakly supervised segmentation, and physical parameter inversion.
Boll-opening concentration is critical for mechanical cotton harvesting, yet it is still assessed mainly by manual records and single time-point indicators that miss temporal dynamics. To bridge the lack of a unified workflow linking vision foundation models, multi-temporal boll-opening monitoring, and harvest decision-making, we developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX. DINO-BollGX couples a DINO v3 backbone, a RetinaNet detection head, and an adaptive refinement-and-suppression module for robust open-boll detection under complex field conditions. Using multi-temporal UAV imagery collected over two years for 383 cultivars, we reconstructed plot-scale time series of open-boll counts, derived dynamic features describing progression and intensity changes, and proposed a Cotton Boll-Opening Temporal Stability Index (CTSI) to quantify boll-opening rhythm and concentration; CTSI was further integrated with a time-based risk function to generate harvest decision curves. Under unified data and training settings, DINO-BollGX achieved precision = 0.91, F1 = 0.88, and AP@0.50 = 0.80, and provided accurate boll-count estimation (R 2 =0.98; MAE=3.10), outperforming YOLOv11, YOLOv12, YOLOv13, and RT-DETR. On an independent cross-year dataset acquired at 5 m altitude, it obtained precision = 0.98 and F1 = 0.87. An internal consistency analysis showed that CTSI had the expected negative association with Window_days (r = −0.83) and positive associations with Max_count (r = 0.78) and the boll-opening efficiency index (r = 0.92), reflecting the co-occurrence of temporal compactness and main-phase opening intensity in the cultivar population. CTSI ranged from −2.72 to 4.69 across cultivars, enabling identification of highly synchronized boll-opening. Harvest decision curves indicated that the relative net income index peaked at day 67 after the first observation and a compact optimal harvest window near the end of monitoring; on a fixed harvest date, Kuche 130292 (CTSI=4.69) produced 486 open bolls versus 182 for Xinluzao 36 (CTSI=0.53) and 90 for Andizhan-60 (CTSI=-2.72). Overall, the framework integrates dynamic boll-opening phenotyping with harvest timing optimization, supporting scalable cultivar screening and mechanization-ready deployment, with potential extension to harvest decision scenarios in other crops.
Why it matches plant phenotyping methodsUAV画像と基盤モデルを用いて綿花の開絮を検出・定量し、時系列表現型指標を構築・検証する方法が研究の中心であるため。
abstractwe developed a cross-scale UAV high-throughput phenotyping framework centered on DINO-BollGX.
Reproduction assets foundThe paper publicly releases its cotton boll-opening UAV image dataset (3638 patches, 94,774 YOLO-format bounding-box annotations) on GitHub, directly supporting the paper's phenotyping analysis. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicentary information for evaluating cross-scale detection performance and characterizing macroscopic spatial patterns. The 5 m imagery acquired on 18 Sept 2024 is used exclusively for cross-year generalization assessment. All cropped images and the corresponding YOLO-format annotation files have been publicly released on GitHub ( https://github.com/mianchen0529/cotton-boll-dataset/tree/main ) to facilitate further research on cotton phenotyping, agricultural remote sensing, and intelligent analytics.
2.3.
Model construction
2.3.1.
Overall architecture of the DINO-BollGX network
The proposed DINO-BollGX network consists of four stages: image preprocessing, feature extraction, object prediction,Open asset ↗mianchen0529/cotton-boll-datasetlines:63-74Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Abstract Cross-sectional analysis is considered the reference method for measuring cotton fiber fineness and maturity; however, its widespread use has been limited by labor-intensive sample preparation and manual image analysis. The objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections. A total of 249 composite light microscopy images of cotton fiber cross-sections were collected and manually annotated to generate training and validation datasets. A YOLO11m instance segmentation model was developed to identify cotton fiber and lumen regions and automatically extract quantitative traits, including fiber area, lumen area, fiber perimeter, and lumen perimeter. The workflow integrates automated image segmentation, post-processing, quantitative trait extraction, and data export to facilitate reproducible cotton fiber phenotyping. Model performance was evaluated using mean Average Precision (mAP), and workflow outputs were validated against Adobe Photoshop using descriptive comparisons of six cross-sectional traits. The model achieved Box mAP50 scores of 0.984 for cotton fiber regions and 0.789 for lumen regions, demonstrating high segmentation accuracy. The automated workflow substantially reduced manual analysis time while producing measurements with central tendencies comparable to those obtained using Adobe Photoshop. To facilitate reproducibility and adoption, the workflow, trained model weights, and supporting documentation are publicly available through GitHub and a Hugging Face web application. The workflow substantially increases analytical throughput while providing a reproducible and publicly accessible method for automated cotton fiber cross-sectional phenotyping, facilitating quantitative analysis for cotton genetics and breeding research.
Why it matches plant phenotyping methods綿繊維横断面の画像分割、形質抽出、検証を目的とした再現可能な深層学習ワークフローの開発であり、植物フェノタイピング手法が研究の中心である。
abstractThe objective of this study was to develop and validate a reproducible deep-learning-based workflow for automated segmentation and quantitative analysis of cotton fiber cross-sections.
Reproduction assets foundThe paper's cotton fiber cross-section phenotyping workflow (YOLO11m segmentation pipeline, trained model weights, example images/outputs) is explicitly stated as publicly available via a GitHub repository and a Hugging Face web application, with URLs matching the allowed list.Code · publicThe complete source code, training scripts, dataset configuration, example input images, example outputs,
and supporting documentation are publicly available through the GitHub repository:
https://github.com/RifeLab/cotton-lumen-microOpen asset ↗RifeLab/cotton-lumen-micropdf-page:11 lines:1-47Code · publicThe cotton fiber image analysis workflow is publicly available through a web-based application hosted on
Hugging Face at: https://huggingface.co/spaces/chaneylc/cotton_fiber_microscopy_measureOpen asset ↗pdf-page:11 lines:1-47Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published8 Aug 2026TAG. Theoretical and applied genetics. Theoretische und angewandte GenetikCited by 0 · OpenAlex ↗
Key messages High-accuracy NIRS models and GWAS identified a novel QTL qPO-A07-1. GhMYB86 was validated to enhance seed protein content and fiber strength, and a functional KASP marker was developed. Cottonseed is rich in protein and oil; improving its nutritional quality is vital for global food security. In this study, near-infrared spectroscopy (NIRS) models were developed for predicting cottonseed protein and oil content using least absolute shrinkage and selection operator (LASSO) regression, achieving validation R 2 of 0.969 (P -4 ). A novel stable quantitative trait locus (QTL), qPO-A07-1, was detected, within which GhMYB86 was prioritized as a candidate gene. This gene exhibited higher expression in high-protein-content varieties during ovule development. Heterologous overexpression in Arabidopsis thaliana increased seed protein content by 2.61-3.34%, whereas expression in Saccharomyces cerevisiae increased protein content by 25.81% and reduced triglyceride content by 30.72% in comparison with the control. These results demonstrate that GhMYB86 positively regulates protein content while negatively affecting oil content. A kompetitive allele-specific PCR (KASP) marker targeting a promoter A/T polymorphism revealed that the AA allele was associated with higher-protein content, lower-oil content, and increased fiber strength across both mapping and validation populations. Furthermore, the protein content- and fiber strength-favorable allele has undergone positive selection during breeding. This study provides phenotyping tools, reliable genetic resources and a molecular marker for cottonseed nutritional quality breeding, laying a foundation for the improvement in cottonseed protein content and fiber strength.
Why it matches plant phenotyping methods綿実のタンパク質・油含量を推定するNIRSモデルを開発・検証しており、植物形質取得法が研究の中心的貢献である。GWASや遺伝子検証も行うが、NIRSによる形質推定が明確な方法論的役割を持つ。
abstractHigh-accuracy NIRS models and GWAS identified a novel QTL qPO-A07-1.
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.
Accurate monitoring of cotton plant moisture content (PMC) is crucial for guiding irrigation practices. To address the limited capacity of single-source remote sensing data to characterize the water status of cotton plants, as well as the lack of quantitative reference values for suitable PMC levels at different growth stages, this study constructed a cotton PMC estimation model based on multimodal UAV remote sensing data. Furthermore, the suitable reference levels of PMC at different growth stages were investigated according to the response relationship between PMC and yield at each growth stage. Five soil moisture gradients were established, and at each growth stage, fresh and dry weights of cotton shoots were measured to calculate the PMC. A UAV platform equipped with multiple sensors was used to collect visible-light (RGB), multispectral (MS), and thermal infrared (TIR) images of the cotton canopy. Three feature selection methods were employed to identify moisture-sensitive parameters: Pearson correlation analysis, principal component analysis (PCA) for dimensionality reduction, and recursive feature elimination (RFE). Using the selected parameters, four machine learning algorithms, AdaBoost, random forest (RF), CatBoost, and k-nearest neighbors (KNN), were applied to construct and validate PMC estimation models. The suitable PMC levels at different growth stages were identified based on the response relationship between measured PMC and yield under different water gradients. The results showed that the RFE feature selection method identified eight water-sensitive parameters, and the CatBoost model integrating multimodal data performed best, with R² and RMSE reaching 0.807 and 0.033%, respectively, on the test set, providing a reliable method for high-resolution spatial mapping of field-scale PMC. On this basis, the response of yield to PMC was analyzed, revealing that when PMC was maintained at 83.8%, 85.9%, 79.3%, 78.0%, and 67.7% at the bud, initial flowering, peak flowering, peak boll-setting, and boll opening stages, respectively, the theoretical maximum yield of 6579–6667 kg/hm² could be achieved. This study realized high-precision remote sensing monitoring of PMC and further explored the appropriate moisture content thresholds for different growth stages, providing a quantitative reference for precision water regulation in cotton fields.
Why it matches plant phenotyping methodsUAVのマルチモーダル画像と機械学習により、綿植物の水分含量を推定・検証する手法が研究の中心であり、植物状態の高解像度マッピングにも応用している。
abstractthis study constructed a cotton PMC estimation model based on multimodal UAV remote sensing data
Cotton leaf diseases significantly affect crop productivity, fiber quality, and agricultural sustainability, making early and accurate disease diagnosis essential for effective crop management. Manual disease identification is time-consuming, subjective, and dependent on expert knowledge, highlighting the need for automated and intelligent diagnostic systems. This study proposes a CNN-based deep learning framework for automated multiclass classification of cotton leaf diseases using digital image analysis. The proposed framework is trained on a curated dataset comprising healthy and diseased cotton leaf images representing multiple disease categories. CNN architecture performs hierarchical feature extraction, automatically learning discriminative spatial representations from input images without manual feature engineering. The network is trained for 100 epochs using optimized hyperparameters to achieve robust feature learning while ensuring high generalization capability. Model performance is quantitatively evaluated using standard classification metrics, including accuracy, precision, recall, F1-score, and confusion matrix analysis, providing a comprehensive assessment of classification effectiveness. Experimental results demonstrate that the proposed framework achieves reliable disease recognition with high classification accuracy and minimal inter-class confusion, validating the effectiveness of CNN-based feature learning for agricultural image analysis. The automated system enables rapid and accurate disease diagnosis, supporting timely intervention, optimized crop protection strategies, and improved decision-making in precision agriculture. Future work will focus on improving model robustness through the integration of larger and more diverse datasets, advanced data augmentation techniques, transfer learning, and hybrid deep learning architectures. Furthermore, deployment of the proposed framework on mobile, edge, and web-based platforms, together with IoT technologies, can facilitate real-time field monitoring and disease surveillance. Overall, the proposed CNN framework provides a scalable, computationally efficient, and intelligent solution for automated cotton leaf disease classification, contributing to the advancement of AI-driven precision agriculture and sustainable crop management.
Why it matches plant phenotyping methodsワタ葉の病徴を画像から分類するCNN手法の開発と性能評価が研究の中心であり、植物の病害状態を直接推定するため。
abstractThis study proposes a CNN-based deep learning framework for automated multiclass classification of cotton leaf diseases using digital image analysis.
CottonField / plotMultimodalFruitClassificationPhysiological trait estimationGrowth / development / phenology
Cotton fiber quality is shaped during boll development, boll opening, fluffing, and harvesting, but current assessment still relies largely on manual field inspection and postharvest laboratory testing. This limits timely harvest scheduling and plot-level quality management. To address this problem, we propose a self-supervised multimodal sensing framework for linking preharvest cotton boll status, environmental conditions, and postharvest fiber quality. First, the Cotton Boll Visual Phenotype Self-Supervised Encoding Module learns maturity-related visual representations by reconstructing masked image patches, so that boll cracking, lint exposure, and surface texture can be captured from unlabeled field images. Second, the Agricultural Sensor Temporal Masked Modeling Module reconstructs masked sensor observations to model temporal patterns in temperature, humidity, light, soil moisture, rainfall, and other environmental variables. Third, the Vision–Environment Cross-Modal Contrastive Fusion Module aligns image features with environmental features and produces a joint representation for downstream prediction. Field experiments were conducted using cotton boll images from different maturity and abnormal states, environmental sensor records, management information, and postharvest fiber quality measurements. The framework was evaluated for maturity classification, harvest-window recognition, and fiber quality prediction. The results showed that the proposed method performed consistently better than representative machine learning, single-modal deep learning, and multimodal fusion baselines, while few-shot and ablation experiments supported the value of self-supervised pretraining and multimodal fusion. These findings indicate that the proposed approach can provide useful information for preharvest cotton maturity assessment and harvest-quality management.
Why it matches plant phenotyping methods綿花の成熟状態を画像・環境センサーから抽出し、成熟度分類や収穫時期認識を行うマルチモーダル手法の開発・評価が中心であり、植物状態の表現型推定に該当する。
abstractwe propose a self-supervised multimodal sensing framework for linking preharvest cotton boll status, environmental conditions, and postharvest fiber quality.
Detecting cotton leaf diseases in open-field environments is challenging due to cluttered backgrounds, scale variation, and irregular lesion morphology. Conventional detectors rely on isotropic receptive fields and coupled box-regression losses, which limit their ability to localize elongated lesions with poorly defined boundaries. We present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages. In the backbone, an Anisotropic Morphological Contrast Aggregation module (AMCA) enhances direction-aware representation and lesion-background contrast via re-parameterizable strip convolutions and high-frequency residual extraction. A Dynamic Semantic Boundary Transfer mechanism (DSBT) then captures boundary priors from shallow layers before they are lost to downsampling and injects them into the neck. A Morphological-Spectral Synergistic Feature Pyramid Network (MFS-FPN) preserves these cues during multi-scale fusion through spatial-domain operations compatible with edge hardware. Finally, an Anisotropic Boundary-Decoupled IoU loss (ABD-IoU) independently penalizes each of the four box boundaries and sustains optimization signals in high-IoU regimes via a logarithmic modulation factor. On the self-constructed Complex Cotton Leaf Disease dataset (CCLD; 6,856 images, 6 classes), the method achieves 78.50% mAP@50 and 65.00% mAP@50:95, improving the YOLOv11n baseline by 4.80% and 2.70% with only 2.73 M parameters at 202 FPS. Cross-domain evaluations on PlantDoc and RWD confirm consistent improvements. The framework runs in real time on NVIDIA Jetson edge platforms with INT8 quantization.
Why it matches plant phenotyping methods綿花葉の病斑・病害状態を画像から検出する手法を開発し、複数データセットとベースラインで性能検証しているため、植物表現型取得が中心である。
abstractWe present an anisotropic boundary-aware detection framework that propagates high-frequency boundary information across four successive pipeline stages.
Reproduction assets foundThe authors explicitly state that their source code, trained models, and implementation details are publicly available, and the data availability statement points to the same repository, which hosts the self-constructed CCLD cotton leaf disease dataset (6,856 images, 6 classes) used for the paper's phenotyping/disease-Code · publicFurthermore, to facilitate future research, our source code, trained models, and implementation details have been made publicly available at https://github.com/DynaVLA/ABAD-CLD .Open asset ↗DynaVLA/ABAD-CLDlines:331-343Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://github.com/DynaVLA/ABAD-CLD .Open asset ↗DynaVLA/ABAD-CLDlines:1278-1317Plant phenotyping relevance match · UnverifiedEurope PMC · Crossref · checked 5 Sept 2026
Abstract Crop disease is a major worldwide problem in agricultural production and food security, adversely affecting yield and quality for a variety of plant species. To overcome these drawbacks, this research provides a Double Transfer Learning-based Capsule Network (DTL-CapsNet) approach for automated plant disease classification with multiple crops. Based on the image pre-processing, segmentation, double transfer learning, and Capsule Networks technologies, the proposed framework extracts discriminative features of the diseases and maintains spatial relations between the leaves symptoms effectively. This double transfer learning approach involves extracting general visual features from pre-trained deep learning models and then fine-tuning these features to classify plant diseases. Capsule Networks then leverage the visual similarity of disease patterns to make the recognition more robust, while simultaneously adding hierarchical part–whole relationships in leaf structures, thereby improving the feature representation. Experiments were performed on a heterogeneous data set consisting of 21,927 leaf images belonging to 17 different healthy and diseased classes of apple, chilli, cotton, corn and potato crops. The experimental results proposed DTL-CapsNet framework is more accurate compared to the traditional CNN-based models and conventional transfer learning models. The proposed method of double transfer learning and Capsule Networks offers an efficient and scalable approach for intelligent plant disease diagnosis, offering significant potential in precision agriculture and real-time crop monitoring systems.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類する画像・計算手法が研究の中心であり、提案手法の開発と既存モデルとの比較検証が行われているため。
abstractBased on the image pre-processing, segmentation, double transfer learning, and Capsule Networks technologies, the proposed framework extracts discriminative features of the diseases and maintains spatial relations between the leaves symptoms effectively.
ABSTRACT Conventional field phenotyping methods are typically manual, time‐consuming, and destructive, creating a bottleneck for breeding progress. To address this challenge, robotics and automation technologies offer efficient sensing tools to monitor field evolution and crop development throughout the season. This study presents an end‐to‐end automated pipeline for terrestrial laser scanning (TLS) in plant breeding trials, built around a Husky ground robot equipped with a high‐resolution survey‐grade FARO 3D LiDAR scanner. Unlike prior TLS phenotyping approaches relying on manual scan placement or heuristic site selection, our system integrates a novel analytical 3D ray‐casting method for optimized TLS site planning with an offline route optimization algorithm that accounts for crop growth stages and field accessibility constraints. This enables efficient planning in complex breeding environments, reduces manual labor, and improves data collection efficiency, addressing scalability challenges in large breeding trials. Leveraging Real Time Kinematic‐Global Navigation Satellite System (RTK‐GNSS) and sensor fusion, the system achieved average errors below 0.6 cm for position and for heading, enabling point cloud registration with mean errors around 2 cm, comparable to traditional manual methods that require artificial targets. The platform was successfully deployed and evaluated in two distinct cotton breeding field layouts, demonstrating the platform's capability to autonomously collect accurate TLS data for quantitative plant phenotyping across varying plot configurations. The proposed autonomous phenotyping system advances scalable, efficient phenotyping workflows to support breeding programs for crop improvement, highlighting the potential for broader deployment in field phenomics.
Why it matches plant phenotyping methods植物育種試験向けの自律走行TLS・LiDAR計測プラットフォームと、3D計画・経路最適化・データ収集の技術的評価が中心であり、定量的植物表現型取得を目的とする。
abstractThis study presents an end‐to‐end automated pipeline for terrestrial laser scanning (TLS) in plant breeding trials
Cotton leaf diseases present a major threat to global cotton production, significantly impacting both yield and fiber quality. Traditional diagnostic methods are labor-intensive, time-consuming, and demand highly skilled professionals, making them inefficient for large-scale agricultural applications. Although earlier deep learning -based approaches have shown promising results in identifying cotton leaf diseases such as Bacterial Blight, Fusarium Wilt, and Curl Virus Disease, their performance is often limited by complex preprocessing requirements and insufficient generalization to real-world field conditions. To address these challenges, this study proposes and optimized transfer learning-based model, CLDP-CNN, designed to enhance feature extraction and classification efficiency using pre-trained deep neural networks. This study demonstrates the development of Cotton Leaf Disease Prediction Convolutional Neural Network (CLDP-CNN) automatically, utilizing Transfer Learning (TL) which operates on meticulously prepared datasets. Two distinct datasets were used to train the model: the first consisted of field images from cotton farms, while the second was sourced from Kaggle. The main goal of this research examines how the model performs on real-world field datasets. The CLDP-CNN model has proven highly accurate by attaining 99.78% detection success rates for cotton leaf diseases when processing primary dataset which surpasses its secondary dataset accuracy rate of 99.62%. Both the primary dataset and secondary dataset resulted in high accuracy values for the VGG16 pre-trained model which achieved 99.56% accuracy on the primary dataset and 98.82% on the secondary dataset. A web-based application enhances the capabilities of the CLDP-CNN model by providing real-time updates on the health status of cotton plants. This technology empowers farmers with valuable information, enabling them to take timely protective actions to prevent potential severe yield losses in their cotton crops.
Why it matches plant phenotyping methods綿花葉の画像から病害状態を推定する深層学習モデルを開発・評価しており、植物フェノタイピング手法が研究の中心です。
abstractThe main goal of this research examines how the model performs on real-world field datasets.
Reproduction assets foundThe paper's cotton leaf disease image datasets are publicly available: the authors' primary field-collected dataset on the first author's GitHub repository, and the secondary Kaggle dataset used for comparison. No analysis code or trained model checkpoints are explicitly deposited.Dataset · publicbia.
Funding: This work was supported by Princess Nourah bint Abdulrahman
University Researchers Supporting Project number (PNURSP2026R760),
Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Data and code availability
The data that support the findings of this study are openly available in Github and
Kaggle at. https://github.com/mnaeem303/Cotton-Leaf_Disease-Dataset),
and https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset/data
Author Contributions
All the authors (Muhammad Naeem, Muhammad Ibrahim, Nadeem Sarwar, Oumaima
Saidani, Asma Irshad, Muhammad Shadab Alam Hashmi, Muhammad Tayyab
Qammar) contributed equally to this work in their respective meaningOpen asset ↗https://github.com/mnaeem303/Cotton-Leaf_Disease-Datasetpdf-raw-page:29 lines:1-54Dataset · publicbdulrahman
University Researchers Supporting Project number (PNURSP2026R760),
Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Data and code availability
The data that support the findings of this study are openly available in Github and
Kaggle at. https://github.com/mnaeem303/Cotton-Leaf_Disease-Dataset),
and https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset/data
Author Contributions
All the authors (Muhammad Naeem, Muhammad Ibrahim, Nadeem Sarwar, Oumaima
Saidani, Asma Irshad, Muhammad Shadab Alam Hashmi, Muhammad Tayyab
Qammar) contributed equally to this work in their respective meaningful ways. All the
authors have read and approved the final manuOpen asset ↗pdf-raw-page:29 lines:1-54Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Accurate monitoring of nitrogen nutrition is critical for optimizing cotton production. Traditional machine learning-based inversion models have limited effectiveness for precision monitoring. Multisource fusion models for small samples were developed in this study to achieve enhanced accuracy through fitting and data complementarity. Cotton plants subjected to different nitrogen treatments were investigated. A two-year pot experiment was conducted to collect main-stem leaf images to construct an image pretraining dataset for model transfer. In a field experiment conducted over one year, main-stem leaf data were collected using hyperspectral, chlorophyll fluorescence, and digital camera sources, thereby providing a multisource dataset for training monitoring models. Two architectures—a neural network (NN) and an interpretable deep forest (DF), which are suitable for small-sample spectral, fluorescence, image color, and texture-sequence features—were constructed to improve the accuracy of nitrogen content inversion. Additionally, a two-dimensional sliding-window processing method was introduced into the DF multigranularity scanning module, and a transfer-learning-based two-dimensional convolutional NN was employed to directly model small-sample two-dimensional images. Building upon the outcome, multilayer fusion models were constructed, with corresponding fusion strategies designed for homogeneous sequence inputs and heterogeneous image–sequence inputs. The results showed that NN and DF can effectively handle limited sample sizes and outperform traditional machine learning models. Among the fusion models, the optimal secondary decision-level fusion model achieved an R² of 0.926 on the independent test set, indicating good performance under small-sample conditions. This study provides a methodological reference for the precise monitoring of crop phenotypic parameters under small-sample conditions.
Why it matches plant phenotyping methods綿花葉の窒素含量という植物形質を、画像・ハイパースペクトル・蛍光データの融合と深層学習で推定する手法を開発・評価しており、形質取得・推定法が研究の中心である。
abstractMultisource fusion models for small samples were developed in this study to achieve enhanced accuracy through fitting and data complementarity.
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.
Accurate crop yield estimation is essential for food-security planning, supply-chain logistics and crop-insurance indexing. However, field-level forecasting in rainfed cotton systems can be affected by human bias, spatial error and seasonal variability. This study evaluated the comparative accuracy, statistical efficiency and predictive reliability of three field sampling protocols for cotton yield estimation under actual farming conditions. Primary data were collected from 31 commercial cotton plots in Sanglud, Morgaon Sadijan and Ural villages in Akola district, Maharashtra. The evaluated methods were the 5 x 5 m crop-cut experiment, plant-based estimation at the open-boll stage and area-based quadrat sampling, assessed across the first and second picking phases. Predictive performance was compared with ground-truth harvested yields using mean actual yield, percentage error, standard deviation and root mean square error (RMSE). The 5 x 5 m crop-cut method showed the closest agreement with actual yield, with low dispersion (s = 2.37) and RMSE (1.24), producing a +5.97% error in the first picking and a -0.29% error in the second picking. The plant-based method overestimated yield by +73.60% in the first picking and underestimated by -31.20% in the second picking, reflecting selection bias and late-season boll attrition. Quadrat sampling showed persistent overestimation (+18.30% and +30.40%; s = 3.73). The findings support the 5 x 5 m crop-cut protocol as the most reliable approach among the methods evaluated.
Why it matches plant phenotyping methods綿花収量という植物形質の推定について、複数のサンプリング手法を比較し、収穫実測値を基準に精度・誤差・RMSEを検証しているため、手法検証が中心である。
abstractThis study evaluated the comparative accuracy, statistical efficiency and predictive reliability of three field sampling protocols for cotton yield estimation under actual farming conditions.
Low-cost RGB imaging is accessible for phenotyping, but color varies with devices and illumination. We tested whether RGB-derived indices from a standardized smartphone setup can proxy cotton (Gossypium hirsutum L.) leaf traits at the early seedling stage. Leaves (n=80) from three growth-chamber experiments were imaged in a closed light-tent with an in-frame gray/white/black card, then corrected in Adobe Photoshop. Mean leaf RGB values (manual ROIs) were used to compute 15 RGB/CIELAB indices, which were screened against SPAD, specific leaf area (SLA), vein density, water content (WC), stomatal density, and stomatal size using Pearson r and second-order regression (adj. R², NRMSE). The strongest relationships were for SLA (h_ab; adj. R²=0.666), vein density (TGI; adj. R²=0.610), and SPAD (G; adj. R²=0.558). WC was moderately associated with c_ab (adj. R²=0.344), while stomatal traits were weakly explained, consistent with scale limits of top-down mean-color metrics. Standardized consumer RGB imaging can therefore support rapid first-pass screening of pigment- and structure-related leaf traits.
Why it matches plant phenotyping methods標準化スマートフォンRGB撮像と色補正・指数計算を用いて葉形質を推定し、SPAD、SLA、葉脈密度などとの関係を定量評価しているため、画像フェノタイピング手法の検証が中心である。
abstractWe tested whether RGB-derived indices from a standardized smartphone setup can proxy cotton (Gossypium hirsutum L.) leaf traits at the early seedling stage.
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
This paper presents a deep learning model to detect cotton plant pests and classify diseases, which must overcome limited datasets, class imbalance, and feature redundancy. At the preprocessing phase, the Gaussian blur filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to sharpen images by improving their clarity and contrast. To increase and diversify the data, Spa-GAN-based data augmentation is used to produce realistic synthetic samples. To obtain an accurate Region of Interest (RoI), an Attention-Guided Multi-Scale Residual U-Net (AGMS-U-Net) is considered to segment local and global structural information. The proposed framework makes three major contributions: (i) a new attention-based feature extractor, TriAttnNet, that incorporates spatial, channel, and contextual attention to represent diseases on a fine-grained level (ii) a new optimization strategy, Hybrid Mongoose Ray Chaotic Optimization (HMRCO), which includes chaotic strategies to better tune the parameters and explore the feature space and (iii) classification layer with focal loss for final decision. Experimental analyses prove that the suggested method is much more effective than the current state-of-the-art models, providing a powerful and understandable solution to precision agriculture and sustainable cotton crop health monitoring. Experimental results show that TriAttnNet achieves 98.66% accuracy, 98.71% recall, and 98.81% F1-score, which is better than the state-of-the-art algorithms, such as EfficientNetB1-CBAM (96.38%) and BERT-ResNet-PSO (95.69%). The proposed system is computationally feasible and interpretable, and it is interpretable to provide a practical solution to precision agriculture and sustainable monitoring of the health of cotton crops.
Why it matches plant phenotyping methods綿花植物の画像から病害を分類する深層学習手法を開発・評価しており、植物の病害状態を直接推定する方法が研究の中心である。
abstractThis paper presents a deep learning model to detect cotton plant pests and classify diseases
Reproduction assets foundThe paper's plant-phenotyping input is the public Kaggle Cotton Plant Disease Dataset (Dhamodharan R), explicitly cited as the study's data source with a matching public URL. The authors' model/code is not publicly deposited (available only upon request), so no qualifying code asset exists.Dataset · publicThe dataset of this study is taken from the publicly available Kaggle repository, Cotton Plant Disease Dataset 43Open asset ↗Kagglelines:48-58Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
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.
Image processing plays a vital role in precision agriculture by enabling automated disease detection and crop health monitoring. This research presents a novel Hybrid Vision Transformer with Dense Attention Capsule Network (HVT-DACapNet) model for accurate cotton plant disease detection. The proposed framework integrates Adaptive Wavelet Transform Filtering (AWTF) for noise removal while preserving disease-related features. A Hybrid Vision Transformer (HVT) is employed to extract both local spatial patterns and global contextual dependencies, and the Dense Attention Capsule Network (DACapNet) captures hierarchical spatial relationships with an attention mechanism that emphasizes infected regions. In addition, a hybrid optimization strategy combining Mayfly and Aquila Optimization (HMAO) is used to fine-tune model hyperparameters for improved convergence. The model is evaluated on a publicly available Kaggle cotton leaf disease dataset containing healthy leaves and multiple disease categories including Target Spot, Powdery Mildew, Bacterial Blight, Army Worm, and Aphids, using a 70:15:15 train-validation-test split under the simulation setup and hyperparameter configuration described in the manuscript. The proposed HVT-DACapNet achieves an F1-score of 99.68%, sensitivity of 99.68%, specificity of 98.89%, and an overall accuracy of 99.79%, outperforming existing models such as ConvLSTM-ZOA, GOA, SFO, Inception-V3, and VGG-16.
Why it matches plant phenotyping methods綿花葉の画像から病害状態を推定する深層学習手法を新規開発し、公開データセットで性能評価しているため、植物フェノタイピング手法が中心である。
abstractThis research presents a novel Hybrid Vision Transformer with Dense Attention Capsule Network (HVT-DACapNet) model for accurate cotton plant disease detection.
Reproduction assets foundThe paper's only qualifying asset is the public Kaggle Cotton Plant Disease Dataset used as the input image dataset for all experiments. The authors' code is explicitly not publicly available (institutional restrictions), with only a supplementary algorithm document and on-request implementation details.Dataset · publicthodological workflow of the proposed model. Additional
implementation details may be made available from the corresponding
author upon reasonable request for academic and non-commercial
research purposes.
Data Availability-The datasets generated and/or analysed during the
current study are available in the Kaggle repository,
https://www.kaggle.com/datasets/dhamur/cotton-plant-disease
Author’s contribution – G.Neelavathi– Research proposal –
construction of the workflow and model – Final Drafting– Survey of
Existing works – Improvisation of the proposed model;
Dr.K.Venkatasalam – Initial Drafting of the paper – Collection of
datasets and choice of their suitability – Formulation of pseudOpen asset ↗Kaggle · dhamur/cotton-plant-diseasepdf-raw-page:39 lines:1-42Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
Abstract Canopy temperature (Tc) is a critical physiological indicator of water and heat stress in cotton. Although weather-driven Tc forecasting is used by 60% of Australian cotton growers for irrigation scheduling, current methods typically rely on a single in situ sensor to represent an entire management area. This uniform assumption overlooks substantial spatial variability in Tc and can lead to suboptimal water application. We propose UAV-linear, a novel spatio-temporal forecasting model that integrates high-accuracy in-situ sensors with weekly Unmanned Aerial Vehicle (UAV) thermal imagery to generate high-resolution hourly spatial Tc forecasts. Experimental results show that UAV-linear forecast stress conditions at unmeasured locations as effectively as models trained on exhaustive historical data, achieving a 25-35% improvement over the standard uniform-forecast assumption. Furthermore, in a large-scale validation across 50,000 hectares of commercially active farms (practical dataset), UAV-linear improved stress-hour prediction by 22% relative to the uniform assumption while maintaining accuracy comparable to historical benchmarks. These findings show that the proposed spatio-temporal framework provides the spatial detail needed for differentiated precision management, with potential to improve crop yield and water-use efficiency.
Why it matches plant phenotyping methods綿花のキャノピー温度という植物の生理状態を、UAV熱画像とセンサーから推定・予測する手法を開発し、大規模に検証しているため、灌漑管理への応用でもフェノタイピング手法が中心です。
abstractWe propose UAV-linear, a novel spatio-temporal forecasting model that integrates high-accuracy in-situ sensors with weekly Unmanned Aerial Vehicle (UAV) thermal imagery to generate high-resolution hourly spatial Tc forecasts.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 5 Sept 2026
Accurate characterization of plant 3D architecture and semantic parsing of key cotton organs represent an essential prerequisite for crop phenotyping, precision field management, and cultivar breeding and selection. However, the overall 3D structure of field-grown cotton plants is highly complex in field environments. The morphological traits and spatial distribution of various organs are modulated by multiple factors including genetics, environmental conditions, and cultivation management practices, resulting in pronounced phenotypic variation under field conditions. Thus, this study proposed an integrated technical framework for UAV-based 3D reconstruction and organ semantic segmentation tailored for field-grown cotton. High-fidelity 3D point clouds of the crop canopy are generated by coupling an field-adapted low-altitude unmanned aerial vehicle (UAV) acquisition strategy with neural radiance fields (NeRF). Despite these attractive characteristics, segmenting and analyzing such intricate point clouds can be quite challenging. To effectively parse these complex geometric structures, a novel deep learning architecture named FieldCotSeg-Net is introduced. This model integrates an Anisotropy-aware Local Attention (ALA) module and a Hierarchical Feature Refinement Gate (HFRG) module to capture fine-grained features for precise point cloud segmentation. Experimental results demonstrate that the proposed model achieves outstanding performance on both the Huaxing3Dcot and Crops3D cotton datasets. On the Huaxing3Dcot dataset, the model yields a mean intersection over union (mIoU) of 75.7%, representing a 4.5% improvement over the baseline model. On the Crops3D cotton dataset, after retraining the model on this dataset, it attains an mIoU of 74.5%, showing substantial adaptability and effectiveness in organ segmentation. This technical system provides a scalable and robust solution for field cotton phenotyping analysis in breeding research and commercial production.
Why it matches plant phenotyping methods綿花の圃場表現型解析を目的に、UAVによる3D再構成と器官セグメンテーション手法を開発・評価しており、植物形態の取得が研究の中心である。
abstractThis technical system provides a scalable and robust solution for field cotton phenotyping analysis in breeding research and commercial production.
Upland cotton (Gossypium hirsutum L.) is a critical economic crop, yet the efficiency of mechanized harvesting is heavily contingent upon effective pre-harvest defoliation. Traditional manual assessment of defoliation is labor-intensive and subjective, posing a significant bottleneck for large-scale genetic dissection of this dynamic trait. In this study, we established an integrated “high-throughput phenotyping-to-gene discovery” framework by utilizing UAV-based multispectral imaging to monitor 306 cotton cultivars across 4 environments. A Partial Least Squares Regression (PLSR) model was optimized to accurately estimate Leaf Area Index (LAI), and Gaussian curve fitting was employed to standardize LAI time series (ΔLAI) into a comparable dynamic phenotypic dataset. Genome-wide association studies (GWAS) based on these dynamic phenotypes identified 472 significant SNPs and 39 candidate genes. By integrating GWAS signals with transcriptome profiling of the petiole abscission zone and haplotype analysis, we identified 3 core regulatory genes: Ghi_A01G08401 (GhPIN3a), Ghi_D08G10716, and Ghi_D11G03091. Functional validation via virus-induced gene silencing (VIGS) and qRT-PCR demonstrated that Ghi_D08G10716 (encoding oxalyl-CoA synthetase) and Ghi_D11G03091 (encoding a VQ motif-containing protein) act as negative regulators in the defoliation process. These results provide a scalable technical paradigm and critical genetic resources for the precision breeding of cotton cultivars optimized for mechanized harvesting.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からLAIを推定し、時系列を動的表現型データへ変換する高スループット表現型解析手法が研究の中心であり、GWASへの実質的適用も行っている。
abstractwe established an integrated “high-throughput phenotyping-to-gene discovery” framework by utilizing UAV-based multispectral imaging to monitor 306 cotton cultivars across 4 environments.
Crop diseases play a significant role in food production globally; therefore, there is an urgent need to develop quick and accurate diagnostic techniques that are more effective than manual inspection methods. The proposed hybrid multimodal learning framework in this research provides a solution that integrates adaptive therapy suggestion, market price prediction, and image-based disease detection. This study also proposes a framework for pesticide recommendation and the treatment of plants. This study experiment on tomato and cotton crop leaf data for disease detection. Experimental results on a tomato crop disease detection dataset show that the proposed model shows high performance. EfficientNetB0 provides more stability and generalization capabilities in different scenarios compared to other models, such as YOLOv8, ResNet50, and a custom CNN model. The use of a knowledge-based decision support system provides sustainable pesticide recommendations based on environmental and symptom-specific parameters. Forecasting of pesticide prices through LSTM methods yields forecasts within 3.2% and 4.1% MAE, enabling improved decision-making by providing instant points of reference for potential price movements. Research uses SHAP and LIME to provide explainability to users, thus improving user buy-in through transparency. Overall, this modular system provides a data-driven decision-making model to improve the efficiency of managing crops.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する画像ベース手法を、複数モデルで比較評価しており、植物病害フェノタイピングがシステムの主要構成要素です。価格予測や農薬推薦も含みますが、病害検出の技術評価が明示されています。
abstractThe proposed hybrid multimodal learning framework in this research provides a solution that integrates adaptive therapy suggestion, market price prediction, and image-based disease detection.
Reproduction assets foundThe paper's disease-detection experiments use publicly available cotton and tomato leaf image datasets (Kaggle, IEEE DataPort, Roboflow), all cited with explicit public URLs in the references. No author analysis code or trained model checkpoints are stated as publicly available; the supplementary material is referencedDataset · publiccholar
View reference in article
19
Muppala C. Guruviah V. ( 2020 ). Machine vision detection of pests, diseases, and weeds: a review . J. Phytol. 12 , 9 – 19 . doi: 10.25081/jp.2020.v12.6145
CrossRef
Google Scholar
View reference in article
20
National College of Ireland ( 2025 ). “Cotton Disease Dataset.” Available online at: https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset (Accessed May 19, 2025).
Google Scholar
View reference in article
21
Naveed Gul and Kaggle ( 2026 ). Tomato Leaf Disease . Kaggle. Available online at: https://www.kaggle.com/datasets/naveedgull/tomato-leaf-disease (Accessed March 29, 2026).
Google Scholar
View reference in article
22
Ngugi H. N. EzugOpen asset ↗Kagglelines:554-633Dataset · publicreference in article
20
National College of Ireland ( 2025 ). “Cotton Disease Dataset.” Available online at: https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset (Accessed May 19, 2025).
Google Scholar
View reference in article
21
Naveed Gul and Kaggle ( 2026 ). Tomato Leaf Disease . Kaggle. Available online at: https://www.kaggle.com/datasets/naveedgull/tomato-leaf-disease (Accessed March 29, 2026).
Google Scholar
View reference in article
22
Ngugi H. N. Ezugwu A. E. Akinyelu A. A. Abualigah L. ( 2024 ). Revolutionizing crop disease detection with computational deep learning: a comprehensive review . Environ. Monit. Assess. 196 : 302 . doi: 10.1007/s10661-024-12454-z
Pubmed AOpen asset ↗Kagglelines:554-633Dataset · publicComputer Vision and Pattern Recognition (CVPR) ( Las Vegas, NV : IEEE ), 779 – 788 . doi: 10.1109/CVPR.2016.91
CrossRef
Google Scholar
View reference in article
29
Roboflow ( 2026a ). A Comprehensive Dataset of Cotton Plant Diseases for National Disease Identification and Treatment Guidance | IEEE DataPort. Available online at: https://ieee-dataport.org/documents/comprehensive-dataset-cotton-plant-diseases-national-disease-identification-and-treatment (Accessed March 29, 2026).
Google Scholar
View reference in article
30
Roboflow ( 2026b ). Cotton Plant Disease Prediction Object Detection Model by National College of Ireland . Available online at: https://universe.roboflow.com/national-colleOpen asset ↗IEEE DataPortlines:554-633Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Published12 Jun 2026Advanced science (Weinheim, Baden-Wurttemberg, Germany)Cited by 0 · OpenAlex ↗
CottonSeed / grainGrowth / time-series analysisGrowth / development / phenology
Seed vigor underpins uniform crop establishment, but its dynamic genetics are understudied. Combining high-resolution temporal phenotyping and genomics in upland cotton, we used the SeedRanger platform to record 17 image-based traits every 30 min over 120 h, revealing stage-specific heritability and identifying 541 seed-vigor loci. These loci show extensive pleiotropy and temporal coordination, forming a genetic network that preserves developmental continuity; 8.9% overlap regions under domestication selection, indicating concurrent optimization with fiber yield. Functional validation of FLA2, a candidate gene underlying a dynamic QTL, implicates auxin-mediated control of radicle elongation and cotyledon development. This temporal framework exposes dynamic genetic architecture and breeding targets for high-vigor crops.
Why it matches plant phenotyping methodsSeedRangerによる高頻度画像計測と17形質の抽出が研究の主要なデータ取得基盤であり、時間的な種子活力表現型を解析する実質的なフェノタイピング応用である。
abstractCombining high-resolution temporal phenotyping and genomics in upland cotton, we used the SeedRanger platform to record 17 image-based traits every 30 min over 120 h
CottonAerial / UAVMultispectral / hyperspectralLeafPhysiological trait estimationWater status / transpiration
Optimizing water and fertilizer management is crucial for improving cotton yield and quality. However, reliable and generalizable models for quickly and accurately estimating cotton canopy leaves water and nutritional status at a low cost throughout the entire growth stage are scarce. Therefore, this study aims to construct the generalization and adaptation retrieval model of cotton canopy leaf nitrogen content (LNC) and equivalent water thickness (EWT) based on PROSAIL, hyperspectral reconstruction with UAV multispectral imagery and module transfer learning. In the hyperspectral reconstruction module, the new hyperspectral reconstruction model (swinT-HSCNN) based on multispectral showing superior performance in reducing pixel-scale systematic errors and effectively captured spectral variations than HSCNN+ and MST++ model. In the PROSAIL module, the proposed Original-E2DCOS method demonstrated greater sensitivity to spectral response characteristics, especially for parameters and bands with low correlation values, and three bands (702 nm, 762 nm, and 938 nm) were selected as the sensitive bands corresponding to chlorophyll content (Cab) and equivalent water thickness (Cw) of cotton. The improved PROSAIL with hyperparameter optimization based on full spectrum and multispectral band shown better fitting performance than the model based on sensitive bands, and achieved high accuracy on simulated data, with R 2 values exceeding 0.98 for both Cab and Cw. Moreover, the new developed modular transfer learning retrieval model of cotton canopy water and nitrogen content through PROSAIL model and hyperspectral reconstruction with UAV multispectral imagery achieved good inversion accuracy with R 2 of 0.83, 0.85, RMSE of 0.0048, 0.0052, for LNC and EWT, respectively after verifying with actual experiment data. In summary, the proposed modular transfer learning retrieval model of cotton canopy water and nitrogen content integrates physical constraints into retrieval models, which enhancing their accuracy and generalization capability, and providing valuable technical support for precision agriculture in cotton production across different regions. • A modular transfer learning model integrates PROSAIL and hyperspectral reconstruction. • Achieves high accuracy for LNC and EWT estimation across diverse environments. • Combines UAV multispectral data with physical constraints for crop monitoring. • Reduces reliance on expensive hyperspectral sensors, ensuring cost-effectiveness. • Validated on multi-regional datasets, demonstrating scalability and generalizability.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像とPROSAIL、ハイパースペクトル再構成、転移学習を統合し、ワタの窒素・水分状態という植物形質を推定する手法を開発・実データで検証しており、フェノタイピング手法が中心である。
abstractTherefore, this study aims to construct the generalization and adaptation retrieval model of cotton canopy leaf nitrogen content (LNC) and equivalent water thickness (EWT) based on PROSAIL, hyperspectral reconstruction with UAV multispectral imagery and module transfer learning.
Attempts to deploy computer vision in agricultural tasks often suffer from a shortage of annotated data. One strategy to alleviate the impact of limited data is Self-Supervised Learning (SSL), which involves pre-training a model on a pretext task that utilizes automatically generated annotations. The primary objective of this study is to leverage a multi-camera view dataset of cotton boll images for contrastive learning in order to enable phenotyping tasks with minimal data annotation. This dataset was collected in the field using six camera views. The efficacy of two contrastive learning frameworks (SimCLR and MoCo) in producing representations when positive examples originate from different cameras was investigated, and a comprehensive study of how the camera positions affect performance was conducted. After self-supervised pre-training, linear evaluation and semi-supervised learning experiments were performed on boll detection and plot status downstream tasks. In general, using multiple camera views with SimCLR and MoCo improves cotton boll detection mean average precision by 14% compared to vanilla SimCLR and MoCo. Through careful investigation using synthetic data, it was determined that relative camera poses with an intermediate amount of overlap seem more likely to perform well. Neither MoCo nor SimCLR was consistently superior to the other in this context. The representations embed meaningful features about the cotton plants, such as overall boll density, but also less meaningful ones, such as lighting variations. This technique could potentially accelerate the development of phenotyping algorithms based on data collected from field robots. • A contrastive learning method based on comparing multi-camera views was developed. • The method was tested with images of cotton bolls from a ground robot. • The method outperformed baseline contrastive learning approaches.
Why it matches plant phenotyping methodsマルチカメラ画像とコントラスト学習による植物表現学習・フェノタイピング手法の開発と評価が中心であり、綿花のボール検出性能を検証している。
abstractThe primary objective of this study is to leverage a multi-camera view dataset of cotton boll images for contrastive learning in order to enable phenotyping tasks with minimal data annotation.
Reproduction assets foundThe paper's data availability statement points to a public GitHub repository containing the authors' code to reproduce the multi-camera contrastive learning phenotyping experiments. A processed-data Zenodo deposit (10.5281/zenodo.18164649) is also mentioned, but its URL is not among the allowed URLs, so only the code资产Code · publicThe code required to reproduce the above findings are available to download from https://github.com/UGA-BSAIL/self-supervised-learning .Open asset ↗UGA-BSAIL/self-supervised-learninglines:200-224Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
Cotton production is highly vulnerable to foliar diseases and pest-induced damage, which significantly reduce yield and compromise fiber quality. Rapid, reliable, and automated disease identification is therefore essential for supporting sustainable crop management. In this study, we propose a hybrid deep learning framework integrating a ResNet50 backbone with Squeeze-and-Excitation (SE) channel attention modules to enhance discriminative feature representation for cotton leaf disease classification. The model is trained on a publicly available disease dataset comprising six classes and optimized using Weighted CrossEntropyLoss, Adam optimization, ReduceLROnPlateau scheduling, and Early Stopping to ensure stable convergence and robust generalization. Experimental results demonstrate outstanding performance, achieving 99.72% training accuracy and 99.31% validation accuracy, with convergence at the 14th epoch. Visualization through Grad-CAM reveals that the model focuses on biologically relevant symptom regions, thereby enhancing interpretability and supporting expert validation. Comparative analysis with state-of-the-art methods shows that the proposed model surpasses existing CNN, transfer learning, and hybrid architectures in both accuracy and model transparency. These results indicate that the proposed SE-ResNet50 framework offers a highly accurate, interpretable, and computationally efficient solution suitable for real-world cotton disease monitoring and precision agriculture applications.Clinical trial registrationThis study is not a clinical trial; therefore, clinical trial registration is not applicable.
Why it matches plant phenotyping methods綿花葉の病徴を画像から分類する深層学習フレームワークの開発が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として適格です。
titleA hybrid SE-ResNet50 deep learning framework for high-accuracy and explainable cotton leaf disease classification.
Reproduction assets foundThe paper's Data Availability statement points to the public Kaggle cotton plant disease dataset used for training the SE-ResNet50 model. No author analysis code or trained model checkpoint is explicitly deposited.Dataset · public“Cotton plant disease.” Accessed: Nov. 21, 2025. [Online]. Available: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease.Open asset ↗Kaggle · dhamur/cotton-plant-diseasehtml-lines:458-493Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Accurate prediction of key agronomic traits in cotton is crucial for advancing its genetic improvement and enabling breeding-by-design. However, when using high-dimensional genomic data (e.g., massive SNP markers) for prediction, traditional models often suffer from overfitting and poor generalization. To address this, we propose an innovative deep learning model-Coformer. Coformer is a hybrid Transformer-autoencoder model: a self-attention Transformer encoder captures long-range SNP dependencies and compresses high-dimensional genotypes into a compact latent representation, which is then decoded by a supervised prediction head to the target phenotype. Through rigorous evaluation on a combined multi-environment, multi-year dataset, Coformer maintains outstanding predictive robustness even without explicitly modeling environmental factors. The model integrates a normalization module and a linear projection layer to enable adaptive input processing and end-to-end training, effectively improving generalization across varying data dimensionalities. Importantly, Coformer is interpretable: it can precisely pinpoint key genetic loci that influence target traits, providing a solid theoretical basis for deeper insight into the genetic underpinnings of phenotypes and for implementing precision breeding. In addition, to accelerate application and dissemination, we have concurrently developed a browser-based Cotton Phenotype Prediction System (CPPS) that seamlessly integrates the full workflow of "data preparation-model training-result interpretation" into a unified graphical interface. The system supports batch processing and result visualization, substantially lowering the barrier for non-specialists and offering an efficient, user-friendly solution to bridge genomics research and breeding practice.
Why it matches plant phenotyping methods遺伝子型データから綿の農業形質を予測する深層学習手法を開発・多環境多年度で評価し、予測・解釈ワークフローのソフトウェアも実装しているため、形質推定法が研究の中心である。
abstractwe propose an innovative deep learning model-Coformer
Understanding diurnal canopy orientation in crops is important for interpreting plant responses to light and environmental conditions, yet field-based quantification remains limited. In this study, we present Heliocot, a field RGB imaging approach that converts time-resolved images into reference-area standardized projected leaf area (PLA) time series to quantify within-day canopy orientation dynamics in early-season cotton. Leaf instance segmentation was performed using YOLOv8m-seg and refined through a 144-combination post-processing optimization. On the held-out early-stage validation/tuning set, the selected workflow showed strong agreement with manual ground truth (R2 = 0.948; NRMSE = 0.082) and destructive leaf area measurements (R2 = 0.836). Derived diurnal metrics, including Daily Orientation Amplitude (DOA) and Peak Orientation Index (POI), consistently revealed a midday maximum (13:15) in canopy projection. Exploratory genotype-level analysis suggested negative associations between orientation indices and selected plant traits, including specific leaf area (SLA) versus DOA (r = −0.71, p = 0.021, R2 = 0.508), destructive leaf area (LA) versus DOA (r = −0.69, p = 0.028, R2 = 0.471), and stem dry weight (SDW) versus POI (r = −0.74, p = 0.014, R2 = 0.554), while plant height was not significantly associated with POI and DOA (p > 0.05). Although currently limited to early-season conditions and two field-imaging dates, this approach provides a practical workflow for field-based monitoring of canopy projection dynamics in cotton, while broader temporal and environmental validation remains necessary.
Why it matches plant phenotyping methods圃場RGB画像から葉面積と日周キャノピー配向動態を推定する手法を開発・検証しており、植物形質取得が研究の中心である。
abstractwe present Heliocot, a field RGB imaging approach that converts time-resolved images into reference-area standardized projected leaf area (PLA) time series to quantify within-day canopy orientation dynamics in early-season cotton.
To address the significant morphological variability of cotton Verticillium wilt lesions and the complex background interference present in field environments, existing detection models often struggle to achieve an effective balance between detection accuracy and model complexity. In this study, a precise and lightweight detection model, YOLO-SCOD, is proposed based on the YOLOv12n framework to enhance lesion recognition performance. During the feature extraction stage, YOLO-SCOD adopts the StarNet architecture as the backbone network. Its efficient feature mapping mechanism enhances the ability to interact with multi-scale features, thereby optimizing the model's capacity to represent multi-scale lesion information. Meanwhile, a channel aggregation block is integrated into the C3k module of the neck network. Through adaptive channel reallocation and enhancement of key lesion features, the perception capability of the C3k2 and A2C2f modules for discriminative lesion features is improved. In the detection head, depthwise convolution is replaced with omni-dimensional dynamic convolution, which dynamically and adaptively adjusts convolutional weights through multi-dimensional attention collaboration, further improving the model's localization accuracy and recognition capability. Experimental results demonstrate that the YOLO-SCOD model achieves improved performance improvements in the task of cotton Verticillium wilt detection. Compared with YOLOv12n, its precision and recall increase to 0.960 and 0.911, respectively, while mAP50-95 improves by 6.436%. In addition, the number of model parameters, FLOPs, and model size are reduced by 13.728%, 20.635%, and 12.727%, respectively, and inference speed increased by 4.167%. While maintaining high detection accuracy, YOLO-SCOD exhibits favorable lightweight characteristics, providing a viable solution for efficient automatic identification and intelligent detection of cotton Verticillium wilt.
Why it matches plant phenotyping methods綿花の萎凋病病斑という植物の病害状態を画像検出するモデルを開発し、精度・計算量・推論速度を比較検証しており、病害表現型の取得手法が中心的です。
abstracta precise and lightweight detection model, YOLO-SCOD, is proposed based on the YOLOv12n framework to enhance lesion recognition performance.
Abstract Cottonseed is rich in protein and oil, making the improvement of its nutritional quality essential for global food security. In this study, high-accuracy near-infrared spectroscopy (NIRS) models were developed for predicting cottonseed protein and oil content using least absolute shrinkage and selection operator (LASSO) regression, achieving validation R 2 values of 0.969 and 0.972, respectively. Using these models, 249 upland cotton accessions were phenotyped across five environments and subjected to a genome-wide association study (GWAS) based on a 10K liquid-phase SNP array, resulting in the identification of 24 significant loci. A novel stable QTL, qPO-A07-1 , was detected, within which GhMYB86 was prioritized as a candidate gene. This gene exhibited higher expression in high protein varieties during ovule development. Functional validation demonstrated that heterologous overexpression in Arabidopsis thaliana increased seed protein content by 2.61% – 3.34%, whereas expression in Saccharomyces cerevisiae reduced triglyceride content by 30.72% relative to the control. These results demonstrate that GhMYB86 positively regulates protein accumulation while negatively affecting oil content. A kompetitive allele-specific PCR (KASP) marker targeting a promoter A/T polymorphism revealed that the AA allele was associated with higher protein content, lower oil content, and increased fiber strength across both mapping and validation populations. Furthermore, the protein- and fiber strength-favorable allele has undergone positive selection during breeding. This study provides robust phenotyping tools, reliable genetic resources and molecular markers for cottonseed nutritional quality breeding, laying a foundation for the synergistic improvement of both fiber quality and nutritional quality in cotton.
Why it matches plant phenotyping methodsNIRSによる綿実タンパク質・油分の非破壊推定モデルを開発・検証し、多数系統の表現型取得に用いており、植物形質の取得法が実質的な中心要素である。
abstracthigh-accuracy near-infrared spectroscopy (NIRS) models were developed for predicting cottonseed protein and oil content using least absolute shrinkage and selection operator (LASSO) regression, achieving validation R 2 values of 0.969 and 0.972, respectively.
Product demand and climate variability are progressively increasing the need for real-time, scalable crop monitoring to support varietal selection and in-season input optimisation. However, producers still have limited information on the temporal and spatial variability of cotton health and performance beyond point-scale field surveying. In addition, given cotton's high phenotypic plasticity, near real-time derived metrics are essential to improve input efficiency and strengthen long-term sustainability of the cotton industry in Australia. Therefore, we proposed a functional integrated predictive sensing framework to estimate and predict cotton canopy morphological (i.e., height) and productivity traits (i.e., dry matter and lint yield) across large plots (12m × 6m). Scalability was validated by applying the proposed framework to estimate and map cotton yield across commercial fields. To do this, we explored the accuracy of high-resolution multispectral imagery from two platforms (unmanned aerial vehicle (UAV) and PlanetScope (PS)) collected across two 144-plot trials for two cotton seasons. These were designed with a large range in nitrogen rates (N), shading, and two growth-regulator doses, thus, creating variable environments. Sensing metrics were obtained from UAV imagery (1.3-1.6 cm pixel size) acquired once in 2022/23 and eight times in 2023/24, while PS composites (3 m pixel size) provided near-daily coverage in both seasons. Time-series gaps were imputed using Savitzky-Golay smoothing in thermal time (GDD), enabling extraction of growth dynamic metrics (GDMs) as single-date (SD; e.g., peak canopy) and multi-date (MD; e.g., daily average growth rate) metrics. After reducing collinearity and dimensionality, random forest (RF), support vector regression (SVR), and gaussian process regression (GPR) were trained and interpreted with SHAP, for feature contribution. UAV single-date models (SD_ UAV) achieved strong accuracy for height (R 2 = 0.77), biomass (R 2 = 0.73), and yield (R 2 = 0.81). Incorporating UAV time-series metrics (MD_UAV) improved the performance R 2 = 0.87, 0.86, and 0.85 for height, biomass and yield, respectively. Application of the derived models using high resolution satellite data (MD_PS) for different farming systems showed highly significant accuracy (R 2 = 0.67) to predict cotton yield at aggregated field scale. As such, enabling the detailed spatial prediction of cotton yield within a field. It is anticipated that the proposed functional sensing framework will improve the estimation of key cotton production traits, supporting field- and within-field decision-making, ultimately contributing to more resilient and sustainable cotton production in Australia.
Why it matches plant phenotyping methodsUAV・衛星時系列画像から綿花の形態・生産性形質を推定するセンシング／予測フレームワークを開発・検証し、商業圃場へ適用しており、表現型取得手法が中心である。
abstractwe proposed a functional integrated predictive sensing framework to estimate and predict cotton canopy morphological (i.e., height) and productivity traits (i.e., dry matter and lint yield) across large plots (12m × 6m).
Cotton is a staple food crop in many developing nations but yield is often decimated (20-40%) by diseases due to late or wrong diagnosis in the field. In this work, we present ResMobNet, a bespoke hybrid convolutional neural network architecture that combines MobileNetV2 depthwise-separable architecture efficiency, ResNet-style residual connections and Squeeze-and-Excitation (SE) channel attention mechanisms within a multi-scale feature fusion approach using a dual-path structure. Using the publicly available Kaggle Cotton Disease Dataset (1951 images, four classes), the proposed model attains 98.31% test accuracy, 98.33% weighted precision, 98.31% recall, 98.30% F1-score and a macro-averaged AUC of 0.9987, outperforming ResNet50, regular MobileNetV2 and VGG16 models by 2.03, 2.70 and 4.05 percentage points respectively. ResMobNet offers a good accuracy-efficiency tradeoff with just 6.58 million parameters and 42.3 ms inference time on the CPU. A rigorous ablation study verifies isolated impacts of 1.17 pp due to SE attention, 1.35 pp due to residual blocks, 2.03 pp due to the double-branch design and 2.57 pp due to the two-stage transfer learning protocol. Grad-CAM results justify biologically meaningful localisation of diseased areas. The model is exported in quantised TFLite format (6.70 MB), for direct use on mobile edge devices for real-time precision agriculture.Keywords— Cotton disease detection; deep learning; MobileNetV2; ResNet; squeeze-and-excitation attention; multi-scale feature fusion; transfer learning; Grad-CAM; precision agriculture; TFLite..
Why it matches plant phenotyping methodsワタ植物の病害領域を画像から識別する深層学習モデルを開発・比較・アブレーション検証しており、植物の病害状態推定が中心的な方法論的貢献である。
abstractwe present ResMobNet, a bespoke hybrid convolutional neural network architecture that combines MobileNetV2 depthwise-separable architecture efficiency, ResNet-style residual connections and Squeeze-and-Excitation (SE) channel attention mechanisms within a multi-scale feature fusion approach using a dual-path structure.
The early detection of plant diseases and pest infestations is essential for improving agricultural productivity and ensuring crop health. Cotton, as a major commercial crop, is highly susceptible to a wide range of diseases and pests that can significantly affect yield and quality if not identified at an early stage. Conventional deep learning approaches for plant disease classification rely heavily on large labeled datasets, which are often difficult and costly to obtain in real-world agricultural environments. In this paper, a self-supervised learning-based framework is proposed for agricultural image classification, focusing on the detection of plant diseases and pests in cotton. The proposed approach begins with dataset curation and cleaning using an automated image inspection technique to remove noisy, blurry, and duplicate samples, thereby improving data quality. Data augmentation techniques are then applied to enhance model generalization. A contrastive learning strategy inspired by SimCLR is employed to learn robust feature representations from unlabeled images using a ResNet18 encoder. The extracted features are further refined using Principal Component Analysis (PCA) for dimensionality reduction and K-Means clustering to improve feature separability. Finally, a pretrained VGG16 model is fine-tuned using labeled data for classification. The integration of self-supervised learning with feature refinement and clustering enhances representation quality while reducing dependency on labeled datasets. The proposed framework is scalable, efficient, and suitable for real-world agricultural applications, including automated crop monitoring and intelligent farming systems.
Why it matches plant phenotyping methods綿花の病害・害虫を画像から分類する自己教師あり学習ワークフローが研究の中心であり、植物の病害状態を直接推定する方法を提案・評価している。
abstracta self-supervised learning-based framework is proposed for agricultural image classification, focusing on the detection of plant diseases and pests in cotton
Abstract Verticillium wilt is one of the main factors hindering cotton yield increase, the more severe the disease, the more severe the yield loss. In order to achieve early detection and prevention of Verticillium wilt, this study investigated cotton plants naturally affected by Verticillium wilt in the field. We analyzed the cotton canopy multispectral data under the stress of Verticillium wilt and the leaf area index (LAI) data collected from the ground. Due to the low prediction accu-racy of traditional empirical models, this paper selected three different back propagation BP neural network models with 5, 10, and 15 hidden layer nodes (hln) and optimized them using four intelligent swarm algorithms: genetic algorithm (GA), particle swarm optimization (PSO), spotted hyena algorithm (SHO), and improved spotted hyena algorithm (ISHO) to estimate the cotton LAI under the stress of Verticillium wilt. Finally, BP algorithm with different node number of hidden layer was optimized by comparing four intelligent swarm optimization al-gorithms to estimate LAI of cotton under verticillium wilt stress. The findings indicated that GA-BP (hln15) was the best in the GA-BP algorithm; In the PSO-BP algorithm, PSO-BP (hln10) performed best; In the SHO-BP model, SHO-BP (hln5) had the highest model performance; In the ISHO-BP model, ISHO-BP (hln5) is the best, with a determination coefficient(R2), root mean square error(RMSE), and prediction accuracy(PA) of 0.952, 0.235, and 89.30%, respec-tively; The estimation results of ISHO-BP (hln5) model can better represent the distribution of Verticillium wilt plants than GA-BP (hln15), PSO-BP (hln10), and SHO-BP (hln5), and its estimation error range is (-0.8,0.5). Therefore, the application of ISHO-BP (hln5) model pro-vides a new method for estimating cotton LAI under pest and disease stress, and also provides technical support to meet the diversified needs of cotton farmers to increase their income and national economic development.
Why it matches plant phenotyping methodsUAVマルチスペクトルデータから綿のLAIを推定するモデルを比較・最適化しており、植物形質の取得・推定手法が研究の中心である。
abstractthis paper selected three different back propagation BP neural network models with 5, 10, and 15 hidden layer nodes (hln) and optimized them using four intelligent swarm algorithms
Accurate and non-destructive acquisition of leaf chlorophyll content (LCC) in cotton plant canopies is of significant importance for real-time monitoring of cotton growth and implementing precise water and nitrogen management in cotton fields. This study utilized UAV-based RGB imagery combined with real-time kinematic (RTK) technology to efficiently and accurately retrieve LCC under different nitrogen application levels in a cotton field, employing 6 machine learning algorithms: Least Absolute Shrinkage and Selection Operator regression (LASSO), Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), Ridge Regression (Ridge), and Support Vector Regression (SVR). Among these, the SVR model demonstrated the best overall performance, with coefficient of determination (R²), root mean square error (RMSE), relative root mean square error (rRMSE), and mean absolute percentage error (MAPE) values of 0.82, 0.14 mg/g, 8.91%, and 6.99% for the training set, and 0.75, 0.14 mg/g, 8.90%, and 7.83% for the testing set, respectively. Furthermore, the SVR model was applied to retrieve LCC pixel-by-pixel from UAV imagery, and pseudo-color rendering techniques were used to generate spatial distribution maps of LCC in the cotton canopy, visually presenting the spatial variability characteristics of LCC within the field. The results indicate that the cotton canopy LCC estimation method based on UAV RGB imagery combined with RTK technology achieves comparable accuracy to the more expensive multispectral and hyperspectral techniques, without a significant reduction in precision. This approach provides an efficient, low-cost, and reliable method for detecting canopy LCC in small-scale cotton fields.
Why it matches plant phenotyping methodsUAV RGB画像とRTK、機械学習を組み合わせ、ワタ群落の葉緑素含量を推定・検証する手法が研究の中心であるため、植物フェノタイピング手法として採用する。
abstractThis study utilized UAV-based RGB imagery combined with real-time kinematic (RTK) technology to efficiently and accurately retrieve LCC under different nitrogen application levels in a cotton field, employing 6 machine learning algorithms
Effective crop monitoring during monsoon growing seasons in Central India faces challenges from persistent cloud cover that limits optical remote sensing during critical agricultural periods. This study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems. Five major monsoon crops (cotton, rice, maize, soybean, and urad) were analyzed across five different agroclimatic zones in Central India using Sentinel-1 data for the 2021 growing season. Phenological features were extracted from VV, VH polarizations, and their ratio, including seasonal extrema, threshold crossings, duration measures, curve shape descriptors, and area under the curve. Distinct crop-specific signatures were observed, with cotton showing extended phenology and cereal–legume crops displaying compressed, overlapping growth patterns. VV polarization achieved the highest statistical discrimination for intensity-based metrics, with 75% thresholds (VV_HP75V: F = 1287) providing higher separability than other thresholds by capturing near-peak biomass differences. VH performed best for duration and integration-based metrics, while VH/VV provided limited additional separability across metric types. For area-under-the-curve metrics, AUC25 outperformed AUC50 and AUC75 by capturing cumulative backscatter across the broader growing season while remaining robust to soil- and residue-dominated backscatter variability at sowing and harvest. Multiclass classification achieved 48.3% overall accuracy with systematic cereal–legume confusion, reflecting fundamental phenological convergence among monsoon-aligned crops. Cotton achieved the highest performance (F1: 0.79), with VH polarization dominating feature importance (65% of top 20 features). Binary classification revealed crop-specific discrimination patterns: cotton was best separated using VV intensity metrics, maize using the VH/VV ratio, and rice using timing-based features. Cross-district transferability showed the highest mean overall accuracy for rice (74%) and cotton (72%), while the remaining crops showed lower accuracy due to their phenological similarity. These findings highlight both the potential and limitations of SAR phenological metrics for monsoon crop discrimination, with effective results for structurally distinct crops but persistent cereal–legume confusion, requiring further investigation with multi-sensor approaches.
Why it matches plant phenotyping methodsSAR時系列から作物のフェノロジー指標を抽出・評価し、識別性能や転移性を検証することが研究の中心であるため、植物フェノタイピング手法として含める。
abstractThis study presents the first attempt to develop a novel set of SAR-derived phenological metrics organized into five thematic categories for monsoon crop discrimination in smallholder agricultural systems.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 5 Sept 2026
Multi-temporal remote sensing data in large-scale crop phenology identification and classification have become increasingly utilized, particularly for precision management in arid oasis agricultural regions with complex cropping systems. In this study, we developed a deep learning framework integrating Sentinel-2 multi-temporal imagery and normalized difference vegetation index (NDVI) time series for mapping cotton, winter jujube, and tiger nut crops in Tumushuke City, Xinjiang Uygur Autonomous Region, China. We employed the minimum redundancy maximum relevance (mRMR) algorithm for spectral and vegetation index feature selection, followed by Savitzky-Golay (S-G) filtering and double logistic function fitting, to automatically extract the key phenological parameters (start of season (SOS), peak of season (POS), and end of season (EOS)), significantly improving phenological feature extraction accuracy. By incorporating multi-temporal Sentinel-2 data and a multi-scale feature fusion approach, we could systematically compare five classification models (multi-layer perceptron (MLP), residual network-18 (ResNet-18), convolutional long short-term memory (ConvLSTM), Transformer, and random forest classifier (RFC)), demonstrating that high-resolution spatial details substantially enhance crop boundary delineation and classification consistency in complex environments. Further optimization of Transformer's spatial representation through multi-scale window analysis revealed that the use of 1×1+3×3+5×5 convolutional windows achieves an optimal balance between accuracy and computational efficiency. Independent validation on unseen areas confirmed robust model transferability, with F1 scores of 94.37%, 87.75%, and 86.35% for the three crops (winter jujube, cotton, and tiger nut), respectively. This study validates the high-precision identification potential of Sentinel-2 temporal data and deep neural networks for multi-crop environments, enabling the precise spatial mapping of crop distributions and providing methodological support for smart agricultural decision-making in arid oasis regions.
Why it matches plant phenotyping methodsSentinel-2時系列から植物の生育季節性（SOS、POS、EOS）を自動抽出し、その精度向上と独立地域での検証を行っており、作物分類を含む解析ワークフローにおける植物フェノタイプ抽出が中心的です。
abstractto automatically extract the key phenological parameters (start of season (SOS), peak of season (POS), and end of season (EOS)), significantly improving phenological feature extraction accuracy.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 5 Sept 2026
Introduction High-throughput field phenotyping (HTFP) holds great potential for elucidating the relationship between genomes and phenotypes. However, obtaining high-quality three-dimensional point cloud data of field populations and achieving single-plant phenotypic analysis remain challenging. Methods This study develops an integrated framework for field crop reconstruction based on 3D Gaussian splatting, incorporating a geometry-aware dynamic constraint algorithm to achieve instance segmentation and extract key phenotypic traits of individual plants. Using 3D Gaussian splatting technology, field-scale cotton population modeling is accomplished, generating dense 3D point clouds for regions of interest. Furthermore, the concept of a crop localization domain is proposed, establishing a longitudinal mapping that associates plant positional coordinates with long-term phenotypic attributes. Finally, through a dynamic spatial constraint mechanism, the accuracy and computational efficiency of instance segmentation for crop population point clouds are significantly improved, enabling rapid extraction of individual plant traits such as cotyledon node height, plant height, and leaf area. Results The results demonstrate that PhenotypeAI successfully reconstructed nine cotton populations with PSNR exceeding 30.0 dB. It successfully extracted regions of interest from 403 cotton plants, achieving an average F-score of 91.32% for instance segmentation and an average accuracy of 91.35%. The extracted traits—cotyledon node height, plant height, and leaf area—exhibited strong correlations with manual measurements, with coefficients of determination ( R 2 ) of 0.90, 0.91, and 0.91, respectively. Discussion The proposed method provides a low-cost solution for high-throughput field phenotypic analysis of field cotton and improves the efficiency of cotton breeding.
Why it matches plant phenotyping methods3D再構成と動的空間制約による個体セグメンテーションおよび形質抽出が研究の中心で、綿花の草丈・葉面積などを検証しているため。
abstractThis study develops an integrated framework for field crop reconstruction based on 3D Gaussian splatting, incorporating a geometry-aware dynamic constraint algorithm to achieve instance segmentation and extract key phenotypic traits of individual plants.
To improve crop genetics, high-throughput, effective and comprehensive phenotyping is a critical prerequisite. While such tasks were traditionally performed manually, recent advances in multimodal foundation models, especially in vision-language models (VLMs), have enabled more automated and robust phenotypic analysis. However, plant science remains a particularly challenging domain for foundation models because it requires domain-specific knowledge, fine-grained visual interpretation, and complex biological and agronomic reasoning. To address this gap, we develop PlantXpert, an evidence-grounded multimodal reasoning benchmark for soybean and cotton phenotyping. Our benchmark provides a structured and reproducible framework for agronomic adaptation of VLMs, and enables controlled comparison between base models and their domain-adapted counterparts. We constructed a dataset comprising 385 digital images and more than 3,000 benchmark samples spanning key plant science domains including disease, pest control, weed management, and yield. The benchmark can assess diverse capabilities including visual expertise, quantitative reasoning, and multi-step agronomic reasoning. A total of 11 state-of-the-art VLMs were evaluated. The results indicate that task-specific fine-tuning leads to substantial improvement in accuracy, with models such as Qwen3-VL-4B and Qwen3-VL-30B achieving up to 78%. At the same time, gains from model scaling diminish beyond a certain capacity, generalization across soybean and cotton remains uneven, and quantitative as well as biologically grounded reasoning continue to pose substantial challenges. These findings suggest that PlantXpert can serve as a foundation for assessing evidence-grounded agronomic reasoning and for advancing multimodal model development in plant science.
Why it matches plant phenotyping methodsPlantXpertは作物フェノタイピング向けの画像ベンチマークとVLM評価基盤を構築しており、表現型解析手法・データセットの開発が研究の中心である。
abstractwe develop PlantXpert, an evidence-grounded multimodal reasoning benchmark for soybean and cotton phenotyping.
Accurate estimation of crop yield from remote sensing remains challenging due to the crop-specific nature of yield drivers and the difficulty of interpreting spectral indicators across agronomic systems. While many studies prioritise predictive accuracy through complex models, fewer explicitly examine the stability and physiological relevance of in-dividual spectral and phenological indicators under controlled analytical conditions. This study investigates yield–spectral relationships in wheat and cotton using a harmonised Sentinel-2 indicator framework applied across multiple growing seasons in a Mediterra-nean agricultural environment. A consistent set of spectral and thermal indicators was derived from two phenologically targeted Sentinel-2 acquisitions per season and analysed using correlation analysis, univariate regression, constrained multivariate modelling, and recurrence analysis within an identical workflow for both crops. Distinct crop-specific patterns were observed. Wheat yield was most strongly associated with water-sensitive and canopy-related indicators, with NDWI-based metrics reaching Pearson correlations up to r = 0.85 and multivariate models explaining a substantial proportion of yield varia-bility (up to R² ≈ 0.82) under controlled analytical conditions. In contrast, cotton yield var-iability was dominated by thermal accumulation, with growing degree day indicators showing correlations up to |r| = 0.59 and multivariate performance reaching R² = 0.76. Recurrence analysis confirmed the stability of these indicator families across analytical stages. Overall, the results indicate that parsimonious, physiologically interpretable indi-cator combinations can account for a substantial proportion of yield variability without reliance on black-box modelling, supporting crop-aware indicator selection for precision agriculture applications.
Why it matches plant phenotyping methodsSentinel-2のスペクトル・熱指標から作物収量を推定する統一ワークフローを構築・比較し、指標の安定性と予測性能を検証しており、収量フェノタイピング手法が中心です。
abstractThis study investigates yield–spectral relationships in wheat and cotton using a harmonised Sentinel-2 indicator framework
Accurate segmentation of fine-scale organs from 3D point clouds poses a substantial challenge in high-throughput plant phenotyping (HTP), where existing methods are hindered by the loss of topological features and the resulting low accuracy. To address this, we introduce CotSkNet, a semantic segmentation network based on structured representation learning. This approach is the first to define plant architecture as a knowledge-based intermediate representation that integrates geometric, topological, and hierarchical relationships, thereby incorporating topological information into the segmentation process. CotSkNet features an innovative Topological Geometric Feature Fusion Extractor and a Two-Way Reinforcement Module to efficiently extract and enhance salient features. Furthermore, the proposed Topological Attention Aggregator enables dynamic focus on key branch points. On a dataset of 403 field-grown cotton plants, our method achieved mean intersection over union values of 91.55%, 92.37%, and 98.21% for the main stem, fruiting branches, and leaves, respectively, far surpassing those of mainstream methods. Moreover, automatically extracted phenotypic parameters, such as plant height and fruiting branch length showed excellent consistency with manual measurements (R 2 > 0.91, root mean square error (RMSE) < 0.14). This study confirms that structured representation learning excels at capturing fine-organ phenotypes, providing an innovative analytical pathway for HTP in complex field crops.
Why it matches plant phenotyping methods3D点群の器官セグメンテーションと形質抽出手法を開発し、圃場データで精度検証しているため、植物フェノタイピング手法が研究の中心である。
abstractwe introduce CotSkNet, a semantic segmentation network based on structured representation learning.
Abstract This paper presents a deep learning model to detect cotton plant pests and classify diseases, which must overcome limited datasets, class imbalance, and feature redundancy. At the preprocessing phase, the Gaussian blur filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to sharpen images by improving their clarity and contrast. To increase and diversify the data, Spa-GAN-based data augmentation is used to produce realistic synthetic samples. To obtain an accurate Region of Interest (RoI), an Attention-Guided Multi-Scale Residual U-Net (AGMS-UNet) is considered to segment local and global structural information. The proposed framework makes three major contributions: (i) a new attention-based feature extractor, TriAttnNet , that incorporates spatial, channel, and contextual attention to represent diseases on a fine-grained level; (ii) a new optimization strategy, Hybrid Mongoose Ray Chaotic Optimization (HMRCO), which includes chaotic strategies to better tune the parameters and explore the feature space; and (iii) classification layer with focal loss for final decision. Experimental analyses prove that the suggested method is much more effective than the current state-of-the-art models, providing a powerful and understandable solution to precision agriculture and sustainable cotton crop health monitoring. Experimental results show that TriAttnNet achieves 98.66% accuracy, 98.71% recall, and 98.81% F1-score, which is better than the state-of-the-art algorithms, such as EfficientNetB1-CBAM (96.38%) and BERT-ResNet-PSO (95.69%). The proposed system is computationally feasible and interpretable, and it is interpretable to provide a practical solution to precision agriculture and sustainable monitoring of the health of cotton crops.
Why it matches plant phenotyping methods綿植物の病害を画像から検出・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性能比較・検証しているため。
abstractThis paper presents a deep learning model to detect cotton plant pests and classify diseases
Reproduction assets foundThe paper's plant image input is the public Kaggle Cotton Plant Disease Dataset, explicitly cited with URL. Authors' code/models are only available upon request, so no public code asset qualifies.Dataset · publicThe dataset of this study is taken from the publicly available Kaggle repository, Cotton Plant Disease
Dataset [25].Open asset ↗Kagglepdf-page:6 lines:1-48Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published31 Mar 2026International Journal for Research in Applied Science and Engineering TechnologyCited by 0 · OpenAlex ↗
Cotton productivity plays a crucial role in the global agricultural economy; however, various leaf diseases significantly threaten crop yield and fiber quality. Early and accurate disease detection is essential for effective crop management, yet traditional inspection methods are time-consuming, labor-intensive, and dependent on expert knowledge, often leading to inconsistent results. Conventional machine learning approaches also face limitations in real-world agricultural environments due to variations in lighting conditions, complex backgrounds, and similarities between disease symptoms. To address these challenges, this research proposes an intelligent framework called Cotton Plant Disease Identification Using ResMobNet with Attention-Guided Localization and Severity Analysis (CPDI-RMN). The proposed system integrates advanced image preprocessing, hybrid feature extraction, deep learning classification, and attention-based localization to create a comprehensive disease detection framework. Initially, cotton leaf images are collected from a comprehensive dataset and preprocessed through image resizing, noise removal, contrast enhancement, and Min–Max normalization to improve visual quality and ensure stable model training. Data augmentation techniques such as rotation, flipping, zooming, and brightness adjustment are applied to enhance dataset diversity and improve model robustness against overfitting. For feature enhancement, contour visualization and geometric feature representation are combined with texture analysis using the Gray-Level Co-occurrence Matrix (GLCM) and Laplacian filtering. The core of the framework is the ResMobNet hybrid architecture, which integrates ResNet-50, EfficientNet-B3, and MobileNet-V2 to capture multi-scale spatial and texture features while maintaining computational efficiency. Gradient-Weighted Class Activation Mapping (Grad-CAM) is employed to generate attention maps for disease localization, followed by segmentation to isolate infected regions. Disease severity is then quantified by calculating the percentage of infected leaf area and classifying it into mild, moderate, and severe categories. Experimental results using five-fold cross-validation demonstrate that the CPDI-RMN model achieves 98.85% classification accuracy, outperforming CNN, ANN, ResNet, and MobileNetV2 models. Additionally, the attention-based localization achieves 96.8% Intersection over Union and 98.0% Dice Score, indicating highly accurate disease region detection. Overall, the proposed framework provides a reliable and scalable solution for intelligent cotton disease monitoring and supports precision agriculture through data-driven crop management.
Why it matches plant phenotyping methodsワタ葉画像から病変領域を抽出し、感染面積率に基づいて病害重症度を定量化する画像解析手法が研究の中心であるため、植物フェノタイピング手法として含める。
abstractThe proposed system integrates advanced image preprocessing, hybrid feature extraction, deep learning classification, and attention-based localization to create a comprehensive disease detection framework.
Summary statement Cotton ( Gossypium hirsutum ) drought sensitivity depends strongly on flowering stage, but stage‐resolved, non‐destructive detection remains limited. Using controlled short‐term droughts imposed at early, mid, or late flowering, we integrated multispectral and hyperspectral canopy phenotyping with physiology and explainable machine learning to identify spectral predictors of metabolic status and recovery. Early and mid‐flowering drought responses were largely recoverable, whereas late‐flowering drought caused the most potent and least reversible losses in photosynthesis, canopy structure, and fiber quality. These results highlight late flowering as a critical vulnerability window and provide a mechanistically grounded framework for rapid phenotyping of stage‐specific drought resilience.
Why it matches plant phenotyping methodsマルチスペクトル・ハイパースペクトルによる非破壊キャノピー表現型計測と説明可能な機械学習を中核に、乾燥耐性を迅速推定する方法・枠組みを提示している。
Introduction Accurate and stable diagnosis of cotton nitrogen status across growth stages is essential for precision fertilization in drip-irrigated systems. However, the instability of conventional nitrogen-related indicators across different phenological stages often reduces diagnostic performance and limits their broader application. Methods A field experiment was conducted in Xinjiang, China, under four irrigation levels (60%, 80%, 100%, and 120% ET c ) and four nitrogen application rates (0, 245, 300, and 350 kg N ha -1 ). UAV multispectral imagery was acquired at the squaring, flowering, boll-setting, and boll-opening stages. Based on ground-measured leaf area index (LAI) and upper-canopy leaf nitrogen weight (LNWupper), an Integrated Nitrogen Diagnosis Index (INDI) was developed. Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Extreme Gradient Boosting (XGBoost) models were used to evaluate the inversion performance of INDI. In addition, the nitrogen nutrition index (NNI), derived from the critical nitrogen dilution curve, was used to validate the diagnostic stability of INDI. Results Multispectral vegetation indices were strongly correlated with LAI, LNWupper, and INDI, with red-edge- and near-infrared-based indices showing the highest sensitivity. Among the three models, XGBoost achieved the best inversion accuracy for INDI (R 2 = 0.85, RMSE = 0.61). INDI was significantly correlated with NNI across growth stages, with R 2 values of 0.58, 0.77, 0.81, and 0.70 at the squaring, flowering, boll-setting, and boll-opening stages, respectively, and the highest accuracy observed at the boll-setting stage. Moreover, the spatial distribution maps of INDI effectively distinguished nitrogen differences under different water-nitrogen treatments and were consistent with NNI-based classifications. Discussion INDI accurately captured nitrogen dynamics throughout cotton growth, and the INDI-XGBoost framework provided a robust approach for high-precision spatial nitrogen diagnosis. These results support precision fertilization management in drip-irrigated cotton fields in Xinjiang.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からワタの窒素状態を推定する指標とXGBoost反転手法を開発・検証しており、植物生理状態の取得・推定が研究の中心である。
abstractBased on ground-measured leaf area index (LAI) and upper-canopy leaf nitrogen weight (LNWupper), an Integrated Nitrogen Diagnosis Index (INDI) was developed.
Abstract Early and accurate diagnosing of crops that contract diseases is critical in sustaining agricultural production and managing economic losses. Despite the massive success of the deep learning in the automated diagnosis of plant disease, new practices are largely only applicable to specific crops, and also need to be in controlled conditions and not in the field. In response to the aforementioned problems, a new Efficient Attention-based Hybrid Deep Learning (EA-HDL) has been suggested in this paper to perform the classification of multi-crop leaf diseases using real-field images. The architecture is based on an EfficientNetV2 backbone pretrained and has an attention-based pooling mechanism to encourage the use of discriminative features by the effective synthesis of information of the disease-relevant areas and the elimination of background noise. It is a tested, validated and benchmarked framework that was experimented on four of the most crucial crops: cotton, chickpea (chana), Black Gram and wheat in different field conditions. Strong and consistent results have been obtained in experiment work with a 100% record of classification accuracy in the cotton case, 98.64% in the chickpea case, 97.53% in the wheat case and competitive results in the Black Gram case in spite of difficult visual variability. It can be compared to the latest state-of-the-art deep learning models to prove that our approach is more accurate, as it generalizes and works with a variety of crops. The results are evidence that attention-based hybrid deep learning models have a tremendous potential of enhancing accuracy in disease classification in real-life agricultural 1 conditions. The EA-HDL is an effective and scalable platform to real-world crop disease surveillance and precision agriculture system.
Why it matches plant phenotyping methods葉画像から植物の病徴・病害を分類する深層学習手法を開発し、複数作物・圃場条件で検証・ベンチマークしており、植物表現型取得が中心である。
abstracta new Efficient Attention-based Hybrid Deep Learning (EA-HDL) has been suggested in this paper to perform the classification of multi-crop leaf diseases using real-field images.
Agriculture currently faces the dual pressures of ensuring global food security and adapting to rapid climate change. To cope with these challenges, researchers have introduced several modern mechanization technologies, including advanced farm machinery, autonomous navigation systems, artificial intelligence, sensing technologies, and communication tools, to enhance productivity and sustainability (Syed et al., 2025a). These technologies enable data-driven decision-making by allowing continuous, large-scale acquisition and analysis of crop and environmental information. Consequently, accurately predicting crop yields and monitoring plant health in real time have become critical prerequisites for precision agricultural management (Syed et al., 2025). Traditional measurement methods-often labor-intensive, destructive, and spatially limited-are increasingly unable to meet the demands of modern large-scale farming. In this context, the integration of Remote Together, these ten contributions illustrate the maturation of agricultural remote sensing, moving towards models that are not only more accurate but also lighter, more interpretable, and more resilient to environmental noise. By combining satellite and UAV data with advanced computational models, these innovative approaches are paving the way for a more resilient and productive global food system. Future research will increasingly focus on improving the precision of crop yield estimation models through multi-dimensional analyses. As agricultural environments grow more complex, integrating AI-powered models with multi-sensor fusion technologies will be essential. Innovations such as lightweight neural networks and multimodal cross-attention frameworks will enable the detection of small, occluded, and densely packed targets with greater accuracy, thereby refining crop-specific metrics such as photosynthetically active radiation (FPAR) and nitrogen content. This, in turn, will enhance crop health monitoring and yield predictions.Additionally, UAV-based remote sensing, combined with multitier feature selection, will improve nitrogen content analysis in crops such as cotton, while image dehazing models and light-use efficiency frameworks will bolster biomass estimation.Emerging technologies such as the Ta-YOLO framework will further optimize small fruit detection in dense canopies, advancing overall crop detection accuracy.A key challenge lies in adapting these models to handle real-world complexities, such as variable environmental conditions. Future work will focus on improving the robustness of these models through dynamic coding networks and performance optimization, ensuring they can operate in heterogeneous agricultural environments.Interdisciplinary collaboration between agriculture, AI, and remote sensing experts will accelerate the development and deployment of these approaches, paving the way for more efficient crop yield estimation systems that are critical for ensuring food security and sustainable agricultural practices.
Why it matches plant phenotyping methods作物収量・健康・バイオマス・窒素含量などの植物形質を、衛星・UAVリモートセンシングと計算モデルで推定する手法群を中心に扱う編集レビューであり、方法論的役割が明確。
titleInnovative approaches in remote sensing for precise crop yield estimation: advancements, applications, and future directions
In agricultural automation, precise cotton segmentation is a key step for tasks such as intelligent harvesting and yield estimation. However, in complex field environments, factors such as background interference and irregular target shapes severely affect segmentation accuracy. Existing deep learning methods offer certain advantages but still generally suffer from limitations including insufficient accuracy, over-segmentation, and misidentification. To address these challenges, this study proposes a novel dual-branch cotton segmentation network, Cotton-aware Mamba-enhanced UNet (CMNet), which optimizes the ParaTransCNN architecture by incorporating the 2D Selective Scan (SS2D) module to replace the original Transformer branch, effectively balancing the extraction of local details and global semantic information while reducing computational burden. To enhance the model's perception of irregularly shaped cotton, a Deformable Convolutional Networks v1 (DCNv1) module is integrated into the Vision Mamba (VMamba) branch, further improving the delineation of target boundaries. Additionally, an Atrous Spatial Pyramid Pooling (ASPP) module is introduced at the end of the Convolutional Neural Network (CNN) branch to strengthen multi-scale feature representation. To optimize the fusion of channel and spatial information, the Spatial and Channel Squeeze-and-Excitation (scSE) attention mechanism replaces the original module, enhancing feature modeling capability. Experimental results on an in-field cotton image dataset demonstrate that CMNet outperforms existing mainstream methods, achieving Dice, mIoU, and Accuracy of 91.06%, 84.18%, and 98.10%, respectively, while reducing parameter count and computational complexity, thus exhibiting excellent performance. Furthermore, generalization experiments on multiple other plant datasets also achieved outstanding results, validating the model's adaptability and potential for broader applications in multi-crop segmentation tasks, providing valuable insights for smart agriculture segmentation research. The source code and dataset of this work are publicly available at https://github.com/halidanmu/CMNet.git.
Why it matches plant phenotyping methods綿花画像から植物領域を抽出する新規セグメンテーション手法を中心に開発・検証しており、植物表現型の画像取得・抽出ワークフローに該当する。
abstractthis study proposes a novel dual-branch cotton segmentation network, Cotton-aware Mamba-enhanced UNet (CMNet)
Reproduction assets foundThe authors explicitly state that the source code and dataset for CMNet are publicly available on GitHub. The paper also uses several public Roboflow plant image datasets in its generalization experiments, cited with public URLs in the references.Code · publicThe source code and dataset of this work are publicly available at https://github.com/halidanmu/CMNet.git.Open asset ↗halidanmu/CMNethtml-lines:106-109Dataset · publicELTE (2023). Assignment 2 dataset. Available online at: https://universe.roboflow.com/elte-msgqy/assignment_2-mjhau (Accessed November 5, 2025).Open asset ↗html-lines:754-834Dataset · publicLaola (2024). Defect banana dataset. Available online at: https://universe.roboflow.com/laola/defect-banana-qf4f6 (Accessed November 5, 2025).Open asset ↗html-lines:754-834Dataset · publicLuffy24312 (2023). Cnn dataset. Available online at: https://universe.roboflow.com/luffy24312/cnn-myqtl.Open asset ↗html-lines:835-919Dataset · publicVyuha T. (2025). Rose dataset. Available online at: https://universe.roboflow.com/tech-vyuha/rose-kfpuf (Accessed November 4, 2025).Open asset ↗html-lines:835-919Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Verticillium wilt (VW), a soil-borne fungal disease of cotton, can lead to significant yield loss and has become a growing problem for global cotton production. From a global perspective, the local biotype has a high pathogenicity. Traditional phenotyping and screening methods for resistance to VW are slow, costly, and prone to human error. However, advancements in object detection models can enable automated, high-throughput screening of resistant varieties, therefore, improving speed, reducing costs, and eliminating operator bias. This study develops and evaluates the effectiveness and generalisation of two widely adopted object detection models: the two-stage Faster R-CNN and the single-stage YOLOv11 for VW in cotton stems across various backbone architectures. Digital cameras were used to collect cotton stem images from several fields. The results showed that the Faster R-CNN with the ResNet-101 model achieved a mean average precision (mAP at intersection over union (IOU) of 0.5) between 5 % and 55 % higher for the most complex YOLOv11-x and simpler YOLOv11-n, respectively, on the test dataset. Further evaluation with an independent dataset confirmed that the Faster R-CNN with ResNet-101 was the most robust and generalisable model, achieving a mAP of 85.68 %, outperforming YOLOv11 models by at least 12 % and up to 82 %. However, this enhanced mAP of the Faster R-CNN model incurred a computational cost approximately 8 % higher than that of YOLOv11-x. Nevertheless, in the context of VW detection for cotton breeding, the value of a higher mAP substantially outweighs the value of a lower computational load.
Why it matches plant phenotyping methods綿花の茎に現れる萎凋病を画像から検出・評価する物体検出手法を開発し、複数モデルの性能と一般化を比較検証しており、植物病害表現型の取得が中心です。
abstractTraditional phenotyping and screening methods for resistance to VW are slow, costly, and prone to human error.
Accurate crop yield estimation is crucial for decision-making and planning in modern agriculture with increasing challenges with food security. Yield predictions provide farmers with insights into expected production, facilitating optimized resource allocation, improved agricultural management strategies, and enhanced profitability. This study investigates the application of machine learning (ML) techniques, including Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM), and Random Forest (RF) models, for predicting crop yields using multi-sensory time-series data that has been collected on two fields over a four-year timeframe. The focus is on corn (Zea mays) and cotton (Gossypium hirsutum) yield, two of the top critical crops in Mississippi region. A multi-sensory dataset was collected using multispectral cameras and LiDAR sensors mounted on unmanned aircraft systems (UAS), along with soil moisture and temperature data from volumetric probes and environmental data from a nearby weather station. Over four years, more than 30 features were extracted weekly from five major categories, with 235 ground truth yield records from plots in the field. The study outlines the methodology for feature selection and examines its impact on yield prediction accuracy. Using percentile root mean square error (RRMSE) and mean absolute percentage error (MAPE) as performance metrics, the study found that the proposed LSTM model produced lower field-wise errors (9 − 21 % MAPE) compared to other models and validation, indicating superior performance in predicting yields across selected weeks. The proposed ML-based approach, validated through year-based and field-wise cross-validation methods, demonstrates the effectiveness of using UAS-collected multi-sensor data for accurate yield estimation in corn and cotton.
Why it matches plant phenotyping methodsUASのマルチセンサー画像・LiDARデータから圃場プロットの収量を推定する計算ワークフローを開発・評価し、交差検証で性能を検証しているため、植物表現型取得が中心である。
abstractThis study investigates the application of machine learning (ML) techniques, including Feedforward Neural Networks (FNN), Long Short-Term Memory (LSTM), and Random Forest (RF) models, for predicting crop yields using multi-sensory time-series data
Cotton (Gossypium hirsutum L.) is a vital fiber crop that thrives with proper management. Accurate decision-making also improves cotton productivity. However, many field experiments assume uniform conditions and ignore within-field variability caused by nitrogen doses and cultivar differences. Therefore, this field experiment was conducted to develop UAV-based yield-prediction models for cotton under varying nitrogen doses. Five nitrogen treatments were applied as urea fertilizer rates: T0 (0 kg/ha), T1 (100 kg/ha), T2 (120 kg/ha), T3 (140 kg/ha), and T4 (160 kg/ha), with other fertilizer doses kept constant across two cotton varieties, CB Hybrid-1 and CB HYV-15. The findings revealed that increasing nitrogen fertilizer doses boosted vegetative growth and height, but yield decreased at the highest fertilizer rate for both varieties. The most efficient urea fertilizer treatments were T2 (120 kg/ha) for the high-yielding variety and T3 (140 kg/ha) for the hybrid variety. Stepwise Multiple Linear Regression (MLR) and Least Absolute Shrinkage and Selection Operator (LASSO) models were used to predict cotton yield during early growth stages using UAV-based vegetation indices. The Normalized Difference Red Edge (NDRE) was identified as the most effective predictor in the Stepwise MLR model, which performed well at 72 days after sowing (DAS) (R2 = 0.89). The LASSO model also showed strong performance at 79 DAS (R² = 0.75), with the Normalized Difference Vegetation Index (NDVI) as a key predictor. Overall, this study demonstrates that UAV-derived vegetation indices can effectively predict cotton yield and highlight the impact of nitrogen fertilizer on yield.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から植生指数を抽出し、綿花収量を予測する手法の開発・評価が研究の中心であり、単なる窒素処理実験における routine 測定ではない。
abstractthis field experiment was conducted to develop UAV-based yield-prediction models for cotton under varying nitrogen doses.
Forecasting crop performance through non-destructive tools is crucial for enabling timely, data-driven decisions for sustainable cotton production. This study evaluated physiological, PIs [chlorophyll index (CI), nitrogen balance index (NBI)], vegetation, VIs [normalized difference vegetation index (NDVI), normalized difference red edge (NDRE)], and biochemical, BIs [leaf nitrogen, petiole nitrate-N] indicators to forecast aboveground biomass accumulation (AGB), nitrogen (N) uptake, and lint yield in cotton. We hypothesized that the predictive strength of each indicator would vary by type and growth stage. Field experiments were conducted across five site-years in West and North Florida, USA, using six N rates (0–252 kg N/ha) in four replications. Quadratic regression (QR) model identified PIs at peak flowering and VIs at first flower and cutout growth stages as the most robust predictors of AGB. A random forest regression (RF) model also showed similar results, with PIs at peak flowering and VIs at peak flowering and first flower as the best indicators for AGB prediction. A strong relationship of BIs with N uptake at bloom and post-bloom growth stages evaluated through QR and RF supports their use for early reproductive assessment. Lint yield predicted using QR and RF was best forecasted by VIs at peak flowering, and cutout. Principal component analysis confirmed mid-to-late season VIs as major drivers of AGB and lint yield variability, while as early reproductive BIs as major indicators of N uptake. This research establishes a robust framework for real-time indicator and growth stage-based biomass, N uptake, and yield forecasting in cotton.
Why it matches plant phenotyping methods綿花の非破壊的な生理・植生指標を用いて、バイオマス、窒素吸収、収量という植物形質を予測し、複数地点年および回帰モデルで指標の予測性能を評価しているため、測定・予測手法が中心である。
abstractForecasting crop performance through non-destructive tools is crucial for enabling timely, data-driven decisions for sustainable cotton production.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 5 Sept 2026
Abstract Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language Summary Temporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.
Why it matches plant phenotyping methods植物画像から形態・スペクトル・テクスチャ形質を抽出する統合パイプラインの開発と評価が中心であり、植物フェノタイピング手法として明確に該当する。
abstractWe developed an integrated multispectral phenotyping framework
Single-boll weight (SBW) is difficult to estimate after defoliant application because canopy spectra include numerous mixed pixels from lint, soil, and senescent leaves, leading to strong background interference. Here we propose a UAV multispectral workflow that combines object-based boll extraction, spectral feature selection, and machine-learning regression to improve SBW mapping. Data were collected from a two-year drip-irrigated cotton experiment in Xinjiang, China involving four varieties evaluated under five planting densities treatments. Boll extraction was treated as a supervised object-based classification problem, and maximum likelihood, mahalanobis distance, and parallelepiped classifiers were compared. Fifteen vegetation indices were computed from the extracted boll pixels; informative features were identified using Pearson correlation and SHapley Additive exPlanations importance ranking. SBW was then estimated with ridge regression, random forest regression, and neural network regression using an independent validation dataset. Maximum likelihood consistently achieved overall accuracy above 97% with Kappa values above 0.93, outperforming the other classifiers. Indices derived from the red, red-edge, and near-infrared bands, particularly those designed to reduce soil background effects, showed the strongest relationships with SBW and ranked highest in SHAP. The best-performing model, which integrated maximum likelihood-based boll extraction with neural network regression, achieved a coefficient of determination of 0.80 and a root mean square error of 0.31 g on the validation set. Relative errors remained below 15% across different years, varieties, and planting densities. This workflow reduces background interference and enables transferable SBW spatial estimation for breeding evaluation and density and harvest management.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から綿花の単一ボール重量を抽出・推定する方法を開発し、分類器と回帰モデルを比較検証しており、植物形質取得が研究の中心である。
abstractHere we propose a UAV multispectral workflow that combines object-based boll extraction, spectral feature selection, and machine-learning regression to improve SBW mapping.
There is great potential for improving agricultural research, ecological monitoring, and biodiversity conservation through computerized plant species cataloging utilizing leaf photos. This work introduces a deep learning-based framework that uses transformer-based architectures, such as the Vanilla Vision Transformer (ViT), Swin Transformer, DeiT (Data-Efficient Image Transformer), and T2T-ViT (Tokens-to-Tokens Vision Transformer), to automatically classify cotton leaf diseases. Images of cotton leaves from four different classes-curl virus, bacterial blight, fusarium wilt, and healthy leaves-make up the dataset. A stratified K-fold hold-out testing technique (K = 1 to 5) is used to maintain the class distribution across training and testing folds in order to guarantee robust model evaluation and address class imbalance. To improve generalization and guarantee compatibility with transformer models, standard image augmentation and normalizing approaches are used. All models begin training using vast collections of images, afterward honed specifically on cotton leaf data to sharpen their ability to tell differences apart. Results spread across multiple test rounds stay steady, one standout reaching nearly perfect accuracy-99.99 percent. This pattern highlights how transformer-driven systems thrive alongside stratified K-fold checks, crafting a dependable way to spot crop issues early, shifting farm oversight toward quicker, smarter responses.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類する深層学習手法の開発・評価が研究の中心であり、植物フェノタイピング手法に該当する。
abstractThis work introduces a deep learning-based framework that uses transformer-based architectures
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · checked 15 Sept 2026
Efficient and non-destructive cotton organ extraction is crucial for automatic cotton phenotyping. However, limited by leaf occlusion, large model parameters, and inefficient manual observation, it fails to meet current high-throughput phenotyping demands. To address these challenges, this paper propose ELGCot3D, a lightweight 3D point cloud-based cotton organ segmentation method, enabling high-precision segmentation in resource-constrained environments. First, a new module called ELG3D replaces traditional Set Abstraction structures, enhancing local cotton data learning capability via multi-mechanism feature fusion and boosting segmentation accuracy. Second, a cotton-specific feature enhancement module is proposed to secondary optimize the features output from the Feature Propagation layer. This module significantly increases feature discriminability while substantially reducing redundant and high consumption network layers, achieving a balance between performance and efficiency. Finally, a cotton point cloud-adapted training strategy improves model training stability and prediction accuracy. Experimental results on the Crops3D dataset show ELGCot3D achieves 76.7% mIoU and 86.1% OA for cotton segmentation, Meanwhile, the number of parameters and computational complexity are reduced by 50.1% and 50.7%, respectively, demonstrating the model's lightweight characteristics. Notably, it performs well in segmenting other Crops3D crops and exhibits strong generalization on the other cotton point cloud datasets. The proposed method offers a reliable approach for cotton phenotyping and precision agriculture. Future work will extend its high-throughput extraction capability for individual plant organs in large cotton fields, providing breeders with accurate data to support efficient breeding and new variety development.
Why it matches plant phenotyping methods綿花器官の3D点群セグメンテーション手法を開発し、精度・計算量・汎化性能を評価しており、植物表現型取得の方法が中心である。
abstractEfficient and non-destructive cotton organ extraction is crucial for automatic cotton phenotyping.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を融合し、綿花の葉水分状態を3D推定・可視化する手法の開発と検証が研究の中心である。
abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).
CottonAerial / UAVLiDAR / point cloudMultispectral / hyperspectralThermalLeafPhysiological trait estimation2D/3D reconstructionImage / point-cloud registrationWater status / transpiration
Accurate estimation of crop water status is essential for monitoring plant senescence and enabling intelligent agricultural management. This study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT). To address the low spatial resolution of thermal imagery, a downsampling–upsampling simulation framework was developed to evaluate interpolation errors. This framework quantitatively compares three common interpolation methods—nearest neighbor, bilinear, and bicubic interpolation—using RMSE and PSNR metrics. Results show that bicubic interpolation performs best in preserving spatial details and minimizing errors, and is therefore adopted in the subsequent image fusion process. A 3D grid was constructed based on the digital surface model (DSM), enabling grid-cell (pixel-aligned) spectral and thermal features to be mapped onto point-cloud units. Vegetation and thermal indices extracted from the mapped features were used as input variables. Combined with recursive feature elimination (RFE) and random forest (RF) models, the prediction of LEWT and LWC achieved R² values of 0.792 and 0.752, and rRMSE values of 13.84% and 9.68%, respectively. These results significantly outperformed those of partial least squares regression (PLSR), support vector machine (SVM), and extreme learning machine (ELM) models. By integrating the predicted results with the point cloud data, a 3D representation of canopy water parameters was constructed, revealing a typical top-down gradient of water loss. The experiment also revealed that nitrogen treatment significantly influenced the vertical distribution of water content. High-nitrogen application delayed water loss in the middle and lower canopy layers, highlighting the coupled regulation between nitrogen and water. Parameter comparisons showed that LEWT exhibited higher sensitivity than LWC across both temporal and spatial scales, making it a more robust indicator for canopy water monitoring. Additionally, point clouds generated from Cross-circling oblique (CCO) photogrammetry outperformed UAV LiDAR systems in terms of point density, structural completeness, and image fusion potential. In summary, this study validated the feasibility and effectiveness of integrating point cloud, MS, and thermal imagery via the proposed pixel-aligned co-registration and DSM-grid fusion framework for 3D crop water monitoring. The proposed method provides a reliable technical foundation for drought detection, irrigation management, and yield prediction in precision agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル・熱画像・点群を統合し、綿花キャノピーの水分形質を3D推定・可視化する手法の開発と検証が中心である。
abstractThis study proposes a pixel-aligned co-registration and DSM-grid fusion framework that integrates high-resolution point clouds, multispectral (MS) images, and thermal imagery acquired by Unmanned Aerial Vehicles (UAVs) to enable three-dimensional prediction and visualization of cotton canopy leaf water content (LWC) and equivalent water thickness (LEWT).
High-throughput field phenotyping bridges genotype, environment, and phenotypic performance. Conventional plot-level approaches relying on manual surveys are labor-intensive, and error-prone and fail to capture variability among individual plants, limiting seed cotton yield estimation and genotype screening under natural conditions. To address these limitations, a complex framework was developed, integrating single-plant instance segmentation, multi-trait inversion, plot-level stability characterization, and yield estimation. The enhanced vision-model framework, TopoRefineSAM, combines YOLOv12 detection with SAM2 segmentation and incorporates adaptive enhancement and topological refinement modules, enabling efficient, robust, and cost-effective single-plant identification under weak annotation. Based on this segmentation, multi-source UAV imagery (RGB, multispectral, thermal infrared, and DSM) was used to build ensemble learning models for inversion of physiological and biomass traits. A Stability Index Group (SIG) translates inter-plant variability into plot-level stability features, improving interpretability and consistency in yield estimation and cultivar screening. Results demonstrated that TopoRefineSAM achieved high segmentation accuracy for single-plant extraction under complex field conditions. In multi-trait inversion, Gradient Boosting Decision Trees (GBDT) achieved the highest performance. Our results demonstrated strong consistency between multimodal features and measured traits. In yield estimation, incorporating the SIG substantially improved predictive performance across growth stages. In cultivar screening, the method achieved high agreement with field measurements, showing robust identification of top-performing cultivars. Collectively, the findings establish a scalable, cost-effective, and high-accuracy framework for field-based phenotypic analysis and yield estimation, providing both methodological innovations and practical support for precision breeding and large-scale crop improvement.
Why it matches plant phenotyping methods単一植物のセグメンテーション、マルチモーダル画像による形質推定、安定性指標、収量推定を統合した圃場フェノタイピング手法の開発が中心である。
abstractTo address these limitations, a complex framework was developed, integrating single-plant instance segmentation, multi-trait inversion, plot-level stability characterization, and yield estimation.
This study addresses the challenge of organ-level instance segmentation in cotton point clouds, which arises from significant morphological variations and leaf occlusion across growth stages. To achieve high-precision leaf extraction, a hybrid framework integrating PointNeXt and DBSCAN is proposed. A dataset containing 1,065 cotton plants from seedling to boll-opening stages was constructed via multi-view image reconstruction and augmented through random rotation and scaling. Methodologically, a two-stage pipeline was designed: semantic segmentation was first performed using the PointNeXt network, where its residual MLP blocks enhanced edge and local feature learning; instance segmentation was then conducted by applying density-adaptive DBSCAN clustering to the semantic results, effectively mitigating over-segmentation in emerging leaves. Experimental results indicate that the semantic segmentation achieved an mIoU of 0.9846, representing a 7.2% improvement over PointNet++. The subsequent instance segmentation attained an ARI of 0.983, reduced the over-segmentation rate by 63%, and maintained an error below 3% for leaves smaller than 5 cm 2 . The framework provides reliable technical support for the automated extraction of key phenotypic traits such as leaf area index and leaf inclination distribution.
Why it matches plant phenotyping methods綿花葉の点群から器官レベルの葉を自動抽出するセグメンテーション手法を開発・評価し、葉面積指数や葉傾斜分布などの表現型形質への応用を示しているため、方法が中心的である。
abstractTo achieve high-precision leaf extraction, a hybrid framework integrating PointNeXt and DBSCAN is proposed.
CottonMultispectral / hyperspectralSeed / grainPhysiological trait estimationWater status / transpiration
Rapid, non-destructive and accurate prediction of cotton seed moisture content is important for assessing seed vigour and improving storage capacity. In this study, a prediction approach for cotton seed moisture content was developed based on machine learning (ML) and hyperspectral imaging. Using the cultivar Jinken 1161 as the experimental material, spectral data in the range of 935-1720 nm were acquired. Outliers were removed using the Isolation Forest algorithm, and the samples were divided into calibration and prediction sets using the spectral-physicochemical value coordinate algorithm. The raw spectra were pre-processed using four methods, including Savitzky-Golay (SG) smoothing and standard normal variate transformation, before constructing traditional ML models [partial least square regression and multiple linear regression (MLR)] and deep learning (DL) models [convolutional neural network and long short-term memory network]. To reduce data redundancy and improve computational efficiency, feature wavelengths related to moisture content were selected using the successive projection algorithm and the least absolute shrinkage and selection operator (LASSO). Comparative analysis of different algorithmic combinations identified the optimal model, which was subsequently applied to hyperspectral images for pixel-wise prediction. This application enabled visualisation of the spatial distribution of moisture within individual cotton seeds. The results showed that, given the current sample size, ML models outperformed DL models. The SG-LASSO-MLR model achieved the best performance, with a prediction correlation coefficient (R2 p), root mean square error of prediction and residual predictive deviation of 0.9557, 0.908 and 4.77, respectively. These findings provide a feasible and effective technical solution for rapid and non-destructive detection of cotton seed moisture content. These outcomes offer valuable insights for seed quality evaluation and intelligent crop monitoring.
Why it matches plant phenotyping methods綿実の水分含量という植物器官の状態を、ハイパースペクトル画像と機械学習で非破壊推定する手法を開発・比較・検証しており、表現型取得が中心です。
abstracta prediction approach for cotton seed moisture content was developed based on machine learning (ML) and hyperspectral imaging.
Introduction: Accurately segmenting cotton seedling organs from 3D point clouds is fundamental for high-throughput plant phenotyping and digital breeding. However, cotton seedling segmentation remains challenging due to fine-scale and complex organ morphology, uneven point density with noise, and the lack of high-quality annotated datasets. Methods: To address these issues, we propose DCSFormer, a tailored extension of Point Transformer V3 designed for cotton seedling point cloud segmentation. The model introduces the DCS Block, which leverages dynamic sparse expert routing and dual-channel attention to adaptively capture global semantic dependencies and subtle local geometric variations, thereby improving stem-leaf boundary discrimination. In addition, the proposed CLFSkip replaces traditional skip connections with a cross-layer fusion strategy, effectively integrating multi-scale features while preserving organ-level details. We also constructed an annotated cotton seedling dataset to support training and evaluation. Results and Discussion: Experimental results show that DCSFormer achieves 93.67% mIoU, 95.83% mPrec, 97.35% mRec, and 96.56% mF1, outperforming multiple comparison models. Furthermore, when evaluated against baseline models on two public datasets, Crops3D and Pheno4D, DCSFormer exceeds the baseline across all four metrics, further validating its effectiveness and generalizability. This work provides an effective solution for precise cotton seedling organ segmentation.
Why it matches plant phenotyping methods綿花幼苗の3D点群から器官を抽出する手法を開発し、アノテーション済みデータセットの構築と複数データセットでの性能検証を行っており、植物表現型取得が中心である。
abstractAccurately segmenting cotton seedling organs from 3D point clouds is fundamental for high-throughput plant phenotyping and digital breeding.
Reproduction assets foundThe authors constructed an annotated cotton seedling point cloud dataset (100 samples with semantic/instance organ labels and ground-truth traits) used for training and evaluating DCSFormer, and the data availability statement points to a public Kaggle repository containing it. No author analysis code or trained model/Dataset · publicThe datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.kaggle.com/datasets/tengfeiliu333/dcsformer-cotton/croissant/download .Open asset ↗Kaggle · tengfeiliu333/dcsformer-cottonlines:957-1015Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · checked 5 Sept 2026
Point clouds and digital surface models (DSMs) derived from unmanned aircraft system (UAS) imagery are widely used for plant height estimation in plant phenotyping and precision agriculture. However, comprehensive evaluations across multiple crops, flight altitudes, and image overlaps are limited, restricting guidance for optimizing flight strategies. This study evaluated the effects of flight altitude, side and front overlap, and image processing parameters on point cloud generation and plant height estimation. UAS imagery was collected at four altitudes (30–120 m, corresponding to 0.5–2.0 cm ground sampling distance, GSD) with multiple side and front overlaps (67–94%) over a 2–ha field planted with corn, cotton, sorghum, and soybean on three dates across two growing seasons, producing 90 datasets. Orthomosaics, point clouds, and DSMs were generated using Pix4Dmapper, and plant height estimates were extracted from both DSMs and point clouds. Results showed that point clouds consistently outperformed DSMs across altitudes, overlaps, and crop types. Highest accuracy occurred at 60–90 m (1.0–1.5 cm GSD) with RMSE values of 0.06–0.10 m (R2 = 0.92–0.95) in 2019 and 0.07–0.08 m (R2 = 0.80–0.89) in 2022. Across multiple side and front overlap combinations at 60–120 m, reduced overlaps produced RMSE values comparable to full overlaps, indicating that optimized flight settings, particularly reduced side overlap with high front overlap, can shorten flight and processing time without compromising point cloud quality or height estimation accuracy. Pix4Dmapper processing parameters strongly affected 3D point cloud density (2–600 million points), processing time (1–16 h), and plant height accuracy (R2 = 0.67–0.95). These findings provide practical guidance for selecting UAS flight and processing parameters to achieve accurate, efficient 3D modeling and plant height estimation. By balancing flight altitude, image side and front overlap, and photogrammetric processing settings, users can improve operational efficiency while maintaining high-accuracy plant height measurements, supporting faster and more cost-effective phenotyping and precision agriculture applications.
Why it matches plant phenotyping methodsUAS画像からの点群・DSM生成と草丈推定について、飛行条件および処理パラメータの影響を体系的に評価・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 evaluated the effects of flight altitude, side and front overlap, and image processing parameters on point cloud generation and plant height estimation.
Achieving an optimal plant architecture is a key objective in cotton breeding for enhancing yield potential, and accurate quantification of the fruit branch angle (FBA) is essential for understanding genotype–phenotype relationships and advancing ideotype breeding. However, in-field FBA measurement remains technically challenging due to severe occlusion, variable illumination, and background interference. To overcome these limitations, we propose a streamlined 3D phenotyping framework that integrates 3D Gaussian Splatting (3DGS) with a novel structural segmentation model, the Linear Point Cloud Reverse Model (LPCRM). The framework decomposes reconstructed cotton point clouds into linear micro-elements using RANSAC, followed by geometric clustering via K-means to identify and separate the main stem and fruit branches. This process operates without topological priors or large annotated datasets. Model fidelity assessment shows that 80% of point pairs between the LPCRM and the original 3DGS reconstruction exhibit Euclidean distances ≤ 0.5 cm. Phenotypic validation using 268 fruit branches from 25 cultivars demonstrates high measurement accuracy, achieving an R² of 0.874 and an RMSE of 4.01° for FBA extraction. Plant height estimation also shows strong agreement with manual measurements (R² = 0.915, RMSE = 3.858). Overall, this study presents a lightweight and robust solution for extracting 3D structural traits of field-grown cotton. The proposed framework reduces data dependency, adapts well to complex field conditions, and offers an efficient approach for high-throughput phenotyping and cotton ideotype breeding.
Why it matches plant phenotyping methods綿花の果枝角度などの3D植物形質を抽出する画像ベース表現型解析フレームワークを開発し、複数品種・枝で精度検証しており、方法開発と技術検証が研究の中心である。
abstractwe propose a streamlined 3D phenotyping framework that integrates 3D Gaussian Splatting (3DGS) with a novel structural segmentation model, the Linear Point Cloud Reverse Model (LPCRM).
Precise and timely identification of cotton leaf diseases is essential for sustaining crop yield and quality, yet manual inspection remains time-consuming, labor-intensive, and prone to error. Existing automated approaches are limited by insufficient dataset diversity, inconsistent evaluation practices, limited use of explainable AI (XAI), and high computational cost. To address these challenges, we propose an attention-enhanced CNN ensemble, namely CottonLeafNet, which integrates lightweight convolutional neural networks for accurate cotton leaf disease classification across two publicly available datasets. CottonLeafNet achieves state-of-the-art performance, obtaining 98.33% accuracy, a macro F1-score of 0.9833, Cohen's kappa of 0.9800, a mean PPV of 0.9838, and an NPV of 0.9967 on Dataset D1, with an inference time of 0.51 s per image. On Dataset D2, it reaches 99.43% accuracy, a macro F1-score of 0.9942, Cohen's kappa of 0.9924, a mean PPV of 0.9943, and an NPV of 0.9981, with a 0.40 s inference time. Moreover, a unified eight-class dataset created by merging both datasets yields a test accuracy of 99.08%. Robustness analysis under artificially induced class imbalance further confirms the model's stability, with consistently strong macro F1-scores. To evaluate the generalization capability of the proposed CottonLeafNet, we conducted cross-dataset experiments, and the results indicate that the model maintains moderate performance even when trained and tested on different datasets. Gradient-Weighted Class Activation Mapping (Grad-CAM) visualizations demonstrate that CottonLeafNet reliably attends to disease-relevant regions, enhancing interpretability. Finally, real-time feasibility is validated through a web-based deployment achieving ≈1 s inference per image. These results establish CottonLeafNet as an accurate, robust, interpretable, and computationally efficient solution for automated cotton leaf disease diagnosis.
Why it matches plant phenotyping methods綿花葉の画像から病害状態を推定する分類手法を開発・評価しており、植物病害フェノタイピングが中心的である。
abstractwe propose an attention-enhanced CNN ensemble, namely CottonLeafNet, which integrates lightweight convolutional neural networks for accurate cotton leaf disease classification across two publicly available datasets.
Reproduction assets foundThe paper's plant-phenotyping inputs are three publicly available Kaggle cotton leaf disease image datasets (D1, D2, and cross-dataset D3) explicitly named in the Data availability statement. No author analysis code, trained model checkpoints, or supplementary code repository is disclosed in the supplied blocks.Dataset · publicThe datasets analyzed during the current study are publicly available in the Kaggle repository. Dataset D1 can
be accessed atOpen asset ↗Kagglepdf-page:17 lines:68-84Dataset · publicThe dataset used for
cross-dataset testing is publicly available at:Open asset ↗pdf-page:17 lines:68-84Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Timely and accurate prediction of crop traits is critical for precision breeding and regional agricultural production. Previous studies have primarily focused on single crop yield traits, neglecting other crop traits and variety-specific analyses. To address this issue, we employed a Meta-Hybrid Regression Ensemble (MHRE) approach by using multiple machine learning (ML) approaches as base learners, integrating regional multi-year, multi-variety crop field trials with satellite remote sensing indices, meteorological and phenological data to predict major crop traits. Results demonstrated MHRE's optimal performance for rice and cotton, significantly outperforming individual models (RF, XGBoost, CatBoost, and LightGBM). Specifically, for rice crop, MHRE achieved highest accuracy for yield trait (R 2 = 0.78, RMSE = 0.59 t ha -1 ) compared to the best individual model (XGBoost: R 2 = 0.76, RMSE = 0.61 t ha -1 ); traits like effective spike also showed strong predictability (R 2 = 0.64, RMSE = 27.81 10,000·spike ha -1 ). Similarly, for cotton, MHRE substantially improved yield trait prediction (R 2 = 0.82, RMSE = 0.33 t ha -1 ) compared to the best individual model (RF: R 2 = 0.77, RMSE = 0.36 t ha -1 ); bolls per plant accuracy was highest (R 2 = 0.93, RMSE = 2.27 bolls plant -1 ). Moreover, rigorous validation confirmed that crop-specific MHRE models are robust across five rice and three cotton varietal groups and are applicable across six distinct regions in China. Furthermore, we applied the SHAP (SHapley Additive exPlanations) method to analyze the growth stages and key environmental factors affecting major traits. Our study illustrates a practical framework for regional-scale crop traits prediction by fusing multi-source data and ensemble machine learning, offering new insights for precision agriculture and crop management.
Why it matches plant phenotyping methods複数ソースデータとメタ・ハイブリッド回帰アンサンブルにより、イネ・ワタの収量や形態関連形質を推定し、モデル比較と品種群・地域横断検証を行っているため、植物形質推定手法が中心である。
abstractwe employed a Meta-Hybrid Regression Ensemble (MHRE) approach by using multiple machine learning (ML) approaches as base learners, integrating regional multi-year, multi-variety crop field trials with satellite remote sensing indices, meteorological and phenological data to predict major crop traits.
AppleCottonPearField / plotNeRF / 3D Gaussian SplattingFruitCounting2D/3D reconstructionSegmentation
Rigorous crop counting is crucial for effective agricultural management and informed intervention strategies. However, in outdoor field environments, partial occlusions combined with inherent ambiguity in distinguishing clustered crops from individual viewpoints poses an immense challenge for image-based segmentation methods. To address these problems, we introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation. Our approach utilizes 2D images captured from multiple viewpoints and associates independent instance masks for neural radiance field (NeRF) view synthesis. We introduce crop visibility and mask consistency scores, which are incorporated alongside 3D information from a NeRF model. This results in an effective segmentation of crop instances in 3D and highly-accurate crop counts. Furthermore, our method eliminates the dependence on crop-specific parameter tuning. We validate our framework on three agricultural datasets consisting of cotton bolls, apples, and pears, and demonstrate consistent counting performance despite major variations in crop color, shape, and size. A comparative analysis against the state of the art highlights superior performance on crop counting tasks. Lastly, we contribute a cotton plant dataset to advance further research on this topic.
Why it matches plant phenotyping methodsNeRFと3Dインスタンスセグメンテーションを用いて作物個体・器官数を推定する画像ベース表現型計測手法を開発・検証しており、方法が研究の中心である。
abstractwe introduce a novel crop counting framework designed for exact enumeration via 3D instance segmentation.
Reproduction assets foundThe paper contributes a public infield cotton plant dataset (8 plants, ~150 iPhone images each, ground-truth boll counts, SAM instance masks) and states that source code, dataset, and multimedia are available at the authors' public project page, which is an allowed URL. The spectacularai GitHub URL is a generic third-pDataset · publicthat incorporates crop visibility
and mask consistency, enabling robustness against occlusions and annotation
discrepancies.
•
We release a public infield cotton plant dataset designed for 3D
rendering and cotton boll counting tasks.
The source code, dataset, and multimedia material associated with this project
can be found at
https://robotic-vision-lab.github.io/cropnerf .
II Related Work
II-A Image-Based Techniques
Image-based methods typically employ object detection to identify crops within
images. For example, Chen et al. [ 4 ] utilized multiple
convolutional neural networks (CNNs) to map input images to total fruit counts.
Similarly, Häni et al. [ 5 ] formulated crop counting as a
multOpen asset ↗lines:108-187Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jan 2026Computers and Electronics in Agriculture.
Accurately estimating the number of cotton bolls is vital for plant phenotyping, offering essential insights for both breeders and growers. This trait offers valuable phenotypic information on plant productivity and supports crop management decisions to optimize yield and profitability for growers. Manual counting of bolls in the field, however, is impractical because it is labor-intensive and time-consuming. This study presented a video-based cotton boll counting approach that integrated a transformer-based detector (RT-DETR) with multi-object tracking techniques. To prevent double-counting bolls across frames, two motion estimation methods, FlowFormer and TAPIR were explored to predict the movement of bolls between adjacent frames and a two-stage association process combining Intersection over Union (IoU) and Euclidean distances was developed to track bolls across time. To further enhance counting accuracy, a virtual counting line was introduced to reduce ID switch errors. Experimental results demonstrated the effectiveness of the RT-DETR model, achieving an mAP0.5 exceeding 0.93 for dense boll detection. Furthermore, both FlowFormer and TAPIR can be used for tracking cotton bolls in the videos while the tracking performance of the FlowFormer-based method was slightly higher than that of the TAPIR-based method with an MOTA of 73.36 % and an IDF1 of 79.89 %. The tracking approach integrating RT-DETR and FlowFormer exhibited a relatively strong correlation between the predicted and the ground-truth boll number with an R² of 0.60 and an MAPE of 14.34 % on multi-plant plots. In single-plant plots, the approach achieved a high correlation with an R² of 0.97 and a MAPE of 10.33%. These findings indicated the potential of the proposed approach as an effective, automated tool to support breeding programs and yield assessments in cotton production. Both the code and dataset can be accessed at: https://github.com/UGA-BSAIL/Dense_cotton_boll_counting.
Why it matches plant phenotyping methods綿花のボール数という植物生産形質を、動画検出・追跡とロボット収集で自動推定する手法の開発・評価が研究の中心であり、mAP、MOTA、IDF1、R²、MAPEによる技術検証も行っている。
abstractThis study presented a video-based cotton boll counting approach that integrated a transformer-based detector (RT-DETR) with multi-object tracking techniques.
Exposed soil, due to low vegetation cover or in open canopy crops, influences scene reflectance derived from remotely sensed data. An experiment was conducted in College Station, TX, to investigate the potential of six unmanned aerial systems (UASs)‐derived and proximally sensed vegetation indices (VIs) in suppressing soil background brightness of four treatments in 2020 and 2021. The treatments were dry soil, dry soil with winter wheat (Triticum aestivum L.) crop residue, wet soil (WS), and wet soil with winter wheat crop residue (CRWS) in 2020. In 2021, WS and CRWS were replaced with dry sand and dry compost (DC). The VIs were calculated from remotely sensed data of treatment plots. Cotton (Gossypium hirsutum L.) canopy cover (%) on different dates of UAS flight was extracted using unsupervised classification. Factors such as shadows, crop residue, soil moisture, and uneven canopy growth influenced the scene reflectance. The shadow on the soil decreased the soil background reflectance to 30% in 2020. Similarly, higher NDVI was observed for DC treatment plots at an estimated mean canopy cover of <35% in 2021. The perpendicular vegetation index was least influenced by canopy cover or soil background variations. The study suggests that UAS can be used for large‐scale research without being affected by soil variability when vegetation cover is above 30%.
Why it matches plant phenotyping methodsUAS・近接リモートセンシングによる植生指数と作物キャノピー被覆率の抽出を評価し、土壌背景の影響や手法の頑健性を検証しているため、植物表現型取得法が中心である。
abstractinvestigate the potential of six unmanned aerial systems (UASs)‐derived and proximally sensed vegetation indices (VIs) in suppressing soil background brightness
The cotton sector has recently encountered various obstacles, and traditional methods persist in the identification of cotton leaf diseases. This study has established an automated approach for diagnosing cotton leaf blast disease via deep learning methodologies and image processing. The research included deep learning architectures like Convolutional Neural Network, InceptionV3, ResNet50, VGG16, VGG19, and Xception. The extensive collection consists of over 4200 images, including around 3000 depicting cotton leaf blight and 1200 representing healthy leaves. The results demonstrated that the Convolutional Neural Network models InceptionV3, ResNet50, VGG16, and VGG19 attained final validation accuracies of 92.92 %, 64.1 %, 96.81 %, 95.42 %, and 95.97 %, respectively. The ResNet50 approach has exhibited greater accuracy than previous models, whereas the VGG19 model has achieved the second-highest accuracy. This research enhances precision agriculture by delivering a reliable and precise automated approach for predicting cotton diseases. Subsequent inquiries have been undertaken to enhance the precision and efficacy of deep learning models by the incorporation of cutting-edge technologies, including ResNet50, RegNet, EfficientNetB, and Vision Transformers. This study has resulted in a significant enhancement of cotton leaf diseases through identification with the model of surpassing existing leading methodologies in accuracy, complexity, and inference speed. Thus, the creation of these reliable and precise automated diagnostic tools for cotton leaf diseases markedly enhances precision agriculture. The current investigation could equip farmers with a dependable and effective method to detect and mitigate cotton leaf diseases prior to inflicting significant harm on cotton crops.
Why it matches plant phenotyping methods綿花葉の画像から病害状態を深層学習で診断する手法が研究の中心であり、植物病害表現型の画像ベース推定に該当する。
abstractThis study has established an automated approach for diagnosing cotton leaf blast disease via deep learning methodologies and image processing.
Accurate and rapid estimation of aboveground biomass (AGB) in cotton is crucial for precise agricultural management. However, current AGB estimation methods are limited by data homogeneity and insufficient model accuracy, which fail to comprehensively reflect the cotton growth status. This study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB. Two experimental treatments, involving varied nitrogen fertilizer rates and organic manure applications, were conducted from 2022 to 2023. MS data from UAVs were collected across multiple cotton growth stages, while AGB and canopy SPAD values were synchronously measured. Using the coefficient of variation method, SPAD values were coupled with existing vegetation indices to develop a novel vegetation index termed CGSIVI. Moreover, the applicability of various machine learning algorithms—including Random Forest Regressor (RFR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Particle Swarm Optimization-XGBoost (PSO-XGBoost), and Particle Swarm Optimization-CatBoost (PSO-CatBoost)—was evaluated for inverting cotton AGB. The results indicated that, compared to the original vegetation indices, the correlation between the improved vegetation index (CGSIVI) and AGB was enhanced by 13.60% overall, with the CGSICIre exhibiting the highest correlation with cotton AGB (R2 = 0.87). The overall AGB estimation accuracy across different growth stages, spanning the entire growth period, ranged from 0.768 to 0.949, peaking during the flowering stage. Furthermore, when the CGSIVI was used as an input parameter in comparisons of different machine learning algorithms, the PSO-XGBoost algorithm demonstrated superior estimation accuracy across the entire growth stage and within individual growth stages. This high-throughput crop phenotyping analysis method enables rapid and accurate estimation. It reveals the spatial heterogeneity of cotton growth status, thereby providing a powerful tool for accurately identifying growth differences in the field.
Why it matches plant phenotyping methods綿花の地上部バイオマスをUAVマルチスペクトルデータと新規植生指数・機械学習で推定する手法を開発・評価しており、植物形質取得が中心である。
abstractThis study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB.
This study investigates the potential of utilizing nonparametric, nonlinear machine learning (ML) algorithms, in conjunction with vegetation indices (VIs) derived from unmanned aerial vehicles (UAVs), to estimate the height-to-node ratio and the fourth internode length in cotton plants. The objective was to enhance the monitoring of these traits, thereby providing more accurate guidance on the optimal timing of plant growth regulator (PGR) applications. Data was collected from eight plots in our experimental field, with six plots used for model training and two for testing. During model development, the performance was assessed using nested 5-fold cross-validation, repeated three times with different partitions. For each algorithm, hyperparameters were tuned on the inner folds via Bayesian optimization with a Gaussian process surrogate, and the tuned model was evaluated on the corresponding outer test fold. We evaluated the performance of the ML algorithms using the Friedman test and interpreted their result using the Wilcoxon signed-rank test. The results demonstrate that VIs, combined with ML algorithms, can reliably estimate both the height-to-node ratio and the length of the fourth internode. Additionally, among the tested ML algorithms, Support Vector Regression (SVR) demonstrated superior performance for predicting height-to-node ratio, with an R² value of 0.8257 (95% CI: 0.7404 - 0.9110), RMSE value of 0.0998 (95% CI: 0.0953 - 0.1044), and rRMSE value of 5.51 (95% CI: 5.30 - 5.7). Meanwhile, the CatBoost demonstrated higher performance in estimating the fourth internode length, with an R² value of 0.799 (95% CI: 0.7570 - 0.8415), an RMSE of 0.1788 (95% CI: 0.1631 - 0.1945), and a rRMSE of 10.64 (95% CI: 9.90 - 11.38). Furthermore, using the Shapley Additive exPlanations (SHAP) approach, we revealed the contribution of each of the VI to the model's prediction. Overall, the findings demonstrate that UAV-derived VIs, combined with a machine learning algorithm, can consistently estimate these cotton traits. Additionally, this approach can replace traditional field-based measurements, thereby supporting more efficient monitoring and precise PGR management decisions.
Why it matches plant phenotyping methodsUAV由来の植生指数と機械学習により綿花の節間長・節位比を推定し、交差検証やアルゴリズム比較で性能を評価しており、形質取得手法が研究の中心である。
abstractThis study investigates the potential of utilizing nonparametric, nonlinear machine learning (ML) algorithms, in conjunction with vegetation indices (VIs) derived from unmanned aerial vehicles (UAVs), to estimate the height-to-node ratio and the fourth internode length in cotton plants.
Precision agriculture progressively relies on remote sensing (RS) technologies to enhance crop classification and monitoring. Among various RS platforms, spectroradiometer offers the highest spectral precision, making them essential for validating the accuracy and performance of other RS methods. Each crop exhibits a unique spectral signature that corresponds to its biophysical characteristics. This spectral information plays a crucial role in accurately classifying crop types and assessing their health status, including water and nutrient availability. Specifically, evaluating crop chlorophyll content enables effective nitrogen management and yield optimization. This study focuses on collecting spectral data using a spectroradiometer (350-1050 nm) at a height of 30 cm above the crop canopy from eight crops, i.e., rice, finger millet, cotton, sunflower, sweet corn, broccoli, cauliflower, and brinjal, classifying the collected data, and measuring chlorophyll content using a Soil Plant Analysis Development (SPAD) meter and predicting the same using key spectral bands and machine learning (ML) techniques. Six supervised ML algorithms, i.e., Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Light Gradient-Boosting Machine (LGBM), Extreme Gradient Boosting (XGBoost), and Multi-Layer Perceptron (MLP) were employed for crop classification. The feature selection process revealed that the spectral range of 710-750 nm is the most significant for crop classification. The MLP model achieved the highest accuracy of 97% during training, 93% in testing, and 85% during validation stage, outperforming other ML classifiers. For chlorophyll content prediction, the RF demonstrated the best performance, with coefficient of determination values of 0.92 for training and 0.72 for testing stage. The ML-based framework, developed in this study, can be applied to various RS platforms, including satellites and unmanned aerial vehicles (UAVs), for crop classification and prediction of chlorophyll content. The developed modelling framework would assist government agencies and policymakers in identifying crop types accurately, enhancing agricultural planning, and optimizing resource allocation to support sustainable on-farm practices.
Why it matches plant phenotyping methods分光反射センシングと機械学習により作物のクロロフィル含量という植物形質を推定する枠組みが研究の中心であり、モデル性能の検証も行っているため。
abstractpredicting the same using key spectral bands and machine learning (ML) techniques
Introduction Cotton is a vital global economic crop and textile material, yet its yield and quality are threatened by leaf diseases such as brown spot, verticillium wilt, wheel spot, and fusarium wilt. Methods We propose ViTKAB, a cotton disease recognition model based on an enhanced Vision Transformer that integrates a Kolmogorov-Arnold network and a BiFormer module. The model optimizes the Vision Transformer architecture to improve inference speed, employs nonlinear feature representation to better capture complex disease characteristics, and incorporates sparse dynamic attention to enhance robustness and accuracy. Results Experiments show that ViTKAB achieves an average recognition accuracy of 98.05% across four cotton leaf diseases, outperforming models such as CoAtNet-7, CLIP, and PaLI. Conclusions This method offers valuable insights for advancing intelligent crop disease detection systems and exhibits strong potential for deployment on edge devices.
Why it matches plant phenotyping methods綿花葉の病害状態を画像から認識する深層学習モデルを開発・評価しており、植物病害フェノタイピング手法が中心である。
abstractWe propose ViTKAB, a cotton disease recognition model based on an enhanced Vision Transformer that integrates a Kolmogorov-Arnold network and a BiFormer module.
This study introduces a hybrid deep learning model that combines CNN-based hierarchical feature extraction with light-efficient vision transformer self-attention to classify multiple types of cotton leaf diseases and fabric defects. Using Explainable AI (XAI) techniques, the framework enhances interpretability, allowing domain experts to better understand the model's decisions. Evaluated on four benchmark datasets, the proposed XCottL-FebViT achieved consistent improvements in accuracy, MCC, and F1 Score compared with leading transformer-based models, while maintaining computational efficiency through hyperparameter optimization. For CottonLeafNet and SAR-CLD, it attained training accuracies of 99.97% and 99.95%, with validation accuracies of 99.93% and 99.91%, respectively. In fabric defect classification, the model achieved 99.97% training accuracy on CottonFabricImageBD and FabricSpotDefect, with validation accuracies of 99.93% and 99.95%, respectively. A lightweight web-based application enables practical deployment for remote disease and defect detection. This work highlights the integration of interpretability, efficiency, and high performance in AI-driven agricultural and textile quality assessment.
Why it matches plant phenotyping methods綿花葉の病害を画像から分類する深層学習手法の開発・比較評価が中心であり、植物の病害状態を直接推定するため、フェノタイピング方法論として採用する。
abstractThis study introduces a hybrid deep learning model that combines CNN-based hierarchical feature extraction with light-efficient vision transformer self-attention to classify multiple types of cotton leaf diseases and fabric defects.
Reproduction assets foundThe paper's cotton leaf disease image datasets (CottonLeafNet, SAR-CLD-2024) are publicly available and directly used as phenotyping inputs, and the authors' analysis code is publicly deposited on GitHub and archived on Zenodo. Fabric defect datasets are excluded as non-plant assets; generic PyPI libraries are excludedCode · publicCode: Source code of the study is available at https://github.com/rezaul-h/CottonVerse.Open asset ↗github · rezaul-h/CottonVersehtml-lines:2058-2083Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Cotton has, in recent years, become one of the most important cash crops worldwide while being impacted in yield from leaf disease which generally goes unnoticed in the early stage. Detection methods depend on manual efforts producing slow processes and human errors. Automated detection methods establish low accuracies, limited scalability and real time applications. To tackle the research issue, this study proposes the CLD-Net which stands for Cotton Leaf Disease Detection Network a novel deep learning-based framework which combines Faster-RCNN and YOLOv5 algorithms into a single action to achieve ultimately real time detection of accurate diseases the combination helps identify both the high detection speed of YOLOv5 along with Faster-RCNN regional proposal accuracy. The new method is that the compilation of these two modern object detection methods has been compiled and designed specifically for detecting leaf disease across varying environmental conditions. Notable contributions to this method include increases in classification accuracy, processing speed, real time detection making these methods suitable for farmers agronomists and sensor deployment. CLD-Net integrates YOLOv5 and Faster R-CNN, combining real-time detection capability with precise classification, to deliver robust cotton leaf disease identification. Experimental validation on a curated dataset of cotton leaf images demonstrates the superiority of CLD-Net, achieving an accuracy of 96.7%, which surpasses that of traditional models. These results confirm the potential of the proposed approach to revolutionize crop disease detection, leading to timely intervention and increased yield.
Why it matches plant phenotyping methods綿花葉の病害状態を画像から推定する深層学習手法を開発し、画像データセットで性能検証しており、植物フェノタイピング手法が中心です。
abstractthis study proposes the CLD-Net which stands for Cotton Leaf Disease Detection Network a novel deep learning-based framework which combines Faster-RCNN and YOLOv5 algorithms
Reproduction assets foundThe paper's Data Availability statement points to a public Kaggle cotton leaf disease image dataset used for the CLD-Net experiments; no author code or trained model deposit with a public URL is provided despite a mention of 'reproducible code and trained models'.Dataset · publicThe data used in this research are available in the following links: https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset.Open asset ↗Kaggle · seroshkarim/cotton-leaf-disease-datasethtml-lines:541-573Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Dec 2025Artificial Intelligence in Agriculture
CottonRiceRootMorphology / geometry measurementSegmentationRoot system architecture
Beyond its fundamental roles in nutrient uptake and plant anchorage, the root system critically influences crop development and stress tolerance. Rhizobox enables in situ and nondestructive phenotypic detection of roots in soil, serving as a cost-effective root imaging method. However, the opacity of the soil often results in intermittent gaps in the root images, which reduces the accuracy of the root phenotype calculations. We present a root inpainting method built upon Generative Adversarial Networks (GANs) architecture In addition, we built a hybrid root inpainting dataset (HRID) that contains 1206 cotton root images with real gaps and 7716 rice root images with generated gaps. Compared with computer simulation root images, our dataset provides real root system architecture (RSA) and root texture information. Our method avoids cropping during training by instead utilizing downsampled images to provide the overall root morphology. The model is trained using binary cross-entropy loss to distinguish between root and non-root pixels. Additionally, Dice loss is employed to mitigate the challenge of imbalanced data distribution Additionally, we remove the skip connections in U-Net and introduce an edge attention module (EAM) to capture more detailed information. Compared with other methods, our approach significantly improves the recall rate from 17.35 % to 35.75 % on the test dataset of 122 cotton root images, revealing improved inpainting capabilities. The trait error reduction rates (TERRs) for the root area, root length, convex hull area, and root depth are 76.07 %, 68.63 %, 48.64 %, and 88.28 %, respectively, enabling a substantial improvement in the accuracy of root phenotyping. The codes for the EU-GAN and the 8922 labeled images are open-access, which could be reused by researchers in other AI-related work. This method establishes a robust solution for root phenotyping, thereby increasing breeding program efficiency and advancing our understanding of root system dynamics.
Why it matches plant phenotyping methods根画像の欠損を補完するGAN手法と再利用可能なデータセットを開発・評価し、根形態形質の推定誤差改善を実証しており、植物フェノタイピング手法が中心である。
abstractWe present a root inpainting method built upon Generative Adversarial Networks (GANs) architecture
Cost-effective remote sensing solutions are critically needed to democratize precision agriculture technologies. While hyperspectral and LiDAR systems deliver high accuracy, their prohibitive costs limit widespread adoption. This study demonstrates that systematic multi-modal feature integration transforms standard UAV-based RGB imagery into a powerful phenotyping instrument, achieving crop trait prediction accuracy comparable to systems costing 10–50 times more. We developed a comprehensive framework integrating spectral indices, geometric parameters, and texture metrics from commodity RGB sensors to predict five critical cotton traits: leaf area index (LAI), intercepted photosynthetically active radiation (IPAR), above-ground biomass, lint yield, and seed cotton yield. The progressive integration approach employed Random Forest regression with four feature configurations: baseline color indices (CIbₐₛₑ), refined color indices (CIᵣₑf), geometric parameters (CIᵣₑf + GP), and texture metrics (CIᵣₑf + GP + T). Field experiments across three trials over two growing seasons (2022–2023) with varying genotypes, planting densities, and sowing dates provided 2,126 ground truth measurements for model development and validation. The optimal multi-modal model achieved R² = 0.97 for IPAR (rRMSE = 6 %), R² = 0.91 for LAI (rRMSE = 15 %), and R² = 0.85 for biomass (rRMSE = 32 %), with lint yield and seed cotton yield demonstrating R² values of 0.92 and 0.77, respectively. Variance partitioning analysis revealed texture features as the dominant contributor (16.2 % ± 7.1 %), followed by spectral indices (9.1 % ± 4.2 %) and geometric parameters (8.0 % ± 2.8 %), with substantial shared variance (45–65 %) indicating strong feature complementarity. Phenological analysis demonstrated that flowering-stage imagery outperformed boll opening stage measurements, while stage-general models showed superior robustness. Cross-temporal validation confirmed model generalizability, with trial-general models achieving R² values of 0.91–0.97 for IPAR across diverse environmental conditions. The framework enables sub-meter spatial resolution trait mapping while maintaining operational simplicity and cost-effectiveness, demonstrating that systematic feature engineering can democratize high-precision phenotyping technologies for broader agricultural applications.
Why it matches plant phenotyping methodsUAV-RGB画像から複数の綿形質を推定する特徴統合フレームワークを開発・検証しており、形質取得・抽出手法が研究の中心である。
abstractThis study demonstrates that systematic multi-modal feature integration transforms standard UAV-based RGB imagery into a powerful phenotyping instrument
Cotton is a significant broadacre crop globally, and monitoring its growth is crucial for improving agricultural productivity. With the development of unmanned aerial vehicle (UAV) remote sensing technology, the inversion of cotton growth parameters (including plant height (PH), and leaf chlorophyll content (LCC), leaf area index (LAI), above-ground biomass (AGB)) from remote sensing data has emerged as a prominent research area. To address the issue of limited accuracy in traditional stacking algorithm for remote sensing inversion, this study proposes an enhanced stacking algorithm (ESA). First, multi-source remote sensing data is acquired using UAV equipment equipped with an integrated payload of a LiDAR sensor and a visual RGB camera and raw features are extracted from the data. Then, principal component analysis (PCA) is used to reduce the dimensionality of these features. The model construction is optimized through the following steps: first, explore all feature combinations and train each using multi-class learners to construct the entire set of base models; second, remove over- or under-fitting models to build a candidate pool; third, introduce iterative screening to the pool—each round incorporates the algorithm with the greatest performance gain and removes those with negative contributions, iterating to construct an efficient subset of base models; finally, RidgeCV is used to fuse base-model outputs. The experimental results show that ESA outperforms other traditional methods in terms of prediction performance for the four growth parameters. Specifically, on the test set, the R2 values for PH, LCC, LAI, and AGB are 0.9320, 0.8015, 0.8638, and 0.8272 , respectively. Compared with the second-best model, the relative improvement is approximately 4.6% (PH), 3.6% (LCC), 7.5% (LAI), and 11.2% (AGB) . ESA offers an effective approach for high-precision inversion of cotton growth parameters, providing new insights for the precision management of other crops.
Why it matches plant phenotyping methodsUAVのLiDAR・RGBデータから綿花の複数生育形質を推定するスタッキングアルゴリズムを開発し、他手法と性能比較しており、表現型取得・推定法が中心です。
abstractthis study proposes an enhanced stacking algorithm (ESA)
Cotton fiber friction is crucial in regulating fiber flow during spinning and can influence yarn quality. Cotton wax acts as a natural lubricant and may impact fiber-to-fiber friction. Previously, a new method was developed for measuring cotton fiber-to-fiber friction. In the present study, the new method was employed to investigate the effects of noncellulosic materials on fiber-to-fiber friction using white, green, and brown cotton samples. All three cotton samples were subjected to xylene treatments under varying sample forms, durations, and temperatures to remove wax and other noncellulosic components. The new method was then used to measure fiber-to-fiber friction of both control and treated samples. Xylene treatment resulted in increased friction values across all cotton types. Fourier Transform Infrared (FTIR) spectroscopy was used to evaluate changes in the surface chemistry due to xylene treatment. The FTIR analysis showed a reduction in noncellulosic materials, primarily wax, in treated samples. Furthermore, the principal component analysis (PCA) of FTIR data revealed a clear distinction between control and treated samples, supporting the reduction of noncellulosic materials. Overall, wax reduction resulted in higher friction values of xylene-treated samples, and the new method effectively measured this, demonstrating further validation of its effectiveness for cotton fiber-to-fiber friction measurement.
Why it matches plant phenotyping methods綿繊維間摩擦という植物由来器官の物性を測定する新手法の有効性を、処理試料との比較で検証しており、測定法が研究の中心である。
abstractPreviously, a new method was developed for measuring cotton fiber-to-fiber friction.
Verticillium wilt poses a severe threat to cotton growth and significantly impacts cotton yield. It is of significant importance to detect Verticillium wilt stress in time. In this study, the effects of Verticillium wilt stress on the microstructure and physiological indicators (SOD, POD, CAT, MDA, Chlₐ, Chlb, Chlₐb, Car) of cotton leaves were investigated, and the feasibility of utilizing hyperspectral imaging to estimate physiological indicators of cotton leaves was explored. The results showed that Verticillium wilt stress-induced alterations in cotton leaf cell morphology, leading to the disruption and decomposition of chloroplasts and mitochondria. In addition, compared to healthy leaves, infected leaves exhibited significantly higher activities of SOD and POD, along with increased MDA amounts, while chlorophyll and carotenoid levels were notably reduced. Furthermore, rapid detection models for cotton physiological indicators were constructed, with the Rₚ of the optimal models ranging from 0.809 to 0.975. Based on these models, visual distribution maps of the physiological signatures across cotton leaves were created. These results indicated that the physiological phenotype of cotton leaves could be effectively detected by hyperspectral imaging, which could provide a solid theoretical basis for the rapid detection of Verticillium wilt stress.
Why it matches plant phenotyping methods綿葉の生理指標をハイパースペクトル画像から推定・可視化するモデルを構築しており、植物フェノタイピング手法の開発と応用が中心である。
abstractthe feasibility of utilizing hyperspectral imaging to estimate physiological indicators of cotton leaves was explored
India is an agro-based country. The major goal of agriculture is to produce disease-free healthy crops. For Indian agronomists, cotton is a profitable commercial and fibre crop, it is the world's second-biggest export crop after China. Cotton production is also affected in a negative way by high use of water, authority of soil erosion and the practice of using dangerous fertilizers and pesticides. The two greatest threats to the rapid growth of the crop are the sucking bugs and cotton diseases. Prompt detection and accurate identification of diseases is vital to ensure healthy crop growth and achieve better yields. The primary objective of this research is to build a model by implementing deep learning-based approaches to spot infections in cotton crops. Deep learning is used because of its exceptional results in classification and image processing tasks. To address this issue, we developed CottonNet-MHA a novel deep learning framework to identify pathological symptoms in cotton leaves. The model employs multi-head attention mechanisms to strengthen feature learning and highlight the diseased-affected regions. To evaluate the performance of the proposed model, five pretrained transfer learning architectures-VGG16, VGG19, InceptionV3, Xception, and MobileNet were used as benchmark models. Furthermore, Gradient-weighted Class Activation Mapping (Grad-CAM) visualization was applied to enhance the trustworthiness and interpretability of the model. A web-based application was developed to deploy the trained model for real-world applicability. The performance analysis is carried out on the developed model based on the conventional models and the results indicate that CottonNet-MHA dominates the conventional models with respect to its accuracy as well as efficiency in the detection of diseases. The use of attention mechanisms approach strengthens the model's diagnostic accuracy and overall reliability. Grad-CAM results further demonstrated that the model effectively targets diseased areas, enhancing interpretability and reliability. Discussion: The study shows that CottonNet-MHA not only automates disease detection but also enhances interpretability through Grad-CAM analysis. The developed web platform allows the model to be applied in real-world environments, supporting live disease monitoring. The proposed framework not only improves the accuracy of cotton disease diagnosis but also offers potential for extension to other crop disease detection systems.
Why it matches plant phenotyping methods綿花葉の病徴を画像から検出・分類する深層学習手法を開発し、既存モデルとの比較検証とGrad-CAMによる病変領域の解釈を行っており、植物病害状態の表現型取得が中心である。
abstractThe primary objective of this research is to build a model by implementing deep learning-based approaches to spot infections in cotton crops.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset used in this work is downloaded from Akash Zade (Data Scientist) which is openly accessible and can be found at: https://drive.google.com/drive/folders/1vdr9CC9ChYVW2iXp6PlfyMOGD-4Um1ue.Open asset ↗Akash Zadehtml-lines:312-354Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Cotton, as a globally important economic crop, requires early and accurate disease detection to ensure stable yield and promote sustainable development. However, due to the small size of certain leaf lesions, traditional detection methods often suffer from missed or false detections. To address this issue, we propose an improved YOLOv8-based model, CM-YOLO, aimed at enhancing the detection performance for small cotton leaf disease targets. Specifically, the SS2D module from VMamba is introduced into the backbone network to achieve comprehensive feature extraction through multi-directional scanning. Furthermore, the MSDA module is embedded prior to the SPPF module to reduce performance degradation caused by redundant computations and to enhance the model's focus on critical small targets. Finally, the original bounding box loss function is replaced with DIoU, enabling precise localization of small targets by optimizing anchor center point distances and accelerating model convergence. Experimental results demonstrate that CM-YOLO achieves superior performance in cotton leaf disease detection, with an mAP50 of 0.933 and a recall of 0.891. Compared with state-of-the-art methods, YOLOv8n and YOLOv11n achieve mAP50 values of 0.874 and 0.930, respectively, both lower than CM-YOLO, thereby validating the effectiveness of the proposed method. Additionally, generalization experiments indicate that the model maintains high detection accuracy and robustness across different plant datasets, highlighting its strong applicability in complex scenarios and providing a valuable reference for intelligent agricultural disease detection research.
Why it matches plant phenotyping methods綿花葉の病斑という植物の病害状態を画像から検出・局在化するYOLOベース手法を開発し、性能比較と汎化実験で検証しており、フェノタイピング手法が中心である。
abstractwe propose an improved YOLOv8-based model, CM-YOLO, aimed at enhancing the detection performance for small cotton leaf disease targets.
Early monitoring of crop development is crucial for precision agriculture, particularly for detecting acute stress and ensuring maximum yield potential. This study introduces a robust, high-throughput phenotyping framework that combines deep learning with biologically grounded feature engineering to classify cotton seedling growth stages and detect early stress signals in real-world field conditions. At its core is SeedlingNet, a lightweight residual CNN trained on a rigorously curated dataset of high-resolution UAV and ground-level images. We extract handcrafted phenotypic features like area, greenness, solidity, and texture and merge them into a Composite Stress Index (CSI) that integrates multivariate stress signals in order to improve biological interpretability. By using clustering to divide the field into intervention tiers, this CSI not only makes it possible to identify at-risk seedlings early on, but also facilitates data-driven zone management. According to experimental data, the CSI obtains considerable stage-wise separation and reliable ROC-AUC performance, while the proposed model achieves a classification accuracy of 97.32% with strong F1 and precision-recall balance. The suggested pipeline is ideal for high-throughput phenotyping and adaptive crop management since it can perform supervised classification and unstructured growth pattern identification. By fusing model performance with biological understanding and useful deployability, this work advances the area of image-based plant monitoring. • Developed SeedlingNet, a CNN model that achieved 97.32% accuracy in growth stage classification. • Introduced CSI to detect early stress using phenotypic features and clustering. • Created a diverse cotton seedling dataset via UAV and ground-based imaging. • Integrated deep learning with interpretable traits for precision agriculture use.
Why it matches plant phenotyping methods綿花幼苗の画像から生育段階とストレス関連形質を抽出・分類する高スループット表現型解析フレームワークを開発しており、画像取得、特徴量設計、CNN、データセット、検証が研究の中心である。
abstractThis study introduces a robust, high-throughput phenotyping framework that combines deep learning with biologically grounded feature engineering to classify cotton seedling growth stages and detect early stress signals in real-world field conditions.
Cotton diseases and pests pose significant threats to cotton production, necessitating accurate and efficient classification methods. Despite existing advanced methods, there is a research gap in utilizing both local feature extraction and global context capture for enhanced classification accuracy. Hence, this study developed and evaluated three advanced models for cotton disease and pest classification: a convolutional neural network (CNN)-based model, a Vision Transformer (ViT)-based model, and a hybrid CNN-ViT model. These models were trained on a dataset comprising eight classes of cotton diseases and pests, namely aphids, armyworm, bacterial blight, cotton boll rot, green cotton boll, healthy, powdery mildew, and target spot. The results demonstrated that the hybrid CNN-ViT model achieved the highest overall performance with an average test accuracy of 98.5%. The CNN model showed strong performance with an average accuracy of 97.9%. The ViT models, while having self-attention mechanisms to capture context and dependencies, exhibited improved performance with increased depth. The ViT model having four transformer layers outperformed the two-layer variant, achieving an average accuracy of 97.2% compared to 96.3%. The hybrid model effectively combined the strengths of CNN's local feature extraction and ViT's global feature capture, resulting in superior classification accuracy across most classes. Future research should focus on expanding the dataset to include more diverse diseases and pests and integrating the models with autonomous platforms for spraying the chemicals, thus facilitating real-world adoption and application in agricultural settings.
Why it matches plant phenotyping methods綿花の病害・害虫を画像から分類するCNN、ViT、ハイブリッド手法を開発・評価しており、植物の病害状態推定が研究の中心である。
abstractthis study developed and evaluated three advanced models for cotton disease and pest classification
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset for this study was downloaded from Kaggle ( https://www.kaggle.com/datasets/saeedazfar/customized-cotton-disease-dataset ) and comprises images of various cotton diseases and pests.Open asset ↗Kaggle · saeedazfar/customized-cotton-disease-datasetlines:78-87Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Nov 2025Computers and Electronics in Agriculture.
Verticillium wilt (VW) is a highly detrimental disease of cotton that causes significant reductions in yield and fiber quality. Efficient and accurate screening of VW-resistant varieties is essential for cotton breeding and production. However, traditional identification methods, such as manual observation, are inefficient and costly. Unmanned aerial vehicle (UAV) and remote sensing technologies have opened new insights into the screening of field crops for disease-resistant germplasm. This study utilized a UAV multispectral platform to collect data from five growth stages of 150 cotton varieties with different VW resistances. The normalized difference vegetation index (NDVI) was identified as a reliable predictor of chlorophyll levels through hierarchical segmentation analysis. We further compared four deep learning models for chlorophyll monitoring: 1D-CNN, CNN-BiLSTM, CNN-BiLSTM-Adaboost, and CNN-BiLSTM-Attention, with the CNN-BiLSTM-Attention model performing best (R² = 0.92). The optimum model was then used to invert the extent of VW infection using single- and multi-period chlorophyll, and the latter was found to have the best results with the highest R² value of 0.96. Multidimensional clustering of chlorophyll content over multiple periods was used to screen different cotton VW-resistant germplasm, and the ISODATA cluster method outperformed the other three methods (K-means, K means++, and GMM). This study highlights that combining a UAV multispectral platform with an accurate chlorophyll inversion model can enable high-throughput assessment of the cotton VW infection in the field, providing a powerful tool for screening cotton VW-resistant germplasm and thus supporting cotton breeding efforts.
Why it matches plant phenotyping methodsUAVマルチスペクトル計測、クロロフィル推定モデル、感染程度の推定、抵抗性判別を技術的に比較・評価しており、植物表現型取得手法が研究の中心である。
abstractThis study utilized a UAV multispectral platform to collect data from five growth stages of 150 cotton varieties with different VW resistances.
Accurate assessment of cotton defoliation (DF) and boll opening (BO) is essential for optimizing yield and fiber quality during mechanized harvesting, as improper timing can reduce yield and impair fiber quality. Unmanned aerial vehicle (UAV)-based remote sensing has become an effective tool for monitoring these indicators, but most current methods rely on single-sensor data, limiting diagnostic accuracy and generalizability. To address this limitation, we propose a multi-source data fusion framework integrating RGB, multi-spectral (MS), and thermal infrared (TIR) sensors for comprehensive canopy information. The fused dataset includes vegetation indices (VIs), color indices (CIs), texture features (Tex), and canopy temperature (TC). Feature selection was performed using pearson correlation coefficients (PCCs), recursive feature elimination with cross-validation (RFECV), and the Boruta algorithm to identify key variables. Three machine learning models—partial least-squares regression (PLSR), random forest regression (RFR), and extreme gradient boosting regression (XGBR)—were developed and compared. The RFECV-selected RGB+MS+TIR features in the XGBR model achieved the highest predictive accuracy, with R² values of 0.918 for defoliation rate and 0.867 for boll opening rate, improving by 1.9 % and 4.3 %, respectively, over single-sensor models. Root mean square error (RMSE) and relative RMSE (rRMSE) were reduced by 1.11 %-1.99 % and 1.66 %-2.28 %, respectively. These findings demonstrate that multi-source UAV data fusion, combined with advanced machine learning techniques, significantly enhances the accuracy and robustness of cotton defoliation and boll opening diagnosis. This approach offers a practical solution for precision agriculture to improve harvest scheduling and defoliant management.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル・熱赤外データを融合し、綿花の落葉率と綿花開絮率という植物状態を推定する手法を開発・比較しており、フェノタイピング手法が研究の中心である。
abstractwe propose a multi-source data fusion framework integrating RGB, multi-spectral (MS), and thermal infrared (TIR) sensors for comprehensive canopy information.
Accurate monitoring of cotton water status is crucial for optimizing irrigation management and improving water-use efficiency in precision agriculture. UAV-based remote sensing offers high-resolution, flexible, and efficient data acquisition for agricultural monitoring, presenting significant potential for assessing crop water stress. However, existing approaches often treat spectral and texture features separately, overlooking their complementary nature across resolutions. This increases model complexity and reduces generalizability across phenological stages. To address these limitations, we propose a Dual-Cycle Cognitive Learning (DCCL) framework that integrates multi-resolution vegetation indices and texture features through a two-stage interpretable training pipeline. In the first stage, all extracted features are fed into a random forest model, and their contributions are quantified using SHapley Additive exPlanations (SHAP). The top 20 SHAP-ranked features are further refined using Recursive Feature Elimination (RFE) to select the 10 most informative features. These features are reintroduced into a pretrained model to form a distilled final monitoring model in the second stage, enhancing interpretability and cross-scale monitoring accuracy. Knowledge distillation further facilitates feature integration across different resolutions and growth stages, eliminating the need for manual feature engineering. Experiments conducted on real UAV datasets demonstrate the effectiveness of the proposed DCCL framework. It achieved a training R² of 0.9577 and an RMSE of 0.0026, while maintaining a cross-validation R² of 0.6514 on unseen datasets. In contrast, the baseline random forest model yielded a training R² of 0.9484 but a considerably lower cross-validation R² of 0.4268. These results confirm the improved robustness and generalizability of our approach under real-world field conditions. The DCCL framework offers a scalable, interpretable, and high-precision solution for UAV-based cotton water status monitoring, with significant potential to support sustainable irrigation strategies and intelligent crop management in large-scale agricultural systems.
Why it matches plant phenotyping methodsUAV画像の植生指数・テクスチャからワタの水分状態を推定する解釈可能な学習手法を開発し、実データ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。
abstractwe propose a Dual-Cycle Cognitive Learning (DCCL) framework that integrates multi-resolution vegetation indices and texture features through a two-stage interpretable training pipeline.
The low spatial resolution of satellite remote sensing has become a major limiting factor in monitoring crop growth. Existing studies have effectively enhanced the resolution of remote-sensing imagery through super-resolution reconstruction (SR) techniques. Although these methods demonstrate good generalizability across different scenarios, they do not adequately address the anisotropic spatial structural characteristics of agricultural fields, particularly the influence of crop ridge orientation distribution on reconstruction outcomes. To address this issue, this study developed a super-resolution direction-aware generative adversarial network (SRDGAN) that incorporates multi-attention mechanisms and orientation-aware convolutions specifically designed for RGB satellite imagery of cotton farmlands. Large-scale monitoring models for defoliation and boll-opening rates were developed using feature selection and machine-learning techniques. The key findings include the following: (1) Significant correlations exist between features (vegetation indices, color components, and texture features) extracted from RGB satellite images and both defoliation and boll-opening rates. (2) A comparative analysis of SR methods revealed a progressive improvement in accuracy in the order Bicubic < EDSR< HAT< SRGAN < SRDGAN. (3) The RFE-XGBoost model achieved the optimal defoliation-rate monitoring accuracy using SRDGAN-enhanced imagery, whereas the RF-XGBoost model achieved the optimal boll-opening-rate monitoring accuracy. This study innovatively addresses the inherent conflict between spatial resolution and coverage range by incorporating directional perception into traditional GAN frameworks. The proposed methodology enables high-spatial resolution and large-scale precision management of cotton defoliation effects, providing crucial technical support for ensuring high-quality and efficient mechanical cotton harvesting in Xinjiang's modern agricultural systems.
Why it matches plant phenotyping methods綿花の脱葉率・吐絮率という植物状態を衛星画像から推定する超解像および機械学習手法を開発・比較しており、表現型取得・推定が研究の中心である。
abstractthis study developed a super-resolution direction-aware generative adversarial network (SRDGAN) that incorporates multi-attention mechanisms and orientation-aware convolutions specifically designed for RGB satellite imagery of cotton farmlands.
Current cotton disease detection technologies often rely on complex computations and large parameter counts to achieve high accuracy. However, the trade-off between precision and resource demands constrains their applicability in smart agriculture. To address this limitation, a lightweight Feature Dynamic Reweighting Network (FDRW-Net) is proposed based on the YOLO11 model for cotton disease detection in natural scenes, enhancing detection performance while reducing resource consumption. First, an Enhanced Multi-Scale Spatial Attention (EMSA) mechanism is developed and integrated into a novel Information Sharing Head (ISH). Multi-scale dynamic sensing capability is enhanced and background noise is suppressed through this design. Then, an Adaptive Downsampling Module (ADM), based on ADown, incorporates adaptive weight allocation and input feature weighting, effectively reducing computational costs. Finally, the C3T combining MetaFormer and a Convolutional Gated Linear Unit (CGLU) is introduced into the feature extraction framework. Limitations of convolutional neural networks are addressed and sensitivity to fine-grained features is enhanced by this structure. The performance of the proposed model was evaluated on a self-built dataset containing fusarium wilt, brown spot disease, verticillium wilt, and red leaf blight. Two public datasets were also included in this assessment. Results from the self-built dataset indicate that mAP50 is improved by 3.2 % to 93% compared to the baseline network. Parameters, floating-point operations, and model size are reduced by 46.5 %, 39.7 %, and 43.4 %, respectively, in this evaluation. Experiments on public datasets further validate that the proposed model outperforms existing cotton disease detection methods in overall performance. Based on this improved model, a real-time cotton disease diagnosis system was developed for field monitoring and early warning.
Why it matches plant phenotyping methods綿花の病害状態を自然画像から推定する軽量画像解析モデルを開発し、複数データセットで性能検証しているため、植物病害フェノタイピング手法が中心です。
abstracta lightweight Feature Dynamic Reweighting Network (FDRW-Net) is proposed based on the YOLO11 model for cotton disease detection in natural scenes
In the development of precision agriculture, accurate monitoring of growth dynamics and yield prediction are important for optimizing the cotton production. However, the reliability of yield prediction for different cotton accessions based on monitoring chlorophyll dynamics using remote sensing techniques remains to be further improved. In this study, field images of 419 cotton accessions were collected at eight time points using an unmanned aerial vehicle (UAV) equipped with RGB and multispectral sensors. Four vegetation indices (VIs) and three color indices (CIs) highly correlated with leaf chlorophyll content (LCC) were screened by a hierarchical segmentation method. Subsequently, four machine learning algorithms were used to construct a chlorophyll prediction model. The random forest (RF) model outperformed the ridge regression (RR), support vector machine (SVM), and partial least squares regression (PLSR) models in predicting the LCC with the best accuracy (R² = 0.827) after fusing the VIs and CIs. Yield prediction was then performed using multiple linear regression, with LCC data as predictors from the squaring, flowering, boll development, and boll opening stages (as well as a combined multi-stage input). The model using multi-stage LCC data achieved the highest accuracy (R² = 0.723). Finally, the K-means algorithm was successfully used to demonstrate a strong association between LCC profiles and yield outcomes of 419 cotton accessions. This study fused RGB and multispectral data to predict multiple-stage LCC data and construct a yield prediction model, which was innovatively applied to 419 cotton accessions with rich genetic diversity for the first time. It is superior to methods relying on a single remote sensing dataset or applied to a few genetic materials, providing an innovative method for large-scale phenotypic analysis of cotton varieties and a scientific basis for promoting cotton germplasm screening and precision breeding.
Why it matches plant phenotyping methodsUAVのRGB・マルチスペクトル画像と機械学習により、綿の葉緑素含量を推定する方法を開発・比較検証し、多数系統への大規模表現型解析に適用しているため、フェノタイピング手法が中心である。
abstractfield images of 419 cotton accessions were collected at eight time points using an unmanned aerial vehicle (UAV) equipped with RGB and multispectral sensors.
Accurate monitoring of chlorophyll content is of great significance for the sustainable development of precision agriculture. However, currently the widely used vegetation indices (VIs) for estimating field chlorophyll content are susceptible to external factors such as soil, vegetation, and weeds. Texture features can extract more information about crop growth from the spatial distribution of images to mitigate the limitations of VIs. Therefore, this study combines vegetation indices (VIs) extracted from remote sensing images with texture information (TFs) and utilizes four machine learning methods—partial least squares regression (PLSR), elastic net regression (ENR), random forest regression (RFR), and extreme gradient boosting regression (XGBoost)—to construct a model for inverting the chlorophyll content of cotton canopy leaves. The single model (VIs) was compared with the coupled model (VIs + TFs). The results indicate that the RFR model outperforms other models in terms of multi-indicator fusion feature processing (R² = 0.87). The RFR model demonstrated the highest accuracy with a low error rate, indicating that integrating spectral features is a key strategy for improving model accuracy. The results of 5-fold and 10-fold cross-validation, along with model evaluation metrics, showed that the random forest algorithm performed best when handling complex datasets, exhibiting strong stability and generalization capabilities. Therefore, it was selected as the optimal inversion model in this study. Explainable machine learning (SHAP) revealed the contribution of each indicator. This study demonstrates the effectiveness of integrating multispectral information in improving the accuracy of crop growth monitoring models. Based on RFR, a spatial distribution map of chlorophyll content was created. The research results provide a scientific basis for drone-based crop growth monitoring and field crop nutrient management.
Why it matches plant phenotyping methodsドローンマルチスペクトル画像から綿葉のクロロフィル含量を推定するモデルを構築・比較・検証しており、植物形質の取得手法が研究の中心である。
abstractthis study combines vegetation indices (VIs) extracted from remote sensing images with texture information (TFs) and utilizes four machine learning methods—partial least squares regression (PLSR), elastic net regression (ENR), random forest regression (RFR), and extreme gradient boosting regression (XGBoost)—to construct a model for inverting the chlorophyll content of cotton canopy leaves.
Plant diseases pose a severe threat to global agricultural production, significantly challenging crop yield, quality, and food security. Therefore, accurate and efficient disease detection is crucial. Current detection methods have clear limitations: CNN-based methods struggle to model long-range dependencies effectively and have weak generalization abilities. Transformer-based methods, while adept at long-range feature modeling, face issues with large parameter sizes and inefficient calculations due to the quadratic complexity of the self-attention mechanism in relation to image size. To address these challenges, this paper proposes the MamSwinNet model. Its core innovation lies in: using the Efficient Token Refinement module with an overlapping space reduction method, relying on depthwise separable convolutions designed with “stride + 3” convolution kernels to expand the image block overlap area and fully preserve boundary spatial structure. This generates high-quality tokens and converts them into a fixed number of latent tokens, reducing computational complexity while maximizing the retention of key features. It integrates the Spatial Global Selective Perception (SGSP) module and the Channel Coordinate Global Optimal Scanning (CCGOS) module. The SGSP module uses a dual-branch structure (the spatial modeling branch introduces 2D-SSM to scan four directions for capturing long-range dependencies, and the residual compensation branch supplements features to prevent loss; the two branches are combined using Hadamard product to enhance spatial detail modeling). The CCGOS module combines channel and spatial attention by embedding positional information through global average pooling in the height and width dimensions, using the Mamba block for channel-selective scanning and generating an attention map, enabling precise association of key channel features like color with spatial distribution. Experimental results show that the model achieves F1 scores of 79.47%, 99.52%, and 99.38% on the PlantDoc, PlantVillage, and Cotton datasets, respectively. The model has only 12.97M parameters (52.9% less than the Swin-T model) and a computational cost as low as 2.71GMac, significantly improving computational efficiency. This study provides an efficient and reliable intelligent solution for large-scale crop disease detection.
Why it matches plant phenotyping methods植物病害を画像から検出・判定するMamSwinNetモデルの開発とデータセット評価が研究の中心であり、感染植物の病態を直接推定する画像ベース表現型解析に該当する。
abstractExperimental results show that the model achieves F1 scores of 79.47%, 99.52%, and 99.38% on the PlantDoc, PlantVillage, and Cotton datasets, respectively.
Reproduction assets foundThe paper's phenotyping inputs are three public plant-disease image datasets (PlantDoc, PlantVillage, Cotton Disease) used for all experiments. No author analysis code, trained model checkpoints, or paper-specific supplements are disclosed.Dataset · publicwhile enhancing its practical relevance. Owing to these characteristics, PlantDoc has become a key benchmark dataset for evaluating the robustness and applicability of plant disease detection models.
Figure 5
illustrates several representative samples from the dataset. The dataset is publicly accessible via the following link: https://github.com/pratikkayal/PlantDoc-Dataset
Figure 5
Sample images from the PlantDoc dataset.
Grid of images showing various diseased leaves. Top row: Apple rust leaf with red spots, apple scab leaf with black lesions, bell pepper leaf with dark spots. Middle row: Corn gray leaf with discolored areas, two corn leaves with blight showing yellow and brown patterns. BOpen asset ↗PlantDoc-Datasetlines:151-174Dataset · publicility and distinct disease features, PlantVillage is frequently employed for model pre-training and performance benchmarking, and has become an important reference dataset in plant disease detection research.
Figure 6
presents several representative samples from this dataset. PlantVillage can be accessed via the following link: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset
Figure 6
Sample images from the PlantVillage dataset.
Grid of nine leaves showing various plant diseases. Top row: Apple Black rot, Apple scab, Grape Leaf blight. Middle row: Peach Bacterial spot, Potato Early blight, Squash Powdery mildew. Bottom row: Tomato Early blight, Tomato Septoria Leaf spot, TOpen asset ↗plantvillage-datasetlines:151-174Dataset · publicdataset’s class design not only covers the major and prevalent diseases in cotton production but also provides a reliable benchmark for evaluating models in multi-class disease classification tasks.
Figure 7
presents several representative image samples from this dataset. The dataset is publicly available at the following link: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease .
Figure 7
Sample images from the cotton disease dataset.
Nine images of leaves show different conditions: three with aphids, showing yellowing and damage; two with bacterial blight, displaying dark spots; three healthy with vibrant green; and three with powdery mildew, covered in white residue. Each conditioOpen asset ↗cotton-plant-diseaselines:151-174Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Light intensity and spectral distribution within plant canopies provides insights into the effects of optimizing canopy architecture on light use efficiency. Breeding crop varieties with a "smart" canopy, characterized by erect upper-layer leaves and flat lower-layer leaves, can be supported with a 3D canopy model which can simulate light distribution for a particular canopy architecture. Leaf optical properties are required parameters for such canopy photosynthesis model to accurately predict canopy microclimate and hence photosynthetic efficiency. In this study, we developed a strategy to estimate the leaf optical properties based on leaf anatomical features. We developed a Directional Spectrum Detection Instrument (DSDI) system and associated Bidirectional Reflectance Distribution Function (BRDF) analysis software to precisely describe leaf light distribution. BRDF parameters were quantified with high accuracy ( R2>0.95 ) for adaxial and abaxial surfaces of maize, rice, cotton, and poplar leaves across canopy layers. Leaf phenotypic traits, surface roughness, pigments content, specific leaf weight and thickness were also assessed. Ensemble learning (EL) model showed excellent predictive performance for leaf optical properties based on phenotypic traits with R 2 between 0.83 and 0.99. Compared to existing BRDF measurement systems, the DSDI achieves broader angular coverage (-π/36 to 35π/36) via mechanical rotation design, and the ensemble learning model establishes the first direct predictive relationship between BRDF parameters and leaf phenotypic traits. This work presents a new approach to quantify leaf optical properties and offers predictive models for leaf optical properties, which can support canopy light distribution prediction and hence support design leaf features for higher canopy photosynthesis efficiency.
Why it matches plant phenotyping methods葉の光学特性と表現型形質を取得・予測する測定機器、BRDF解析ソフトウェア、機械学習モデルを開発しており、植物フェノタイピング手法が研究の中心である。
abstractthe ensemble learning model establishes the first direct predictive relationship between BRDF parameters and leaf phenotypic traits.
Reproduction assets foundThe paper's BRDF analysis code (adaptive grid search fitting and Roughness Calculator) is publicly available at github.com/PlantSystemsBiology/brdf, and the modified fastTracer ray tracing software used for canopy light simulations is at github.com/PlantSystemsBiology/fastTracerPublic. Phenotype/measurement data are '…Code · publicAn adaptive grid search algorithm was developed in this study, and this algorithm utilized a 2-layered grid (step sizes of 1 × 10 − 2 and 1 × 10 − 4 respectively) structure to incrementally optimize each parameter, providing a more precise approximation of true values. By iteratively narrowing the search range and increasing resolution, this method gradually converges on the optimal solution. The source code of Python for adaptive grid search algorithm was available at https://github.com/PlantSystemsBiology/brdf .Open asset ↗PlantSystemsBiology/brdflines:212-227Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 6 Sept 2026
Introduction Plant type is an important part of plant phenotypic research, which is of great significance for practical applications such as plant genomics and cultivation knowledge modeling. The existing plant type judgment mainly relies on subjective experience, and lacks automatic analysis and identification methods, which seriously restricts the progress of efficient crop breeding and precision cultivation. Methods In this study, the digital structure model of cotton plant was constructed based on multi-dimensional vision, and the rapid analysis and identification method of cotton plant type was established. 50 cotton plants were used as experimental objects in this study. Firstly, multi-view images of cotton plants at boll opening stage were collected, and a three-dimensional point cloud model of cotton plants was constructed based on Structure From Motion and Multi View Stereo (SFM-MVS) algorithm. The original cotton point cloud data was preprocessed by coordinate correction, statistical filtering, conditional filtering and down-sampling to obtain a high-quality three-dimensional model. The three-dimensional model is projected in two dimensions to obtain the two-dimensional projection data of cotton plants from multiple perspectives. Secondly, based on the fast convex hull algorithm, the cotton plant two-dimensional convex hull was constructed from multiple perspectives, and the distribution range and corner change rate of each corners of the convex hull were analyzed, and the identification basis of cotton plant type was established. Results The R2 of plant height and width extracted from the model were greater than 0.90, and RMES were 0.372 cm and 0.387 cm, respectively. When the maximum number of point clouds is 75335, the point cloud reading time, cotton multi-view projection time, and convex hull automatic construction time are 0.402 S, 2.275 S, and 0.018 S, respectively. Finally, the cotton cylinder type classification interval is 0-0.2, and the tower type classification interval is 0.4-1.5. Discussion The cotton plant type identification method proposed in this study is fast and efficient. It provides a solid theoretical basis and technical support for cotton plant type identification.
Why it matches plant phenotyping methods多視点画像とSfM-MVS点群から綿花の草型および高さ・幅を自動抽出・識別する手法を開発し、精度と処理時間も評価しており、植物表現型取得が中心である。
abstractthe digital structure model of cotton plant was constructed based on multi-dimensional vision, and the rapid analysis and identification method of cotton plant type was established.
Cotton production is a crucial agricultural industry, a raw material source for the textiles sector and a major source of livelihood for more than 30 million farmers globally. The yield and quality of cotton (Gossypium) are influenced by different types of stress and diseases. Deep Learning as a solution for disease prevention, detection, and management can increase the yield, reduce the cost and improve the quality of crop. This study presents a robust method using 10-fold cross-validation with the YOLOv8 DL model for precise cotton leaf disease recognition. The k-fold cross-validation mitigates overfitting by training the model on diverse data subsets, which leads to enhanced generalizability while ensuring reliable performance. The proposed method achieved 99.60% and 100% as Top_1 and Top_5 accuracy, respectively. The method also achieved a recall of 99.53%, a precision of 99.53%, and an F1 score of 99.60%. During 10 trials, the method consistently performed with an average. Top_1 and Top_5 accuracy of 98.41% and 100% respectively, recall 98.53%, precision 98.39% and F1 score 98.42%.This study is among the first to apply YOLOv8 classification with 10-fold cross-validation for multi-class cotton leaf disease identification using field-captured images.
Why it matches plant phenotyping methodsYOLOv8による葉画像からの植物病害認識が研究の中心で、交差検証による性能評価も実施しているため、植物病害状態を推定するフェノタイピング手法として含める。
abstractThis study presents a robust method using 10-fold cross-validation with the YOLOv8 DL model for precise cotton leaf disease recognition.
Flow cytometry (FCM) and genome sequencing are complementary methods for estimating plant genome size (GS). However, discrepancies between the GS estimates derived from genome assemblies and FCM create ambiguity regarding the accuracy of these approaches. Approximately 12,000 plant GS measurements have been reported, with hardly any of them based on genome assemblies. Currently, FCM is the most frequently used method. Accurate GS estimation by FCM relies on internal standards with known GS values. However, previous GS calibrations, often based on incomplete reference genome assemblies, have led to significant discrepancies in GS estimates. Historically, the GS of a diploid plant species was estimated by doubling the size of a consensus genome assembly. However, consensus assemblies collapse homologous chromosomes into a single sequence, typically favouring the larger haplotype and potentially overestimating GS, especially in highly heterozygous species. Here, we applied haplotype-resolved genome assemblies to accurately recalibrate the reference standards. We utilized a recent gapless, telomere-to-telomere (T2T) consensus and the most complete phased genome assemblies of the Nipponbare rice as a primary standard to recalibrate five commonly used plant standards. Using the consensus genome as a reference revealed an overestimation of over 30% in widely used previous GS estimates for Pisum sativum and Nicotiana benthamiana , approximately 18% for Arabidopsis thaliana , and 5% for Sorghum bicolor and Gossypium hirsutum . The GS estimates based on phased haplotype assemblies suggested an additional 6%–7% overestimation. Haplotype-resolved genome assemblies allow the recalibration of GS estimates with the potential to yield more accurate values by capturing haplotype-specific variations previously missed in consensus assemblies.
Why it matches plant phenotyping methods植物のゲノムサイズ推定に用いるフローサイトメトリー標準の再校正が研究の中心であり、測定精度の検証・改善に該当する。
titleRe-calibration of flow cytometry standards for plant genome size estimation
Cotton, often referred to as "white gold" or the "king of fibers," is one of the most widely used natural fibers in the global textile industry, supporting approximately 250 million people worldwide. However, cotton plants suffer from a variety of diseases, particularly leaf diseases, which can significantly reduce the yield and fiber quality. To overcome this problem, we propose a carefully curated image dataset that enables research toward early and automated disease detection and health monitoring of cotton plants. The dataset comprises 1373 original and 4963 augmented high-resolution images of cotton leaves with healthy, damaged, and infected samples. The images were captured under different environmental conditions from plants grown at the Sher-e-Bangla Agricultural University in Dhaka, Bangladesh to provide natural variability and realism. The dataset considers four common cotton leaf diseases-Fusarium wilt, Alternaria leaf spot, Verticillium wilt, and bacterial blight-each labeled and classified to support machine learning applications. Captured from different angles and devices, the images have rich visual content that enables the development of strong deep learning models for disease classification. The dataset was designed to advance research relevant to precision agriculture by supporting early disease detection studies, crop health monitoring, and sustainable cotton-growing methods.
Why it matches plant phenotyping methods綿花葉の病害・健全状態を画像で分類するためのデータセットであり、植物の病害状態を直接観測する再利用可能なフェノタイピング資源が中心です。
abstractwe propose a carefully curated image dataset that enables research toward early and automated disease detection and health monitoring of cotton plants.
Reproduction assets foundThe paper is a data article describing a cotton leaf image dataset (1373 original + 4963 augmented images) for disease classification, publicly deposited on Mendeley Data with DOI 10.17632/t9hgvk2h9p.1 and a direct URL. This is the paper's own plant-phenotyping (leaf disease image) dataset and is directly actionable.Dataset · publicRepository name: Mendeley Data
Data identification number: 10.17632/t9hgvk2h9p.1
Direct URL to data: https://data.mendeley.com/datasets/t9hgvk2h9p/1Open asset ↗Mendeley Data · 10.17632/t9hgvk2h9p.1lines:1-48Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
The early detection of Verticillium wilt (VW) in cotton is a critical challenge in agricultural disease management. Cotton, a vital global textile resource, is severely threatened by this devastating disease. Traditional diagnostic methods, which often rely on manual expertise or destructive sampling, are limited by low efficiency and high subjectivity. In recent years, Raman spectroscopy has emerged as a promising solution due to its rapid, non-destructive, and highly sensitive characteristics for plant disease detection. In this study, we analyzed cotton stems using Raman spectroscopy, applying Savitzky-Golay (SG) smoothing combined with multiple preprocessing methods including Scaling and Shifting (SS), Standard Normal Variate (SNV), inverse first-order differential (1/SG)', and multiplicative scatter correction (MSC). For baseline correction, we employed polynomial fitting (PolyFit) and adaptive iterative weighted penalized least squares (airPLS). Feature selection was performed using principal component analysis (PCA), successive projection algorithm (SPA), and competitive adaptive reweighted sampling (CARS).Three optimized models were developed: support vector machine (SVM) with weighted mean of vectors (INFO) algorithm, random forest (RF) enhanced by particle swarm optimization (PSO), and long short-term memory (LSTM) network optimized via chameleon swarm algorithm (CSA).The results show that the INFO-SVM model with SG-airPLS-(1/SG)' -CARS preprocessing demonstrated superior performance, achieving 97.5% accuracy (0.974 F1-score) on training data and 90.0% accuracy (0.867 F1-score) on validation data, outperforming both PSO-RF and CSA-LSTM models. These results confirm that Raman spectroscopy integrated with optimized machine learning enables accurate VW classification in cotton stems. This method enables early disease detection during infection, facilitating timely fungicide application and reducing yield losses.
Why it matches plant phenotyping methodsランダ分光と機械学習により、ワタ茎の病徴・萎凋病の早期検出および重症度分類を開発・検証しており、植物状態の取得手法が研究の中心である。
titleEarly detection and severity classification of verticillium wilt in cotton stems using Raman spectroscopy and machine learning.
A precise and timely assessment of the cotton leaf area index (LAI) plays a critical role in advancing precision agriculture practices. Verticillium wilt reduces LAI by disrupting leaf structure and physiological functions.Verticillium wilt is a significant disease that constrains the yield potential of cotton. The emergence of unmanned aerial vehicle (UAV) technology offers a revolutionary technology for cost-effective, fine-scale inversion of the LAI in cotton under Verticillium wilt stress. The study investigated the relevant potential of classical-based algorithms and deep learning-based algorithms to estimate the LAI using multi-spectral images acquired by drones under Verticillium wilt stress. Prior to constructing the model, Gaussian blur was applied to enhance the original images. Inputs to the Random Forest (RF) and Extreme Learning Machine (ELM) algorithms included vegetation indices (VI) and texture features (TF), and the LAI of cotton under Verticillium wilt stress was estimated in conjunction with ground-measured LAI data. The enhanced images were used as input to the Convolutional Neural Network (CNN) algorithm to estimate the LAI of cotton under Verticillium wilt stress. The results demonstrated that, in the classical-based models, the prediction accuracy of LAI inversion using both VI and TF was slightly higher than that achieved using VI or TF alone. After Gaussian blur processing, the accuracy of RF, ELM, and CNN models showed significant improvement. Among these, the CNN model based on Gaussian blur achieved the highest LAI inversion accuracy (R²≥0.88), followed by the LSM model (R²=0.83) and the RF model (R²=0.82). Consequently, the CNN model under Gaussian blur is employed to achieve rapid and accurate inversion of the LAI of cotton under Verticillium wilt stress. This finding of the study provides a significant reference for real-time monitoring of cotton growth and effective field management under Verticillium wilt conditions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から綿花のLAIを推定する画像解析・機械学習手法の比較と精度評価が研究の中心であり、植物表現型の取得方法に該当する。
abstractThe study investigated the relevant potential of classical-based algorithms and deep learning-based algorithms to estimate the LAI using multi-spectral images acquired by drones under Verticillium wilt stress.
Cotton is a major industrial crop and a primary raw material for textiles worldwide. Intelligently detecting pests and diseases is crucial for automating cotton cultivation management. This study proposed a cross-platform detection method for cotton pests and diseases using unmanned aerial vehicle (UAV) and smartphone imaging. The method employed a self-built improved YOLO, namely CPD-YOLO. The bidirectional feature pyramid network with the reparameterization vision transformer was constructed to achieve an optimal balance between multi-scale feature fusion and inference efficiency. Secondly, the head network, featuring four dynamic detection heads, was designed to enhance the multi-dimensional dynamic awareness capability. Finally, the Inner-Shape intersection over union was proposed as the bounding box regression loss function, which can improve positioning accuracy and accelerate convergence. The improvement strategy significantly improved the detection accuracy of multi-scale objects in cross-platform scenarios. Compared with the original model, the F1-score and mean average precision (mAP) were increased by 7.44 % and 7.08 %, respectively. The CPD-YOLO also outperformed typical object detection models, with an F1-score of 88.86 % and a mAP of 90.42 %. Moreover, the CPD-YOLO model had superior generalization capability, with a 1.30 % decrease in F1-score and a 0.55 % decrease in mAP. Furthermore, a mobile application named Doctor Cotton, utilizing the CPD-YOLO, was developed. It promises to be a new farming tool for the real-time and accurate detection of cotton pests and diseases. These findings provide a valuable reference for the cross-platform application of consumer-grade UAVs and smartphones in detecting other crop pests and diseases.
Why it matches plant phenotyping methodsUAV・スマートフォン画像から綿花の病害状態を検出する画像解析手法を開発・評価しており、植物状態の取得が中心的です。害虫検出単独ではなく病害検出も含むため対象範囲に該当します。
abstractThis study proposed a cross-platform detection method for cotton pests and diseases using unmanned aerial vehicle (UAV) and smartphone imaging.
Pre-harvest defoliation of cotton is a key agricultural measure to improve mechanical harvesting efficiency and raw cotton purity. Collecting data on cotton defoliation traits for genetic localization and thus breeding defoliation-prone varieties is an essential alternative to traditional defoliant spraying. Nevertheless, it is hampered by low throughput and artificial error in manual field surveys. In this study, a framework for collecting high-throughput defoliation data in large fields was established. Three spectral indices (MTCI, VDVI, CI) and leaf area index (LAI) were first screened as core predictors through hierarchical segmentation analysis in three levels: leaf number (LN), leaf number difference (LND), and defoliation rate (DR). Four deep learning architectures (CNN, BiGRU, CNN-BiGRU, and CNN-BiGRU-Attention) were developed, and the CNN-BiGRU-Attention hybrid model demonstrated superior performance at all three levels, with R 2 values exceeding 0.85. Importantly, the inversion accuracy of this model at the LN and LND levels was superior to that at the DR level, which was also confirmed by the results of the genome-wide association study (GWAS). We combined GWAS and transcriptome results to identify a new gene, GhDR_UAV1 , associated with defoliation traits. The overexpression of GhDR_UAV1 significantly promoted the wilting of cotton leaves, indicating that GhDR_UAV1 plays a positive regulatory role in cotton defoliation. This study proposed a strategy to invert cotton defoliation data at three levels using deep learning fusion of UAV remote sensing data and LAI data and confirmed that LND can provide accurate phenotypic data for GWAS analysis. This study provides a new theoretical basis for cotton defoliation regulation and genetic improvement by integrating cotton high-throughput defoliation phenomics and genomics from an innovative perspective.
Why it matches plant phenotyping methodsUAVリモートセンシング、LAI、深層学習を統合し、ワタの落葉形質を高スループット推定する方法を開発・評価しており、表現型取得が研究の中心である。
abstracta framework for collecting high-throughput defoliation data in large fields was established
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the study's data and code (UAV/LAI defoliation phenotyping data and analysis code) in a public GitHub repository, which matches an allowed URL.Code · publicThe data and code utilized in this study are available at GitHub ( https://github.com/xbw322/Data_upload.git ).Open asset ↗https://github.com/xbw322/Data_upload.gitlines:169-205Code / dataset availability confirmedarXiv · checked 15 Sept 2026
Cotton is one of the most important natural fiber crops worldwide, yet harvesting remains limited by labor-intensive manual picking, low efficiency, and yield losses from missing the optimal harvest window. Accurate recognition of cotton bolls and their maturity is therefore essential for automation, yield estimation, and breeding research. We propose Cott-ADNet, a lightweight real-time detector tailored to cotton boll and flower recognition under complex field conditions. Building on YOLOv11n, Cott-ADNet enhances spatial representation and robustness through improved convolutional designs, while introducing two new modules: a NeLU-enhanced Global Attention Mechanism to better capture weak and low-contrast features, and a Dilated Receptive Field SPPF to expand receptive fields for more effective multi-scale context modeling at low computational cost. We curate a labeled dataset of 4,966 images, and release an external validation set of 1,216 field images to support future research. Experiments show that Cott-ADNet achieves 91.5% Precision, 89.8% Recall, 93.3% mAP50, 71.3% mAP, and 90.6% F1-Score with only 7.5 GFLOPs, maintaining stable performance under multi-scale and rotational variations. These results demonstrate Cott-ADNet as an accurate and efficient solution for in-field deployment, and thus provide a reliable basis for automated cotton harvesting and high-throughput phenotypic analysis. Code and dataset is available at https://github.com/SweefongWong/Cott-ADNet.
Why it matches plant phenotyping methods綿花の花・ボール認識を対象とする画像解析手法を開発し、データセット作成、外部検証、性能評価まで行っており、植物器官の表現型取得が中心である。
abstractWe propose Cott-ADNet, a lightweight real-time detector tailored to cotton boll and flower recognition under complex field conditions.
Reproduction assets foundThe paper explicitly states that its code and curated cotton boll/flower detection dataset (4,966 labeled images plus a 1,216-image external validation set) are publicly released at the authors' GitHub repository. The ultralytics repository is a generic third-party library, not a paper-specific asset.Code · publicy 7.5 GFLOPs, maintaining stable performance under multi-scale and rotational variations. These results demonstrate Cott-ADNet as an accurate and efficient solution for in-field deployment, and thus provide a reliable basis for automated cotton harvesting and high-throughput phenotypic analysis. Code and dataset is available at https://github.com/SweefongWong/Cott-ADNet .
† † footnotetext: ∗ * Corresponding author: cuij@wfu.edu
Index Terms :
cotton, cotton boll detection, lightweight object detection, rotational convolution
1 Introduction
Cotton is one of the most critical economic crops worldwide, accounting for nearly 35% of global natural fiber production. It underpins industries such as Open asset ↗SweefongWong/Cott-ADNetlines:1-57Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract This study investigates the complex regulatory mechanisms of genotype-environment interactions (GEI) in cotton phenotype formation and explores the genetic basis of environmental adaptation through an integrated analytical approach. The research methodology encompasses four key components: (1) deep learning model construction, (2) phenotypic plasticity analysis, (3) environmental adaptation assessment, and (4) genome-wide association study (GWAS). Based on the multi-head self-attention mechanism and the deep feature interaction, we constructed the AttGEI-Net deep learning framework. The model demonstrates remarkable predictive performance with an average accuracy of 0.96 in fixed environments, though this decreases to 0.39-0.44 in novel environments, revealing fundamental differences between genotype-dominated and environment-dominated prediction scenarios. A total of 10,215 significant SNP loci is identified by GWAS, including 2,705 Main-SNPs, 41 phenotype plasticity loci (PP-SNPs), and 9,022 environmental adaptation loci (EvA-SNPs). The regulation of phenotypes by these loci has a distinct hierarchical character: the basic genetic architecture (Main-SNPs) maintains the basic expression of traits, the PP-SNPs mediates the immediate response of phenotypes to environmental changes, and the EvA-SNPs constitutes the highest-level adaptive regulatory network that coordinates the expression of multiple traits by integrating environmental signals. Shared loci of interpretability analyses of model and GWAS may be the key genetic basis adapting to different environments. Broadly adapted varieties in the Yellow River basin (e.g., F096, L090, etc.) can be used as the backbone parents for suitability breeding.
Why it matches plant phenotyping methods綿花の表現型を予測する深層学習フレームワークを構築し、環境間で予測性能を評価しているため、計算的な表現型推定手法が研究の中心です。
abstractdeep learning model construction
Reproduction assets foundThe paper's authors publicly released the AttGEI-Net model code used for cotton phenotype prediction and interpretability analysis on GitHub. The phenotype/trait datasets themselves are not publicly deposited (available only on request), and the genomic deposits are molecular omics data, which do not qualify.Code · public841 The code of our model has been made available at https://github.com/hezikang-git/AttGEI-Net,Open asset ↗hezikang-git/AttGEI-Netpdf-page:32 lines:1-53Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Hyperspectral indices integrated with physiology predicted metabolites such as Rubisco activity across early, mid, and late flowering drought, establishing a rapid, non-destructive framework to detect sink limitations and identify cotton resilience to stage-specific stress and fiber quality decline.
Why it matches plant phenotyping methods綿花のスペクトル表現型と生理形質を機械学習で統合し、乾燥ストレス耐性や品質低下を非破壊・迅速に推定する枠組みが中心であるため。
abstractHyperspectral indices integrated with physiology predicted metabolites such as Rubisco activity
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
Abstract Genomic and phenomic analyses suggest additional heritable phenomic features can improve modeling of important end traits like senescence or yield. Field phenotyping generally uses trait values averaged across individual experimental units (plants or numerous plants within plots), ignoring the full distributional pattern of collected measures. Images of plants or plots, as captured by drones (unoccupied aerial vehicles / UAVs / drones), can be viewed as individual distribution functions that capture biological information. This study introduces and validates distributional data analysis in two crops and experiment types – cotton ( Gossypium hirsutum L.) single plant vegetation index (VI) analysis and maize ( Zea mays L.) plot-level yield predictions. In both crops, the concept of within-day variance decomposition was demonstrated. In cotton, genotypes exerted significant influences on temporal quantile functions of VIs. Maize yield prediction using distributional data with elastic-net regression indicated improvements in yield prediction between 12.7%-21.6% with quantiles outside the conventionally used median responsible for added predictive power. A novel data visualization method for per-pixel heritability allowed distributional features to be explainable and interpretable. These results have implications for future plant phenomic studies, indicating that distributional data analysis applied across temporal imagery captures novel, heritable, and interpretable biological signal that is lost when working with conventional measures of central tendency such as mean or median summary values of experimental units. Significance Repeated aerial imaging of agricultural experiments produces image data sets that capture plant development in high spatial and temporal resolutions. Frequently, images are summarized by measures of central tendency, such as mean or median values. Here, functional data distributional methods were applied to cotton ( Gossypium hirsutum L.) and maize ( Zea mays L.) image data, capturing more information than standard approaches. Cotton genotypes significantly impacted distributional spectral data while in maize, distributional data enabled more accurate predictions of grain yield versus models trained with median data alone. Distributional data were more explainable by genetics, with novel data visualization techniques able to shine light on specific parts of plant imagery with high and low genetic variance.
Why it matches plant phenotyping methodsドローン画像から植物表現型を抽出する分布データ解析手法を導入・検証し、遺伝率解析、収量予測、可視化まで行っており、表現型取得・解析法が研究の中心である。
abstractThis study introduces and validates distributional data analysis in two crops and experiment types
The precise segmentation of crop organs plays a crucial role in optimizing crop cultivation strategies and enhancing yield potential. This study proposes a novel deep learning network, CotSegNet, which enables precise and non-destructive segmentation of cotton organs facilitating the extraction of phenotypic characteristics. In CotSegNet, an improved attention mechanism known as CGLUConvFormer is designed. This mechanism significantly improves segmentation accuracy by emphasizing important features while diminishing redundant information. Furthermore, CotSegNet integrates the SegNext attention mechanism. This mechanism facilitates the efficient extraction and integration of multi-scale features, thereby significantly enhancing the ability of CotSegNet to comprehend and segment point cloud data. To address issues related to leaf adhesion and coplanarity that lead to over-segmentation problems, this study proposes an improved region-growing algorithm. This algorithm enhances the accuracy of leaf instance segmentation through the incorporation of distance constraints. In comparative experiments with five advanced deep learning networks (PointNet, PointNet++, DGCNN, SPoTr and CurveNet), CotSegNet demonstrated outstanding performance. Its Precision, Recall, F1-score, and IoU reached 95.06 %, 93.32 %, 94.61 %, and 89.80 %, respectively. The experimental results demonstrated that the proposed method effectively extracted the phenotypic parameters of stem height, leaf length, leaf width, and leaf area in cotton plants. These measurements exhibited a high degree of consistency with manual assessments, yielding determination coefficients of 0.947, 0.948, 0.955, and 0.961 for each parameter respectively. The corresponding root mean square errors were recorded as 0.852 cm, 0.492 cm, 0.551 cm, and 1.674 cm² respectively. The research findings demonstrate that this approach offers essential technical support for the collection and analysis of high throughput phenotyping data in field crops.
Why it matches plant phenotyping methods綿花器官の点群セグメンテーションと表現型形質抽出のためのCotSegNetおよび改良領域成長法を開発し、手動測定との検証も行っており、表現型取得が研究の中心である。
abstractThis study proposes a novel deep learning network, CotSegNet, which enables precise and non-destructive segmentation of cotton organs facilitating the extraction of phenotypic characteristics.
Accurate quantification of open bolls and their distribution is crucial for understanding cotton growth, development, and yield in optimized crop management and enhanced plant breeding. Manual boll counting methods are time-consuming, labor-intensive, and subjective. Leveraging the potential of high-resolution images for high-throughput phenotyping offers a promising avenue for efficient trait quantification. The objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources. A DJI Phantom 4 RTK Unmanned Aerial System (UAS) equipped with a 4 K RGB camera was used to acquire high-resolution RGB images, and a DJI Matrice 300 RTK with a Zenmuse L1 sensor was used to acquire LiDAR point cloud data. The RGB images were converted to point cloud using photogrammetry by measuring multiple points of overlapping images. The boll detection workflow involved data filtering and clustering using the density-based spatial clustering of applications with noise (DBSCAN) method. Evaluation of the methods involved 48 plots representing small, medium, and large plant sizes using metrics including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (r²). The methods using both data sources performed well in estimating open bolls, with LiDAR point cloud data slightly outperforming those derived from RGB images. Generally, the performance of the DBSCAN method in boll detection improved with decreasing plant sizes. Specifically, LiDAR data yielded MAPE values of 5.03 %, 8.05 %, and 13.46 %, RMSE values of 7.26, 14.33, and 23.40 bolls per m², and r² values of 0.93, 0.84, and 0.84 for small, medium, and large plant sizes, respectively. RGB image-based data exhibited MAPE values of 7.21 %, 6.49 %, and 16.41 %, RMSE values of 11.05, 13.66, and 26.49 bolls per m², and r² values of 0.82, 0.74, and 0.83 for small, medium, and large plant sizes, respectively. The method demonstrates the potential of RGB imagery and LiDAR data for estimating boll counts, offering valuable tools for enhanced plant phenotyping in plant breeding and site-specific crop management. Both data sources underestimated boll counts, with smaller plants showing less undercounting, likely due to improved light penetration and separation of bolls. These findings highlight the influence of plant structure on boll detection accuracy and the need to address challenges posed by dense canopies to enhance detection reliability.
Why it matches plant phenotyping methodsLiDARとRGB画像を用いた綿花の開花ボール数の検出・計数手法を開発し、比較評価した研究であり、植物表現型取得が中心です。
abstractThe objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources.
Cotton is one of most important economic crops in the world. Cotton yield has been significantly affected by frequent infestations of Verticillium wilt (VW). Currently, most detection methods for cotton VW are implemented based on the clearly visible symptoms, leading to delayed interventions and control. Therefore, early detection of cotton VW is crucial for minimizing economic losses. However, existing early detection methods for cotton VW face substantial challenges due to the subtle nature of early-stage symptoms and the limited availability of data, which result in considerable error. To address this, an early detection method for cotton VW by integrating Generative Adversarial Networks (GANs) with hyperspectral imaging technology was proposed, focusing primarily on cotton hyperspectral data augmentation. GANS-based spectral enhancement model (Spe-GAN) and GANS-based spatial-enhancement model (Spa-GAN) were developed to capture subtle early-stage symptoms of VW under limited data from both spectral and spatial perspectives. Compared to traditional machine learning methods (RF and SVM) and deep learning methods (LSTM and ResNet18), the proposed Spe-GAN and Spa-GAN achieved better detection performance, with accuracy rates of 94.52 % and 91.78 %, respectively. Moreover, this study also explored the underlying reasons for the superiority of the proposed method from various perspectives, further enhancing the model interpretability. The data augmentation method proposed in this study provided a new perspective and opened up possibilities for achieving the early detection of cotton VW and other plant diseases.
Why it matches plant phenotyping methods綿花の病害状態（Verticillium wilt）を、ハイパースペクトル画像とGANによって早期推定する方法を開発・比較評価しており、植物フェノタイピング手法が研究の中心である。
abstractan early detection method for cotton VW by integrating Generative Adversarial Networks (GANs) with hyperspectral imaging technology was proposed
CottonChlorophyll fluorescencePhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
A thorough understanding of the biochemical, stomatal, and mesophyll components that limit photosynthetic induction is crucial for targeted improvement of crop productivity. However, compared with biochemical activation and stomatal conductance (gs), mesophyll conductance (gm) remains underexplored in induction studies. The fluorescence method (the variable J method) is a valid and widely accessible tool for gm measurement under steady-state conditions. Here, we experimentally validated the applicability of the fluorescence method under nonsteady-state conditions, demonstrating comparable induction kinetics of gm with the well-established carbon isotope method. Building on this validation, we combined the fluorescence method with gas-exchange measurements to comprehensively examine the induction kinetics of photosynthetic rate (A) and its associated components in a set of historical cotton (Gossypium hirsutum L.) cultivars. Our results showed no significant effect of the year of cultivar release on A during induction, suggesting that dynamic photosynthesis has not benefited from past selection efforts in cotton. Nonetheless, significant among-cultivar variations were observed in all measured induction traits, hinting at breeding opportunities for leveraging dynamic photosynthetic variation to boost crop productivity. Through induction-period-integrated limitation analysis, we further identified gs as the single most important limiter of photosynthetic induction across all cotton cultivars. Moreover, the analysis also demonstrated that accurately accounting for gm kinetics is essential for the unbiased acquisition of mechanistic insights into nonsteady-state photosynthetic physiology. We recommend that future induction studies incorporate gm measurements whenever possible to strengthen the knowledge base necessary for genetically enhancing dynamic carbon gain and crop yield in the field.
Why it matches plant phenotyping methods非定常条件での蛍光法による葉肉コンダクタンス測定を同位体法と検証し、ガス交換と組み合わせて光合成誘導形質を評価しており、測定法の検証が中心的です。
abstractHere, we experimentally validated the applicability of the fluorescence method under nonsteady-state conditions, demonstrating comparable induction kinetics of gm with the well-established carbon isotope method.
ABSTRACT Driven by the dual pressures of rapid global population growth and escalating climate change, there is a growing demand for real‐time monitoring of crop nitrogen levels to support precision agriculture. This necessity has catalysed the integration of crop modelling techniques with remote sensing technologies. Addressing challenges such as multi‐source remote sensing data heterogeneity and limited generalisation in nitrogen inversion models for cotton canopies, this paper designs a novel inversion framework based on the assimilation of diverse remote sensing sources and mechanistic crop models. Firstly, this paper employed spectral resampling techniques, fuzzy logic for uncertainty quantification, and Pearson correlation analysis to harmonise differences in spectral characteristics and spatial resolution between Sentinel‐2A and Landsat 8 imagery, ultimately identifying eight nitrogen‐sensitive features. Subsequently, a multi‐scale feature enhancement module was developed to improve representational richness. Additionally, the paper employed a satellite image fusion module, which effectively reduced data heterogeneity errors by 12.7% across sources. Building on this, a hybrid GNN‐EnKF model was proposed. GNN was used to establish spatial neighbourhood dependencies, while EnKF dynamically adjusted the parameters within the WOFOST crop model. This approach successfully fuses data‐driven learning with physically based modelling. Experimental evaluations revealed that the proposed architecture attained a mAP of 95.83%, outperforming baseline models such as ResNet18 (83.92%) and Transformer (92.84%), demonstrating robust adaptability in complex agricultural settings. In conclusion, the framework presented in this paper offers a high‐accuracy nitrogen monitoring solution tailored for precision farming, and provides strong data support for cotton nitrogen deficiency and additional fertilisation.
Why it matches plant phenotyping methods綿花キャノピーの窒素状態という植物形質を、マルチソースリモートセンシング融合とGNN-EnKFで推定する枠組みを開発・評価しており、形質取得・推定手法が研究の中心である。
abstractAdditionally, the paper employed a satellite image fusion module, which effectively reduced data heterogeneity errors by 12.7% across sources.
The accurate classification of plant diseases is crucial for effective crop management and yield improvement. This study explores the application of deep learning models for the automated classification of cotton leaf and plant conditions. A data set comprising 2,316 images, classified into four classes: diseased cotton leaf, diseased cotton plant, fresh cotton leaf, and fresh cotton plant, was used for training and evaluation. Three well-known convolutional neural network (CNN) architectures, namely AlexNet, GoogLeNet, and SqueezeNet, were employed for classification. The experimental results indicate that GoogLeNet outperformed the other models, achieving a recognition accuracy of 73.67%, followed by SqueezeNet at 66.33% and AlexNet at 39.71 %. These findings highlight the efficiency of GoogLeNet in feature extraction and classification for cotton leaf and plant disease detection. The study demonstrates the potential of deep learning techniques in smart agriculture by automating disease detection processes.
Why it matches plant phenotyping methods綿花の葉・植物体の病害状態を画像から分類する深層学習手法が研究の中心であり、植物の病害表現型を直接推定しているため含める。
abstractThis study explores the application of deep learning models for the automated classification of cotton leaf and plant conditions.
Background: Cotton is an important crop globally and early detection of plant diseases is crucial for maintaining yields. Traditional methods for disease detection are manual and inefficient, highlighting the need for advanced technology like AI to enhance productivity. Methods: The study utilized the Inception-v3 deep learning model along with techniques such as transfer learning and hyper-parameter tuning. These approaches helped design an efficient system to classify whether a cotton plant is healthy or diseased. Comparisons were made with other pre-trained models like VGG16, ResNet50 and ResNet152V2. Result: The Inception-v3 model showed exceptional performance: • Achieved 87.52% accuracy without tuning. • Achieved 98.85% accuracy after hyper-parameter tuning, marking an improvement of ~11%. This approach also demonstrated faster and more precise predictions for diseases like bacterial blight, army worms and aphids. It supports sustainable farming by reducing chemical usage while maintaining crop quality and yield.
Why it matches plant phenotyping methods綿花の健康・罹病状態を画像ベースの深層学習で分類する手法が研究の中心であり、植物病害状態のフェノタイピング手法に該当する。
abstractThe study utilized the Inception-v3 deep learning model along with techniques such as transfer learning and hyper-parameter tuning.
Agricultural is the primary source of essential provisions almost all nation and remains as a vital survival tool for human race for past, present and future. The agriculture which remains as the bedrock of the civilizations is frequently being affected by devastating plant diseases leading to the loss of economy and food shortage. The manual testing requires high expertise, time and often leads to human error. Recent advancements in computer vision and AI models have highlighted the potential for building automatic plant disease detection models based on visible signs using image classification tasks. However, the task becomes complex and erroneous due to the complex nature of data. Consequently Deep Learning (DL) models tend to outperform others traditional methods through utilizing network topology with convolution layers in core. Projected system uses high quality Cotton Disease dataset sourced by Kaggle and provide a suitable solution to the aforementioned problem using advanced DL neural network namely Blend Unity Resqueeze Resnet approach which yields high accuracy with modification including Resqueeze layer and blend unity weights applied to do the tedious job of diagnosing plant diseases on image based classification. The proposed research outperforms the conventional methods achieving better accuracy of 92% with better precision and reliability. The outcome of the respective research is analyzed with suitable metrics and compared with the recent developed conventional algorithms in which the proposed model proves to be a better suited for the efficient plant disease classification. Hence, the proposed method is intended to contribute in the plant disease classification and assist the agriculturists significantly to prevent the losses due to crop diseases.
Why it matches plant phenotyping methods植物画像から病害状態を分類する深層学習手法の開発・比較が中心であり、植物表現型（病害状態）の取得・推定に該当する。
abstractbuilding automatic plant disease detection models based on visible signs using image classification tasks
Reproduction assets foundThe paper's plant-phenotyping measurements are based on the public Cotton Disease Dataset from Kaggle, explicitly cited with URL and CC BY 4.0 license. No author analysis code or trained model is shared.Dataset · publicTable 3. Significant Features of the Validation Dataset
S. Features Feature percentage
no %
1. DL 16.97
2. DP 31.17
3. FL 24.70
4. FP 27.16
The utilized dataset is licensed with creative commons attribution 4.0 international. The dataset is acquired from
the following link:
https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset/data
4.2 Performance Metrics
The metrics used for evaluating the system’s performance are precision, accuracy, f1-score and recall.
1. Precision: The metric signifies the count of accurate positive predictions. Precision is calculated by
taking the ratio of true positives to the total number of positive predictions, and it is expresOpen asset ↗Kaggle · janmejaybhoi/cotton-disease-datasetpdf-layout-page:10 lines:1-51Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
To address the limitations of the existing cotton seedling growth prediction methods in field environments, specifically, poor representation of spatiotemporal features and low visual fidelity in texture rendering, this paper proposes an algorithm for the prediction of cotton seedling growth from images based on FCA-STNet. The model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image. A novel STNet structure is designed to enhance the representation of spatiotemporal dependencies, while an Adaptive Fine-Grained Channel Attention (FCA) module is integrated to capture both global and local feature information. This attention mechanism focuses on individual cotton plants and their textural characteristics, effectively reducing the interference from common field-related challenges such as insufficient lighting, leaf fluttering, and wind disturbances. The experimental results demonstrate that the predicted images achieved an MSE of 0.0086, MAE of 0.0321, SSIM of 0.8339, and PSNR of 20.7011 on the test set, representing improvements of 2.27%, 0.31%, 4.73%, and 11.20%, respectively, over the baseline STNet. The method outperforms several mainstream spatiotemporal prediction models. Furthermore, the majority of the predicted phenotypic traits exhibited correlations with actual measurements with coefficients above 0.8, indicating high prediction accuracy. The proposed FCA-STNet model enables visually realistic prediction of cotton seedling growth in open-field conditions, offering a new perspective for research in growth prediction.
Why it matches plant phenotyping methods画像系列から綿実生の将来画像を予測し、そこから37形質を抽出する計算・画像ベースの表現型推定法を開発・評価しており、方法が研究の中心である。
abstractThe model leverages historical sequences of cotton seedling RGB images to generate an image of the predicted growth at time t + 1 and extracts 37 phenotypic traits from the predicted image.
Accurate crop yield prediction is essential for enhancing agricultural sustainability and guiding economic policy decisions. It is effective to fuse multi-source remote sensing data to predict crop yields, but difficult to reveal the effects of physiological processes on yield estimation models, and challenging to guide crop field production and management. In this study, an innovative framework was introduced to construct plant height (PH) and leaf chlorophyll content (LCC) inversion models for UAV LiDAR and multispectral data through different strategies. PH and LCC, two key growth features affecting cotton yield, were evaluated using multiple linear regression (MLR), partial least squares regression (PLSR), and extreme gradient boosting (XGBoost) algorithms for single-feature and multi-feature fusion, respectively. The multi-feature fusion model based on the XGBoost algorithm was significantly better than the single-feature model (R²=0.744). Further optimization of the multi-feature fusion model revealed that multi-temporal growth features as input variables significantly improved the accuracy of the multi-feature fusion model compared with that based on single-temporal (R²=0.802). Shapley additive explanations (SHAP) analysis revealed the key contribution of LCC to yield formation at the flowering and boll development stage in different cotton varieties. Cluster analysis confirmed that the dynamic trends of PH and LCC were closely related to yield, indicating that PH and LCC could be used as a bridge between remote sensing data and yield. This study highlights the value of UAV-based multi-dimensional and multi-temporal data fusion of growth features in yield estimation models, enabling a deeper understanding of yield formation mechanisms and providing novel methodological tools for phenomics research and precision agriculture management.
Why it matches plant phenotyping methodsUAV LiDAR・マルチスペクトルデータから植物高と葉クロロフィル含量を推定するセンサ融合・機械学習手法が研究の中心であり、収量推定への応用も技術的に評価されている。
abstractan innovative framework was introduced to construct plant height (PH) and leaf chlorophyll content (LCC) inversion models for UAV LiDAR and multispectral data through different strategies.
Accurate crop yield prediction is essential for enhancing agricultural sustainability and guiding economic policy decisions. It is effective to fuse multi-source remote sensing data to predict crop yields, but difficult to reveal the effects of physiological processes on yield estimation models, and challenging to guide crop field production and management. In this study, an innovative framework was introduced to construct plant height (PH) and leaf chlorophyll content (LCC) inversion models for UAV LiDAR and multispectral data through different strategies. PH and LCC, two key growth features affecting cotton yield, were evaluated using multiple linear regression (MLR), partial least squares regression (PLSR), and extreme gradient boosting (XGBoost) algorithms for single-feature and multi-feature fusion, respectively. The multi-feature fusion model based on the XGBoost algorithm was significantly better than the single-feature model (R²=0.744). Further optimization of the multi-feature fusion model revealed that multi-temporal growth features as input variables significantly improved the accuracy of the multi-feature fusion model compared with that based on single-temporal (R²=0.802). Shapley additive explanations (SHAP) analysis revealed the key contribution of LCC to yield formation at the flowering and boll development stage in different cotton varieties. Cluster analysis confirmed that the dynamic trends of PH and LCC were closely related to yield, indicating that PH and LCC could be used as a bridge between remote sensing data and yield. This study highlights the value of UAV-based multi-dimensional and multi-temporal data fusion of growth features in yield estimation models, enabling a deeper understanding of yield formation mechanisms and providing novel methodological tools for phenomics research and precision agriculture management. • UAV-LiDAR multi-temporal data achieved accurate reconstruction of PH by linear regression. • UAV-multispectral multi-temporal data enabled precise inversion of LCC by XGBoost algorithm. • PH and LCC dynamics data fusion provide mechanistic insights into cotton yield formation. • LCC at the flowering and boll development stage makes a key contribution to yield formation.
Why it matches plant phenotyping methodsUAV LiDAR・マルチスペクトルデータから草丈と葉クロロフィル含量を推定するモデル開発・融合・最適化が研究の中心であり、植物形質の取得手法として実質的です。
abstractan innovative framework was introduced to construct plant height (PH) and leaf chlorophyll content (LCC) inversion models for UAV LiDAR and multispectral data through different strategies.
The primary goal of this study was to create a Bayesian framework that would incorporate remote sensing data to automatically calibrate the AquaCrop model for simulating cotton responses to irrigation strategies in the northern border of the United States Cotton Belt, which faces a lack of observational data. Multiple regression models (linear and non-linear) were fitted to establish a correlation between cotton canopy cover (CC) values and aerial vegetation indices (EVI, EVI2, MACARI, NDRE, NDVI, NDSVI, OSAVI, and VARI) obtained from sUAS multispectral imagery for 2021 and 2022 growing seasons. The highest correlation was found between RGB-Based VARI index and cotton CC by fitting the linear model (R² = 0.83 and RMSE = 0.12), which contradicted the results of other studies that emphasized the importance of using red-edge and near-infrared for monitoring crop canopy cover. A considerably less accurate correlation was detected for fitting the polynomial model (0.4 <RMSE<4.2). Furthermore, the MCARI index was found unsuitable for cotton monitoring under water stress conditions. Afterward, the Bayesian theorem-based Generalized Likelihood Uncertainty Estimation (GLUE) algorithm was linked to AquaCrop in the R environment to calibrate the model based on the remotely sensed CCs by seeking posterior distributions of the parameters through the Monte Carlo approach. Then, the model was validated for its key outputs, including cotton biomass, yield, and soil water content. The simulated CC results showed the model's automatic calibration success. The best performances of the model were found for simulating cotton biomass under 70 % and 80 % deficit irrigation conditions Pe = 0.88 % and −0.38 %) in 2022 and full irrigation conditions in 2021 Pe = 3.19 %); however, the biomass simulations were satisfactorily under all irrigation conditions. The outstanding performance of the AquaCrop was confirmed for reproducing cotton yield values regardless of irrigation conditions. The accurate retrieval of soil water dynamics by the model can introduce the framework created in this study as a robust tool to derive soil water content at cotton rootzone by having access to aerial RGB images. Overall, the findings of this study revealed that by supplying the introduced framework with one seasonal remote sensing data, the AquaCrop could successfully be turned into a decision support system for exploring irrigation scheduling strategies for producers of cotton in Kansas.
Why it matches plant phenotyping methods航空マルチスペクトル画像から綿花のキャノピー被覆を推定し、Bayesian/AquaCrop frameworkで植物形質・収量を検証する測定・解析手法が研究の中心である。
abstractMultiple regression models (linear and non-linear) were fitted to establish a correlation between cotton canopy cover (CC) values and aerial vegetation indices
Cotton leaf diseases significantly impact global cotton yield and quality, threatening the livelihoods of millions of farmers. Traditional diagnostic methods are often slow, subjective, and unsuitable for large-scale agricultural monitoring. This study proposes an interpretable and efficient deep learning (DL) framework for the accurate classification of cotton leaf diseases using a hybrid architecture that combines EfficientNetB3 and InceptionResNetV2. The system demonstrates excellent performance, achieving 98.0% accuracy, 98.1% precision, 97.9% recall, an F1-score of 98.0%, and an AUC-ROC of 0.9992. Minimal overfitting was observed, with low training and validation losses and high per-class performance, even in visually similar disease cases such as bacterial blight and target spot. In addition to strong predictive accuracy, the framework incorporates explainable AI (XAI) techniques, including LIME and SHAP, to enhance model transparency. These tools highlight the key visual features used in predictions, providing valuable insights for agronomists and improving trust in AI-based systems. The model is lightweight and scalable, making it deployable on mobile or edge devices for real-time field applications. Overall, this research demonstrates the potential of combining transfer learning and XAI to develop reliable, interpretable, and field-ready diagnostic tools for precision agriculture.
Why it matches plant phenotyping methods綿花葉の病害状態を画像から分類する深層学習・説明可能AI手法の開発が研究の中心であり、植物病害表現型の取得・推定に該当する。
abstractThis study proposes an interpretable and efficient deep learning (DL) framework for the accurate classification of cotton leaf diseases using a hybrid architecture that combines EfficientNetB3 and InceptionResNetV2.
Reproduction assets foundThe paper's Data Availability Statement explicitly points to two public sources for the cotton leaf disease image dataset used in this study: a GitHub repository and a Kaggle dataset. Both are paper-specific, public, and actionable. No author analysis code or trained model checkpoints are explicitly deposited.Dataset · publicThe dataset used in this study is publicly available at [ https://github.com/gurjot000/cotton‐leaf‐disease/tree/main ] and [ https://www.kaggle.com/datasets/ataher/cotton‐leaf‐disease‐dataset/data ].Open asset ↗lines:650-650Dataset · publicThe dataset used in this study is publicly available at [ https://github.com/gurjot000/cotton‐leaf‐disease/tree/main ] and [ https://www.kaggle.com/datasets/ataher/cotton‐leaf‐disease‐dataset/data ].Open asset ↗gurjot000/cotton‐leaf‐diseaselines:650-650Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published21 Jul 2025International Journal of Scientific Research in Science and TechnologyCited by 0 · OpenAlex ↗
Cotton, often referred to as “white gold,” is one of India's most critical cash crops, forming the backbone of both the agricultural and textile sectors. However, despite its economic significance, cotton cultivation is increasingly threatened by the emergence of severe plant diseases such as Bacterial Blight, Cotton Leaf Curl Virus (CLCuV), and Fusarium Wilt. These diseases not only diminish yield but also exacerbate rural distress, especially in drought-prone regions like Marathwada, Maharashtra. This research explores the biological characteristics and economic impact of these diseases, reviews conventional and advanced detection methods, and proposes a hybrid technological framework involving IoT sensors and Convolutional Neural Networks (CNN) for early disease diagnosis. With real-time monitoring, image-based classification, and predictive analytics, this model aims to empower farmers, reduce production losses, and promote sustainable cotton farming practices.
Why it matches plant phenotyping methods綿花の病害症状を画像・IoTセンサー・CNNで早期診断する枠組みを中心的に提案しており、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します。
abstractproposes a hybrid technological framework involving IoT sensors and Convolutional Neural Networks (CNN) for early disease diagnosis.
Accurate quantification of open bolls and their distribution is crucial for understanding cotton growth, development, and yield in optimized crop management and enhanced plant breeding. Manual boll counting methods are time-consuming, labor-intensive, and subjective. Leveraging the potential of high-resolution images for high-throughput phenotyping offers a promising avenue for efficient trait quantification. The objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources. A DJI Phantom 4 RTK Unmanned Aerial System (UAS) equipped with a 4 K RGB camera was used to acquire high-resolution RGB images, and a DJI Matrice 300 RTK with a Zenmuse L1 sensor was used to acquire LiDAR point cloud data. The RGB images were converted to point cloud using photogrammetry by measuring multiple points of overlapping images. The boll detection workflow involved data filtering and clustering using the density-based spatial clustering of applications with noise (DBSCAN) method. Evaluation of the methods involved 48 plots representing small, medium, and large plant sizes using metrics including mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (r²). The methods using both data sources performed well in estimating open bolls, with LiDAR point cloud data slightly outperforming those derived from RGB images. Generally, the performance of the DBSCAN method in boll detection improved with decreasing plant sizes. Specifically, LiDAR data yielded MAPE values of 5.03 %, 8.05 %, and 13.46 %, RMSE values of 7.26, 14.33, and 23.40 bolls per m², and r 2 values of 0.93, 0.84, and 0.84 for small, medium, and large plant sizes, respectively. RGB image-based data exhibited MAPE values of 7.21 %, 6.49 %, and 16.41 %, RMSE values of 11.05, 13.66, and 26.49 bolls per m², and r 2 values of 0.82, 0.74, and 0.83 for small, medium, and large plant sizes, respectively. The method demonstrates the potential of RGB imagery and LiDAR data for estimating boll counts, offering valuable tools for enhanced plant phenotyping in plant breeding and site-specific crop management. Both data sources underestimated boll counts, with smaller plants showing less undercounting, likely due to improved light penetration and separation of bolls. These findings highlight the influence of plant structure on boll detection accuracy and the need to address challenges posed by dense canopies to enhance detection reliability. • The study developed methods to count open cotton bolls using LiDAR point clouds and RGB images. • LiDAR slightly outperformed the RGB image-derived point cloud, with better accuracy for smaller plants due to less canopy density. • Dense canopies reduced detection accuracy, highlighting the influence of plant structure.
Why it matches plant phenotyping methodsLiDARとRGB画像を用いて綿花の開花ボール数を検出・計数する手法を開発し、精度比較・検証しており、植物表現型取得が研究の中心です。
abstractThe objectives of this study were to develop methods to detect and count open cotton bolls using LiDAR point cloud and RGB images and to compare the effectiveness of these two data sources.
Cotton leaf diseases can lead to substantial yield losses and economic burdens. Traditional detection methods are challenged by low accuracy and high labor costs. This research presents the ACURS-YOLO network, an advanced cotton leaf disease detection architecture developed on the foundation of YOLOv11. By integrating a medical image segmentation model, it effectively tackles challenges including complex background interference, the missed detection of small targets, and restricted generalization ability. Specifically, the U-Net v2 module is embedded in the backbone network to boost the multi-scale feature extraction performance in YOLOv11. Meanwhile, the CBAM attention mechanism is integrated to emphasize critical disease-related features. To lower the computational complexity, the SPPF module is substituted with SimSPPF. The C3k2_RCM module is appended for long-range context modeling, and the ARelu activation function is employed to alleviate the vanishing gradient problem. A database comprising 3000 images covering six types of cotton leaf diseases was constructed, and data augmentation techniques were applied. The experimental results show that ACURS-YOLO attains impressive performance indicators, encompassing a mAP_0.5 value of 94.6%, a mAP_0.5:0.95 value of 83.4%, 95.5% accuracy, 89.3% recall, an F1 score of 92.3%, and a frame rate of 148 frames per second. It outperforms YOLOv11 and other conventional models with regard to both detection precision and overall functionality. Ablation tests additionally validate the efficacy of each component, affirming the framework's advantage in addressing complex detection environments. This framework provides an efficient solution for the automated monitoring of cotton leaf diseases, advancing the development of smart sensors through improved detection accuracy and practical applicability.
Why it matches plant phenotyping methods綿花葉の病徴を画像から検出するYOLOベース手法を開発し、データセット、比較実験、アブレーション試験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。
abstractThis research presents the ACURS-YOLO network, an advanced cotton leaf disease detection architecture developed on the foundation of YOLOv11.
Drought stress has a significant impact on cotton growth, development, and productivity. This study conducted drought stress treatment and normal water treatment (control group) on 502 cotton accessions and analyzed data on eight phenotypic traits closely related to drought stress tolerance. The results showed that all indicators changed significantly under drought stress conditions compared to the control group, with varying degrees of response among different indicators. To comprehensively evaluate the drought resistance of cotton during the germination period, the values of drought resistance comprehensive evaluation (D-value), weight drought resistance coefficient (WDC-value), and comprehensive drought resistance coefficient (CDC-value) were calculated based on membership function analysis and principal component analysis. Cluster analysis based on the D-value divided the germplasm into five drought-resistant grades, followed by the selection of one extreme material, each from the strongly drought-resistant and strongly drought-sensitive groups. An evaluation model was established using stepwise regression analysis, including the following effective indicators: Relative Fresh Weight (RFW), Relative Hypocotyl Length (RHL), Relative Seeds Water Absorption Rate (RAR), Relative Germination Rate (RGR), Relative Germination Potential (RGP), and Relative Drought Tolerance Index (RDT). The validation of the D-value prediction model based on the Best Linear Unbiased Prediction (BLUP) showed that the results obtained from two independent biological replicates were highly consistent. The comprehensive evaluation system and screening indicators established in this study provide a reliable method for identifying drought tolerance during the germination period.
Why it matches plant phenotyping methods発芽期の干ばつ耐性を複数の植物形質から統合評価・予測する評価システムとモデルを構築し、独立反復で検証しており、表現型評価手法が研究の中心である。
abstractTo comprehensively evaluate the drought resistance of cotton during the germination period, the values of drought resistance comprehensive evaluation (D-value), weight drought resistance coefficient (WDC-value), and comprehensive drought resistance coefficient (CDC-value) were calculated based on membership function analysis and principal component analysis.
CottonAerial / UAVFlowerFruitGrowth / time-series analysisGrowth / development / phenology
Societal Impact Statement Plant breeding is a critical tool for increasing the productivity, climate resilience, and sustainability of agriculture, but current phenotyping methods are a bottleneck due to the amount of human labor involved. Here, we demonstrate high‐throughput phenotyping with an unmanned aerial vehicle (UAV) to analyze the season‐long flowering pattern in cotton, subsequently mapping relevant genetic factors underpinning the trait. Season‐long flowering is a complex trait, with implications for adaptation of perennials to specific environments. We believe our approach can improve the speed and efficacy of breeding for a variety of woody perennials. Summary Many perennial plants make important contributions to agroeconomies and agroecosystems but have complex architecture and/or long flowering duration that hinders measurement and selection. Iteratively tracking productivity over a long flowering/fruiting season may permit the identification of genetic factors conferring different reproductive strategies that might be successful in different environments, ranging from rapid early maturation that avoids stresses, to late maturation that utilizes the full seasonal duration to maximize productivity. In cotton, a perennial plant that is generally cultivated as an annual crop, we apply aerial imagery and deep learning methods to novel and stable genetic stocks, identifying genetic factors influencing the duration and rate of fruiting. Our phenotyping method was able to identify 24 QTLs that affect flowering behavior in cotton. A total of five of these corresponded to previously identified QTLs from other studies. While these factors may have different relationships with crop productivity and quality in different environments, their determination adds potentially important information to breeding decisions. With transfer learning of the deep learning models, this approach could be applied widely, potentially improving gains from selection in diverse perennial shrubs and trees essential to sustainable agricultural intensification.
Why it matches plant phenotyping methodsUAV画像と深層学習による綿花の季節的開花・結実形質の高スループット表現型計測が研究の中心であり、遺伝解析にも用いられているため。
abstractHere, we demonstrate high‐throughput phenotyping with an unmanned aerial vehicle (UAV) to analyze the season‐long flowering pattern in cotton
Cotton is the most widely cultivated natural fiber crop worldwide, yet it is highly susceptible to various diseases and pests that significantly compromise both yield and quality. To enable rapid and accurate diagnosis of cotton diseases and pests-thus supporting the development of effective control strategies and facilitating genetic breeding research-we propose a lightweight model, the Resource-efficient Cotton Network (RF-Cott-Net), alongside an open-source image dataset, CCDPHD-11, encompassing 11 disease categories. Built upon the MobileViTv2 backbone, RF-Cott-Net integrates an early exit mechanism and quantization-aware training (QAT) to enhance deployment efficiency without sacrificing accuracy. Experimental results on CCDPHD-11 demonstrate that RF-Cott-Net achieves an accuracy of 98.4%, an F1-score of 98.4%, a precision of 98.5%, and a recall of 98.3%. With only 4.9 M parameters, 310 M FLOPs, an inference time of 3.8 ms, and a storage footprint of just 4.8 MB, RF-Cott-Net delivers outstanding accuracy and real-time performance, making it highly suitable for deployment on agricultural edge devices and providing robust support for in-field automated detection of cotton diseases and pests.
Why it matches plant phenotyping methods綿花の病害を画像から分類する軽量深層学習モデルと画像データセットを開発・評価しており、植物の病害状態を抽出するフェノタイピング手法が中心である。
abstractwe propose a lightweight model, the Resource-efficient Cotton Network (RF-Cott-Net), alongside an open-source image dataset, CCDPHD-11, encompassing 11 disease categories.
Reproduction assets foundThe paper's authors publicly released their self-constructed cotton disease/pest image dataset CCDPHD-11 (18,953 images, 11 classes) on GitHub, as stated in the Data Availability Statement. No code or trained model deposit is mentioned.Dataset · publicew and editing, K.C., H.W., P.W.C. and R.-F.W.; visualization, H.-W.Z. and R.-F.W.; supervision, H.W., P.W.C. and R.-F.W.; project administration, H.W., P.W.C. and R.-F.W. All authors have read and agreed to the published version of the manuscript.
Data Availability Statement
The proposed CCDPHD-11 dataset can be found online ( https://github.com/SweefongWong/CCDPHD-11-Dataset , accessed on 9 March 2025).
Conflicts of Interest
The authors declare no conflicts of interest.
Funding Statement
This research received no external funding.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributOpen asset ↗SweefongWong/CCDPHD-11-Datasetlines:384-405Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Cotton, the backbone of global textile production, demands sustainable agricultural practices to ensure fiber, food, and environmental security. Cotton crop play an essential role in farming economies; however, production is sometimes affected by various diseases that harm production. We proposed a methodology that uses formal modeling and verification for requirements confirmation to improve the monitoring and detection of cotton crop diseases. The correct information and requirements about disease symptoms can improve disease monitoring and prediction. The Temporal Logic of Action (TLA+) is used to construct a mathematical model to verify requirements by providing disease symptoms and then model checking to ensure correctness properties. Using model checking in TLA + ensures the reliability and correctness of disease symptom detection. We consequently used deep learning models to predict cotton diseases, i.e., Aphids, Armyworms, Bacterial Blight, Powdery Mildew, Target Spot, and Healthy leaf. Our results show that the Convolutional Neural Network (CNN) model achieved an overall accuracy of 98.7% with class-specific accuracy ranging from with F1-scores across all classes (e.g., 0.90 for Powdery Mildew and 0.87 for Army Worm).
Why it matches plant phenotyping methods綿花葉の病徴を画像から深層学習で分類・検出する手法が研究の中心であり、植物の病害状態を推定するため、植物フェノタイピング手法として収録対象。
abstractWe proposed a methodology that uses formal modeling and verification for requirements confirmation to improve the monitoring and detection of cotton crop diseases.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publictoolbox modules, are publicly available on GitHub at https://github.com/abdulpk/MuhammadMusa for furtherOpen asset ↗https://github.com/abdulpk/MuhammadMusapdf-page:18 lines:1-64Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Jul 2025Computers and Electronics in Agriculture.
The research introduced a novel vegetation index (DCBVI) to effectively monitor dusky cotton bug infestations, addressing the limitations of existing indices, which show low sensitivity to these infestations. Non-imaging hyperspectral data and UAV multispectral data of the cotton canopy were collected during the flowering and boll development stages over two years, and the first-order differential values of each hyperspectral band were calculated. The Pearson correlation coefficient and Relief-F algorithm were used to select the hyperspectral bands associated with insect stress caused by the dusky cotton bug. Four novel vegetation indices were constructed based on difference, ratio, normalization and chlorophyll vegetation index. The vegetation index most strongly correlated with insect pest stress was selected and designated as the Dusky Cotton Bug Vegetation Index (DCBVI). Combined with the existing vegetation indices, GDLR, SVM and CatBoost were used to construct the pest stress classification model of dusky cotton bug, and DCBVI was derived using relevant bands from UAV multispectral remote sensing data. The pest discrimination model for the cotton bug was established, and the pest distribution in cotton fields was visualized. To research the effect of dusky cotton bug infestation on cotton growth, SPAD inversion model for different infestation levels were established by KNN, RF and CatBoost. The Firefly algorithm (FA), Bat algorithm (BA), and Genetic algorithm (GA) were used to optimize both the classification and discrimination models for dusky cotton bug infestation and the cotton SPAD inversion model. The results showed that the correlation between DCBVI and the pest stress level of the dusky cotton bug was 0.833, and the correlation between DCBVI and SPAD was higher than that of existing vegetation indices. The accuracy, precision, recall, and F1 score of the CatBoost dusky cotton bug insect pest classification model, optimized by FA based on DCBVI and the existing vegetation index, reached 93.1%, 93.2%, 92.9%, and 92.8%, respectively. When the BA-CatBoost model was used to invert the SPAD values of the dusky cotton bug under different insect stress, the R² and RMSE of the optimal model were 0.949 and 1.619. This research significantly improves the accuracy of dusky cotton bug pest monitoring and provides a method for constructing a novel sensitivity index based on hyperspectral data for monitoring other insect pests.
Why it matches plant phenotyping methods綿花の害虫ストレスとSPADという植物状態を、ハイパースペクトル・UAVデータから推定する新規植生指数と分類／反演モデルの開発・検証が研究の中心である。
abstractThe research introduced a novel vegetation index (DCBVI) to effectively monitor dusky cotton bug infestations
Leaf shape is of great significance in plant phenotype research. Landmarks method is a widely used morphometric approach, which can comprehensively describe the morphological differences among leaves. However, the selection of landmarks is time-consuming and laborious. An automatic landmarking algorithm is proposed here. Based on conformal mapping, the leaf outline can be transformed into a monotonically increasing function curve, referred to as the ’fingerprint function’. The Dynamic Time Warping (DTW) algorithm was introduced to match landmarks between different leaves. Two leaf datasets were used to validate the algorithm separately in different species and developmental stages. Dataset1 is a public dataset which covers 26 different types of leaves. The average positional difference between automatic and manual landmarks for dataset1 was only 2.95%. Dataset2 consists of cotton leaves collected in the field at various growth stages, and the positional difference for this dataset was all below 5%. These results validate that our algorithm is applicable to a wide range of leaf types and capable of identifying and locating novel features that emerge during leaf growth. The automatic landmarking algorithm can simulate manual landmarking to a great extent. It provides a new approach for automated acquisition of plant leaf shape homology tailored to the research needs of botanists.
Why it matches plant phenotyping methods葉形態のランドマークを自動抽出する手法を開発し、複数の葉データセットで精度検証しているため、植物表現型取得法が研究の中心です。
abstractAn automatic landmarking algorithm is proposed here.
Cotton is an economically important crop cultivated worldwide for textile production. Breeding programs focus on selecting genotypes with favorable traits for high yields. This study introduced 3D Gaussian Splatting (3DGS) to reconstruct high-fidelity three-dimensional (3D) models and developed a segmentation workflow, Cotton3DGaussians, to analyze cotton bolls and extract architectural traits from single plants. Cotton plants were scanned 360° using a smartphone, and photogrammetry was used to estimate camera parameters and reconstruct a sparse point cloud, which was then optimized into a 3DGS model. In Cotton3DGaussians, 2D masks of bolls segmented from four views were mapped to 3D space, and redundant bolls were removed through cross-view clustering. YOLOv11x and a foundation model, segment anything model (SAM), were compared to obtain 2D masks, with YOLOv11x achieving an F1-score 5.9 % higher than SAM. Phenotypic traits such as boll number, volume, plant height, and canopy size were estimated. The 3DGS model exhibited superior rendering quality, achieving a peak signal-to-noise ratio (PSNR) that was 6.91 higher than NeRF. Cotton3DGaussians effectively segmented 3D bolls from multiple views, with mean absolute percentage errors (MAPE) of 9.23 % for boll number, 3.66 % for canopy size, 2.38 % for plant height, and 8.17 % for boll volume compared to LiDAR ground truth. The regression analysis between convex boll volume and boll weight showed a 19.3 % weight error per plant. This study demonstrates the potential of 3DGS for low-cost, high-fidelity 3D modeling, enabling high-resolution phenotyping and advancing cotton breeding programs. The methodology can also be applied to other crops for improved 3D trait measurement research and enhanced productivity.
Why it matches plant phenotyping methods3Dガウシアンスプラッティングと多視点画像・セグメンテーションを組み合わせ、ワタのボールおよび植物体形質を抽出・検証する手法が研究の中心である。
abstractThis study introduced 3D Gaussian Splatting (3DGS) to reconstruct high-fidelity three-dimensional (3D) models and developed a segmentation workflow, Cotton3DGaussians, to analyze cotton bolls and extract architectural traits from single plants.
Uncrewed Aircraft Systems (UAS) are widely used for crop growth monitoring and yield estimation in Precision Agriculture (PA). However, UAS are limited by their relatively small area coverage, high cost, and high data processing needs. High resolution satellites (such as SkySat) are valuable alternatives to UAS in PA. Nonetheless, persistent cloud cover, especially in regions like the South of Texas, limits their utility. This study compared and explored the integration of satellite and UAS imagery for cotton yield estimation. The rationale was to determine the best performing platform among the two, as well as leverage their synergy to mitigate data gaps caused by persistent cloud cover. Using deep learning model, vegetation indices derived from SkySat and P4M (Phantom 4 Multispectral) images were correlated with crop yield data collected during the 2023 season. Results demonstrated that SkySat slightly outperformed P4M in yield estimation, with median accuracies of R² = 0.81 and RMSE = 0.20 ton/ha for SkySat, compared to R² = 0.80 and RMSE = 0.21 ton/ha for P4M. More importantly, when all the SkySat and P4M datasets were combined, accuracy improved by 3 % compared to SkySat-only data. In addition, data collected between 74 and 114 days after planting contributed most significantly to yield prediction. The fusion approach used in this study allows for better spatial and temporal coverage, ultimately enhancing yield prediction reliability in PA. Future research should explore the inclusion of additional sensors such as Synthetic Aperture Radar (SAR) and thermal imagery, which could further improve yield prediction accuracy, especially in cloud-prone regions.
Why it matches plant phenotyping methods衛星・UAS画像と深層学習を用いた綿花収量推定およびプラットフォーム比較が研究の中心であり、植物の収量形質を直接推定するため含める。
abstractThis study compared and explored the integration of satellite and UAS imagery for cotton yield estimation.
Early crop disease detection remains challenging for precision agriculture. This research presents an AI-drone surveillance system using YOLOv12 deep learning model to automatically identify diseases for real-time monitoring in potato, banana, and cotton crops. The complete pipeline includes automated image acquisition, intelligent preprocessing, and real-time analysis. Compared to traditional manual inspection, this approach reduces diagnosis time from days to minutes while improving reliability. Key innovations include optimized model architectures for resource-limited environments and multi-spectral disease pattern recognition. Field tests confirm the system's robustness across varying weather conditions and growth stages. Proposed method processes the drone-captured images through Raspberry Pi edge computing, achieving 99.5%, 98.1%, and 89.7% detection accuracy of potato, banana, and cotton crops respectively. The lightweight YOLO-Nano variants enable efficient field deployment while maintaining precision. A merged dataset across 28 disease classes demonstrates 91.8% overall accuracy through comprehensive validation metrics. Farmers receive immediate alerts for targeted treatment, reducing pesticide use by 30-45% in trial implementations. This scalable solution outperforms existing methods in both speed (4.2ms per image) and accuracy. Results demonstrate practical potential for transforming global agricultural monitoring through accessible AI technology.
Why it matches plant phenotyping methods植物の病害状態をドローン画像から推定するYOLOベースの取得・解析パイプラインを開発し、圃場で検証しており、表現型取得法が研究の中心である。
abstractThis research presents an AI-drone surveillance system using YOLOv12 deep learning model to automatically identify diseases for real-time monitoring in potato, banana, and cotton crops.
Image-based phenotyping represents an innovative approach in breeding research, allowing rapid and reliable measurements that aid in decision-making. This study used image analysis to quantify the intensity of fiber coloration in Gossypium hirsutum L. genotypes and investigate the impact of bolls from the upper, middle and lower thirds of the plants. Seventeen cotton genotypes (5 cultivars and 12 F2:3 generation genotypes) were evaluated in relation to the intensity of fiber coloration. Three representative bolls were manually harvested per plot, per third development, and were photographed, including a colorcard, and these were processed with ImageJ software for correction of brightness and coloration. Data analysis was performed in R software, and the FIELDimageR package was used to calculate the BI, SI and NGRDI indices. Calibration of the images with colorcards resulted in higher averages but did not change the grouping of the observed averages. Compared with the other thirds, the upper third presented lower fiber staining intensity, with a variation of 6.25%, but there was no interaction between the genotypes and thirds. The SI allowed the distinction of genotypes by the saturation of the fiber color. The BI was correlated with the fiber characteristics UI, STR and UHML, with coefficients of determination of 0.77, 0.80 and 0.88, respectively. This digital cotton fiber quality evaluation method streamlines breeding decisions, enhances data accuracy and reduces costs.
Why it matches plant phenotyping methods綿花繊維の着色強度という植物器官形質を、RGB画像、色カード補正、ImageJ、FIELDimageR指標で定量化する方法が研究の中心であり、育種判断への適用と指標の評価も行っている。
abstractImage-based phenotyping represents an innovative approach in breeding research, allowing rapid and reliable measurements
Cotton is one of the most important cash crops globally, contributing significantly to the agricultural economy and textile industries.However, cotton production is often affected by various diseases such as bacterial blight, leaf curl virus, and fungal infections, which lead to substantial yield losses and reduced fiber quality.Traditionally, disease detection in cotton relies on manual observation and expert knowledge, which is time-consuming, labor-intensive, and prone to human error.Cotton is a vital cash crop whose productivity is significantly affected by various diseases.Early and accurate detection of these diseases is essential to prevent crop loss and improve yield.This study explores the application of machine learning techniques for detecting cotton leaf diseases using image processing and classification models.Various algorithms, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forests, are evaluated for their effectiveness in identifying common cotton diseases.The study aims to assist farmers and agronomists in disease management, promoting healthier crops and improved yield through technology-driven solutions.
Why it matches plant phenotyping methods綿花葉の病害状態を画像から推定する機械学習手法を比較評価しており、植物病害フェノタイピングが中心的な方法論的貢献である。
abstractThis study explores the application of machine learning techniques for detecting cotton leaf diseases using image processing and classification models.
Cotton is one of the most valuable non-food agricultural products in the world. However, cotton production is often hampered by the invasion of disease. In most cases, these plant diseases are a result of insect or pest infestations, which can have a significant impact on production if not addressed promptly. It is, therefore, crucial to accurately identify leaf diseases in cotton plants to prevent any negative effects on yield. This paper presents a hybrid deep learning approach based on Bidirectional Encoder Representations from Transformers with Residual network and particle swarm optimization (BERT-ResNet-PSO) for detecting cotton plant diseases. This approach starts with image pre-processing, which they pass to a BERT-like encoder after linearly embedding the image patches. It results in segregating disease regions. Then, the output of the encoded feature is passed to ResNet-based architecture for feature extraction and further optimized by PSO to increase the classification accuracy. The approach is tested on a cotton dataset from the Plant Village dataset, where the experimental results show the effectiveness of this hybrid deep learning approach, achieving an accuracy of 98.5%, precision of 98.2% and recall of 98.7% compared to the existing deep learning approaches such as ResNet50, VGG19, InceptionV3, and ResNet152V2. This study shows that the hybrid deep learning approach is capable of dealing with the cotton plant disease detection problem effectively. This study suggests that the proposed approach is beneficial to help avoid crop losses on a large scale and support effective farming management practices.
Why it matches plant phenotyping methods葉画像から綿花の病害領域を抽出・分類する深層学習手法を開発し、既存手法と比較評価しており、植物病害状態の表現型取得が中心である。
abstractThis paper presents a hybrid deep learning approach based on Bidirectional Encoder Representations from Transformers with Residual network and particle swarm optimization (BERT-ResNet-PSO) for detecting cotton plant diseases.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicdataset can be obtained from the following link: https://www.kaggle.com/datasets/Open asset ↗Kagglepdf-page:10 lines:1-17Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Early monitoring of cotton Verticillium wilt (VW) is crucial for preventing significant yield losses and quality deterioration. Current hyperspectral approaches often overlook the bottom-up disease progression and the impact of leaf stratification on VW detection. To address this, vertical spectral traits were examined to improve early diagnosis. A total of 551 in-situ leaf spectra were averaged from thousands of measurements, alongside corresponding RGB images from top, middle, and bottom leaf layers. Five severity levels (SL=0-4) were classified based on lesion coverage. Various vegetation indices and signal features were extracted for VW identification. Three feature selection methods, Relief-F, Lasso, and Random Forest (RF), were integrated with five machine learning models, including LightGBM, ANN, XGBoost, RF, and SVM. Results showed that spectral reflectance varied significantly by severity and layer, with the most pronounced variations in the bottom layer's visible spectrum. LightGBM with RF-selected features achieved the best performance and fastest training, with accuracies of 0.82, 0.81, and 0.91 for the top, middle, and bottom leaf layers, respectively. Early-stage detection (SL=0-2) was most effective in the lowest layer, showing 38% and 34% higher precision (SL=1) than the upper two. Critical spectral features varied with vertical leaf layers and disease severity, with blue and red-edge bands identified as most important. For assessing five disease severity levels, the most informative features for the top, middle, and bottom layers were Ant Gitelson , Blue Index (B), and PRI 570 . For detecting early symptoms (SL=1), the blue band was particularly effective, followed by water-related bands. At the initial infection stage, the most significant indicators for top, middle, and bottom layers were Blue/red index (BRI), B, and WSCT, respectively. This study deepens understanding of vertical leaf spectral dynamics and enables rapid, non-destructive in vivo detection of cotton Verticillium wilt, enhancing the applicability of portable hyperspectral devices and informing leaf-layer-aware precision disease management strategies.
Why it matches plant phenotyping methods本研究は、葉の分光・RGB観測と機械学習によりワタ萎凋病の病徴および重症度を推定する手法を開発・評価しており、植物表現型取得が中心である。
abstractvertical spectral traits were examined to improve early diagnosis.
Real-time monitoring of cotton moisture status and determination of appropriate irrigation thresholds are essential for achieving precision irrigation. Currently employed diagnostic methods based on physiological indicators, remote sensing, or soil moisture measurements typically present limitations including cumbersome procedures, high labor intensity, requirements for specialized technical expertise, and delayed results. To address these challenges, this study investigated the relationship between plant water content and leaf RGB color values (red, green, and blue color values measured using LScolor technology) during the bud, flowering, and boll development stages, with the objective of establishing a predictive model for rapid, real-time moisture status monitoring. Given that leaf position and color values (R, G, and B) of different functional leaves may influence the relationship between leaf color and plant water content, and this relationship varies across different temporal periods, a two-year experiment was conducted. In 2023, leaf color data from the top five functional leaves were measured at five time points daily throughout the irrigation cycle. In 2024, the following four irrigation treatments were established: one conventional irrigation control treatment (CK) and three irrigation treatments at 72% (T1), 70% (T2), and 68% (T3) plant water content thresholds. Results demonstrated that the following: (1) plant water content initially declined during the day and subsequently showed slight recovery, indicating cotton’s particular susceptibility to water stress between 2:30 p.m. and 7:00 p.m.; (2) plant water content continuously decreased across five measurement periods following irrigation during the bud, flowering, and boll development stages, with R and G color values of the five functional leaves showing declining trends between 2:30 p.m. and 7:00 p.m., while B color values exhibited no consistent pattern; (3) correlation analysis revealed significant positive correlations between plant water content and R and G color values of the five functional leaves during the 2:30 p.m. to 5:00 p.m. period, with highly significant correlations observed for the third and fourth leaves from the apex; (4) univariate and bivariate linear regression models were successfully established between cotton water content and R and G color values of the third and fourth leaves from the top; and (5) under 72% plant water content conditions, cotton achieved the highest yield and Irrigation Water Use Efficiency, indicating that 72% represents the optimal irrigation threshold. In conclusion, integrating leaf color–plant water content relationships with the 72% irrigation threshold enables rapid, non-destructive, large-scale diagnosis of cotton moisture status, providing a robust foundation for implementing effective precision irrigation strategies.
Why it matches plant phenotyping methods綿花葉のRGB色値から植物含水状態を推定する非破壊・リアルタイム診断モデルの構築が研究の中心であり、単なる灌漑試験の routine 測定ではない。
abstractwith the objective of establishing a predictive model for rapid, real-time moisture status monitoring
CottonSunflowerLeafPhysiological trait estimationWater status / transpiration
Precision agriculture aims to increase crop yield while reducing the use of harmful chemicals, such as pesticides and excess fertilizer, using minimal, tailored interventions. However, these strategies are limited by factors such as sensor quality, which typically relies on visual plant expression, and the manual, destructive nature of many nonvisual measurement methods, including the Scholander pressure bomb. By automating more intimate interactions with foliage in vivo, it would be possible to inject chemical and biological probes that reveal more phenotypes—such as water stress in response to varying environmental conditions and visible gene expression to measure the success of gene engineering applications. To address this, we developed a soft robotic leaf gripper and stamping-injection method to improve foliar delivery of nanoscale synthetic and biological probes. This allows for nondestructive, in situ, multispecies applications. We used two probes: Agrobacterium tumefaciens carrying the RUBY gene as a reporter system for plant transformation and nanoparticle hydrogels for measuring leaf water potential (ψ). Our hourglass-shaped design enabled the gripper to exert higher forces with reduced radial expansion compared with conventional designs, achieving an injection success rate above 91%. Studies on sunflower ( Helianthus annuus L.) and cotton ( Gossypium hirsutum L.) showed that our method achieved an average 12-fold increase in infiltration areas, with substantially less leaf damage—3.6% in sunflower and none in cotton—compared with the needle-free syringe method. Enabling long periods of successful in vivo phenotyping on both species after precise and safe foliar delivery underscores the potential of the leaf gripper for robotic plant bioengineering.
Why it matches plant phenotyping methods植物葉への非破壊プローブ導入と光学的・生理的表現型取得を可能にするソフトロボティクス手法の開発が中心であり、単なる生物学的処理実験ではない。
abstractwe developed a soft robotic leaf gripper and stamping-injection method to improve foliar delivery of nanoscale synthetic and biological probes.
Cotton is a major cash crop, and increasing its production is extremely important worldwide, especially in agriculture-led economies. The crop is susceptible to various diseases, leading to decreased yields. In recent years, advancements in deep learning methods have enabled researchers to develop automated methods for detecting diseases in cotton crops. Such automation not only assists farmers in mitigating the effects of the disease but also conserves resources in terms of labor and fertilizer costs. However, accurate classification of multiple diseases simultaneously in cotton remains challenging due to multiple factors, including class imbalance, variation in disease symptoms, and the need for real-time detection, as most existing datasets are acquired under controlled conditions. This research proposes a novel method for addressing these challenges and accurately classifying seven classes, including six diseases and a healthy class. We address the class imbalance issue through synthetic data generation using conventional methods like scaling, rotating, transforming, shearing, and zooming and propose a customized StyleGAN for synthetic data generation. After preprocessing, we combine features extracted from MobileNet and VGG16 to create a comprehensive feature vector, passed to three classifiers: Long Short Term Memory Units, Support Vector Machines, and Random Forest. We propose a StackNet-based ensemble classifier that takes the output probabilities of these three classifiers and predicts the class label among six diseases-Bacterial blight, Curl virus, Fusarium wilt, Alternaria, Cercospora, Greymildew-and a healthy class. We trained and tested our method on publicly available datasets, achieving an average accuracy of 97%. Our robust method outperforms state-of-the-art techniques to identify the six diseases and the healthy class.
Why it matches plant phenotyping methods綿花の病害症状を画像から分類する深層学習手法の開発・評価が研究の中心であり、植物の病害状態を直接推定しているため。
abstractThis research proposes a novel method for addressing these challenges and accurately classifying seven classes, including six diseases and a healthy class.
Reproduction assets foundThe paper's plant-phenotyping inputs are public cotton leaf disease image datasets. The Data Availability statement names four public datasets (Kaggle Serosh Karim, Mendeley Cotton Plant Disease, and two Roboflow datasets), and the external validation section cites a fifth public Mendeley dataset. No author analysis/trDataset · publicmodels, which affirms the ability of the model to handle diverse data in the real world and validates its potential for reliable use in practical agricultural applications.
Data Availability
The data underlying the results presented in the study are available from the following sources: (1) Kaggle: Cotton Leaf Disease Dataset ( https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset ); (2) Mendeley Data: Cotton Plant Disease Dataset ( https://data.mendeley.com/datasets/6hm6pc3y43/2 ); (3) Roboflow: Cotton Plant Disease Prediction Dataset ( https://universe.roboflow.com/national-college-of-ireland/cotton-plant-disease-prediction-igthk/dataset/3 ); (4) Roboflow: Cotton Plant DiOpen asset ↗Kagglelines:545-557Dataset · publicble use in practical agricultural applications.
Data Availability
The data underlying the results presented in the study are available from the following sources: (1) Kaggle: Cotton Leaf Disease Dataset ( https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset ); (2) Mendeley Data: Cotton Plant Disease Dataset ( https://data.mendeley.com/datasets/6hm6pc3y43/2 ); (3) Roboflow: Cotton Plant Disease Prediction Dataset ( https://universe.roboflow.com/national-college-of-ireland/cotton-plant-disease-prediction-igthk/dataset/3 ); (4) Roboflow: Cotton Plant Disease Dataset ( https://universe.roboflow.com/roboflow-100/cotton-plant-disease/dataset/2) .
Funding Statement
This work was Open asset ↗Mendeley Datalines:545-557Dataset · publicin the study are available from the following sources: (1) Kaggle: Cotton Leaf Disease Dataset ( https://www.kaggle.com/datasets/seroshkarim/cotton-leaf-disease-dataset ); (2) Mendeley Data: Cotton Plant Disease Dataset ( https://data.mendeley.com/datasets/6hm6pc3y43/2 ); (3) Roboflow: Cotton Plant Disease Prediction Dataset ( https://universe.roboflow.com/national-college-of-ireland/cotton-plant-disease-prediction-igthk/dataset/3 ); (4) Roboflow: Cotton Plant Disease Dataset ( https://universe.roboflow.com/roboflow-100/cotton-plant-disease/dataset/2) .
Funding Statement
This work was supported by KUCARS, Department of Mechanical and Nuclear Engineering, Khalifa University under Award numberOpen asset ↗Roboflowlines:545-557Dataset · publich healthy and diseased cotton leaves across different conditions, including Bacterial blight (250 images), Cotton curl virus (431 images), Herbicide growth damage (280 images), Leaf hopper Jassids (225 images), Leaf reddening (578 images), Leaf variegation (116 images), and Healthy leaf (257 images). The dataset is available at https://data.mendeley.com/datasets/b3jy2p6k8w/2
Each image captures critical disease-specific features such as leaf discoloration, curling, wilting, necrosis, and other symptomatic indicators. The dataset is particularly valuable as it includes images collected from real field environments during different growth stages of the cotton plant. These were taken under varyOpen asset ↗lines:417-499Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Phenotypic data of cotton can accurately reflect the physiological status of plants and their adaptability to environmental conditions, playing a significant role in the screening of germplasm resources and genetic improvement. Therefore, this study proposes a cotton phenotypic data extraction algorithm that integrates ResDGCNN with an improved region-growing method and constructs a 3D point cloud dataset of cotton covering the entire growth period under real growth conditions. To address the challenge of significant structural variations in cotton organs across different growth stages, we designed an innovative point cloud segmentation algorithm, ResDGCNN, which integrates residual learning with dynamic graph convolution to enhance organ segmentation performance throughout all developmental stages. In addition, to address the challenge of accurately segmenting overlapping regions between different cotton organs, we introduced an optimization strategy that combines point distance mapping with curvature-based normal vectors and developed an improved region-growing algorithm to achieve fine segmentation of multiple cotton organs, including leaves, stems, and flower buds. Experimental data show that, in the task of organ segmentation throughout the entire cotton growth cycle, the ResDGCNN model achieved a segmentation accuracy of 67.55%, with a 4.86% improvement in mIoU compared to the baseline model. In the fine-grained segmentation of overlapping leaves, the model achieved an R 2 of 0.962 and an RMSE of 2.0. The average relative error in stem length estimation was 0.973, providing a reliable solution for acquiring 3D phenotypic data of cotton.
Why it matches plant phenotyping methods綿花の全生育期間にわたる3D点群から器官を分割し、葉・茎・花蕾の表現型データを抽出する手法の開発と評価が研究の中心である。
abstractthis study proposes a cotton phenotypic data extraction algorithm that integrates ResDGCNN with an improved region-growing method
High resolution three-dimensional (3D) point clouds enable the mapping of cotton boll spatial distribution, aiding breeders in better understanding the correlation between boll positions on branches and overall yield and fiber quality. This study developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants. The data processing workflow includes two independent approaches to map the vertical and horizontal distribution of cotton bolls. The vertical distribution was mapped by segmenting bolls using PointNet++ and identifying individual instances through Euclidean clustering. For horizontal distribution, TreeQSM segmented the plant into the main stem and individual branches. PointNet++ and Euclidean clustering were then used to achieve cotton boll instance segmentation. The horizontal distribution was determined by calculating the Euclidean distance of each cotton boll relative to the main stem. Additionally, branch types were classified using point cloud meshing completion and the Dijkstra shortest path algorithm. The results highlight that the accuracy and mean intersection over union (mIoU) of the 2-class segmentation based on PointNet++ reached 0.954 and 0.896 on the whole plant dataset, and 0.968 and 0.897 on the branch dataset, respectively. The coefficient of determination (R 2 ) for the boll counting was 0.99 with a root mean squared error (RMSE) of 5.4. For the first time, this study accomplished high-granularity spatial mapping of cotton bolls and branches, but directly predicting fiber quality from 3D point clouds remains a challenge. This method provides a promising tool for 3D cotton plant mapping of different genotypes, which potentially could accelerate plant physiological studies and breeding programs.
Why it matches plant phenotyping methods3D点群の分割・個体抽出ワークフローを開発し、綿花の果実数と枝・果実の空間分布という植物形質を定量化・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 developed a segmentation workflow for point clouds of 18 cotton genotypes to map the spatial distribution of bolls on the plants.
Reproduction assets foundThe authors explicitly state that the code, data, and trained PointNet++ weights for cotton boll and branch mapping are publicly available in their GitHub repository, which directly reproduces this paper's phenotyping analysis.Code · publicThe code, data, and training weights for cotton boll and branch mapping are available at https://github.com/UGA-BSAIL/cotton_organ_mapping.git .Open asset ↗UGA-BSAIL/cotton_organ_mappinglines:101-109Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Introduction Verticillium wilt is a severe soil-borne disease that affects cotton growth and yield. Traditional monitoring methods, which rely on manual investigation, are inefficient and impractical for large-scale applications. This study introduces a novel approach combining machine learning with feature selection to identify sensitive spectral features for accurate and efficient detection of cotton Verticillium wilt. Methods We conducted comprehensive hyperspectral measurements using handheld devices (350-2500 nm) to analyze cotton leaves in a controlled greenhouse environment and employed Unmanned Aerial Vehicle (UAV) hyperspectral imaging (400-995 nm) to capture canopy-level data in field conditions. The hyperspectral data were pre-processed to extract wavelet coefficients and spectral indices (SIs), enabling the derivation of disease-specific spectral features (DSSFs) through advanced feature selection techniques. Using these DSSFs, we developed detection models to assess both the incidence and severity of leaf damage by Verticillium wilt at the leaf scale and the incidence at the canopy scale. Initial analysis identified critical spectral reflectance bands, wavelet coefficients, and SIs that exhibited dynamic responses as the disease progressed. Results Model validation demonstrated that the incidence detection models at the leaf scale achieved a peak classification accuracy of 85.83%, which is about 10% higher than traditional methods without feature selection. The severity detection models showed improved precision as disease severity of damage increased, with accuracy ranging from 46.82% to 93.10%. At the canopy scale, UAV-based hyperspectral data achieved a remarkable classification accuracy of 93.0% for disease incidence detection. Discussion This study highlights the significant impact of feature selection on enhancing the performance of hyperspectral-based remote sensing models for cotton wilt monitoring. It also explores the transferability of sensitive spectral features across different scales, laying the groundwork for future large-scale early warning systems and monitoring cotton Verticillium wilt.
Why it matches plant phenotyping methods綿花の病害状態をハイパースペクトル計測と特徴選択・機械学習で葉および群落レベルに推定し、モデル性能も検証しているため、病害フェノタイピング手法が中心です。
abstractThis study introduces a novel approach combining machine learning with feature selection to identify sensitive spectral features for accurate and efficient detection of cotton Verticillium wilt.
Cellulose microfibrils that are essential for mechanical strength and overall quality of cotton fibers. This study quantifies and compares the nanoscale structural and mechanical properties of cellulose microfibrils such as microfibril dimensions, crossover count and angles, roughness, and Youngs modulus for two popular cotton species: Gossypium hirsutum (Gh) and Gossypium barbadense (Gb) fibers across four growth stages (8, 12, 18, and 22 days post-anthesis) using atomic force microscopy (AFM). Our results revealed for the first time that Gb fibers exhibit a better alignment, finer dimensions, and higher stiffness compared to Gh fibers at nanoscale, resulting in smoother fiber surfaces, and improved quality at macroscale. We are also the first to develop machine-learning models to predict macroscale phenotypic traits specifically boll length and cellulose content using nanoscale features alone and in combination with multi-omics modalities, substantially enhancing the predictive accuracy and highlighting opportunities for robust cross-species modeling of cotton fiber traits.
Why it matches plant phenotyping methodsAFMによる綿繊維のナノスケール形態・力学形質の定量と、ナノスケール特徴からボール長などの植物表現型を予測する機械学習モデルの開発が研究の中心である。
The accurate and timely detection of leaf phosphorus content (LPC) is extremely important for the fertilization management of crop growth and yield. This study aimed to establish an estimating model of LPC in cotton based on hyperspectral data. Under field experimental conditions with different phosphorus treatments, the spectral data and LPC were measured. The spectral characteristics of different cotton cultivars and leaves with varying phosphorus content were analyzed. Optimized spectral indices most correlated to phosphorus were calculated with combinations of arbitrary bands using the Fractional Differential Order (FOD) transform. Then, the random forest-based(RF) estimation model for cotton LPC was established. The research results indicated that (1) the spectral changes of 24 cotton cultivars were basically consistent, and spectral differences between the cultivars became more obvious within the 760-960 nm spectral region; (2) in the visible region, the reflectance of cotton under different phosphorus treatments did not show obvious regularity, while in NIR, the reflectance of cotton increased with the increase in phosphorus content, showing a certain difference in phosphorus; (3) the RF model using a difference spectral index (DSI) had the best performance for LPC estimations in calibration (R 2 = 0.78) and validation (R 2 = 0.85), which was superior to the other models based on two spectral indices (the NDSI and RSI). This study provides technical support for the hyperspectral estimation of LPC in cotton.
Why it matches plant phenotyping methods綿花葉のリン含量という植物形質を、ハイパースペクトルデータ、FOD最適化スペクトル指標、ランダムフォレストで推定する手法を構築・検証しており、形質取得・推定法が研究の中心である。
abstractThis study aimed to establish an estimating model of LPC in cotton based on hyperspectral data.
With a rich historical background, the US cotton industry consistently maintains its position as one of the leading global producers. Due to the direct correlation between cottonseed quality and germination rate, conducting non-destructive testing to identify defects in cottonseeds becomes important to optimize yield performance. In this study, we propose an objective method for detecting cottonseed defects which classifies cottonseeds into four categories (Normal, Pinhole, Damage, and Very Damaged) and fourteen subcategories (N, R, C, RH, EH, CH, R Cut, C Cut, RV, CV, RH Expose, EH Expose, CH Expose, and V). Leveraging our customized cottonseed image dataset, we introduce a cottonseed defect detection and classification method based on a lightweight YOLOv5n model enhanced with Swin Transformer and an improved two-stage deep learning classification model. For cottonseed detection, our method achieves a 30.11 % reduction in model size and a 7.7 % increase in mAP50:95 compared to YOLOv5n. For individual cottonseed image classification, the accuracy, precision, recall, and F1 scores of our two-stage deep learning model are 97.34 %, 97.7 %, 97.3 %, and 97.3 %, respectively. The gradient-weighted class activation mapping (Grad-CAM) algorithm was then used to visually explain the model’s classification mechanism. Moreover, our algorithm demonstrates superior performance compared to six commonly used classification algorithms, including ResNet-18, ResNet-50, AlexNet, GoogleNet, VGG-16, and VGG-19, achieving a notable 1.65 % increase in accuracy over the best-performing algorithm among them. We then compared its performance with four state-of-the-art (SOTA) cottonseed damage classification methods. The findings demonstrate the potential for this design to advance the development of non-destructive seed damage detection.
Why it matches plant phenotyping methods綿実の損傷状態という植物器官の表現型を画像から分類・検出する深層学習手法を開発し、精度比較・検証しており、表現型取得・抽出法が研究の中心である。
abstractwe propose an objective method for detecting cottonseed defects which classifies cottonseeds into four categories
CottonMicroscopyCell / cellular structureSeed / grainVisualization / data managementGrowth / development / phenology
Cotton fibers, as highly extended, thickened epidermal seed structures, are a crucial renewable resource in textile production. Cotton plants produce two main types of fiber cells: wide, hemisphere-shaped fibers and narrow, tapered fibers. Both types stabilize through secondary cell wall development, with the mature narrow fibers being particularly valued for spinning into fine, strong yarns, suitable for premium cotton fabrics. Traditional methods for studying fiber development and cell types, such as scanning electron microscopy (SEM), are often time-intensive and costly. SEM preparation steps, including fixation, dehydration, and sputter coating, can cause shrinkage and other image distortions, limiting the accuracy of observations. Variable-pressure scanning electron microscopy (VP-SEM) offers an alternative approach, operating under low pressure rather than a high-vacuum environment, which can be advantageous for imaging live samples with minimal sample preparation. In this study, we applied VP-SEM to observe fiber cell initiation and early elongation in the conventional upland cotton cultivar UGA 230 at 0 and 1-day post-anthesis. Two SEM detectors, the ultra-variable-pressure detector and backscattered electrons, were used to capture detailed images. Optimal imaging conditions were identified with a 15 keV accelerating voltage and a 50 Pa pressure setting, enabling clear visualization of early fiber development without the need for extensive preparation. This VP-SEM protocol not only facilitates high-resolution imaging of cotton fibers at early developmental stages but also reduces time and expense, minimizing sample damage. Additionally, this optimized approach can be adapted for other fresh biological samples, making it a versatile tool for real-time imaging across various studies in plant biology and beyond.
Why it matches plant phenotyping methods綿花繊維の発生・伸長を高解像度で取得するVP-SEMプロトコルの条件最適化と技術的利点を中心に扱っており、植物形質取得法が主題である。
abstractOptimal imaging conditions were identified with a 15 keV accelerating voltage and a 50 Pa pressure setting, enabling clear visualization of early fiber development without the need for extensive preparation.
The number of cotton bolls is an important phenotyping trait not only for breeders but also for growers. It can provide information on the physiological and genetic mechanisms of plant growth and aid decision-making in crop management. However, traditional visual inspection in the field is time-consuming and laborious. With the application of drones in the agricultural domain, there is promising potential to collect data expediently. In this paper, we integrated the improved Distribution Matching for crowd Counting (DM-Count) and Segment Anything Model (SAM) to predict cotton boll number, size, and yield in aerial images. The cotton plots were first extracted from the raw aerial images using boundaries derived from orthophotos. Then, a convolutional neural network (DM-Count) was introduced as a baseline and customized by replacing the VGG19 backbone and adding a pixel loss. The customized network was first pretrained on ground images and then fine-tuned on aerial images to predict the density map, where the number and locations of cotton bolls can be obtained. The zero-shot foundation model SAM was investigated to segment cotton bolls with the point prompts provided by customized DM-Count. The respective numbers of bolls and segmented pixels were compared for seed cotton yield estimation. The experimental results showed that the customized model obtained a mean absolute error (MAE) of 1.78 per square meter and a mean absolute percentage error (MAPE) of 4.39 % on the testing dataset, with a high correlation between the predicted boll number and ground truth (R² = 0.91). The AP50 of SAM for cotton boll segmentation was 0.63. The segmented masks were used to delineate the boll size differences among the four genotypes, and it was found that the average boll size of Pima was 452 pixels, which was significantly smaller than Acala Maxxa, UA 48 and Tamcot Sphinx. Moreover, the yield estimation using the boll number was better than that using the pixel number, with an R² = 0.70. Combing the boll number and the pixel number can achieve a slightly higher R² of 0.72 for yield estimation. Overall, the customized model can count cotton bolls in aerial images accurately and estimate seed cotton yield effectively, which could significantly benefit breeders in developing genotypes with high yields, as well as help growers in yield estimation and crop management.
Why it matches plant phenotyping methods綿花のボール数・サイズ・収量を航空画像から推定する画像解析手法を開発・評価しており、植物形質取得が研究の中心である。
abstractIn this paper, we integrated the improved Distribution Matching for crowd Counting (DM-Count) and Segment Anything Model (SAM) to predict cotton boll number, size, and yield in aerial images.
Extracting cotton boll phenotypic parameters from imaging data is a prerequisite for intelligently characterizing boll growth and development. However, current methods relying on manual measurements are inefficient and often inaccurate. To address this, we developed a cotton boll phenotypic parameter extraction program (CPVS), a tool designed to estimate the morphological characteristics of unopened cotton bolls from images. CPVS integrates semi-automatic data extraction with advanced algorithms to calculate length, width, volume, and surface area. Length and width estimation algorithms were developed using a custom “Fixed” image set, which links pixel dimensions to actual measurements. Volume and surface area models were based on shape classification using a custom “Random” image set, trait correlations, and measured data. Testing showed strong performance, with R² values of 0.880 and 0.769 and root mean square error (RMSE) values of 0.173 and 0.188 for length and width, respectively. The volume model achieved an R² of 0.91 and an RMSE of 1.76, while surface area models had R² values of 0.76 and RMSEs of 2.37 and 2.41. These results indicate that CPVS is a robust tool, providing theoretical and practical support for efficient, accurate characterization of cotton boll morphology.
Why it matches plant phenotyping methods画像から未開裂綿花果の形態形質を抽出するソフトウェアと推定モデルを開発・検証しており、植物表現型取得が研究の中心です。
abstractwe developed a cotton boll phenotypic parameter extraction program (CPVS), a tool designed to estimate the morphological characteristics of unopened cotton bolls from images.
In this study, novel methods using portable NIR and Raman spectroscopy instruments associated with multivariate classification were developed to classify cotton fibers according to their length. The Upper Half Mean (UHM) length is considered a quality parameter by the cotton fiber market and is traditionally determined using a high-volume system (HVI), which entails high installation costs and labor-intensive analyses. As UHM correlates with cellulose polymerization, its determination can be achieved through vibrational spectroscopy techniques such as near-infrared (NIR) and Raman. These technologies offer advantages such as low cost, ease of handling, and rapid data acquisition, making them suitable for field use. This study aimed to develop a method and demonstrate the feasibility of using portable NIR and Raman spectrometers coupled with pattern recognition (PR) methods for routine analysis of cotton fibers, serving as a proof of concept for practical application in the industry. A total of 142 samples of cotton fibers from cotton improvement experiments conducted by the Brazilian Agricultural Research Corporation (EMBRAPA) were employed. Two classification approaches based on cotton lint length and the related economic value were employed. The first aimed to differentiate between short (SM) and long (LF) fibers, while the second aimed to further classify long fibers into internal classes (L, VL, and EL). Overall, methods using portable Raman spectrometer exhibited 100% accuracy performance regardless of the PR technique used. Meanwhile, methods based on NIR spectrometers achieved accuracies of 100% depending on the PR method and variable selection employed. The use of GLSW resulted in a reduction of a latent variable. In conclusion, the use of portable NIR and Raman spectrometers combined with PR methods emerges as an innovative and viable technology for the classification of cotton fibers based on their length.
Why it matches plant phenotyping methods携帯型NIR・ラマン分光と多変量解析を用いて、綿繊維長という植物由来形質を分類する手法を開発・実証しており、測定手法が研究の中心である。
abstractnovel methods using portable NIR and Raman spectroscopy instruments associated with multivariate classification were developed to classify cotton fibers according to their length.
Cotton stickiness, mostly resulting from honeydew depositions of whiteflies and aphids, presents a worldwide problem for cotton growers and processors consistently. To meet the challenge of measuring the cotton stickiness, a few direct and indirect techniques exist. Previous study showed that Fourier transform near-infrared (FT-NIR) spectroscopy can be used to detect Minicard stickiness in raw cotton from partial least-squares (PLS) analysis. In the present investigation, visible-NIR (vis-NIR) as an analytical technique was explored for potential classification of four-class Minicard cotton stickiness, in combination mainly with the data-driven version of soft independent modeling of class analogy (DD-SIMCA) as one-class classification. Both PLS prediction-based classification and DD-SIMCA models in different spectral regions were developed to optimize the identification efficiency. Compared to an optimal PLS prediction-based classification model indicating a four-class correct classification of 77.8% in the calibration set and 69.2% in the validation set from the 750-1850 nm NIR region, an optimal DD-SIMCA model from the same spectral region could reach an improved discrimination of >95.0%, with a 98.1% correct identification in the calibration set and a 96.2% success in the validation set. This observation emphasized that vis-NIR spectroscopy with an DD-SIMCA approach could be a rapid and nondestructive tool for screening the Minicard stickiness in cottons.
Why it matches plant phenotyping methodsVis-NIR分光とDD-SIMCAによって綿花のスティッキネスという作物由来形質を非破壊分類し、モデル開発と検証を行っており、表現型取得・抽出法が中心である。
abstractvisible-NIR (vis-NIR) as an analytical technique was explored for potential classification of four-class Minicard cotton stickiness
This study introduces a method for optimizing flight modes using unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR) technology, aiming for the efficient and accurate estimation of crop phenotypes in wide-narrow row planting patterns, for cotton. It proposes specialized flight plans that take into account the unique growth stages of cotton and recommends the s-along flight path, which is derived from a detailed analysis of the cross flight path, to facilitate effective and precise data collection. A comprehensive phenotypic index, labeled as ‘P', and a fitting function are developed to describe the relationship between flight parameters, paths, and digital elevation model (DEM) data. The study also introduces two flight strategies, one focusing on accuracy and the other on efficiency, utilizing a sophisticated multi-objective optimization method. Comparative analyses show that the s-along flight path significantly improves efficiency without sacrificing accuracy, compared to traditional cross flight path techniques. The use of high-precision prior DEM data greatly enhances the precision in estimating critical phenotypic parameters such as plant height (PH) and leaf area index (LAI), especially during key stages of canopy growth. By carefully adjusting flight height, speed, and overlap during different growth stages, an ideal balance is achieved between the precision and efficiency of data collection. These strategies markedly increase the accuracy of estimating phenotypic features (P > 0.75) and efficiency (by 42 %–44 %). This research highlights the potential of these approaches in facilitating large-scale phenotypic data collection for precision agriculture, demonstrating their ability to simultaneously improve data quality and operational efficiency. Future research will aim to expand the applicability and robustness of these methods across various planting conditions and crops, further enhancing essential tools for the advancement of precision agriculture practices and development.
Why it matches plant phenotyping methodsUAV LiDARの飛行経路・パラメータ最適化と多目的最適化を開発し、綿の草丈・葉面積指数などの表現型推定精度と効率を評価しており、表現型取得法が研究の中心である。
abstractThis study introduces a method for optimizing flight modes using unmanned aerial vehicles (UAVs) and light detection and ranging (LiDAR) technology, aiming for the efficient and accurate estimation of crop phenotypes
Verticillium wilt (VW) is one of the most common and devastating diseases in cotton production, and early diagnosis is very important to alleviate the damage of VW. Recent studies have shown that early diagnosis and prevention of soil-borne diseases can be achieved by detecting spectral changes related to chlorophyll fluorescence and transpiration. However, there are no systematic studies to report the heterogeneity of photosynthetic characteristics and their spectral responses of plant leaves at the early stage of VW. In this study, the spatial heterogeneity characteristics in chlorophyll fluorescence of cotton leaves during the incubation period of VW were discussed, and the pixel-level inversion of the heterogeneity characteristics of leaf chlorophyll fluorescence was realized with hyperspectral imaging information, aiming to realize the early diagnosis of VW of cotton. The results showed that the chlorophyll fluorescence parameters Y(NPQ) (quantum yield of regulated energy dissipation) and NPQ/4 (non-photochemical quenching/4) values of cotton increased and the Y(II) (effective quantum yield of photosystem II) decreased significantly during the asymptomatic period of VW, indicating heterogeneity in photosynthetic capacity of leaves in the early stage of VW, i.e., VW developed from leaf margins to leaf center, and leaf margin was the area where chlorophyll fluorescence changed firstly. Furthermore, the multi-task learning model constructed with vegetation index and wavelet features accurately inversed the pixel-level heterogeneous characteristics of leaf Y(NPQ) and Y(II). The spectral information had the best inversion performance for the local heterogeneous regions of Y(II), with a classification accuracy of 85.6 %, a Kappa coefficient of 0.71, an r² (coefficient of determination) of 0.66, and a RMSE (root mean square error) of 0.06. According to the inversion results of the local heterogeneous region of Y(II), the accurate diagnosis of early-stage VW was realized, with an accuracy of 87.4 % and a Kappa coefficient of 0.75. This study will provide a new method for the early prevention and control of VW.
Why it matches plant phenotyping methods綿花葉のクロロフィル蛍光という植物生理形質を、ハイパースペクトル画像と機械学習で画素レベル推定し、萎凋病の早期診断へ応用しており、形質取得・抽出手法が中心的である。
abstractthe pixel-level inversion of the heterogeneity characteristics of leaf chlorophyll fluorescence was realized with hyperspectral imaging information
The agricultural industry is experiencing revolutionary changes through the latest advances in artificial intelligence and deep learning-based technologies. These powerful tools are being used for a variety of tasks including crop yield estimation, crop maturity assessment, and disease detection. The cotton crop is an essential source of revenue for many countries highlighting the need to protect it from deadly diseases that can drastically reduce yields. Early and accurate disease detection is quite crucial for preventing economic losses in the agricultural sector. Thanks to deep learning algorithms, researchers have developed innovative disease detection approaches that can help safeguard the cotton crop and promote economic growth. This study presents dissimilar state-of-the-art deep learning models for disease recognition including VGG16, DenseNet, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet models. For this purpose, real cotton disease data is collected from fields and preprocessed using different well-known techniques before using as input to deep learning models. Experimental analysis reveals that the ResNet152 model outperforms all other deep learning models, making it a practical and efficient approach for cotton disease recognition. By harnessing the power of deep learning and artificial intelligence, we can help protect the cotton crop and ensure a prosperous future for the agricultural sector.
Why it matches plant phenotyping methods綿花の病害症状を画像から認識する深層学習手法の比較・評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用。
abstractThis study presents dissimilar state-of-the-art deep learning models for disease recognition including VGG16, DenseNet, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet models.
Leaf shape is of great significance in plant phenotype research. Landmarks method is a widely used morphometric approach, which can comprehensively describe the morphological differences among leaves. However, the selection of landmarks is time-consuming and laborious. An automatic landmarking algorithm is proposed here. Based on conformal mapping, the leaf outline can be transformed into a monotonically increasing function curve, referred to as the ’fingerprint function’. The Dynamic Time Warping (DTW) algorithm was introduced to match landmarks between different leaves. Two leaf datasets were used to validate the algorithm separately in different species and developmental stages. Dataset1 is a public dataset which covers 26 different types of leaves. The average positional difference between automatic and manual landmarks for dataset1 was only 2.95%. Dataset2 consists of cotton leaves collected in the field at various growth stages, and the positional difference for this dataset was all below 5%. These results validate that our algorithm is applicable to a wide range of leaf types and capable of identifying and locating novel features that emerge during leaf growth. The automatic landmarking algorithm can simulate manual landmarking to a great extent. It provides a new approach for automated acquisition of plant leaf shape homology tailored to the research needs of botanists.
Why it matches plant phenotyping methods葉形態の自動ランドマーク取得アルゴリズムを開発し、複数の葉データセットで手動測定と比較検証しているため、植物表現型の取得手法が中心である。
abstractAn automatic landmarking algorithm is proposed here.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Cotton phenomics plays a crucial role in understanding and managing the growth and development of cotton plants. The segmentation of point clouds, a process that underpins the measurement of plant organ structures through 3D point clouds, is necessary for obtaining precise phenotypic parameters. This study proposes a cotton point cloud organ semantic segmentation method named TPointNetPlus, which combines PointNet++ and Transformer algorithms. Firstly, a dedicated point cloud dataset for cotton plants is constructed using multi-view images. Secondly, the attention module Transformer is introduced into the PointNet++ model to increase the accuracy of feature extraction. Finally, organ-level cotton plant point cloud segmentation is performed using the HDBSCAN algorithm, successfully segmenting cotton leaves, bolls, and branches from the entire plant, and obtaining their phenotypic feature parameters. The research results indicate that the TPointNetPlus model achieved a high accuracy of 98.39% in leaf semantic segmentation. The correlation coefficients between the measured values of four phenotypic parameters (plant height, leaf area, and boll volume) ranged from 0.95 to 0.97, demonstrating the accurate predictive capability of the model for these key traits. The proposed method, which enables automated data analysis from a plant's 3D point cloud to phenotypic parameters, provides a reliable reference for in-depth studies of plant phenotypes.
Why it matches plant phenotyping methods綿花の3D点群から器官を分割し、草丈・葉面積・莢体積などの表現型形質を自動抽出する手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractThis study proposes a cotton point cloud organ semantic segmentation method named TPointNetPlus
The increasing prevalence of plant diseases poses a significant threat to global cotton production, leading to substantial economic losses and reduced crop yield. Traditional manual disease detection methods are time-consuming, labour-intensive, and often inaccurate. To address this challenge, this research proposes an advanced deep learning-based approach for automated cotton plant disease detection using Convolutional Neural Networks (CNNs). The study evaluates multiple CNN architectures, including GoogleNet, VGG16, DenseNet201, ResNet50, and TLResnet152V2, to determine their effectiveness in identifying and classifying diseased cotton leaves. The proposed methodology leverages normalized and augmented datasets, utilizing data pre-processing, feature extraction, and transfer learning techniques to enhance model performance. Extensive experimental evaluations demonstrate that data augmentation significantly improves classification accuracy, enabling CNN models to generalize better across diverse disease conditions. Among the tested architectures, TLResnet152V2 achieved the highest accuracy (92.03%) and F1-score (0.8842), outperforming all other models, followed closely by ResNet50. These results highlight the superiority of deep residual learning in plant disease classification, ensuring robust feature extraction and precise detection. This studies also explores the combination of CNN-primarily based disorder detection into clever agriculture structures, allowing actual-time sickness classification via cell packages and IoT-based totally answers. The findings affirm that deep gaining knowledge of-pushed plant disorder detection can considerably enhance precision farming, reducing dependency on professional agronomists while improving early disorder intervention techniques. destiny studies will awareness on deploying light-weight CNN models for facet computing, integrating climate statistics for predictive disorder modelling, and exploring hybrid deep studying strategies for enhanced accuracy. The examine demonstrates that CNN-based automatic cotton plant disease detection is a transformative step closer to sustainable, AI-enabled smart agriculture, ensuring better productivity, decreased crop losses, and advanced food safety.
Why it matches plant phenotyping methods綿花葉の病害状態を画像から分類・検出するCNN手法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 research proposes an advanced deep learning-based approach for automated cotton plant disease detection using Convolutional Neural Networks (CNNs).
Aims Hyperspectral remote sensing technology can quickly obtain above-ground biomass (AGB) information of cotton, playing an important role in realizing accurate management for cotton cultivation. Methods Using Tahe-2 as the research object, nitrogen application rates and irrigation amounts were set to 0 (N 0 ), 100 (N 1 ), 150 (N 2 ), 200 (N 3 ), 250 (N 4 ) kg ha - 1 and 4500 (W 1 ), 6000 (W 2 ), 7500 (W 3 ) m³ ha - 1 under the coupled conditions of water and nitrogen. Through correlation analysis between cotton AGB and canopy spectral reflectance, the intersection of feature wavelengths screened by the successive projection algorithm (SPA) and highly significant wavelengths was used as the input vector for modeling. Support vector machine (SVM), regression tree (RT), and convolutional neural network (CNN) were employed to verify the accuracy. Results The results revealed the following: (1) The AGB of cotton at the bud stage was highest under the W 1 N 2 gradient. At the flowering stage, the highest AGB was observed under the W 3 N 2 gradient. At the boll stage, the highest AGB was under the W 3 N 0 gradient. (2) The optimal spectral model based on SVM for cotton AGB identification had higher R 2 values and lower RMSE values at the boll stage, with R 2 = 0.76, RMSE = 0.35 g and RPD = 17.59. The optimal spectral model based on RT had higher R 2 values and lower RMSE values at the bud stage, with R 2 = 0.79, RMSE = 0.24 g and RPD = 16.18. The optimal spectral model based on CNN also had higher R 2 values and lower RMSE values at the bud stage, with R 2 = 0.70, RMSE = 0.42 g and RPD = 4.50. These results indicated that the inversion effect at the bud stage was better than at other stages. Conclusions In terms of model testing, the RT model was found to be the most accurate for estimating cotton AGB, outperforming SVM and CNN.
Why it matches plant phenotyping methods綿花の地上部バイオマスをハイパースペクトル反射から推定するモデルを開発・比較し、精度検証しており、植物表現型取得手法が研究の中心である。
abstractHyperspectral remote sensing technology can quickly obtain above-ground biomass (AGB) information of cotton
Plant height (PH) is a key agronomic trait influencing plant architecture. Suitable PH values for cotton are important for lodging resistance, high planting density, and mechanized harvesting, making it crucial to elucidate the mechanisms of the genetic regulation of PH. However, traditional field PH phenotyping largely relies on manual measurements, limiting its large-scale application. In this study, a high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging (LiDAR) was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field. Different strategies were used to extract PH values from two sets of sensor data, and the extracted values were used to train using linear regression and machine learning methods to obtain PH predictions. These predictions were consistent with manual measurements of the PH for the LiDAR (R 2 = 0.934) and RGB (R 2 = 0.914) data. The predicted PH values were used for GWAS analysis, and 34 PH-related genes, two of which have been demonstrated to regulate PH in cotton, namely, GhPH1 and GhUBP15 , were identified. We further identified significant differences in the expression of a new gene named GhPH_UAV1 in the stems of the G. hirsutum cultivar ZM24 harvested on the 15th, 35th, and 70th days after sowing compared with those from a dwarf mutant ( pag1 ), which presented shortened stem and internode phenotypes. The overexpression of GhPH_UAV1 significantly promoted cotton stem development, whereas its knockout by CRISPR-Cas9 dramatically inhibited stem growth, suggesting that GhPH_UAV1 plays a positive regulatory role in cotton PH. This field-scale high-throughput phenotype monitoring platform significantly improves the ability to obtain high-quality phenotypic data from large populations, which helps overcome the imbalance between massive genotypic data and the shortage of field phenotypic data and facilitates the integration of genotype and phenotype research for crop improvement.
Why it matches plant phenotyping methodsUAV搭載RGB・LiDARによる綿花草丈の高スループット取得・推定プラットフォームの開発と精度検証が研究の中心であり、GWASや遺伝子機能解析は応用部分です。
abstracta high-throughput phenotyping platform based on UAV-mounted RGB and light detection and ranging (LiDAR) was developed to efficiently and accurately obtain time series PHs of 419 cotton accessions in the field
Reproduction assets foundThe paper's Data availability statement explicitly deposits the authors' source code, UAV-captured images, and analysis datasets in a public GitHub repository, which directly supports this paper's cotton plant-height phenotyping measurements and computational analysis.Code · publicThe source code, images captured by UAVs, data obtained from the analysis, and other datasets supporting the results presented here are available at https://github.com/Liqiangfan/419-cotton-plant-height-datasets .Open asset ↗Liqiangfan/419-cotton-plant-height-datasetslines:142-154Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
Light Detection and Ranging (LiDAR) technology can be used to assess canopy height in cotton (Gossypium hirsutum L.), but standardized data acquisition and processing guidelines are lacking. Accurate canopy height estimation is crucial in cotton for optimizing growth regulator application and maximizing yield. The main goal of this study was to determine the optimal unmanned aerial vehicle flight settings—altitude and speed—and to assess specific processing parameters impact on data accuracy, processing time, and file size. Nine flight settings comprising three altitudes (12.2 m, 24.4 m, and 48.8 m) and three speeds (4.8 km/h, 9.6 km/h, and 14.8 km/h) were tested. LiDAR data were processed using DJI Terra software, where two user-defined processing steps were examined: point-cloud thinning via grid size sub-sampling (0, 10, 20, 30, 40, and 50 cm) and slope classification (flat, gentle, and steep). The optimal flight altitude was 24.4 m, with no effect of flight speed. Grid sub-sampling up to 20 cm produced balanced accuracy, processing time, and file size. The choice of slope category had no significant effect on LiDAR-derived canopy height. These findings contribute to the development of standardized LiDAR data acquisition and processing guidelines for cotton to support crop management decision.
Why it matches plant phenotyping methodsUAV LiDARによるワタ群落高推定を対象に、飛行条件と点群処理条件を比較・最適化しており、植物形質取得法の技術開発・検証が中心である。
abstractstandardized data acquisition and processing guidelines are lacking
Accurate and rapid acquisition of cotton canopy phenotypic traits is relevant for growth monitoring, yield prediction, precise spraying of pesticides, and other scientific management. Manual measurements are time-consuming and labor-intensive. Light detection and ranging (LiDAR) can accurately acquire point cloud data of the agricultural environment. However, LiDAR data require specific algorithms for processing and interpretation, making them unsuitable for direct use in agricultural applications. This study proposes a high-throughput detection method for phenotypic traits in cotton canopies based on an unmanned aerial vehicle (UAV) LiDAR platform. It comprises three key components: first, high-throughput data collection of field-grown cotton at the boll stage is conducted using the UAV LiDAR platform. Second, the three-dimensional deep neural network PointNet++ is used to process the raw data for semantic segmentation to extract cotton single-plant and block. Finally, six single-plant cotton phenotypic analysis algorithms and five block-level cotton phenotypic analysis algorithms are used to extract canopy structural information, such as cotton plant height, porosity, and canopy volume. In the final result, the extraction rate of the neural network for cotton single plants reached 86.3 %. Among the six methods for calculating cotton phenotypes, the plant height method was the most effective for calculating plant height, with an R² value of 0.91 and the smallest root mean square error (RMSE) of 0.034 m, compared with the manually measured data. In the calculation result of cotton canopy porosity algorithms, the highest R² value is 0.87 and the smallest RMSE value is 0.012. In the calculation result of cotton canopy volume algorithms, the highest achievable R² value is 0.96 and the smallest RMSE value is 0.019 m³. The method can effectively partition cotton, extract phenotypic information, and provide technical support for cotton growth monitoring, yield prediction, and scientific management.
Why it matches plant phenotyping methodsUAV LiDARと3次元深層学習を用いた綿花の表現型取得・抽出法を開発し、草高、透過性、群落体積を手測定と比較検証しており、方法が研究の中心である。
abstractThis study proposes a high-throughput detection method for phenotypic traits in cotton canopies based on an unmanned aerial vehicle (UAV) LiDAR platform.
• Integral difference model best fits the spatiotemporal variation of FIPAR in canopy. • Photometric sensor could monitor the spatiotemporal variation of crop canopy and PAR. • Monitoring the spatiotemporal variability of PAR in canopy represents crop growth. • The spatiotemporal variation of FIPAR is positively correlated with growth traits. Crop growth monitoring technology holds great potential to enable timely management adjustments, optimize resource use, and support sustainable agriculture practices, achieving efficient intelligent agriculture for data-driven cultivation. Traditional field measurement and monitoring methods are often inefficient and provide limited, outdated information. The photon sensor-based fraction of intercepted photosynthetically active radiation (FIPAR) monitoring system was demonstrated to provide accurate real-time tracking of crop growth. It was designed to capture spatial variations in FIPAR across the canopy profile throughout the entire crop growth season. Subsequently, spatiotemporal models were applied to simulate variations in FIPAR across the entire canopy throughout the crop's growth. Finally, leveraging these model simulations, spatiotemporal variations in specific FIPAR values were derived to effectively characterize and describe crop growth dynamics. The technology was proved in a two-year monoculture cotton experiment. Results demonstrated that the post-simulation R² values of the dynamic spatiotemporal model were 0.940 for 2020 and 0.749 for 2021. Common agronomic traits used to measure cotton growth, including plant height (PH), aboveground biomass (AGB), and leaf area index (LAI), showed the highest correlations with FIPAR at 0.2 and 0.3 for PH, 0.5 and 0.6 for AGB, and 0.4 and 0.5 for LAI, all exhibiting significant positive relationships. Spatial variations of these FIPAR values within the canopy structure exhibited a linear relationship with PH, AGB, and LAI. This study demonstrated the feasibility of using photometric sensors as a non-destructive technology for real-time crop growth monitoring. The technology was developed to provide reasonably accurate crop growth information while balancing cost requirements for applications in both scientific research and agricultural production, offering high potential for guiding smart crop management to enhance agricultural productivity.
Why it matches plant phenotyping methods光量子センサーでキャノピーFIPARを取得し、時空間モデルで作物成長形質を推定する測定技術の開発・検証が研究の中心である。
abstractThe photon sensor-based fraction of intercepted photosynthetically active radiation (FIPAR) monitoring system was demonstrated to provide accurate real-time tracking of crop growth.
• A 3D neural architecture search was proposed to improve cotton plant segmentation. • The searched network outperformed the baselines with manually designed networks. • The approach can also search architectures that meet memory and time limits. Accurately segmenting plant parts from imagery is vital for improving crop phenotypic traits. However, current 3D deep learning models for segmentation in point cloud data require specific network architectures that are usually manually designed, which is both tedious and suboptimal. To overcome this issue, a 3D neural architecture search (NAS) was performed in this study to optimize cotton plant part segmentation. The search space was designed using Point Voxel Convolution (PVConv) as the basic building block of the network. The NAS framework included a supernetwork with weight sharing and an evolutionary search to find optimal candidates, with three surrogate learners to predict mean IoU, latency, and memory footprint. The optimal candidate searched from the proposed method consisted of five PVConv layers with either 32 or 512 output channels, achieving mean IoU and accuracy of over 90% and 96%, respectively, and outperforming manually designed architectures. Additionally, the evolutionary search was updated to search for architectures satisfying memory and time constraints, with searched architectures achieving mean IoU and accuracy of more than 84% and 94%, respectively. Furthermore, a differentiable architecture search (DARTS) utilizing PVConv operation was implemented for comparison, but the proposed method demonstrated better segmentation performance with a margin of more than 2% and 1% in mean IoU and accuracy, respectively. Overall, the proposed method can be applied to segment cotton plants with an accuracy over 94%, while adjusting to available resource constraints.
Why it matches plant phenotyping methods綿花の植物部位を点群画像から分割する3Dニューラルアーキテクチャ探索法を開発・評価しており、植物表現型抽出の計算手法が研究の中心である。
abstractA 3D neural architecture search was proposed to improve cotton plant segmentation.
Phosphorus (P) is an essential macronutrient for cotton (Gossypium hirsutum L.) growth, and plays a crucial role in yield formation. In this context, P deficiency reduces yield due to the limited leaf photosynthesis caused by the disruption of photosynthetic apparatus, and thus can be detected early via photosynthesis-related chlorophyll a fluorescence before visible leaf changes. In addition, the leaf subtending to cotton boll (LSCB) is the primary source of photosynthates, contributing to the boll biomass accumulation. Therefore, it is necessary to develop methods for early assessment of P status in the LSCB, facilitating rapid intervention in cotton production. To satisfy above demand, this study conducted a field experiment to explore the impact of different P application levels [0 (Deficient P), 100 (Critical P), and 200 (Excess P) kg P₂O₅ ha⁻¹] on cotton yield, boll weight accumulation and LSCB photosynthesis. Results showed that the increase of boll weight under P application is a significant factor contributing to yield improvement, and 15-25 days post anthesis is the key development period of cotton boll regulated by P. During this key period, P deficiency decreases the I and P steps of chlorophyll a fluorescence transients (indicating the damage to oxygen-evolving complex), and thus lead to the impaired photosystem II (PSII) and the reduction of electron transfer capacity. Then, the relationship between the leaf phosphorus concentration (LPC) and JIP-test parameters was fitted by Partial Least Squares Regression (PLSR) and showed good accuracy (R²=0.61 in calibration; RMSE=0.05 %, RRMSE=14.00 % in validation). Among the JIP-test parameters, the six ones (i.e. RC/CSₘ, FV/FO, ETO/CSₘ, FV/FM, DIO/RC and PIABS) show the highest correlation to LPC. This study demonstrated that the PLSR model generated using JIP-test parameters has the potential to detect P status in cotton during the key development period, and provided a new insight for optimizing P nutrient management in cotton production.
Why it matches plant phenotyping methods綿花葉のクロロフィル蛍光からリン栄養状態を推定する生理フェノタイピング手法を開発・検証しており、PLSRモデルの検証も中心的に行っている。
abstractit is necessary to develop methods for early assessment of P status in the LSCB
Cotton (Gossypium hirsutum L.), as a significant economic crop, has undergone significant modernization in planting methods, and its smart irrigation management relies heavily on accurate cotton water content (CWC) estimation. Existing ground-based methods for measuring CWC are constrained by their limited scope and high monitoring costs. Although the development of unmanned aerial vehicle (UAV) technology has provided a new opportunity for large-scale CWC measurements, there remains a gap in the study of CWC estimation in cotton using multi-source and multi-stage data. In this study, we used UAV-based data, including texture features, vegetation indices, and a heat index, and applied four machine learning algorithms, i.e., partial least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XGB), to estimate CWC. The findings demonstrate that in a single growth stage, the boll setting stage performs the best, and multi-source and multi-stage inputs can improve the accuracy of CWC estimation, with the best performance of XGB (R2 = 0.860). Overall, this study highlights that the synergistic use of multi-source and multi-stage data can effectively improve CWC estimation in cotton, suggesting UAV-based data will lead to a brighter future for precision agriculture.
Why it matches plant phenotyping methodsUAVマルチソース画像・センサーデータと機械学習により、綿花の水分含量という植物生理形質を推定する手法が研究の中心であり、フェノタイピング手法の開発・適用に該当する。
abstractwe used UAV-based data, including texture features, vegetation indices, and a heat index, and applied four machine learning algorithms, i.e., partial least-squares regression (PLSR), support vector regression (SVR), random forest regression (RFR), and extreme gradient boosting (XGB), to estimate CWC.
The portable X-ray fluorescence (pXRF) spectrometry has been very useful for the characterization of different earth materials, and its application for foliar analysis is really promising. The performance of pXRF for foliar analysis depends on several factors such as concentration of the elements, fluorescence yield which is influenced by atomic number, spectral interference, and water content. Mn is one of the elements that present a prominent fluorescence peak. In this sense, it was hypothesized that pXRF can directly determine the Mn concentration on foliar samples, even when used on intact leaves (fresh or dry) being a useful tool for agronomic and environmental purposes. Thus, the objective was to assess the performance of a pXRF to determine Mn concentration in two different foliar datasets from Brazil/South America and Mali/Africa. In the Brazilian dataset, leaves from eight crops (common bean, castor plant, coffee, eucalyptus, guava tree, maize, mango, and soybean) were scanned via pXRF at the following conditions: intact and fresh leaves, intact and dry leaves, and powdered samples). In the Malian dataset, powdered samples from cotton and maize were analyzed via pXRF. For comparison, Mn concentration was also determined after nitro-perchloric digestion followed by quantification via inductively coupled plasma optical emission spectroscopy (ICP-OES). After descriptive statistics, linear regressions were performed for all sample preparation conditions in both datasets, using Mn concentrations obtained through pXRF and the acid digestion method. The data quality level of all linear regressions was considered quantitative with high R (0.93 to 0.98) and R 2 (0.87 to 0.96) values. The direct analysis of Mn via pXRF on intact and fresh leaves yielded R of 0.93, R 2 of 0.87, and a low relative standard deviation (< 10%). The manufactured pXRF calibration used in this work allowed an accurate direct Mn determination in plant leaves. Considering the importance of Mn as a plant micronutrient and its potential toxicity depending on soil redox conditions, the fast, in situ, non-destructive, and eco-friendly determination via pXRF has a tremendous agronomic and environmental application worldwide.
Why it matches plant phenotyping methods植物葉のMn濃度という生理・元素形質を、携帯型XRFで非破壊測定する方法の性能評価と検証が中心であり、単なる生物学的実験での routine 測定ではない。
abstractThe direct analysis of Mn via pXRF on intact and fresh leaves yielded R of 0.93, R 2 of 0.87, and a low relative standard deviation (< 10%).
Diagnosis of cotton plant diseases is essential to maintain agricultural sustainability and output. This study proposes a YOLO-based deep learning model for leaf disease detection to maximize cotton plant leaf disease detection accuracy. This method ensures a comprehensive evaluation of cotton plant health by combining various image processing techniques, improving the accuracy of disease identification. This study provides a viable path to improve crop health monitoring and management in cotton farming systems and emphasizes the importance of utilizing cutting-edge image processing techniques in agricultural activities. ROC curve performance and classification metrics were better for YOLOv5 than for VGG16 and ResNet50, as it had the highest F1 score (99.21%), recall, and precision. Consistent performance in classification tests was demonstrated by all models, which showed balanced precision, recall, and F1 scores. ResNet50 marginally outperformed VGG16 in terms of true positive rates, F1 score (98.88% vs. 98.65%), recall, and precision. More sophisticated models, such as YOLOv5 and ResNet50, showed higher efficiency and accuracy than VGG16, which makes them more appropriate for applications demanding low false positive rates and high precision. The proposed YOLO-based method improves the accuracy of disease identification, ensuring a thorough assessment of cotton plant health using image processing techniques. The results show that the proposed approach is quite successful in correctly detecting and classifying a variety of diseases that affect cotton plants.
Why it matches plant phenotyping methods綿花葉の病害状態を画像から検出・分類するYOLOベース手法の提案とモデル比較が中心であり、植物病害フェノタイピング手法に該当する。
abstractThis study proposes a YOLO-based deep learning model for leaf disease detection to maximize cotton plant leaf disease detection accuracy.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe leaf images of cotton plants used in this research were
collected from a public Kaggle dataset [21, 22].Open asset ↗Kagglepdf-raw-page:2 lines:1-83Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Remote sensing, particularly through unmanned aerial vehicles (UAVs), has emerged as a pivotal tool in precision agriculture, especially for nitrogen (N) management. Traditional methods, while effective in quantifying crop N status using the Nitrogen Nutrition Index (NNI), fall short in providing quantitative fertilization strategies. This study bridges this gap by developing a comprehensive method that leverages multispectral remote sensing data from UAVs to refine N fertilizer management in cotton cultivation within arid environments. By integrating both field observations and UAV-derived multispectral data, we established robust models capable of estimating both leaf and overall cotton nitrogen contents (CNC-leaf and CNC-all), as well as NNI, throughout the growing season. This facilitated real-time calculation of required N fertilizer doses in cotton fields. We uniquely applied covariance diagnosis and full subset screening techniques, underscoring the efficacy of vegetation index categories (VIs) in enhancing prediction accuracy. The Random Forest (RF) model exhibited superior performance in predicting plant nitrogen content, particularly in CNC-leaf prediction (Calibration: R²=0.92, RMSE=7.7 g m⁻², MAE=5.5 g m⁻²; Validation: R²=0.60, RMSE=16.5 g m⁻², MAE=12.2 g m⁻²) as opposed to CNC-all prediction (Calibration: R²=0.78, RMSE=117.0 g m⁻², MAE=154.3 g m⁻²; Validation: R²=0.34, RMSE=138.4 g m⁻², MAE=190.4 g m⁻²). The RF model also demonstrated optimal performance in NNI prediction (Calibration: R²=0.93, RMSE=0.05, MAE=0.04; Validation: R²=0.73, RMSE=0.12, MAE=0.10), surpassing the predictions for CNC. Utilizing CNC and NNI estimates derived from the optimized RF model, this study succeeded in generating a comprehensive map detailing the N fertilizer requirement across cotton Fertilization zones were established for different treatments, revealing that biochar application levels primarily determine nitrogen fertilizer needs. As biochar application increases, nitrogen fertilizer demand decreases. Moreover, nitrogen application rates typically increase when irrigation levels reach either 120 % ETc or 60 % ETc. This innovative approach not only empowers farmers with intuitive and accurate tools for real-time cotton N management but also fosters enhanced agricultural practices by integrating advanced remote sensing technologies with sophisticated data analysis methods. The findings of this study have significant implications for sustainable and efficient agricultural practices, particularly in arid regions, setting a new precedent in precision nitrogen management.
Why it matches plant phenotyping methodsUAVマルチスペクトルセンシングと機械学習により、ワタの窒素含量およびNNIという植物形質を推定・検証する方法が中心であり、単なる施肥試験ではない。
abstractdeveloping a comprehensive method that leverages multispectral remote sensing data from UAVs to refine N fertilizer management in cotton cultivation
Plant disease detection is a critical task in agriculture, directly impacting crop yield, food security, and sustainable farming practices. This study proposes FourCropNet, a novel deep learning model designed to detect diseases in multiple crops, including CottonLeaf, Grape, Soybean, and Corn. The model leverages an advanced architecture comprising residual blocks for efficient feature extraction, attention mechanisms to enhance focus on disease-relevant regions, and lightweight layers for computational efficiency. These components collectively enable FourCropNet to achieve superior performance across varying datasets and class complexities, from single-crop datasets to combined datasets with 15 classes. The proposed model was evaluated on diverse datasets, demonstrating high accuracy, specificity, sensitivity, and F1 scores. Notably, FourCropNet achieved the highest accuracy of 99.7% for Grape, 99.5% for Corn, and 95.3% for the combined dataset. Its scalability and ability to generalize across datasets underscore its robustness. Comparative analysis shows that FourCropNet consistently outperforms state-of-the-art models, such as MobileNet, VGG16, and EfficientNet, across various metrics. FourCropNet’s innovative design and consistent performance make it a reliable solution for real-time disease detection in agriculture. This model has the potential to assist farmers in timely disease diagnosis, reducing economic losses and promoting sustainable agricultural practices.
Why it matches plant phenotyping methods植物葉の病害状態を画像から推定するCNNモデルを開発し、複数作物データセットで性能評価しているため、植物フェノタイピング手法が中心です。
abstractThis study proposes FourCropNet, a novel deep learning model designed to detect diseases in multiple crops, including CottonLeaf, Grape, Soybean, and Corn.
Reproduction assets foundThe paper evaluates FourCropNet on a public Kaggle multi-class crop disease image dataset (reference [28]), which is the plant image input used for the paper's phenotyping/disease-detection measurements. No author code or trained model checkpoints are reported as publicly available.Dataset · public[28] “20k+ Multi-Class Crop Disease Images.” Accessed: Jan. 28, 2024. [Online]. Available:
https://www.kaggle.com/datasets/jawadali1045/20k-multi-class-crop-disease-imagesOpen asset ↗jawadali1045/20k-multi-class-crop-disease-imagespdf-page:10 lines:1-59Code / dataset availability confirmedbioRxiv · checked 15 Sept 2026
Leaf osmotic potential at full turgor ({pi}0) has been used frequently to indicate turgor loss point of plant leaves. However, even a rapid measurement of{pi} 0 using osmometry is time-consuming, if numerous leaf samples need to be measured. Because of this, researchers tend to use a small sample size to determine{pi} 0 and relate it to indices of crop performance. Yet the statistical and agronomic significance of using a small sample size of{pi} 0 to indicate crop performance is not known. We address this question using field measurements and statistical resampling. Six mature leaf samples were collected at the peak bloom stage from each of the 54 cotton plots in Texas, USA in 2024. The{pi} 0 of the collected leaves were measured using an osmometer. Seed cotton yields from the field plots were measured near the end of cotton season. To test the effect of sample size on strength of the linear relation between{pi} 0 and cotton yield, 1-6 resamples of{pi} 0 were randomly drawn with replacement from the original 6 measurements per plot for the 54 plots. The resampled data of{pi} 0 were then used as independent variable to predict cotton yield. We found that, considering the labor and cost, sampling 3 or 6 leaves per plot may not make a significant difference for the linear regression between{pi} 0 and cotton yield.
Why it matches plant phenotyping methods葉の浸透ポテンシャル測定におけるサンプル数の妥当性と、収量との関係に対する影響を再サンプリングで評価しており、測定プロトコルの技術的検証が中心である。
titleDoes sample size of leaf osmotic potential affect its relationship with cotton yield?
Reproduction assets foundThe paper's field-measured leaf osmotic potential and seed cotton yield dataset, plus the authors' resampling/regression computer code, are explicitly deposited publicly on Zenodo (record 14635663), as stated in the Data availability section.Dataset · publicect 9574-
2, is appreciated. We thank Jose Teran and Joe Gonzalez,
Farm Manager and Farm Foreman, respectively, at Uvalde
Research Center, and collaborating farmer Rick Kruger for
time/efforts invested in crop management.
Data availability
The data and computer code for reproduc-
ing the results of this paper are available from
https://zenodo.org/records/14635663.Bibliography
1. Megan K. Bartlett, Ya Zhang, Christine Scoffoni, Shanwen Sun, Rico Ardy, Kunfang Cao,
and Lawren Sack. Rapid determination of comparative drought tolerance traits: using an
osmometer to predict turgor loss point. Methods in Ecology and Evolution, 3:880–888, 2012.
2. Y. N. S. Cheung, M. T. Tyree, and J. Dainty. WOpen asset ↗Zenodo · 14635663pdf-raw-page:3 lines:1-85Plant phenotyping relevance match · UnverifiedCrossref · checked 13 Sept 2026
Water use efficiency (WUE) relates two important processes of the plant atmosphere continuum namely net carbon assimilation (via photosynthesis) and water utilization (via evapotranspiration). Our desire to trade-off WUE between accurate measurement at leaf level (WUEL) and effective implementation at plant level (WUEP) demands accurate scaling relations. Conventional mid-day, fully expanded, single-leaf measurements of WUEL are found to be poorly correlated with WUEP, thus questioning the applicability of scaling relations. This research is aimed at obtaining optimal time-window and leaf canopy position to characterize and upscale WUEL for effective field level implementation. Leaf gas exchange parameters were monitored in a rainfed Cotton field at five canopy positions for one crop cycle, and further correlated with WUEP considering individual measurements as well as their spatial averages. Optimal time-window showing highest correlation with WUEP has occurred during 15:00 to 16:00 hours irrespective of canopy leaf position and growth stage. Deviation with mid-day measurements of WUEL low during boll bursting stage (7.38 ± 4.69 %) and high during germination and seedling emergence stage (17.27 ± 5.37 %). These changes are largely attributed to stomatal regulation of water vapour via unregulated water stress conditions. Scaling relations between WUEL and WUEP are linear with correlation strengths ranging from 0.52 (west bottom) to 0.80 (plant top). At leaf level, WUE is controlled by variations in photosynthetic photon flux density (ρ = 0.80) and vapour pressure deficit (ρ = 0.78), whereas at plant level, WUE is controlled by relative humidity (ρ = 0.77) and net solar radiation (ρ = 0.85). Our findings can help in developing alternate water management strategies to improve WUE in rainfed Cotton fields of tropical humid climate.
Why it matches plant phenotyping methods葉レベルWUEを植物レベルへ拡大するため、測定時間帯と葉冠位置を最適化し、相関関係を評価することが研究の中心であり、単なる生理測定の付随利用ではない。
abstractOur desire to trade-off WUE between accurate measurement at leaf level (WUEL) and effective implementation at plant level (WUEP) demands accurate scaling relations.
The spectral vegetation indices derived from remote sensing data provide a detailed spectral analysis for assessing vegetation characteristics. This study investigated the relationship between cotton yield and canopy spectral indices to develop yield estimation models. Spectral reflectance data were collected at various growth stages using an ASD FieldSpec Pro VNIR 2500 spectrometer. Six prediction models were developed using spectral vegetation indices, including the Normalized Difference Vegetation Index ( NDVI ) and Ratio Vegetation Index ( RVI ), to estimate the Leaf Area Index ( LAI ) and above-ground biomass. For LAI estimation using the NDVI , the power function model ( y = 10.083x 11.298 ) demonstrated higher precision, with a multiple correlation coefficient of R 2 = 0.8184 and the smallest root mean square error ( RMSE = 0.3613). These results confirm the strong predictive capacity of NDVI for LAI , with the power function model offering the best estimation accuracy. In estimating above-ground biomass using RVI , the power function model of y = 6.5218x 1.33917 achieved the higher correlation ( R 2 = 0.8851) for fresh biomass with an RMSE of 0.1033, making it the most accurate. For dry biomass, the exponential function model ( y = 9.1565 × 10 -5 ∙exp(1.1146x) ) was the most precise, achieving an R 2 value of 0.8456 and the lowest RMSE value of 0.0076. These findings highlight the potential of spectral remote sensing for accurately predicting cotton canopy structural parameters and biomass weights. By integrating spectral analysis techniques with remote sensing, this research offers valuable insights for precision cotton planting and field management, enabling optimized agricultural practices and enhanced vegetation health monitoring.
Why it matches plant phenotyping methods綿花のLAI・地上部バイオマスという植物形質を分光反射データと植生指数から推定するモデルを開発・精度評価しており、形質取得手法が中心である。
abstractSix prediction models were developed using spectral vegetation indices, including the Normalized Difference Vegetation Index ( NDVI ) and Ratio Vegetation Index ( RVI ), to estimate the Leaf Area Index ( LAI ) and above-ground biomass.
High-throughput phenotyping (HTP) has become a powerful tool for gaining insights into the genetic and environmental factors that affect cotton (Gossypium spp.) growth and yield. With the recent advances in the field of computer vision, namely the integration of deep learning algorithms, the accuracy and efficiency of HTP systems have improved dramatically, enabling them to automatically quantify such fundamental phenotypic traits as fruit identification and enumeration. However, there is currently no HTP system available for counting all the reproductive phases of cotton crop that can be deployed in agronomic field conditions throughout the growing season. This study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll. Consequently, CottonSense enhances agronomic management through increased opportunities for data collection and analysis. Using RGB-D cameras, it captures and processes both two and three-dimensional data, facilitating a wider range of phenotypic trait extractions such as crop biomass and plant architecture. To segment the cotton fruits, a Mask-RCNN model is trained and optimized for faster inference using TensorRT. The model yields an average AP score of 79% in segmentation across the four fruit categories. Moreover, the model's accuracy in estimating total fruit count per image is validated by a strong agreement with the counts given by ten domain experts, as reflected by an R² value of 0.94. Furthermore, to accurately count the segmented fruits over large populations of plants, an enumeration algorithm based on a tracking strategy is developed that achieves an R² value of 0.93 when compared to hand-counted fruits in the field. The proposed HTP system, which is implemented entirely on an edge computing device, is cost-effective and power-efficient, making it an effective tool for high-yield cotton breeding and crop improvement. The code for CottonSense is publicly available at https://github.com/FeriBolour/CottonSense.
Why it matches plant phenotyping methods綿花の果実を画像から分割・計数する高スループット表現型計測システムを開発し、専門家および手作業計数で検証しているため、方法が研究の中心である。
abstractThis study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll.
In the textile industry, cotton serves as a crucial raw material; however, diseases affecting cotton leaves can result in substantial financial losses for farmers. Conventional illness detection techniques are frequently costly, time‐consuming, and inaccurate. Existing deep learning models can detect and classify affected leaves, but they face several limitations, including high error rates, excessive time consumption, a tendency for overfitting, and suboptimal performance. To overcome these issues, this work proposes a hybrid deep learning model with meta‐heuristic support integrated with Internet of Things applications to efficiently classify cotton plant diseases. This creative concept seeks to give the textile sector and farmers a more precise and efficient solution. The proposed approach consists of two phases: first, high‐resolution images of cotton leaves are captured using a Canon EOS 450D digital camera, and potential diseases are identified through IoT sensors. In the second step, advanced techniques like pre‐processing, segmentation, feature extraction, feature selection, and classification are implemented. Disease segmentation is accomplished via the modified dilated u‐net (MDU‐Net) model. Feature selection utilising the Binary Guided Whale‐Dipper Throated Optimizer (BGW‐DTO) helps to identify the most relevant properties. Using the Harris Whale Optimization Method, the best weight coefficients for every classifier are found; next, a stacking ensemble model using the most recent deep learning approaches performs classification. In a collection of photos of cotton plant leaves, the optimal ensemble model shows a 99.66% classification rate, thereby precisely diagnosing a range of illnesses comprising Army Worms, Powdery Mildew, Bacterial Blight, Aphids, and Target Spots.
Why it matches plant phenotyping methods綿花葉の病害状態を画像からセグメンテーション・特徴抽出・分類する手法が研究の中心であり、植物病害表現型の取得・推定方法を開発している。
abstractDisease segmentation is accomplished via the modified dilated u‐net (MDU‐Net) model.
Plant height is an important phenotypic trait used to estimate crop height, biomass, and yield for various crops, including cotton. Traditionally, plant heights were manually measured using rulers and tapes, a tedious and time-consuming task. To address such limitations, several studies have developed and applied different techniques for estimating plant height, including stereo-vision camera systems. However, the lack of standardized camera mounting positions across different studies raises concerns about consistency in plant height measurement as the plant height increases, but the camera position does not. To address these limitations, this study investigates how the mounting position of a stereo-vision camera, specifically the ZED2 stereo-camera from StereoLabs, influences the accuracy of cotton plant height estimation. Furthermore, the study proposes an optimal mounting position for the ZED2 camera for accurate cotton height measurements. In our experiment, we used ten potted cotton plants grown and measured weekly for over six weeks. We mounted the ZED2 camera at five predefined positions (H1, H2, H3, H4, H5) directly above the plant. We ensured that the camera was positioned perpendicular to the plant canopy when measuring and recording the plant heights. The manual measurements served as the ground truth for comparative analysis with camera measurements, using both a mixed-effects model and a simple regression model. Experimental results indicated that there were no statistically significant differences between camera measurements and manual measurements when the camera was 38 to 47 inches above the plant canopy. This observation was also demonstrated by a strong linear correlation between the manual and the camera measurement when we ran a simple linear regression model (R 2 = 0.987). The findings from this study emphasize the importance of mounting cameras at the proposed positions to obtain an accurate estimate of plant height, thereby enhancing agronomic field decisions.
Why it matches plant phenotyping methodsステレオカメラによる綿花の草丈という植物形質の推定について、カメラ取付位置を最適化し、手測定を基準に精度を検証しているため、フェノタイピング手法が中心である。
abstractthis study investigates how the mounting position of a stereo-vision camera, specifically the ZED2 stereo-camera from StereoLabs, influences the accuracy of cotton plant height estimation.
Chemical defoliation and ripening are a prerequisite for mechanical harvesting of cotton, and the boll opening rate is a critical determinant of timing and rate of defoliates and ripening agents as well as harvest. Given the low efficiency and poor timeliness of manual determination of boll opening rates, we have developed a rapid method based on digital images. Field images were collected 7 days before and 7, 14, and 21 days after the application of harvest aids, with the boll opening rates (BOR) varying from 25 to 95%. We set four shooting heights, five shooting angles and two shooting directions, and a total of 912 original images (each 5,184×3,456 pixels) were obtained. Actual ground boll opening rates were monitored simultaneously. Each single image was segmented into 500×500 pixels sub-images. The four deep learning networks were used to identify opened and unopened cotton bolls, and YOLOv5 performed best in balancing recognition time and accuracy. To address the issue of boundary boll recognition caused by image segmentation, the original images were segmented into 10 different sizes (100, 200, 300, 400, 500, 600, 700, 800, 900, and 1,000 pixels), and YOLOv5 model was then used to identify bolls in each size of the sub-images. The bounding boxes marking cotton bolls at the same position of two different sizes of sub-images, were combined to obtain new corrected bounding boxes in merged image. Based on the true values of BOR, the best combination of sub-images is 400×400 pixels with 700×700 pixels. This combination was used to examine the recognition results of various shooting parameters, and we found that the optimal shooting height for the digital camera was 20-30 cm above the canopy, with a downward angle of 0-30° (BOR higher than 40%) and 15-30° (BOR lower than 40%) from the horizontal and shooting direction parallel to the planting rows. The method established in this study can enable a less-destructive and rapid detection of BOR in the range of 25 to 95% boll opening rate, with a model R²; value >92% and a relative root mean square error <10%, suggesting its high precision and stability for field application.
Why it matches plant phenotyping methods綿花のboll opening rateという植物器官・生育状態の形質を、デジタル画像と深層学習で迅速・非破壊に推定する手法を開発し、撮影条件と精度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。
abstractwe have developed a rapid method based on digital images.
In the past decade, Unmanned Aerial Systems (UAS) have made a significant impact on various sectors, including precision agriculture, by enabling remote monitoring of crop growth and development. Monitoring and managing crops effectively throughout the growing season are crucial for optimizing crop yield. The integration of UAS-monitored data and machine learning has greatly advanced crop production management, resulting in improvements in key areas such as irrigation scheduling, crop termination analysis, and predicting yield. This study presents the development of a Digital Twin (DT) for cotton crops using UAS captured RGB data. The primary objective of this DT is to forecast various cotton crop features during the growing season, including Canopy Cover (CC), Canopy Height (CH), Canopy Volume (CV), and Excess Greenness (EXG). Predictive analytics as part of DT development employs machine learning regression to extract crop feature growth patterns from UAS data collected from 2020 to 2023. During the current season, real-time UAS data and historical growth patterns are combined to generate growth patterns using a novel hybrid model generation strategy for forecasting. Comparisons of the DT-based forecasts to actual data demonstrated low RMSE for CC, CH, CV, and EXG. The proposed DT framework, which accurately forecasts cotton crop features up to 30 days into the future starting 80 days after sowing, was found to outperform existing forecasting methods. Notably, the RRMSE for CC, CH, CV, and EXG was measured to be 9, 13, 14, and 18 percent, respectively. Furthermore, the potential applications of forecasted data in biomass estimation and yield prediction are highlighted, emphasizing their significance in optimizing agricultural practices.
Why it matches plant phenotyping methodsUAS画像と機械学習を統合したDigital Twinによる綿花のキャノピー形質（被覆、草高、体積、緑度）の抽出・将来予測が研究の中心であり、実測値との比較検証も行っている。
abstractThis study presents the development of a Digital Twin (DT) for cotton crops using UAS captured RGB data.
Cotton and soybeans are important crops for the country's economic growth. Due to the rapid spread of disease, plants are susceptible to bacterial and viral diseases. Early identification and classification using machine or deep learning models aid farmers in reducing potential losses. Model‐based detection necessitates a large number of training samples and high‐quality images. Thus, this study generates new datasets to diagnose soybean and cotton plant diseases. The images are collected with the help of the Central Institute for Cotton Research (CICR) in Nagpur, Maharashtra, to create a clean and comprehensive dataset for research purposes. The dataset contains 5200 images, including both diseased and healthy images. The collected images are labelled using the Robo flow tool, masked with the Photoshop tool and stored in the dataset. The generated dataset is examined through pre‐processing and classification using the novel proposed algorithms. Initially, the Gabor filter is used for pre‐processing to eliminate unwanted noise from the collected images. Afterwards, the Position attention‐based capsule network (PA‐CapNet) model is proposed to perform multidisease classification for the soybean and cotton datasets. Finally, the performances are assessed by evaluating varied metrics. The result analysis shows that the proposed method obtains better results than the other existing models. The proposed method obtains an accuracy of 98% for the soybean dataset and 96.89% for the cotton dataset.
Why it matches plant phenotyping methods植物葉の画像から病害状態を分類するデータセットの生成・前処理・評価が中心であり、植物病害フェノタイピング手法および再利用可能なデータセットに該当する。
abstractThus, this study generates new datasets to diagnose soybean and cotton plant diseases.
Cotton is the most significant cash crop in India. Each year cotton production is decreasing because of the attack of the disease. Plant diseases are usually produced by pathogens and pest insects and reduce the yield to a large scale if not controlled in time. The hour requires an effective plant disease diagnosis system that can assist the farmers in their farming and cultivation. Nevertheless, cotton production is harmfully affected by the presence of viruses, pests, bacterial pathogens, and so on. For the past decade or so, numerous image processing or deep learning (DL)--based automated plant leaf disease recognition techniques have been established but, unluckily, they infrequently focus on the cotton leaf diseases. Therefore, this article develops an Intelligent Detection and Classification of Cotton Leaf Diseases Using Transfer Learning and the Honey Badger Algorithm (IDCCLD-TLHBA) model with Satellite Images. The proposed IDCCLD-TLHBA technique intends to determine and classify various kinds of cotton leaf diseases using satellite imagery. In the IDCCLD-TLHBA technique, the wiener filtering (WF) model is used to reduce noise and enhance image quality for subsequent analysis. For feature extraction, the IDCCLD-TLHBA technique applies the MobileNetV2 model to capture relevant features from the satellite images while maintaining computational efficiency. In addition, the stacked long short-term memory (SLSTM) method is employed for the classification and recognition of cotton leaf diseases. Eventually, the honey badger algorithm (HBA) is used to optimally select the parameters involved in the SLSTM model to ensure a better configuration of the network to enhance results. The performance validation of the IDCCLD-TLHBA method is carried out against the benchmark dataset and the stimulated results highlight the better results of the IDCCLD-TLHBA model across the existing techniques.
Why it matches plant phenotyping methods綿花葉の病害を画像から検出・分類する深層学習ワークフローを開発・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 article develops an Intelligent Detection and Classification of Cotton Leaf Diseases Using Transfer Learning and the Honey Badger Algorithm (IDCCLD-TLHBA) model with Satellite Images.
Protecting plants from diseases involves recognizing the symptoms and identifying practical, safe, and reasonable treatment methods. Holistic approaches based on particular times or seasons can reduce plant resistance and minimize tedious work. Technological advancements have led to the development of microscopic examinations and computational methods using machine learning techniques to detect diseases automatically and quickly using leaf images. This study builds the prediction model using EfficientNet and YOLO neural network architectures from computer vision. The development of a model that assists farmers in identifying cotton disease so that they use pesticides that may treat it further utilizes this concept. In the physical world, the input is accepted from many different sources, so observing the model’s output is necessary. This work concentrates on model response to the inputs from physical devices, and analysis shows that the monitoring varies the results. A novel convolutional neural network (CNN) based on the EfficientNet architectures and variations of YOLO architectures is used to classify and identify the objects in cotton leaf. The EfficientNetB4 yielded 100% accuracy for healthy leaf and powdery mild leaf classes, and YOLO v4 version with 96%, 98.3%, 99.2%, and 0.70 for precision, recall, mAP@0.5, mAP120.5:095 respectively. These results indicate that consequences vary in real-time per environmental parameters such as light effect and devices, and analysis shows that monitoring affects the results.
Why it matches plant phenotyping methods綿花葉の画像から病徴・病害状態を推定する画像解析モデルを開発・評価しており、植物フェノタイピング手法が中心である。
abstractThis study builds the prediction model using EfficientNet and YOLO neural network architectures from computer vision.
Although season-long cotton flower counts have value to breeders and growers, a manual data collection process is too laborious to be practical in most cases. In recent years, several fully automated flower counting approaches have been proposed. However, such approaches are typically designed to run offline and require a significant amount of computation. Furthermore, little thought has gone into developing convenient interfaces and integrations so that a layperson can use such systems without extensive training. The goal of this study is to develop a lightweight flower tracking system that is deployable on a ground robot and can operate in real-time. We modify a previous GCNNMatch++ approach to increase the inference speed. Additionally, we fuse data from multiple cameras in order to avoid canopy occlusions, and extract three-dimensional flower locations by integrating GPS data from the robot. We show that our approach significantly outperforms UAV-based counting and single-camera counting while running at above 40 FPS on an edge device, achieving a counting error of 15% and an average localization error of 19 cm. This level of performance is enough to observe significant differences in flowering behavior between genotypes. Overall, we believe that our highly-integrated, automated, and simplified flower counting solution makes significant strides towards a practical commercial cotton phenotyping platform.
Why it matches plant phenotyping methodsリアルタイムの花数・三次元位置推定を行う画像解析・ロボット統合手法を開発し、精度と処理速度を評価した綿花フェノタイピング研究であり、方法が中心である。
abstractThe goal of this study is to develop a lightweight flower tracking system that is deployable on a ground robot and can operate in real-time.
Introduction Cotton, being a crucial cash crop globally, faces significant challenges due to multiple diseases that adversely affect its quality and yield. To identify such diseases is very important for the implementation of effective management strategies for sustainable agriculture. Image recognition plays an important role for the timely and accurate identification of diseases in cotton plants as it allows farmers to implement effective interventions and optimize resource allocation. Additionally, deep learning has begun as a powerful technique for to detect diseases in crops using images. Hence, the significance of this work lies in its potential to mitigate the impact of these diseases, which cause significant damage to the cotton and decrease fibre quality and promote sustainable agricultural practices. Methods This paper investigates the role of deep transfer learning techniques such as EfficientNet models, Xception, ResNet models, Inception, VGG, DenseNet, MobileNet, and InceptionResNet for cotton plant disease detection. A complete dataset of infected cotton plants having diseases like Bacterial Blight, Target Spot, Powdery Mildew, Aphids, and Army Worm along with the healthy ones is used. After pre-processing the images of the dataset, their region of interest is obtained by applying feature extraction techniques such as the generation of the biggest contour, identification of extreme points, cropping of relevant regions, and segmenting the objects using adaptive thresholding. Results and Discussion During experimentation, it is found that the EfficientNetB3 model outperforms in accuracy, loss, as well as root mean square error by obtaining 99.96%, 0.149, and 0.386 respectively. However, other models also show the good performance in terms of precision, recall, and F1 score, with high scores close to 0.98 or 1.00, except for VGG19. The findings of the paper emphasize the prospective of deep transfer learning as a viable technique for cotton plant disease diagnosis by providing a cost-effective and efficient solution for crop disease monitoring and management. This strategy can also help to improve agricultural practices by ensuring sustainable cotton farming and increased crop output.
Why it matches plant phenotyping methods綿花の画像から植物病害状態を推定する深層学習手法を比較・評価しており、病害フェノタイピング手法が中心である。
abstractThis paper investigates the role of deep transfer learning techniques such as EfficientNet models, Xception, ResNet models, Inception, VGG, DenseNet, MobileNet, and InceptionResNet for cotton plant disease detection.
Reproduction assets foundThe paper analyzed a public Kaggle cotton plant disease image dataset, explicitly linked in its data availability statement. No author code or trained models are shared.Dataset · publicPublicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease .Open asset ↗Kaggle · dhamur/cotton-plant-diseaselines:1296-1311Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
The soil-borne disease Verticillium wilt causes significant yield losses in cotton. Developing resistant cotton varieties is a long-term solution to manage the disease. The phytopathological parameter used to assess variety resistance is the presence or absence of infection, where transverse sectioning of infected stems is discoloured/brown. The traditional method of selecting resistant varieties involves manual scoring of visual stem discolouration after cutting. However, this process is associated with high labour costs, delayed processing time and cognitive biases. Therefore, automatic detection of resistant varieties with scalable, cost-effective, and rapid phenotyping tools is needed in cotton breeding.
Why it matches plant phenotyping methodsYOLO-v8を用いて綿のVerticillium病抵抗性を自動評価する画像ベース表現型計測が主題であり、育種における従来の目視判定を代替する方法開発に該当する。
titleYOLO-v8 for verticillium disease phenotyping for cotton breeding under complex field background condition
Plant height is a critical biophysical trait indicative of plant growth and developmental conditions and is valuable for biomass estimation and crop yield prediction. This study examined the effects of flight altitude and camera angle in quantifying cotton plant height using unmanned aerial system (UAS) imagery. This study was conducted in a field with a sub-surface irrigation system in Lubbock, Texas, between 2022 and 2023. Images using the DJI Phantom 4 RTKs were collected at two altitudes (40 m and 80 m) and three sensor angles (45°, 60°, and 90°) at different growth stages. The resulting images depicted six scenarios of UAS altitudes and camera angles. The derived plant height was subsequently calculated as the vertical difference between the apical region of the plant and the ground elevation. Linear regression compared UAS-derived heights to manual measurements from 96 plots. Lower altitudes (40 m) outperformed higher altitudes (80 m) across all dates. For the early season (4 July 2023), the 40 m altitude had r2 = 0.82–0.86 and RMSE = 2.02–2.16 cm compared to 80 m (r2 = 0.66–0.68, RMSE = 7.52–8.76 cm). Oblique angles (45°) yielded higher accuracy than nadir (90°) images, especially in the late season (24 October 2022) results (r2 = 0.96, RMSE = 2.95 cm vs. r2 = 0.92, RMSE = 3.54 cm). These findings guide optimal UAS parameters for plant height measurement.
Why it matches plant phenotyping methodsUAS画像からワタの草丈を抽出する方法について、飛行高度・カメラ角度の比較と手測定による精度検証を中心に扱っているため、植物フェノタイピング方法研究に該当する。
abstractThis study examined the effects of flight altitude and camera angle in quantifying cotton plant height using unmanned aerial system (UAS) imagery.
Accurate and timely detection and classification of cotton aphid damage are essential for the control of cotton aphids, a major pest in cotton in China and many other countries. However, use of existing convolutional neural networks (CNN) to classify the levels of damage caused by the pest is undesirable because of their low accuracy caused by complex field backgrounds and different lighting conditions. In this study, a lightweight classification network, CA_DenseNet_BC_40, with improved DenseNet was proposed by introducing the network architecture of DenseNet and Coordinate Attention module for classifying the levels of damage caused by cotton aphids in a natural field environment. The results of analyses show that the CA_DenseNet_BC_40 network outperformed the existing networks ResNet50, ShuffleNet, Ghost, MobileNetv3, and DenseNet on the accuracy of classification for cotton aphid damages. The classification accuracy of the proposed network reached as high as 97.3 % and the size of parameters was only 0.18 M that was smaller than those of the lightweight convolutional neural network models such as Mobinenet and GhostNet. The proposed model can be used to automatically detect and classify the levels of damage caused by cotton aphids in natural field conditions with a high accuracy.
Why it matches plant phenotyping methods綿花のアブラムシ被害レベルという植物状態を画像から分類するモデルを開発し、既存モデルとの精度比較も行っており、表現型取得・推定手法が中心である。
abstractThe proposed model can be used to automatically detect and classify the levels of damage caused by cotton aphids in natural field conditions with a high accuracy.
The growth of precision agriculture has allowed farmers access to more data and greater efficiency for their farms. With consistently tight profit margins, farmers need ways to take advantage of the advancement of technology to lower their costs or increase their revenue. One area where these advancements can prove beneficial are in the measurement of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE). Color maps representing these vegetation indices can be used to identify problem areas, plant health, or even places where spot applications are needed. These color maps help farmers to visualize these areas. Currently, a multi-thousand dollar multispectral camera, typically attached to an Unmanned Aerial Vehicle (UAV) during flight, is required for measuring these indices. This makes obtaining NDVI and NDRE somewhat cost prohibitive for most farmers. This work demonstrates a solution to this cost issue. The solution involves the use of a conditional Generative Adversarial Network known as Pix2Pix. By using Pix2Pix along with training data from UAV flights of corn, soybeans, and cotton, this paper highlights the potential for predicting comparable NDVI and NDRE with a low-cost Red-Green-Blue (RGB) camera. This paper proposes and assesses a cost-efficient method that can comparably predict these vegetation indices, resulting in cost-savings in the range of $5000 per UAV system.
Why it matches plant phenotyping methodsRGB航空画像とPix2Pixを用いてNDVI・NDREという植物状態指標を推定する手法を提案・評価しており、植物表現型の取得・推定が研究の中心である。
abstractThis work demonstrates a solution to this cost issue.
Cotton diseases and pests are essential factors affecting the quality and yield of cotton in agricultural production. To meet the needs of intelligent agriculture development and solve the problems of low efficiency and poor reliability of cotton diseases and pests detection, this study proposed a real-time high-performance detection model based on improved YOLOX. The model introduced Efficient Channel Attention (ECA), hard-Swish activation function, and Focal Loss function into YOLOX, which improved the ability of the model to extract image features, solved the problem of sample imbalance, improved the detection speed and accuracy, and enhanced the detection effect of cotton diseases and pests. A total of 5760 manually labeled cotton diseases and pests images (including five kinds of red leaf blight, verticillium wilt, cotton spider mite damage, double-spotted leaf beetle damage, and brown spot disease) were used to fine-tune and test the model. The mean Average Precision (mAP) of cotton diseases and pests detection reached 94.60%, the precision was 94.04%, the F1-score was 0.90, and the FPS was 74.21. Furthermore, the results were compared with five classical object detection algorithms (Faster R-CNN, SSD, YOLOv3, YOLOv4, and YOLOv5). The comparative results showed that the mAP of the improved model was 11.50%, 21.17%, 9.34%, 10.22%, and 8.33% higher than the other five algorithms, and the detection speed can meet the real-time requirements. Finally, a cotton diseases and pests detection software was designed and developed based on the improved model, deployed on the smartphone to complete the real-time detection of cotton diseases and pests in the field environment. The improved model can effectively detect the infected area of cotton leaves in the field and provide theoretical reference and technical support for controlling cotton diseases and pests.
Why it matches plant phenotyping methods綿花葉の病害症状・害虫被害を画像から検出するYOLOX改良モデルを開発・比較検証し、圃場用ソフトウェアにも実装しており、植物の病害状態を取得する方法が中心である。
abstractthis study proposed a real-time high-performance detection model based on improved YOLOX
PURPOSE: A generalised approach to downscale areal observations of crop production data is illustrated using cotton yield and fibre quality (length and micronaire) data which is measured as a module (areal/block) average. METHODS: Two features of the downscaling algorithm are; (i) to estimate spatial trends in yield and quality using regression with fine resolution predictors such as remote sensing imagery, and (ii) use area-to-point kriging (A2PK) to downscale either the observations in the absence of a useful spatial trend model or the residuals from the trend model (if useful) from areal averages. RESULTS: Correlations with remote sensing covariates were stronger for cotton fibre yield than for cotton fibre micronaire, and much stronger compared to those for cotton fibre length. Spatial trends in cotton fibre yield and micronaire could be estimated with good model quality using regression with remote sensing covariates with or without A2PK in almost all fields. Conversely, model quality was poorer for cotton fibre length and there was only a small difference in model performance between the null and trend models. When the downscaling approach was tested using fine-resolution yield observations, model performance was poorer at a fine-resolution compared to the module-resolution, which was to be expected. CONCLUSION: This approach enables the creation of high-resolution raster maps of variables of interest with a much finer spatial resolution compared to the areal observations, and can be applied for any areal averaged crop production data in a range of broadacre and horticultural industries (e.g. sugarcane, apples, citrus). The finer spatial resolution may allow growers or agronomists to better understand the drivers of variability within fields, assess management implications, and create management plans at a higher resolution.
Why it matches plant phenotyping methods綿花の収量・繊維品質という作物形質を、リモートセンシングと回帰・面積対点クリギングで高解像度推定する手法を開発・検証しており、形質取得・抽出法が中心である。
abstractTwo features of the downscaling algorithm are; (i) to estimate spatial trends in yield and quality using regression with fine resolution predictors such as remote sensing imagery, and (ii) use area-to-point kriging (A2PK) to downscale either the observations in the absence of a useful spatial trend model or the residuals from the trend model (if useful) from areal averages.
The leaf rolling index (LRI) is a phenotype with significant physiological implications under drought stress. However, research on the quantification of the cotton LRI is lacking, limiting its application in drought diagnosis, irrigation guidance, and physiological assessments. This study conducted a 3D reconstruction of cotton using Structure from Motion (SFM) and Multi-View Stereo (MVS). Algorithms for leaf point cloud preprocessing and phenotype extraction were developed using the PCL point cloud library and integrated into software to calculate the leaf area and perimeter. The LRI was quantified in 3D space based on the point cloud area ratio. On this basis, we analyze the relationships between LRI and leaf physiological indicators such as leaf water potential (LWP), relative water content (RWC), stomatal conductance (gs), and electron transport rate (ETR) at the seedling and flowering stages. The results indicate that the cotton LRI provides a stable indicator of drought stress, which is mainly reflected in the stable correlation between the LRI and water physiological parameters (LWP, and RWC), with coefficients of determination (R²) exceeding 0.70. Furthermore, the correlation between the LRI and the ETR suggests that the LRI could be used to assess photosynthetic efficiency under drought stress. This study demonstrates that LRI based on 3D vision in cotton may serve as a reliable morphological indicator for indicating drought stress and evaluating photosynthetic efficiency.
Why it matches plant phenotyping methods3D画像から綿花葉のローリング指数を抽出する手法を開発し、生理指標との相関で妥当性を検証しており、植物表現型取得が研究の中心である。
abstractAlgorithms for leaf point cloud preprocessing and phenotype extraction were developed using the PCL point cloud library and integrated into software to calculate the leaf area and perimeter.
Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
Cotton (Gossypium hirsutum L.) leaf chlorophyll (Chl) has been targeted as a phenotype for breeding selection to improve cotton tolerance to environmental stress. However, high-throughput phenotyping methods based on hyperspectral reflectance sensing are needed to rapidly screen cultivars for chlorophyll in the field. The objectives of this study were to deploy a cart-based field spectroradiometer to measure cotton leaf reflectance in two field experiments over four growing seasons at Maricopa, Arizona and to evaluate 148 spectral vegetation indices (SVI’s) and 14 machine learning methods (MLM’s) for estimating leaf chlorophyll from spectral information. Leaf tissue was sampled concurrently with reflectance measurements, and laboratory processing provided leaf Chl a, Chl b, and Chl a+b as both areas-basis (µg cm-2) and mass-basis (mg g-1) measurements. Leaf reflectance along with several data transformations involving spectral derivatives, log-inverse reflectance, and SVI’s were evaluated as MLM input. Models trained with 2019–2020 data performed poorly in tests with 2021–2022 data (e.g., RMSE=23.7% and r2 = 0.46 for area-basis Chl a+b), indicating difficulty transferring models between experiments. Performance was more satisfactory when training and testing data were based on a random split of all data from both experiments (e.g., RMSE=10.5% and r2 = 0.88 for area basis Chl a+b), but performance beyond the conditions of the present study cannot be guaranteed. Performance of SVI’s was in the middle (e.g., RMSE=16.2% and r2 = 0.69 for area-basis Chl a+b), and SVI’s provided more consistent error metrics compared to MLM’s. Ensemble MLM’s which combined estimates from several base estimators (e.g., random forest, gradient booting, and AdaBoost regressors) and a multi-layer perceptron neural network method performed best among MLM’s. Input features based on spectral derivatives or SVI’s improved MLM’s performance compared to inputting reflectance data. Spectral reflectance data and SVI’s involving red edge radiation were the most important inputs to MLM’s for estimation of cotton leaf chlorophyll. Because MLM’s struggled to perform beyond the constraints of their training data, SVI’s should not be overlooked as practical plant trait estimators for high-throughput phenotyping, whereas MLM’s offer great opportunity for data mining to develop more robust indices.
Why it matches plant phenotyping methods圃場ハイパースペクトル計測とスペクトル指標・機械学習による綿葉クロロフィル推定を開発・評価しており、植物形質取得法が研究の中心である。
abstracthigh-throughput phenotyping methods based on hyperspectral reflectance sensing are needed to rapidly screen cultivars for chlorophyll in the field
The nitrogen (N) and phosphorus (P) contents in cotton leaves can directly reflect growth conditions. Rapid and nondestructive acquisition of the N and P content in cotton leaves at the field scale is essential for rational fertilization strategies and precision agriculture. However, traditional direct destructive sampling in the field is performed at the sample point scale, which cannot rapidly obtain cotton leaf N and P content from the entire field. In this study, we propose that post-classification modeling based on differences in spectral features is beneficial for improving the prediction of N and P contents in cotton leaves. To test this hypothesis, we first used principal component analysis to downscale the hyperspectral data and then used Gaussian mixture modeling (GMM) to segment the hyperspectral data for spectral differences. The in-situ measured data was then combined with the random forest model to establish N and P prediction models for cotton leaves with spectral differences and full samples. Finally, the predictive model was utilized for leaf N and P spatial mapping of cotton in the field using UAV hyperspectral images as the input data. The results demonstrate that the spectral reflectance features of the different clusters classified by the GMM differ significantly in intensity and shape. The accuracy of the cotton leaf N and P prediction model based on the spectral differences was attributed to the full sample. The results validate the existence of spectral differences between crop leaf content by UAV hyperspectroscopy, and modeling based on spectral differences can improve the accuracy of predicting the spatial distribution of nitrogen and phosphorus in cotton leaves in the field.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像、GMMによるスペクトル分割、ランダムフォレスト予測を組み合わせ、綿葉のN・P含量を非破壊推定・空間マッピングする方法が研究の中心である。
abstractwe propose that post-classification modeling based on differences in spectral features is beneficial for improving the prediction of N and P contents in cotton leaves
Cotton (Gossypium spp.), an economically and strategically significant crop in China, faces challenges such as rising cultivation costs and conflicts between grain and cotton cultivation. These challenges underscore the need for enhancing yields per unit area. In response, this study employs deep learning techniques, combined with high-throughput angle detection, to conduct genome-wide association studies (GWAS) on 355 upland cotton accessions, identify key SNPs and candidate genes for plant architecture by fruit branch angle (FBA) influences planting density, yield, and mechanized harvesting. A convolutional neural network (CNN)-based software was developed for rapid and accurate branch angle detection, showing high correlation with both AutoCAD and manual measurements. Significant phenotypic variation in FBA was observed across various cotton planting regions, with the Northwest Inland Region (NIR) exhibiting notably smaller angles. In total, 107 significant Single Nucleotide Polymorphisms (SNPs) were detected across 45 quantitative trait loci (QTL), and three potential candidate genes (Ghir_A11G034910, Ghir_D05G007790, and Ghir_D05G031350) were identified, providing insights into the genetic basis of FBA and presenting valuable genetic resources for cotton breeding programs.
Why it matches plant phenotyping methodsCNNソフトウェアによる果枝角度の高速・高精度検出を開発し、AutoCADおよび手動測定と比較検証しているため、植物表現型取得法が中心的である。
abstractthis study employs deep learning techniques, combined with high-throughput angle detection
Cotton is one of the major cash crop in the agriculture led economies across the world. Cotton leaf diseases affects its yield globally. Determining cotton lesions on leaves is difficult when the area is big and the size of lesions is varied. Automated cotton lesion detection is quite useful; however, it is challenging due to fewer disease class, limited size datasets, class imbalance problems, and need of comprehensive evaluation metrics. We propose a novel deep learning based method that augments the data using generative adversarial networks (GANs) to reduce the class imbalance issue and an ensemble-based method that combines the feature vector obtained from the three deep learning architectures including VGG16, Inception V3, and ResNet50. The proposed method offers a more precise, efficient and scalable method for automated detection of diseases of cotton crops. We have implemented the proposed method on publicly available dataset with seven disease and one health classes and have achieved highest accuracy of 95% and F-1 score of 98%. The proposed method performs better than existing state of the art methods.
Why it matches plant phenotyping methods綿花葉の病変・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、植物病害フェノタイピングに該当する。
abstractWe propose a novel deep learning based method that augments the data using generative adversarial networks (GANs) to reduce the class imbalance issue and an ensemble-based method that combines the feature vector obtained from the three deep learning architectures including VGG16, Inception V3, and ResNet50.
Reproduction assets foundThe authors publicly released their analysis code (GitHub + Zenodo DOI) and used two publicly available Kaggle cotton leaf image datasets for their phenotyping/disease-detection analysis; all are paper-specific and actionable.Code · publicon-disease-dataset/data .
The code is available at GitHub and Zenodo:
- https://github.com/FrnazAkbar/Cotton-Lesion-Detection/tree/991640ddd25ad2fee85ee41f1bc92d1ea406a55b
- FrnazAkbar. (2024). FrnazAkbar/Cotton-Lesion-Detection: Automated Lesion Detection in Cotton Leaf Visuals using Deep Learning: Code Release (v1.0). Zenodo. https://doi.org/10.5281/zenodo.13324708 .
References
Abdalla et al. (2024) Abdalla A, Wheeler TA, Dever J, Lin Z, Arce J, Guo W. Assessing fusarium oxysporum disease severity in cotton using unmanned aerial system images and a hybrid domain adaptation deep learning time series model. Biosystems Engineering. 2024;237:220–231. doi: 10.1016/j.biosystemseng.2023.12.014.Open asset ↗Zenodo · 10.5281/zenodo.13324708lines:551-578Code · public:
The cotton plant disease data is available at Kaggle:
https://www.kaggle.com/datasets/dhamur/cotton-plant-disease/data , DOI: 10.34740/kaggle/dsv/5127834 .
The Cotton Disease Dataset is available at Kaggle: https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset/data .
The code is available at GitHub and Zenodo:
- https://github.com/FrnazAkbar/Cotton-Lesion-Detection/tree/991640ddd25ad2fee85ee41f1bc92d1ea406a55b
- FrnazAkbar. (2024). FrnazAkbar/Cotton-Lesion-Detection: Automated Lesion Detection in Cotton Leaf Visuals using Deep Learning: Code Release (v1.0). Zenodo. https://doi.org/10.5281/zenodo.13324708 .
References
Abdalla et al. (2024) Abdalla A, Wheeler TA, Dever J, Lin ZOpen asset ↗GitHub · FrnazAkbar/Cotton-Lesion-Detectionlines:551-578Dataset · publice dataset to conduct an analysis that involved the application of various deep learning models, namely Inception V3, ResNet50, VGG16, and a Transfer Learning approach. This led to the development of a comprehensive ensemble of pre-trained models through training procedures. Datasets used in this study are publicly available at: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease/data and https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset/data .
By incorporating a diverse range of models, there is a potential to encompass a broader array of leaf attributes compared to relying solely on a singular paradigm. Inception V3, VGG 16 and ResNet 50 results are combined on theOpen asset ↗Kaggle · dhamur/cotton-plant-diseaselines:410-480Dataset · publicious deep learning models, namely Inception V3, ResNet50, VGG16, and a Transfer Learning approach. This led to the development of a comprehensive ensemble of pre-trained models through training procedures. Datasets used in this study are publicly available at: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease/data and https://www.kaggle.com/datasets/janmejaybhoi/cotton-disease-dataset/data .
By incorporating a diverse range of models, there is a potential to encompass a broader array of leaf attributes compared to relying solely on a singular paradigm. Inception V3, VGG 16 and ResNet 50 results are combined on the bases of voting in order to extract a wide range of leaf features frOpen asset ↗Kaggle · janmejaybhoi/cotton-disease-datasetlines:410-480Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
In the textile industry, cotton plays a significant role as the primary raw material. However, cotton farming often faces challenges from various diseases, resulting in reduced productivity and financial losses for farmers. To address this issue, this study proposes a new method for predicting cotton diseases in Internet of Things (IoT)-based applications. The proposed solution combines meta-heuristic techniques with deep learning (DL). The aim of the proposed method leverages IoT, deep learning, and meta-heuristic techniques to detect and classify cotton plant diseases, providing an accurate and efficient solution for farmers and the textile industry. The process begins with capturing high-resolution images of cotton leaves in agricultural fields using a camera. These images are then processed using IoT to identify potential diseases. Noise removal and quality enhancement are performed using a Probabilistic Hybrid Wiener Filter (PHWF). Next, the Modified Dilated U-Net (MDU-Net) model segments significant disease regions from the images. Effective features are extracted from these segments using Improved Local Binary Pattern (ILBP), Gray Level Co-Occurrence Matrix (GLCM), and Gray Level Run Length Matrix (GLRM). Feature dimensionality is reduced by selecting optimal features through a Binary Guided Whale-Dipper Throated Optimizer (BGW-DTO). Classification is carried out using a stacking ensemble model, which combines EfficientNet-B7, ResNet50, VGG19, DenseNet121, and InceptionV3 models. To optimize the ensemble, a Harris whale optimization algorithm determines optimal weight coefficients for each classifier. The optimized ensemble model classifies various diseases, including Army Worms, Powdery Mildew, Bacterial Blight, Aphids, and Target Spots. Using our dataset of cotton plant leaf images, the proposed technique achieves a high accuracy of 99.66%. By integrating IoT sensor data and DL, early detection of cotton plant leaf diseases is enabled.
Why it matches plant phenotyping methods綿花葉の画像から病変領域を抽出・分類する画像ベースの植物病害表現型推定手法を開発しており、取得・抽出ワークフローが研究の中心である。
abstractThe process begins with capturing high-resolution images of cotton leaves in agricultural fields using a camera.
Much research has been invested in infrared temperature (IRT)-based methods for cotton (Gossypium hirsutism L.) water stress detection using in-field sensors, but adoption of these is low, perhaps due to logistical challenges. Alternatively, the Water Deficit Index (WDI) was developed for crop water stress assessment using remote sensors not embedded in the canopy. The objective of this research was to evaluate the performance of a sensor package-including modern IRT and normalized difference vegetation index (NDVI) sensors facing downward at 45˚, and a mini weather station-attached unintrusively to a center pivot irrigation system for detecting cotton water stress using WDI. Sensor packages were evaluated in a two-year field study that included four irrigation treatments (0, 30, 60, and 90% ET replacement) and in two production cotton fields. Overall, the tested system was effective at distinguishing crop water stress among irrigation rates. Comparison of the results to a ground-based station and simulations indicated that WDI overestimated water stress at the highest irrigation rate, but performed well otherwise. Accuracy of the system could be improved by measuring canopy coverage (Fc) from the same vantage point as the IRT and NDVI sensors (from the pivot, downward at a 45˚ angle).
Why it matches plant phenotyping methods綿花の水ストレスという植物状態を、センターピボット搭載センサーパッケージとWDIで検出・評価する方法が研究の中心であり、技術性能の比較検証も行っている。
abstractThe objective of this research was to evaluate the performance of a sensor package-including modern IRT and normalized difference vegetation index (NDVI) sensors facing downward at 45˚, and a mini weather station-attached unintrusively to a center pivot irrigation system for detecting cotton water stress using WDI.
Monitoring the number of cotton flowers can provide important information for breeders to assess the flowering time and the productivity of genotypes because flowering marks the transition from vegetative growth to reproductive growth and impacts the final yield. Traditional manual counting methods are time-consuming and impractical for large-scale fields. To count cotton flowers efficiently and accurately, a multi-view multi-object tracking approach was proposed by using both RGB and depth images collected by three RGB-D cameras fixed on a ground robotic platform. The tracking-by-detection algorithm was employed to track flowers from three views simultaneously and remove duplicated counting from single views. Specifically, an object detection model (YOLOv8) was trained to detect flowers in RGB images and a deep learning-based optical flow model Recurrent All-pairs Field Transforms (RAFT) was used to estimate motion between two adjacent frames. The intersection over union and distance costs were employed to associate flowers in the tracking algorithm. Additionally, tracked flowers were segmented in RGB images and the depth of each flower was obtained from the corresponding depth image. Those flowers tracked with known depth from two side views were then projected onto the middle image coordinate using camera calibration parameters. Finally, a constrained hierarchy clustering algorithm clustered all flowers in the middle image coordinate to remove duplicated counting from three views. The results showed that the mean average precision of trained YOLOv8x was 96.4%. The counting results of the developed method were highly correlated with those counted manually with a coefficient of determination of 0.92. Besides, the mean absolute percentage error of all 25 testing videos was 6.22%. The predicted cumulative flower number of Pima cotton flowers is higher than that of Acala Maxxa, which is consistent with what breeders have observed. Furthermore, the developed method can also obtain the flower number distributions of different genotypes without laborious manual counting in the field. Overall, the three-view approach provides an efficient and effective approach to count cotton flowers from multiple views. By collecting the video data continuously, this method is beneficial for breeders to dissect genetic mechanisms of flowering time with unprecedented spatial and temporal resolution, also providing a means to discern genetic differences in fecundity, the number of flowers that result in harvestable bolls. The code and datasets used in this paper can be accessed on GitHub: https://github.com/UGA-BSAIL/Multi-view_flower_counting.
Why it matches plant phenotyping methodsRGB-Dカメラ、ロボットプラットフォーム、物体追跡、深度投影、重複除去を統合してワタ花数を自動計測する手法を開発・検証しており、植物表現型取得が中心である。
abstractTo count cotton flowers efficiently and accurately, a multi-view multi-object tracking approach was proposed by using both RGB and depth images collected by three RGB-D cameras fixed on a ground robotic platform.
Real-time monitoring of leaf area index (LAI) in cotton (Gossypium hirsutum L.) plays a vital role in guiding field fertilization, water management, growth observation and yield prediction. The use of unmanned aerial vehicles (UAVs) equipped with diverse sensors enables flexible and rapid LAI measurement across extensive areas. This study evaluates the efficacy of UAV-mounted LiDAR and high-resolution camera-derived point cloud data in LAI prediction. We assessed the integration of canopy spectral-textural characteristics from multispectral data with structural features from point cloud data for LAI forecasting. Furthermore, we compared various machine learning and deep learning models, selected the optimal one, and applied the SHAP (Shapley Additive Explanations) method to identify key features and their influence patterns in this model. The findings are distilled into four key points: (1) The performance of canopy structure metrics based on the two sensors varied across fertility periods due to differences in canopy closure; (2) The DNN-based (Deep Neural Network) LAI prediction model excelled with a single-period dataset, achieving an R² of 0.81 and an RMSE% of 11.36 %. Similarly, in full-period multimodal data fusion, it demonstrated superior performance, evidenced by an R² of 0.84 and an RMSE% of 9.94 %. (3) Compared to the unimodal data model, the multimodal data model yielded superior results and exhibited greater robustness. (4) In the DNN-based LAI prediction model utilizing multimodal data, texture features contributed most significantly. The results suggest that the DNN model, when employing multimodal data fusion, offers not only relatively precise and robust estimates of crop LAI but also contributes valuable insights for crop phenotyping and enhanced field management. This approach subsequently improves spatial prediction accuracy and the quality of decision-making in crop production.
Why it matches plant phenotyping methodsUAV LiDAR・マルチスペクトル画像・点群を統合し、機械学習で綿花のLAIという植物形質を推定する手法を比較・評価しており、フェノタイピング手法が中心である。
abstractThis study evaluates the efficacy of UAV-mounted LiDAR and high-resolution camera-derived point cloud data in LAI prediction.
Timely and accurate plant growth monitoring is crucial for precision crop management. Traditional remote sensing methods use a single agronomic parameter to evaluate crop growth status (GST), limiting accuracy. To develop a comprehensive growth monitoring index (CGMI) based on multiple parameters. A two-year field experiment in the Mosuwan Reclamation Region of Xinjiang, China was conducted to collect parameter characterization data for cotton growth, including leaf area index, canopy chlorophyll content, above-ground biomass, and boll numbers, and their contributions and interrelationships in relation to yield were analyzed. Entropy and game theory weighting methods were used to establish the CGMIEWM and CGMIGT, and a sequential forward selection algorithm (SFS) was used to screen the most effective remote-sensing monitoring feature variables for the different reproductive stages. Partial least squares regression (PLSR), random forest (RF), and support vector regression (SVR) were used to develop an optimal model to comprehensively monitor cotton growth and draw a spatial distribution map. CGMIEWM and CGMIGT could effectively reflect GST. The correlation between CGMIGT and yield based on a game theory combination weighting method was significantly higher than that between yield and each agronomic parameter. The correlation between CGMIGT and yield (r = 0.75) was slightly higher at the initial boll stage than that of CGMIEWM (r = 0.73), whereas at the initial boll-opening stage, the correlation between CGMIGT and yield (r = 0.74) was significantly higher than that of CGMIEWM (r = 0.63). The weight coefficients used to construct the CGMIGT exhibited stable performance in different years. Feature variables were selected to monitor the comprehensive growth of cotton at different stages based on the SFS algorithm. PLSR, RF, and SVR were used to estimate CGMIGT. The RF algorithm had the best estimation performance in both the initial boll and initial boll-opening stages (R² = 0.63, root mean square error (RMSE) = 0.086, RE = 19.8 % vs. R² = 0.56, RMSE = 0.107, RE = 24.1 %). A comprehensive cotton growth distribution map in the Mosuwan Reclamation Region was drawn using the optimal model, and growth was comprehensively evaluated. Areas with good cotton growth were concentrated in the north, and there was a decreasing trend from north to south. We provide a new comprehensive evaluation tool for cotton growth status large-scale, real-time monitoring. Our results promote differentiated management, improve crop yield prediction accuracy, and aid in the formulation of cotton price strategies.
Why it matches plant phenotyping methodsSentinel-2Aリモートセンシングと複数の植物形質を統合した綿花生育状態の推定・検証手法が研究の中心であり、単なる農業実験の routine 測定ではない。
abstractTo develop a comprehensive growth monitoring index (CGMI) based on multiple parameters.
Agriculture is crucial to India's economy, contributing 17% to the GDP and supporting over 60% of the population. Cotton is a key crop, essential for Indian farmers and the textile industry. However, cotton leaf diseases have long posed a challenge, affecting crop yields. Monitoring these crops manually is time-consuming and expensive.To address this, both traditional and computer-assisted methods have been used for early detection. Convolutional Neural Networks (CNNs) have shown great potential in classifying diseases but require large datasets for training. In this study, a dataset of 1,951 images of cotton leaves, affected by four major diseases, was compiled using optical sensors. These images were processed with Keras to enhance the database for more accurate disease detection.The goal was to develop a CNN-based method that identifies the health of cotton plants through user-uploaded images. The CNN architecture achieved an accuracy of 98.765%, demonstrating its ability to detect diseases early. This approach offers a valuable tool for farmers to address crop diseases promptly, potentially improving cotton yield and minimizing losses.
Why it matches plant phenotyping methods綿花葉の画像から病害という植物状態を推定するCNN手法の開発とデータセット構築が中心であり、植物フェノタイピング手法として適格。
abstracta dataset of 1,951 images of cotton leaves, affected by four major diseases, was compiled using optical sensors
The sustainable management of Earth's complex ecosystems requires an abundance of field data to support long term stewardship. Remotely sensed satellite data provide crucial supplements to field measurements and are essential for deriving key operational products for monitoring Earth systems. However, to accurately calibrate and validate the models used to develop monitoring datasets, coincident field measurements are required. In 2018 and 2019, data related to cotton (Gossypium hirsutum L.) crops were collected from five fields in two farms located in Georgia, USA. Collections were timed to coincide with satellite overpasses to support the development of remote sensing-based crop and soil data products. Data collected include soil moisture, plant water content, above ground biomass, crop height, plant phenology, and field management practices (row direction, row spacing, and plant density). The datasets include 512 records collected in 2018 and 303 records collected in 2019. The data are archived in the National Agricultural Library Ag Data Commons repository and are available for use by researchers seeking crop and soil validation data.
Why it matches plant phenotyping methods植物の草丈、地上部バイオマス、植物フェノロジーなどの形質を含む再利用可能な検証用データセットであり、リモートセンシングデータ製品の開発・検証を支援することが中心目的である。
abstractData collected include soil moisture, plant water content, above ground biomass, crop height, plant phenology, and field management practices
Deep networks play a crucial role in the recognition of agricultural diseases. However, these networks often come with numerous parameters and large sizes, posing a challenge for direct deployment on resource-limited edge computing devices for plant protection robots. To tackle this challenge for recognizing cotton diseases on the edge device, we adopt knowledge distillation to compress the big networks, aiming to reduce the number of parameters and the computational complexity of the networks. In order to get excellent performance, we conduct combined comparison experiments from three aspects: teacher network, student network and distillation algorithm. The teacher networks contain three classical convolutional neural networks, while the student networks include six lightweight networks in two categories of homogeneous and heterogeneous structures. In addition, we investigate nine distillation algorithms using spot-adaptive strategy. The results demonstrate that the combination of DenseNet40 as the teacher and ShuffleNetV2 as the student show best performance when using NST algorithm, yielding a recognition accuracy of 90.59% and reducing FLOPs from 0.29 G to 0.045 G. The proposed method can facilitate the lightweighting of the model for recognizing cotton diseases while maintaining high recognition accuracy and offer a practical solution for deploying deep models on edge computing devices.
Why it matches plant phenotyping methods綿花の病害状態を画像認識するモデルの軽量化・比較評価が中心であり、植物病害フェノタイピング手法の開発・検証に該当する。
abstractTo tackle this challenge for recognizing cotton diseases on the edge device, we adopt knowledge distillation to compress the big networks, aiming to reduce the number of parameters and the computational complexity of the networks.
Verticillium wilt (VW) is a soil-borne vascular disease that affects upland cotton and is caused by Verticillium dahliae Kleb. A rapid and user-friendly early diagnostic technique is essential for the preventing and controlling VW disease. In this study, Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) technology was used to detect VW infection in cotton leaves. About 1800 FTIR spectra were obtained from 348 cotton leaves. The cotton leaves were collected from three categories: VW group, infected group and control group (non-infected). The vibrational peak of chitins at 1558 cm -1 was identified through mean and differential analysis of FTIR spectra as a criterion to differentiate the VW or infected group from the control group. Classification models were constructed using various machine learning algorithms. The support vector machines (SVM) model exhibited the highest predictive accuracy (>96 %) in each group and a total accuracy (>97 %) for the three groups. These results provide a new approach for detecting Verticillium infection in cotton leaves and shows a promising potential for the future applications of the method in plant science.
Why it matches plant phenotyping methods綿葉の感染状態をATR-FTIRと機械学習で直接検出する手法の開発・評価が研究の中心であり、植物病害状態のフェノタイピングに該当する。
abstractIn this study, Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) technology was used to detect VW infection in cotton leaves.
As an essential component of the global economic crop, cotton is highly susceptible to the impact of diseases on its yield and quality. In recent years, artificial intelligence technology has been widely used in cotton crop disease recognition, but in complex backgrounds, existing technologies have certain limitations in accuracy and efficiency. To overcome these challenges, this study proposes an innovative cotton disease recognition method called CANnet, and we independently collected and constructed an image dataset containing multiple cotton diseases. Firstly, we introduced the innovatively designed Reception Field Space Channel (RFSC) module to replace traditional convolution kernels. This module combines dynamic receptive field features with traditional convolutional features to effectively utilize spatial channel attention, helping CANnet capture local and global features of images more comprehensively, thereby enhancing the expressive power of features. At the same time, the module also solves the problem of parameter sharing. To further optimize feature extraction and reduce the impact of spatial channel attention redundancy in the RFSC module, we connected a self-designed Precise Coordinate Attention (PCA) module after the RFSC module to achieve redundancy reduction. In the design of the classifier, CANnet abandoned the commonly used MLP in traditional models and instead adopted improved Kolmogorov Arnold Networks-s (KANs) for classification operations. KANs technology helps CANnet to more finely utilize extracted features for classification tasks through learnable activation functions. This is the first application of the KAN concept in crop disease recognition and has achieved excellent results. To comprehensively evaluate the performance of CANnet, we conducted extensive experiments on our cotton disease dataset and a publicly available cotton disease dataset. Numerous experimental results have shown that CANnet outperforms other advanced methods in the accuracy of cotton disease identification. Specifically, on the self-built dataset, the accuracy reached 96.3%; On the public dataset, the accuracy reached 98.6%. These results fully demonstrate the excellent performance of CANnet in cotton disease identification tasks.
Why it matches plant phenotyping methods綿花の病徴を画像から認識するCNN手法を開発し、自作および公開データセットで性能検証しており、植物病害状態の画像ベース表現型取得が中心である。
abstractthis study proposes an innovative cotton disease recognition method called CANnet
Reproduction assets foundThe paper evaluates CANnet on a self-built Xinjiang cotton disease dataset (no public availability statement) and on a public Kaggle cotton disease dataset explicitly cited with a URL. No author code, models, or supplementary data deposits are mentioned.Dataset · public29. Dhamodharan. Cotton Plant Disease. 2023. Available online: https://www.kaggle.com/datasets/dhamur/cotton-plant-disease
(accessed on 6 May 2024).Open asset ↗Kaggle · dhamur/cotton-plant-diseasepdf-page:21 lines:1-56Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The creation and use of a comprehensive cotton leaf disease dataset offer significant benefits in agricultural research, precision farming, and disease management. This dataset enables the development of accurate machine learning models for early disease detection, reducing manual inspections and facilitating timely interventions. It serves as a benchmark for testing algorithms and training deep learning models, aiding in automated monitoring and decision support tools in precision agriculture. This leads to targeted interventions, reduced chemical use, and improved crop management. Global collaboration is fostered, contributing to the development of disease-resistant cotton varieties and effective management strategies, ultimately reducing economic losses and promoting sustainable farming. Field surveys conducted from October 2023 to January 2024 ensured meticulous image capture under diverse conditions. The images are categorized into eight classes, representing specific disease manifestations, pests, or environmental stress in cotton plants. The dataset comprises 2137 original images and 7000 augmented images, enhancing deep learning model training. The Inception V3 model demonstrated high performance, with an overall accuracy of 96.03 %. This underscores the dataset's potential in advancing automated disease detection in cotton agriculture.
Why it matches plant phenotyping methods綿花葉の病徴を画像で分類するデータセットを構築し、深層学習モデルのベンチマークとして評価しており、植物病害表現型の取得・解析が中心である。
abstractThe creation and use of a comprehensive cotton leaf disease dataset offer significant benefits in agricultural research, precision farming, and disease management.
Reproduction assets foundThe paper is a Data in Brief article describing the authors' own cotton leaf disease image dataset (SAR-CLD-2024), publicly deposited on Mendeley Data with a direct URL and DOI provided in the article.Dataset · publicining and evaluating machine learning models aimed at accurately classifying and diagnosing cotton leaf diseases.
Data source location
The National Cotton Research Institute field in Gazipur, Dhaka, Bangladesh
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/b3jy2p6k8w.2
Direct URL to data: https://data.mendeley.com/datasets/b3jy2p6k8w/2
Related research article
None
1.
Value of the Data
•
The presence of diseases such as Cotton Leaf Curl Disease and leaf hopper in cotton plants poses significant challenges to farmers worldwide, leading to substantial yield losses, reduced crop quality, and economic hardships. Timely detection and effective management ofOpen asset ↗Mendeley Data · 10.17632/b3jy2p6k8w.2lines:1-44Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Published1 Sept 2024Computers and Electronics in Agriculture.
Multiple source images acquired from diverse sensors mounted on unmanned aerial vehicles (UAVs) offer valuable complementary information for ground vegetation analysis. However, accurately aligning heterogeneous UAV images poses challenges due to differences in geometry, intensity, and noise resulting from varying imaging principles. This paper presents a two-stage registration method aimed at fusing visible RGB and multispectral images for cotton leaf lesion grading. The coarse alignment stage utilizes Scale Invariant Feature Transform (SIFT), while the refined alignment stage employs a novel correlation coefficient-based template matching. The proposed method first employs the EfficientDet network to detect infected cotton leaves with lesions in RGB images. Subsequently, lesion leaves in multiple spectral imagery (red, green, red edge, and near-infrared bands) are located using the perspective transformation matrix derived from SIFT and the coordinates of lesion leaves in RGB images. Refined registration between RGB and multispectral imagery is achieved through template matching with the new correlation coefficient. The registered reflectance data from the different spectral bands and RGB components are utilized to classify pixels in each infected leaf into lesion, healthy, and soil parts. The lesion grade is determined based on the ratio of lesion pixels to the total corresponding leaf area. Experimental results, compared with manual assessment, demonstrate a lesion leaves detection model with a mAP@0.5 of 91.01% and a leaf lesion grading accuracy of 92.01%. These results validate the suitability of the proposed method for UAV RGB and multispectral image registration, enabling automated cotton leaf lesion grading.
Why it matches plant phenotyping methodsUAV RGB・マルチスペクトル画像の位置合わせと病斑画素抽出を開発・検証し、ワタ葉の病斑重症度という植物状態を自動推定しているため、フェノタイピング手法が中心である。
abstractThis paper presents a two-stage registration method aimed at fusing visible RGB and multispectral images for cotton leaf lesion grading.
In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R2 of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R2 of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像の分割・混合画素分解と回帰モデルを開発・比較し、ワタ群落のSPADという植物生理形質を推定する方法が研究の中心である。
abstractimage segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · bioRxiv · checked 15 Sept 2026
ABSTRACT The economic value of cotton is based on its long, thin, strong, and twisted trichoblasts that emerge from the ovule epidermis. The mature dried fiber cell reflects the outcome of a rapid tapering of the nascent trichoblast, weeks of polarized diffuse growth, followed by a transition to persistent secondary cell wall synthesis. Highly conserved and dynamic microtubule and cellulose microfibril-based anisotropic growth control modules are central to all of these phases. In this paper, we developed novel quantitative phenotyping and computational modeling pipelines to analyze fiber growth behaviors at a daily resolution. We uncovered unexpected variability in growth rate, cell wall properties, and cell geometry across a critical window of fiber development. Finite element computational modeling of fiber growth was used to analyze the instability of cell diameter control and predict how spatial gradients of fiber and matrix material properties can interact to dictate the patterns of shape change. As an initial step toward gaining insight into the molecular orchestration of cellulose biosynthesis, expression profiles of a broad set of relevant genes were quantified across the same developmental timeline and correlated with fiber phenotypes. This analysis identified specific candidate genes that may serve as targets for fiber quality improvement.
Why it matches plant phenotyping methods綿花繊維の成長挙動を日次で定量化するフェノタイピングおよび計算モデリングのパイプライン開発が中心である。
abstractwe developed novel quantitative phenotyping and computational modeling pipelines to analyze fiber growth behaviors at a daily resolution.
Introduction Individual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed. Methods In this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square ( R 2 ), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model. Results (i) after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (iii) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients ( r ) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R 2 , RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC.
Why it matches plant phenotyping methodsUAV画像の画素分類とスペクトル前処理により、土壌・葉影の影響を低減してワタの葉窒素含量を推定する手法が研究の中心であり、性能評価も行っている。
abstractone classification method to unmanned aerial vehicle (UAV) image pixels is proposed
Cotton is one of the crops that requires the most time and labor. Precision agriculture technology is required for efficient management of cotton, and the identification of cotton attribute information in the field is a necessary and crucial step towards implementing precision agriculture. Unmanned aerial vehicles (UAVs) and Light Detection and Ranging (LiDAR) have evolved into essential instruments for plant phenotyping research. In this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology. This work acquired a dual-view point cloud of a cotton field in order to address the high density and low accuracy of the cotton point cloud attributes. Following pre-processing of the data, the point cloud was first coarsely regenerated using a combination of Fast Point Feature Histograms (FPFH) and Intrinsic Shape Signatures (ISS) techniques. The dual-view point cloud registration was then refined and finished using an Iterative Closest Point (ICP) algorithm. The height of the cotton plant was determined using the reconstructed point cloud of the cotton canopy, and a method combining Graham’s algorithm and the Alpha-Shape algorithm was suggested to determine the porosity of the cotton layers. The findings revealed that the root mean square errors (RMSE) between calculated and measured values of cotton plant height and stratified porosity were, respectively, 3.98 cm and 5.21%, and that their mean absolute percentage errors (MAPE) were 4.39% and 9.31%, with correlation coefficients (R2) of 0.951 and 0.762, respectively. On the whole, our study has demonstrated the effectiveness of the proposed method in terms of providing accurate and reliable cotton parameters in agriculture.
Why it matches plant phenotyping methods綿花キャノピーの点群取得・再構成・特徴量抽出手法を開発し、草高と層別空隙率を実測値と比較して検証しているため、植物フェノタイピング手法が中心である。
abstractIn this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology.
High-quality cottonseed is essential for successful cotton production. The integrity of cottonseed hulls plays a pivotal role in fostering the germination and growth of cotton plants. Consequently, it is crucial to eliminate broken cottonseeds before the cotton planting process. Regrettably, there is a lack of rapid and cost-effective methods for detecting broken cottonseed at this critical stage. To address this issue, this study developed a dual-camera system for acquiring front and back images of multiple cottonseeds. Based on this system, we designed the hardware, software, and control systems required for the online detection of cottonseed breakage. Moreover, to enhance the performance of cottonseed breakage detection, we improved the backbone and YOLO head of YOLOV8m by incorporating MobileOne-block and GhostConv, resulting in Light-YOLO. Light-YOLO achieved detection metrics of 93.8% precision, 97.2% recall, 98.9% mAP50, and 96.1% accuracy for detecting cottonseed breakage, with a compact model size of 41.3 MB. In comparison, YOLOV8m reported metrics of 93.7% precision, 95.0% recall, 99.0% mAP50, and 95.2% accuracy, with a larger model size of 49.6 MB. To further validate the performance of the online detection device and Light-YOLO, this study conducted an online validation experiment, which resulted in a detection accuracy of 86.7% for cottonseed breakage information. The results demonstrate that Light-YOLO exhibits superior detection performance and faster speed compared to YOLOV8m, confirming the feasibility of the online detection technology proposed in this study. This technology provides an effective method for sorting broken cottonseeds.
Why it matches plant phenotyping methods綿実の破損状態という植物器官の形態・品質特性を対象に、二眼カメラによる取得装置、オンライン検出システム、Light-YOLO解析手法を開発し、性能検証まで行っているため、植物フェノタイピング手法が中心である。
abstractthis study developed a dual-camera system for acquiring front and back images of multiple cottonseeds.
Pests and viral plant diseases have proliferated in agriculture. Rats spread plant diseases. Hard to detect viral infections or insect infestations that impair plant growth and yield. It decreases crop size, quality, and marketability, incurring major economic losses. Strong, disease-resistant crops are needed for agriculture. Bacterial, fungal, and insect/pest diseases destroy 70% and 15% of crops. Above 85% of disease severity destroys crops. Early detection systems track pests and diseases. High-definition equipment is costly for rural farmers. Correct diagnosis permits quick infection severity and type assessment and control. In precision agriculture, neural networks and image processing eliminate human error. Sustainable agriculture and organic agricultural cultivation must start early. The proposed system classifies diseases and pests for plant safety. A healthy, fast-growing plantation is our goal. Early detection of viral diseases and insect infestations boosts crop productivity. Plant disease diagnostics focuses on leaves and buds. Stem and insect infections are covered. The suggested method classifies plant health and disease automatically. Peanut leaves develop Bacterial Blight from contagious illnesses like Cercospora Leaf Peanut. Boll Weevils, European Corn Borers, and Fall Army Worms attack cotton. The conditions are categorised. Image accuracy depends on lighting, resolution, location, and backdrop complexity. Various low-resolution pictures are enriched to better classification. Complex leaf intensity variations cause the system to misdiagnose illness or insect infestation. To avoid misclassification, the system segments the sample image using various methods to find lesions. Multilevel Segmentation (MS) improves Deep neural network feature extraction using approximate outlines. This method protects important pixels, eliminates unneeded pixels, and amplifies the lesion to control segmentation and reduce insufficient and excessive segmentation.
Why it matches plant phenotyping methods植物画像から病害・病徴を自動検出し、病変分割とニューラルネットワーク分類を行う手法開発が中心であり、植物の健康状態・病害状態を直接推定している。
abstractThe suggested method classifies plant health and disease automatically.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 7 Sept 2026
Understanding the complex interactions between genotype-environment dynamics is fundamental for optimizing crop improvement. However, traditional phenotyping methods limit assessments to the end of the growing season, restricting continuous crop monitoring. To address this limitation, we developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking. Leveraging multi-scan terrestrial laser scanning (TLS), we captured high-resolution 3D LiDAR data in a cotton breeding field across various stages of the growing season to generate four-dimensional (4D) crop models, seamlessly integrating spatial and temporal dimensions. Our registration procedure involved an initial pairwise terrain-based matching for rough alignment, followed by a bird’s-eye view adjustment for fine registration. Point clouds collected throughout nine sessions across the growing season were successfully registered both spatially and temporally, with average registration errors of approximately 3 cm. We used the generated 4D models to monitor canopy height (CH) and volume (CV) for eleven cotton genotypes over two months. The consistent height reference established via our spatiotemporal registration process enabled precise estimations of CH ( R 2 = 0.95, RMSE = 7.6 cm). Additionally, we analyzed the relationship between CV and the interception of photosynthetically active radiation (IPAR f ), finding that it followed a curve with exponential saturation, consistent with theoretical models, with a standard error of regression (SER) of 11%. In addition, we compared mathematical models from the Richards family of sigmoid curves for crop growth modeling, finding that the logistic model effectively captured CH and CV evolution, aiding in identifying significant genotype differences. Our novel TLS-based digital phenotyping methodology enhances precision and efficiency in field phenotyping over time, advancing plant phenomics and empowering efficient decision-making for crop improvement efforts.
Why it matches plant phenotyping methodsTLSによる時系列3D点群の登録と4D作物モデル構築を開発し、綿の草高・群落体積を継続的に推定・検証することが研究の中心である。
abstractwe developed a methodology for spatiotemporal registration of time-series 3D point cloud data, enabling field phenotyping over time for accurate crop growth tracking.
Abstract Background Zonal application maps aim to represent field variability by means of variables of interest that can further be translated into management practices. Zonal application maps for crop growth regulators in cotton under variable-rate strategies are commonly based exclusively on vegetation index variability. However, saturation by vegetation indexes cannot be avoided by multispectral imagery and dense crop vegetation areas. The objective of this study was to compare zonal application maps for crop growth regulator application under variable-rate conditions via two approaches: (i) relying on field-measured crop height and (ii) not relying on field-measured crop height. During the agricultural season, we developed zonal application maps using an unsupervised framework, representing local variability based on field-collected data, satellite imagery data, soil texture and phenology. Posteriorly, using data from 3 agricultural seasons, we developed a supervised framework to predict plant height by using a machine learning algorithm based on remote sensing and phenology data to test the development of the same maps without field data on plant height. Results The results indicated good performance of the machine learning model for predicting plant height, but these predictions presented much lower variability than that found in field conditions. Ultimately, the predictions went through the same unsupervised process to construct maps, but without requiring field-measured data. We tested both approaches in three fields on two different dates each. Fields with intense textural variability provided the highest compatibility between the maps of both approaches. Conversely, the lowest compatibility was found in fields with lower soil textural variation. Conclusion There is potential for the use of predictive modelling to assist in the construction of zonal application maps, but the adherence to real patterns of crop growth variability found in the field is highly variable and must be assessed on a case-by-case basis.
Why it matches plant phenotyping methodsリモートセンシングと機械学習により綿花の草丈という明示的な植物形質を推定し、圃場測定値との変動性を比較・検証しているため、管理マップ作成への応用を含む実質的な形質推定手法研究である。
abstractwe developed a supervised framework to predict plant height by using a machine learning algorithm based on remote sensing and phenology data
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Background Agriculture is one of the most crucial assets of any country, as it brings prosperity by alleviating poverty, food shortages, unemployment, and economic instability. The entire process of agriculture comprises many sectors, such as crop cultivation, water irrigation, the supply chain, and many more. During the cultivation process, the plant is exposed to many challenges, among which pesticide attacks and disease in the plant are the main threats. Diseases affect yield production, which affects the country’s economy. Over the past decade, there have been significant advancements in agriculture; nevertheless, a substantial portion of crop yields continues to be compromised by diseases and pests. Early detection and prevention are crucial for successful crop management. Methods To address this, we propose a framework that utilizes state-of-the-art computer vision (CV) and artificial intelligence (AI) techniques, specifically deep learning (DL), for detecting healthy and unhealthy cotton plants. Our approach combines DL with feature extraction methods such as continuous wavelet transform (CWT) and fast Fourier transform (FFT). The detection process involved employing pre-trained models such as AlexNet, GoogLeNet, InceptionV3, and VGG-19. Implemented models performance was analysed based on metrics such as accuracy, precision, recall, F1-Score, and Confusion matrices. Moreover, the proposed framework employed ensemble learning framework which uses averaging method to fuse the classification score of individual DL model, thereby improving the overall classification accuracy. Results During the training process, the framework achieved better performance when features extracted from CWT were used as inputs to the DL model compared to features extracted from FFT. Among the learning models, GoogleNet obtained a remarkable accuracy of 93.4% and a notable F1-score of 0.953 when trained on features extracted by CWT in comparison to FFT-extracted features. It was closely followed by AlexNet and InceptionV3 with an accuracy of 93.4% and 91.8% respectively. To further improve the classification accuracy, ensemble learning framework achieved 98.4% on the features extracted from CWT as compared to feature extracted from FFT. Conclusion The results show that the features extracted as scalograms more accurately detect each plant condition using DL models, facilitating the early detection of diseases in cotton plants. This early detection leads to better yield and profit which positively affects the economy.
Why it matches plant phenotyping methods画像解析と深層学習により、綿植物の健全・病害状態を直接分類する手法を開発・評価しており、植物状態の取得が研究の中心である。
abstractwe propose a framework that utilizes state-of-the-art computer vision (CV) and artificial intelligence (AI) techniques, specifically deep learning (DL), for detecting healthy and unhealthy cotton plants.
Introduction Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status. Methods In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the estimation of cotton LAI. Results The RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. Discussion Appropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications.
Why it matches plant phenotyping methods綿花キャノピーのスペクトルから植被指数を構築し、機械学習と指数融合によってLAIを推定する手法の開発・精度評価が研究の中心である。
abstract20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · Crossref · bioRxiv · checked 13 Sept 2026
Summary Senescence is a highly ordered degenerative biological process that affects yield and quality in annuals and perennials. Images from 14 unoccupied aerial system (UAS, UAV, drone) flights captured the senescence window across two experiments while functional principal component analysis (FPCA) effectively reduced the dimensionality of temporal visual senescence ratings (VSRs) and two vegetation indices: RCC and TNDGR. Convolutional neural networks (CNNs) trained on temporally concatenated, or “sandwiched,” UAS images of individual cotton plants ( Gossypium hirsutum L.), allowed single-plant analysis (SPA). The first functional principal component scores (FPC1) served as the regression target across six CNN models (M1-M6). Model performance was strongest for FPC1 scores from VSR (R 2 = 0.857 and 0.886 for M1 and M4), strong for TNDGR (R 2 = 0.743 and 0.745 for M3 and M6), and strong-to- moderate for RCC (R 2 = 0.619 and 0.435 for M2 and M5), with deep learning attention of each model confirmed by activation of plant pixels within saliency maps. Single-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis (FDA). This has applications for fundamental plant biology, monitoring orchards or other spaced plantings, plant breeding, and genetic research.
Why it matches plant phenotyping methodsUAS時系列画像とCNN/FDAを用いて、単一ワタ個体の老化形質・植生指数を推定する高スループット表現型解析手法が研究の中心である。
abstractSingle-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis (FDA).
Cotton is a pivotal global commodity underscored by its economic value and widespread use. In the face of climate change, breeding resilient cultivars for variable environmental conditions becomes increasingly essential. However, the process of phenotyping, crucial to breeding programs, is often viewed as a bottleneck due to the inefficiency of traditional, low-throughput methods. To address this limitation, this study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties. The results from this study demonstrated the effectiveness of four vegetation indices (VIs) in evaluating these varieties for water-use efficiency (WUE). The prediction accuracy for WUE through VIs such as the simple ratio water index (SRWI) and normalized difference water index (NDWI) was higher (up to R2 = 0.66), enabling better detection of phenotypic variations (p < 0.05) among the varieties compared to physiological-related traits (from R2 = 0.21 to R2 = 0.42), with high repeatability and a low RMSE. These VIs also showed high Pearson correlations with WUE (up to r = 0.81) and yield-related traits (up to r = 0.63). We also selected high-performing varieties based on the VIs, WUE, and fiber quality traits. This study demonstrated that the hyperspectral-based proximal sensing approach helps rapidly assess the in-season performance of varieties for imperative traits and aids in precise breeding decisions.
Why it matches plant phenotyping methods綿花品種の水利用効率などの形質を、ハイパースペクトル近接センシングと植生指数で高スループットに推定・評価することが研究の中心であり、予測精度や再現性も検証している。
abstractthis study utilizes hyperspectral remote sensing, a promising tool for assessing crucial crop traits across forty cotton varieties.
Introduction Cotton yield estimation is crucial in the agricultural process, where the accuracy of boll detection during the flocculation period significantly influences yield estimations in cotton fields. Unmanned Aerial Vehicles (UAVs) are frequently employed for plant detection and counting due to their cost-effectiveness and adaptability. Methods Addressing the challenges of small target cotton bolls and low resolution of UAVs, this paper introduces a method based on the YOLO v8 framework for transfer learning, named YOLO small-scale pyramid depth-aware detection (SSPD). The method combines space-to-depth and non-strided convolution (SPD-Conv) and a small target detector head, and also integrates a simple, parameter-free attentional mechanism (SimAM) that significantly improves target boll detection accuracy. Results The YOLO SSPD achieved a boll detection accuracy of 0.874 on UAV-scale imagery. It also recorded a coefficient of determination (R 2 ) of 0.86, with a root mean square error (RMSE) of 12.38 and a relative root mean square error (RRMSE) of 11.19% for boll counts. Discussion The findings indicate that YOLO SSPD can significantly improve the accuracy of cotton boll detection on UAV imagery, thereby supporting the cotton production process. This method offers a robust solution for high-precision cotton monitoring, enhancing the reliability of cotton yield estimates.
Why it matches plant phenotyping methodsUAV画像から綿花のbollを検出・計数する新規YOLOベース手法を開発し、検出精度とboll数推定性能を評価しており、植物形質取得手法が中心である。
abstractthis paper introduces a method based on the YOLO v8 framework for transfer learning, named YOLO small-scale pyramid depth-aware detection (SSPD).
Aerial photogrammetry with drones is being frequently used in agriculture. Since computational tools can optimize various personalized workflows for Digital Agriculture, the objective of this study was to develop modules (scripts) of a computational routine called FindPLANT-V1, leaving it personalized for testing on a preliminary basis, through products aerial photogrammetry obtained with a drone, the spatial and temporal variability of the height of cotton plants. To develop the study, an experimental area of 500 m2 was selected with irrigated cotton cultivation at the State University of Montes Claros (Unimontes) Experimental Farm, in Janaúba/MG. The FindPLANT-V1 routine has four modules, structured through object-oriented programming with Python 3.8 in the Spyder IDE. The following libraries were used to develop the scripts: Gdal, Imageio, Math, Matplolib, Numpy, OpenCV, OS, OSR, OGR, Pandas, Rasterstats and Scipy. A script was also developed to create the essential directories (inputs and outputs) for the operation of this routine. As a result of aerophotogrammetric geoprocessing, the average ground sample distances (GSD) of classic orthophotomosaics was 1.6 cm. The average GSD of the Digital Surface Model (DSM) and Digital Terrain Model (DTM) was 3.2 cm. Such products, routine inputs, made it possible to estimate the spatial and temporal variability (module 1.ST), the canopy height model (module 2.ProCHM), the zonal statistics of the plots (3.ZonalStat). The results of the routine developed, although partial, made it possible to analyze the spatial variability of cotton plants and quantify the height in different phases of the cycle, presenting flexibility of adaptation for herbaceous crops.
Why it matches plant phenotyping methodsドローン空撮画像からワタ植物の高さとキャノピー高を推定する計算ルーチンを開発しており、植物表現型の取得・抽出手法が研究の中心である。
abstractthe objective of this study was to develop modules (scripts) of a computational routine called FindPLANT-V1
Cotton, a vital textile raw material, is intricately linked to people's livelihoods. Throughout the cotton cultivation process, various diseases threaten cotton crops, significantly impacting both cotton quality and yield. Deep learning has emerged as a crucial tool for detecting these diseases. However, deep learning models with high accuracy often come with redundant parameters, making them challenging to deploy on resource-constrained devices. Existing detection models struggle to strike the right balance between accuracy and speed, limiting their utility in this context. This study introduces the CDDLite-YOLO model, an innovation based on the YOLOv8 model, designed for detecting cotton diseases in natural field conditions. The C2f-Faster module replaces the Bottleneck structure in the C2f module within the backbone network, using partial convolution. The neck network adopts Slim-neck structure by replacing the C2f module with the GSConv and VoVGSCSP modules, based on GSConv. In the head, we introduce the MPDIoU loss function, addressing limitations in existing loss functions. Additionally, we designed the PCDetect detection head, integrating the PCD module and replacing some CBS modules with PCDetect. Our experimental results demonstrate the effectiveness of the CDDLite-YOLO model, achieving a remarkable mean average precision (mAP) of 90.6%. With a mere 1.8M parameters, 3.6G FLOPS, and a rapid detection speed of 222.22 FPS, it outperforms other models, showcasing its superiority. It successfully strikes a harmonious balance between detection speed, accuracy, and model size, positioning it as a promising candidate for deployment on an embedded GPU chip without sacrificing performance. Our model serves as a pivotal technical advancement, facilitating timely cotton disease detection and providing valuable insights for the design of detection models for agricultural inspection robots and other resource-constrained agricultural devices.
Why it matches plant phenotyping methods綿花の病害を画像から検出する軽量深層学習モデルを開発・評価しており、植物の病害状態を取得する方法が研究の中心である。
abstractThis study introduces the CDDLite-YOLO model, an innovation based on the YOLOv8 model, designed for detecting cotton diseases in natural field conditions.
Accurate monitoring of nitrogen nutrition is crucial for improving cotton yield and quality, as well as the ecological environment. The mainstream method for monitoring nutrition is to establish traditional machine learning (ML) models using a single data source. However, this approach has limitations such as limited access to feature information, model-fitting problems, and limited generalization. Deep learning (DL), on the other hand, has shown promise in complex nonlinear modeling tasks due to its flexible structure. However, it has its limitations, such as the fact that agronomic sample collection and testing are usually labor- and material-intensive, resulting in sample sizes that are too small to meet its training conditions. Therefore, there is an urgent need for DL models that can effectively integrate features from multiple data sources and accurately monitor crop nitrogen content, especially in scenarios with small samples. In this study, we conducted indoor pot experiments using the cotton variety Xinluzao 53 and subjected it to six nitrogen treatments. The data sources for our analysis included hyperspectral and digital images of the cotton leaves. To enhance representation learning capabilities, we enriched the multi-class base learners within each layer of the deep forest (DF) model and introduced skip connections. These enhancements improved the quality of inversion for both hyperspectral and digital image datasets. We then developed image-spectral fusion models, which combined the DF structure with stacking ensemble learning. Our focus was on three levels of fusion: feature-level fusion, decision-level fusion, and secondary decision-level fusion. This approach aimed to further enhance the accuracy and stability of nitrogen content inversion. The DF model satisfied the training condition for small samples. Compared to traditional ML algorithms and the original DF algorithm, the improved DF model achieved an increase in validation set R² of 13.4–28.5% and 10.9–14.9%, respectively. These findings highlight the enhanced accuracy and stability of the improved DF model. Additionally, compared to the optimal inversion model using two single data sources, the “Image-Spectral” three-level fusion models exhibited improvements in validation set R² of 8.6–9.3%, 10.5–11.2%, and 11.8–12.5% for feature-level, decision-level, and secondary decision-level fusion, respectively. The improved DF and three-level fusion model collectively contributed to the increased accuracy of cotton nitrogen content inversion. Among these models, the secondary decision-level fusion model demonstrated the most marked improvement. This methodology provides valuable insights into monitoring crop phenotypic parameters in situations with limited sample sizes.
Why it matches plant phenotyping methods綿葉のハイパースペクトル画像とデジタル画像から窒素含量を推定する融合モデルを開発・比較検証しており、植物形質推定手法が研究の中心である。
abstractWe then developed image-spectral fusion models, which combined the DF structure with stacking ensemble learning.
Cotton is an important economic crop; therefore, enhancing cotton yield and cultivating superior varieties are key research priorities. The seedling stage, a critical phase in cotton production, significantly influences the subsequent growth and yield of the crop. Therefore, breeding experts often choose to measure phenotypic parameters during this period to make breeding decisions. Traditional methods of phenotypic parameter measurement require manual processes, which are not only tedious and inefficient but can also damage the plants. To effectively, rapidly, and accurately extract three-dimensional phenotypic parameters of cotton seedlings, precise segmentation of phenotypic organs must first be achieved. This paper proposes a neural network-based segmentation algorithm for cotton seedling organs, which, compared to the average precision of 75.4% in traditional unsupervised learning, achieves an average precision of 96.67%, demonstrating excellent segmentation performance. The segmented leaf and stem point clouds are used for the calculation of phenotypic parameters such as stem length, leaf length, leaf width, and leaf area. Comparisons with actual measurements yield coefficients of determination R2 of 91.97%, 90.97%, 92.72%, and 95.44%, respectively. The results indicate that the algorithm proposed in this paper can achieve precise segmentation of stem and leaf organs, and can efficiently and accurately extract three-dimensional phenotypic structural information of cotton seedlings. In summary, this study not only made significant progress in the precise segmentation of cotton seedling organs and the extraction of three-dimensional phenotypic structural information, but the algorithm also demonstrates strong applicability to different varieties of cotton seedlings. This provides new perspectives and methods for plant researchers and breeding experts, contributing to the advancement of the plant phenotypic computation field and bringing new breakthroughs and opportunities to the field of plant science research.
Why it matches plant phenotyping methods綿花幼苗の点群から器官をセグメンテーションし、茎長・葉長・葉幅・葉面積を抽出する手法の開発と精度検証が中心である。
abstractThis paper proposes a neural network-based segmentation algorithm for cotton seedling organs
Effectively monitoring seedling emergence is critical to identify missing cotton seedling at early stages, allowing the prompt replenishment of the seedlings to maintain crop yield. However, traditional manual inspections have the limitations with low efficiency, poor timeliness, and large counting errors. Although UAV imagery has been applied in seedling monitoring, there are still chance in cotton seedling monitoring under complex environment conditions. Therefore, there is a urgent need for an efficient, rapid, and low-cost method for monitoring early seedling emergence in cotton crops. This study utilizes RGB images of early seedling emergence captured from an unmanned aerial vehicle (UAV) at a flight height of 10 m and a resolution of 0.33 cm in Shihezi, Xinjiang, on 7 and 12 days after seeding (DAS). The modified Hough transform method is constructed for line detection, and crop rows are subsequently extracted from the lines and masked images. After masking, the waveform is extracted from the excess green index (ExG) greyscale map and smoothed to determine the peaks. Seedlings are counted and located by numbering and positioning the peaks, meanwhile their coordinates and distance determine the number and location of missing seedlings. Images with varying resolutions (ranging from 0.33 to 2.5 cm) and intensity changes (0.8 to 1.2) verify the accuracy and efficiency of the proposed waveform method(WM). The results shown that at 7 DAS, the root mean square error (RMSE) is less than 2.0 plants/row, the relative root mean squared error (RRMSE) is less than 2 %, and the coefficient of determination (R²) is stable at 0.95–0.96 when the resolution is below 1.0 cm. The RMSE, RRMSE and R² fluctuate more when the resolution is less than 1.0 cm. At 12 DAS, the counting accuracy is stable under different conditions, and the RMSE and RRMSE are distributed smoothly over the range of 1.76–4.39 plants/row and 1.29–5.47 % with different resolutions. The R² is in the range of 0.88–0.97, RRMSE is 1.90–3.39 % and the RMSE is 1.65–2.90 plants/row under different intensities. The results of cotton seedling counting using the waveform method are less affected by image resolution and intensity changes. Therefore, the proposed WM provides efficient, accurate, and automatic counting for cotton seedlings in response to complex weather conditions. Furthermore, it would give a novel approach to accurately countequally spaced row-sown crops based on UAV images.
Why it matches plant phenotyping methodsUAV画像と波形解析を用いてワタ幼苗の出芽数・位置・欠株を自動抽出する手法を開発し、解像度や強度変化に対する精度を検証しており、植物表現型取得が中心である。
abstractThe modified Hough transform method is constructed for line detection, and crop rows are subsequently extracted from the lines and masked images.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
Rapid and precise 3D organ segmentation is crucial for the automatic extraction of phenotypic traits, forming a fundamental prerequisite for intelligent plant breeding. The advancement of deep learning technology has replaced labor-intensive manual measurements and traditional computer vision methods, which are sensitive to parameters, in phenotypic trait extraction. However, current larger network structures not only require extensive point cloud data but also consume substantial computational resources, rendering them unsuitable for agricultural tasks with limited plant samples. Therefore, this study developed a lightweight 3D deep learning network (PEPNet) that achieves precise plant organ segmentation and stem-leaf phenotypic trait extraction. The adopted simple-but-effective network architecture and innovative modern operations, including a high-dimensional feature mapping strategy for preprocessing input points, a local feature extraction module based on inverted residual bottleneck block, and a cost-free attention block for spatial feature fusion, effectively implement multi-scale hierarchies and adaptively reduce computational overheads. Experimental results from cotton stem-leaf segmentation demonstrated that PEPNet not only presented approximately 2 × faster inference speed (9.59 ms) and throughput (146.32 plants per second) but also achieved competitive segmentation performance compared to other six state-of-the-art deep learning networks, namely PointNet++, DGCNN, CurveNet, Point Cloud Transformer, PointMLP, and SPoTr, achieving 95.99 %, 94.66 %, 95.32 %, and 91.31 % in Precision, Recall, F1-score, and mIoU, respectively. In transferability experiments with tomato and soybean plants, PEPNet achieved almost all the best metrics and significantly outperformed the second-best model (CurveNet). Furthermore, ablation study verified the optimal trade-off between efficiency and accuracy in this network. Any modifications to the modules could potentially disrupt the optimal trade-off. This work could contribute to reducing computational resources and annotation costs for applying segmentation methods in high-throughput phenotyping tasks.
Why it matches plant phenotyping methods植物器官の3D点群セグメンテーションと形質抽出を目的とする軽量深層学習ネットワークを開発し、複数作物で性能検証しており、フェノタイピング手法が中心です。
abstractthis study developed a lightweight 3D deep learning network (PEPNet) that achieves precise plant organ segmentation and stem-leaf phenotypic trait extraction
Efficient and accurate spraying of cotton defoliant is a vital part of cotton production; the traditional way of spraying cotton defoliant will cause waste of pesticides and environmental pollution. The fraction of absorbed photosynthetically active radiation(FPAR) in cotton predicted by multi-source remote sensing information from unmanned aerial vehicles(UAVs) offers the possibility of precise control of cotton defoliant dosage. In this study, RGB and multispectral(MS) images of cotton at multiple fertility stages were collected by UAV. Six machine learning models (random forest regression(RFR), support vector regression(SVR), gradient-boosting decision tree(GBDT), extreme gradient boosting(XGBoost), K-nearest neighbor(k-NN), partial least squares regression(PLSR)) were constructed using spectral indices and texture features extracted from UAV remote sensing imagery and cotton FPAR measurements collected, and the performance of each model was evaluated. A spatial distribution map of cotton FPAR based on UAV imagery was constructed from cotton FPAR predicted by the machine learning models. The study results show that (i) image feature changes over multiple periods based on UAV multi-source remote sensing data are consistent with the evolution of cotton FPAR and can be used to predict cotton FPAR. (ii) Overall, texture features are more sensitive to FPAR than spectral indices, and the fusion of texture features with spectral indices increases the prediction accuracy of the model. (iii) The GBDT model based on UAV multi-source remote sensing data not only performs better on cotton FPAR prediction for a large sample dataset (whole fertility period), with R² = 0.88 and rRMSE = 7.43 %, but also for a small sample dataset (single fertility period), with a range of R² of 0.65–0.85 and a range of rRMSE of 7.41 %-19.09 %. In addition, a cotton defoliation spray prescription map was constructed based on the spatial distribution of cotton FPAR predicted by GBDT.
Why it matches plant phenotyping methodsUAV画像から綿花のFPARという植物生理形質を機械学習で推定し、複数モデルの性能評価と空間マッピングまで行っており、形質取得・推定ワークフローが中心的です。
abstractSix machine learning models (random forest regression(RFR), support vector regression(SVR), gradient-boosting decision tree(GBDT), extreme gradient boosting(XGBoost), K-nearest neighbor(k-NN), partial least squares regression(PLSR)) were constructed using spectral indices and texture features extracted from UAV remote sensing imagery and cotton FPAR measurements collected, and the performance of each model was evaluated.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Information on boll distribution within a cotton plant is critical to evaluate the adaptation and response of cotton plants to environmental and biotic stress in cotton production. Cotton researchers have applied available conventional fiber measurements, such as the high volume instrument (HVI) and advanced fiber information system (AFIS), to map the location and the timing of boll development and distribution within plants and further to determine within-plant variability of cotton fiber properties. Both HVI and AFIS require numerous cotton bolls combined for the measurement. As an alternative approach, attenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy was proposed to measure fiber maturity (MIR) and crystallinity (CIIR) of a sample as little as 0.5 mg lint. Extending fiber maturity and crystallinity measurement into a single boll for node-by-node mapping, FT-IR method might be advantageous due to less sampling amount compared with HVI and AFIS methods. Results showed that FT-IR technique enabled the evaluation of fiber MIR and CIIR at a boll level, which resulted in average MIR and CIIR values highly correlated with HVI micronaire (MIC) and AFIS maturity ratio (M). Hence, FT-IR technique possesses a good potential for a rapid and non-destructive node-by-node mapping of cotton boll maturity and crystallinity distribution.
Why it matches plant phenotyping methods綿花の単一ボールにおける繊維成熟度・結晶化度をFT-IRで非破壊測定する手法を提案し、既存測定法との相関で検証しているため、植物フェノタイピング手法が研究の中心である。
abstractattenuated total reflection Fourier transform infrared (ATR FT-IR) spectroscopy was proposed to measure fiber maturity (MIR) and crystallinity (CIIR) of a sample as little as 0.5 mg lint.
The food industry has led the agricultural economy of the state all India to prosperity. India has historically been the largest producing nation having identity of Agricultural Land. Grains , fruits , Vegetables , such as potatoes, oranges, Tomato ,sugarcane and other specially grains and cottons are the chief crops of the India. Citrus and cotton industries have been a driving force behind Maharashtra's impressive economic growth.. The situation has created job opportunities for many people, boosting the state's economic potential. To maintain the prosperity of citrus and cotton industries, Government has been concerned about disease control, labour cost, and global market. During the recent past, citrus canker and citrus greening, Black spot-n cotton has become serious threats to citrus in Maharashtra. Infection by these diseases weakens trees, leading to decline, mortality, lower yields, and decreased commercial value. Likewise, the farmers are concerned about costs from tree loss, scouting, and chemicals used in an attempt to control the disease. An automated detection system may help in prevention and, thus reduce the serious loss to the industries, farmers and Economy of country. This research aims to the development of disease detection with pattern recognition approaches for these diseases in crop. The detection approach consists of three major sub-systems, namely, image acquisition, image processing and pattern recognition. The imaging processing sub-system includes image preprocessing for background noise removal, leaf boundary detection and image feature extraction. Pattern recognition approaches will be use to classify samples among several different conditions on crops. In order to evaluate the classification approaches, results will be compared between classification methods for the different induvial fruits, vegetable, grains disease detection. Obtained results will help in demonstration of classification accuracy which is targeted as better than existing for proposed model as high as 97.00%. This study aimed to assess the potential of identifying plant diseases by examining visible signs on fruits and leaves. These data collection and initial knowledge acquisition is plan in offline approaches. By implementing this simple model, we can achieve a more favourable cost-to-production ratio compared to complex solutions.
Why it matches plant phenotyping methods植物の葉・果実の可視症状から病害状態を画像取得・画像処理・パターン認識で推定する手法の開発が中心であり、植物病害フェノタイピングに該当する。
abstractThis research aims to the development of disease detection with pattern recognition approaches for these diseases in crop.
In the pursuit of sustainable agriculture, efficient water management remains crucial, with growers relying on advanced techniques for informed decision-making. Cotton yield prediction, a critical aspect of agricultural planning, benefits from cutting-edge technologies. However, traditional methods often struggle to capture the nuanced complexities of crop health and growth. This study introduces a novel approach to cotton yield prediction, leveraging the synergy between Unmanned Aerial Vehicles (UAVs) and scale-aware convolutional neural networks (CNNs). The proposed model seeks to harness the spatiotemporal dynamics inherent in high-resolution UAV imagery to improve the accuracy of the cotton yield prediction. The CNN component adeptly extracts spatial features from UAV-derived imagery, capturing intricate details related to crop health and growth, modeling temporal dependencies, and facilitating the recognition of trends and patterns over time. Research experiments were carried out in a cotton field at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, with three replications evaluating four irrigation treatments (rainfed, full irrigation, percent deficit of full irrigation, and time delay of full irrigation) on cotton yield. The prediction revealed that the proposed CNN regression models outperformed conventional CNN models, such as AlexNet, CNN-3D, CNN-LSTM, ResNet. The proposed CNN model showed state-of-art performance at different image scales, with the R2 exceeding 0.9. At the cotton row level, the mean absolute error (MAE) and mean absolute percentage error (MAPE) were 3.08 pounds per row and 7.76%, respectively. At the cotton grid level, the MAE and MAPE were 0.05 pounds and 10%, respectively. This shows the proposed model's adaptability to the dynamic interplay between spatial and temporal factors that affect cotton yield. The authors conclude that integrating UAV-derived imagery and CNN regression models is a potent strategy for advancing precision agriculture, providing growers with a powerful tool to optimize cultivation practices and enhance overall cotton productivity.
Why it matches plant phenotyping methodsUAV画像から綿花の収量という植物形質を推定するCNN手法を開発・比較評価しており、表現型取得・推定ワークフローが中心である。
abstractThis study introduces a novel approach to cotton yield prediction, leveraging the synergy between Unmanned Aerial Vehicles (UAVs) and scale-aware convolutional neural networks (CNNs).
Traditional field phenotyping methods are often manual, time-consuming, and destructive, posing a challenge for breeding progress. To address this bottleneck, robotics and automation technologies offer efficient sensing tools to monitor field evolution and crop development throughout the season. This study aimed to develop an autonomous ground robotic system for LiDAR-based field phenotyping in plant breeding trials. A Husky platform was equipped with a high-resolution three-dimensional (3D) laser scanner to collect in-field terrestrial laser scanning (TLS) data without human intervention. To automate the TLS process, a 3D ray casting analysis was implemented for optimal TLS site planning, and a route optimization algorithm was utilized to minimize travel distance during data collection. The platform was deployed in two cotton breeding fields for evaluation, where it autonomously collected TLS data. The system provided accurate pose information through RTK-GNSS positioning and sensor fusion techniques, with average errors of less than 0.6 cm for location and 0.38$^{\circ}$ for heading. The achieved localization accuracy allowed point cloud registration with mean point errors of approximately 2 cm, comparable to traditional TLS methods that rely on artificial targets and manual sensor deployment. This work presents an autonomous phenotyping platform that facilitates the quantitative assessment of plant traits under field conditions of both large agricultural fields and small breeding trials to contribute to the advancement of plant phenomics and breeding programs.
Why it matches plant phenotyping methods自律走行ロボットとLiDAR/TLSによる圃場フェノタイピング基盤を開発・評価しており、植物形質を定量評価するための取得・解析手法が研究の中心である。
abstractThis study aimed to develop an autonomous ground robotic system for LiDAR-based field phenotyping in plant breeding trials.
CottonField / plotLeafStem / branchPhysiological trait estimationWater status / transpiration
Stem water potential (Ψₛₜₑₘ) is a key indicator for assessing plant water status, which is crucial in understanding plant health and productivity. However, existing measurement methods for Ψₛₜₑₘ, characterized by destructiveness and intermittency, limit its applicability. Microtensiometers, an emerging plant-based sensor, offer continuous monitoring capabilities and have shown success in certain vine and tree species. In this study, we investigate the efficacy of microtensiometers ability to monitor the Ψₛₜₑₘ of cotton (Gossypium hirsutum L.) under three distinct irrigation treatments in Maricopa, Arizona, an extremely hot, arid environment. We analyze the diurnal dynamics of Ψₛₜₑₘ across the irrigation regimes and compare these measurements with midday leaf water potentials (Ψₗₑₐf) obtained using a dewpoint potentiometer. Our results demonstrate that the microtensiometer-derived Ψₛₜₑₘ closely follows known diurnal patterns of Ψₗₑₐf, tracking with vapor pressure deficit (VPD) and responding to variations in irrigation levels and soil moisture content. Time cross-correlation analysis reveals an 80-minute lag in Ψₛₜₑₘ response to changing VPD under non-water limiting conditions, which shortens under water-limiting conditions. Additionally, we establish a robust linear relationship (R²ₐdⱼ = 0.82) between Ψₛₜₑₘ and Ψₗₑₐf, with this relationship strengthening as water availability decreases. Notably, we observe mean gradients of 1.2 and 0.06 MPa between soil vs. stem and stem vs. leaf water potentials, respectively. Moreover, Ψₛₜₑₘ data proves to be more sensitive in distinguishing between irrigation treatments earlier in the growing season compared to Ψₗₑₐf, leaf temperature and leaf gas exchange parameters. These findings highlight the utility of microtensiometers as valuable tools for monitoring water status in smaller-stemmed row crops such as cotton.
Why it matches plant phenotyping methodsマイクロテンシオメータによる綿の茎水ポテンシャル測定を中心に、既存指標との比較、応答特性、灌漑処理の識別性能を検証しており、植物生理状態のセンシング手法として中心的である。
abstractMicrotensiometers, an emerging plant-based sensor, offer continuous monitoring capabilities
Cotton micronaire is an essential fiber quality attribute that characterizes both fiber maturity and fineness components. Micronaire and other attributes are measured on fiber lint routinely in laboratories under controlled environmental conditions following a well-established high-volume instrument protocol. In this study, the attenuated total reflection Fourier transform infrared spectroscopy, characterizing fundamental group vibrations in fiber cellulose from 4000 to 400 cm⁻¹, and using an attenuated total reflection device, was explored for fiber micronaire assessment, especially for seed cotton locule fibers that were mingled with nonlint materials, and varied in fiber maturity within a naturally variable sample. Partial least squares multivariate regression models and the algorithmic infrared maturity approach were developed and then applied to predict micronaire values of validation samples and independent seed cotton samples for comparison. Unlike partial least squares models that showed worse in the coefficient of determination, bias, and percentage of samples within the 95% agreement range for independent samples than for validation samples, the algorithmic infrared maturity approach indicated a similarity in the coefficient of determination, bias, and percentage of samples within the 95% agreement range between the validation samples and independent samples. In particular, the algorithmic infrared maturity approach avoided the need to re-calibrate the model with new samples. Therefore, the development of a robust and effective Fourier transform infrared technique combined with the infrared maturity approach for rapid laboratory micronaire assessment and distribution demonstrated a great potential for its extension to the early micronaire testing in remote/breeding locations, and also to regular cotton fibers, processed cotton yarns and fabrics.
Why it matches plant phenotyping methods綿繊維のミクロンネールという植物由来形質を、ATR-FTIRと回帰・赤外成熟度アルゴリズムで測定・予測する手法を開発し、検証しており、表現型取得が研究の中心である。
abstractPartial least squares multivariate regression models and the algorithmic infrared maturity approach were developed and then applied to predict micronaire values of validation samples and independent seed cotton samples for comparison.
Crop diseases are a main cause for crop losses in agricultural production worldwide. Remote sensing has long been used for crop disease detection, but it is necessary to select a suitable imaging platform and spatial resolution for practical applications. In this study, the category variance ratio (CVR) method was proposed to characterize the separability of healthy and cotton root rot-infested cotton areas on images from three different platforms, including an unmanned aerial vehicle (UAV), a manned aerial vehicle (MAV) and the Sentinel-2 satellite. The visible and near-infrared bands were selected to construct several vegetation indices and the separability parameters for the three types of images were calculated and compared using multiple evaluation indicators. The comparison results showed that, based on the CVR separability index proposed in this study, the MAV image had the best separation between healthy and root rot areas among the three types of images. Through pixel clustering and resampling, the UAV images were upscaled to the resolution of the MAV images, but the upscaled UAV image was not as effective as the original MAV image. Further analysis of the upscaled UAV images at different spatial resolutions showed that the 4-m resolution in the reconstructed image was the optimal resolution for cotton root rot identification. Image classification and accuracy assessment results showed that the resampled 4-m UAV and MAV images had better classification accuracy than the original images and that the upscaled 4-m MAV image achieved the best accuracy among the three types of images. The results from this study demonstrate that the CVR method was effective for selecting the most appropriate spatial resolution and image type to extract crop disease information.
Why it matches plant phenotyping methodsCVR法とマルチスケール画像を用いて、綿花根腐病の状態を画像から識別し、画像プラットフォームと空間解像度を比較・検証しており、植物病害フェノタイピング手法が研究の中心です。
abstractthe category variance ratio (CVR) method was proposed to characterize the separability of healthy and cotton root rot-infested cotton areas on images from three different platforms
The root is an important organ for plants to obtain nutrients and water, and its phenotypic characteristics are closely related to its functions. Deep-learning-based high-throughput in situ root senescence feature extraction has not yet been published. In light of this, this paper suggests a technique based on the transformer neural network for retrieving cotton's in situ root senescence properties. High-resolution in situ root pictures with various levels of senescence are the main subject of the investigation. By comparing the semantic segmentation of the root system by general convolutional neural networks and transformer neural networks, SegFormer-UN (large) achieves the optimal evaluation metrics with mIoU, mRecall, mPrecision, and mF1 metric values of 81.52%, 86.87%, 90.98%, and 88.81%, respectively. The segmentation results indicate more accurate predictions at the connections of root systems in the segmented images. In contrast to 2 algorithms for cotton root senescence extraction based on deep learning and image processing, the in situ root senescence recognition algorithm using the SegFormer-UN model has a parameter count of 5.81 million and operates at a fast speed, approximately 4 min per image. It can accurately identify senescence roots in the image. We propose that the SegFormer-UN model can rapidly and nondestructively identify senescence root in in situ root images, providing important methodological support for efficient crop senescence research.
Why it matches plant phenotyping methods綿花の根の老化状態を画像から抽出・認識する深層学習手法の開発と比較が中心であり、植物表現型測定法に該当する。
abstractthis paper suggests a technique based on the transformer neural network for retrieving cotton's in situ root senescence properties.
One of the most important crops for economic survival is cotton, and one of the biggest challenges it faces is early disease detection that affect productivity. The cotton business may suffer financial losses because of the frequently insufficient visual detection of these diseases by humans. This study presents an intelligent approach for the detection of cotton plant diseases using Convolutional Neural Networks (CNNs) with a focus on ResNet-152V2 architecture. Leveraging deep learning techniques, specifically ResNet-152V2, the model exhibits robust performance in identifying various diseases affecting cotton plants. The research involved training the model on a diverse dataset encompassing different cotton leaf diseases. Results demonstrate a better accuracy, with the proposed approach achieving an impressive precision in disease detection. The utilization of ResNet-152V2 enhances the model's capability to accurately classify and diagnose cotton plant diseases, showcasing its efficacy for real-world applications. The study contributes to the advancement of automated disease detection systems in agriculture, particularly in the context of cotton crops.
Why it matches plant phenotyping methods綿花葉の画像から病害をCNNで自動分類する手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として収録する。
abstractThis study presents an intelligent approach for the detection of cotton plant diseases using Convolutional Neural Networks (CNNs) with a focus on ResNet-152V2 architecture.
Timely and accurate estimation of cotton seedling emergence rate is of great significance to cotton production. This study explored the feasibility of drone-based remote sensing in monitoring cotton seedling emergence. The visible and multispectral images of cotton seedlings with 2 - 4 leaves in 30 plots were synchronously obtained by drones. The acquired images included cotton seedlings, bare soil, mulching films, and PE drip tapes. After constructing 17 visible VIs and 14 multispectral VIs, three strategies were used to separate cotton seedlings from the images: (1) Otsu's thresholding was performed on each vegetation index (VI); (2) Key VIs were extracted based on results of (1), and the Otsu-intersection method and three machine learning methods were used to classify cotton seedlings, bare soil, mulching films, and PE drip tapes in the images; (3) Machine learning models were constructed using all VIs and validated. Finally, the models constructed based on two modeling strategies [Otsu-intersection (OI) and machine learning (Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)] showed a higher accuracy. Therefore, these models were selected to estimate cotton seedling emergence rate, and the estimates were compared with the manually measured emergence rate. The results showed that multispectral VIs, especially NDVI, RVI, SAVI, EVI2, OSAVI, and MCARI, had higher crop seedling extraction accuracy than visible VIs. After fusing all VIs or key VIs extracted based on Otsu's thresholding, the binary image purity was greatly improved. Among the fusion methods, the Key VIs-OI and All VIs-KNN methods yielded less noises and small errors, with a RMSE (root mean squared error) as low as 2.69% and a MAE (mean absolute error) as low as 2.15%. Therefore, fusing multiple VIs can increase crop image segmentation accuracy. This study provides a new method for rapidly monitoring crop seedling emergence rate in the field, which is of great significance for the development of modern agriculture.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、画像分割、機械学習を統合し、綿実生の出芽率という植物形質を推定する方法を開発・検証しており、フェノタイピング手法が中心である。
titleImproving the accuracy of cotton seedling emergence rate estimation by fusing UAV-based multispectral vegetation indices.
The use of drones in crop assessment has become increasingly common. The use of drones presents benefits for evaluating the height of plants in various crops, including cotton, checking nutritional, phytosanitary, genetic parameters, abiotic and biotic effects. The objective of this study was to evaluate the use of drones to aid in the evaluation of trials in cotton cultivation. The study was carried out at the Goiano Institute of Agriculture, located in the municipality of Montividiu, State of Goiás, Brazil. A Phantom 4 Pro drone was used to carry out the mapping and WebODM was used to carry out photogrammetry and obtain a digital model of the surface area in the cotton field. In the experimental design, 4 randomized blocks with 10 treatments were used. The results demonstrated that there was no difference between the four blocks for vegetative index. The digital surface model proved to be efficient in detecting possible differences between the analyzed blocks. This analysis is essential to understand variations in response to cotton cultivation in different blocks and identify possible factors that contribute to these differences.
Why it matches plant phenotyping methodsドローン画像とフォトグラメトリによる綿花の草丈・圃場差の推定が研究の中心であり、植物表現型取得法の実質的な応用に該当する。
abstractThe objective of this study was to evaluate the use of drones to aid in the evaluation of trials in cotton cultivation.
Background Cotton accounts for 80% of the global natural fibre production. Its leaf hairiness affects insect resistance, fibre yield, and economic value. However, this phenotype is still qualitatively assessed by visually attributing a Genotype Hairiness Score (GHS) to a leaf/plant, or by using the HairNet deep-learning model which also outputs a GHS. Here, we introduce HairNet2, a quantitative deep-learning model which detects leaf hairs (trichomes) from images and outputs a segmentation mask and a Leaf Trichome Score (LTS). Results Trichomes of 1250 images were annotated (AnnCoT) and a combination of six Feature Extractor modules and five Segmentation modules were tested alongside a range of loss functions and data augmentation techniques. HairNet2 was further validated on the dataset used to build HairNet (CotLeaf-1), a similar dataset collected in two subsequent seasons (CotLeaf-2), and a dataset collected on two genetically diverse populations (CotLeaf-X). The main findings of this study are that (1) leaf number, environment and image position did not significantly affect results, (2) although GHS and LTS mostly correlated for individual GHS classes, results at the genotype level revealed a strong LTS heterogeneity within a given GHS class, (3) LTS correlated strongly with expert scoring of individual images. Conclusions HairNet2 is the first quantitative and scalable deep-learning model able to measure leaf hairiness. Results obtained with HairNet2 concur with the qualitative values used by breeders at both extremes of the scale (GHS 1-2, and 5-5+), but interestingly suggest a reordering of genotypes with intermediate values (GHS 3-4+). Finely ranking mild phenotypes is a difficult task for humans. In addition to providing assistance with this task, HairNet2 opens the door to selecting plants with specific leaf hairiness characteristics which may be associated with other beneficial traits to deliver better varieties.
Why it matches plant phenotyping methods綿花葉の毛状突起という植物形質を画像から定量抽出する深層学習モデルを開発し、複数データセットで検証しており、フェノタイピング手法が研究の中心である。
abstractwe introduce HairNet2, a quantitative deep-learning model which detects leaf hairs (trichomes) from images and outputs a segmentation mask and a Leaf Trichome Score (LTS).
Reproduction assets foundThe paper's Availability of data and materials section explicitly deposits the four paper-specific image/annotation datasets (AnnCoT, CotLeaf-1, CotLeaf-2, CotLeaf-X) with public CSIRO DOIs, all present in allowed_urls. No code or model checkpoint deposit is stated.Dataset · publicThe CotLeaf-1 image dataset is available at https://doi.org/10.25919/9vqw-7453 .Open asset ↗10.25919/9vqw-7453lines:256-289Dataset · publicThe CotLeaf-2 image dataset is available at https://doi.org/10.25919/v0qb-er50 .Open asset ↗10.25919/v0qb-er50lines:256-289Dataset · publicThe CotLeaf-X image dataset is available at https://doi.org/10.25919/eqhx-1x73 .Open asset ↗10.25919/eqhx-1x73lines:256-289Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published15 Mar 20242024 5th International Conference on Innovative Trends in Information Technology (ICITIIT)Cited by 1 · OpenAlex ↗
Agriculture is a vital component of any nation’s economy, and India is renowned for being an agro-based economy. One of the main objectives in agriculture is to cultivate robust crops that are free from diseases. Cotton has a crucial role in generating money in India. India is the world’s leading producer of cotton. Premature leaf abscission or the onset of diseases can have detrimental effects on cotton harvests. However, throughout generations, farmers and agricultural experts have consistently faced numerous problems and chronic issues in the realm of planting, including the prevalence of various cotton diseases. There is a pressing demand in the agricultural information sector for a rapid, efficient, cost-effective, and reliable technique to detect cotton infections. This is crucial since severe cotton diseases can result in a complete failure of grain harvest. Deep learning is utilized to address the challenges of image processing and classification due to its exceptional performance. This technique employs the MobileNet paradigm. Based on the experimental results, the model attains a training accuracy of 0.95 and a validation accuracy of 0.98.
Why it matches plant phenotyping methods綿花の病害を画像から深層学習で検出・分類する手法が研究の中心であり、植物の病害状態を直接推定しているため含める。
titleRecognition of Cotton Plant Diseases Using Deep Learning Architecture
INTRODUCTION: The precise and timely detection of plant diseases plays a crucial role in ensuring efficient crop management and disease control. Nevertheless, conventional methods of disease identification, which heavily rely on manual visual inspection, are often time-consuming and susceptible to human error. The knowledge acquired from this research paper enhances the overall comprehension of the discipline and offers valuable direction for future progressions in the application of deep learning for the identification of plant diseases.[1][2] AIM: to investigate the utilization of deep learning techniques in identifying various Malvaceae plant diseases. METHODS: AlexNet, VGG, Inception, REsNet and other CNN architectures are analyzed on Malvaceae plant diseases specially on Cotton, Ocra and Hibiscus, different data collection methods ,Data augmentation and Normalization techniques. RESULTS: Inception V4 have Training Accuracy 98.58%, VGG-16 have Training Accuracy 84.27%, ResNet-50 have Training Accuracy 98.72%, DenseNet have Training Accuracy 98.87%, Inception V4 have Training Loss 0.01%, VGG-16 have Training Loss 0.52%, ResNet-50 have Training Loss 6.12%, DenseNet have Training Loss 0.016%, Inception V4 have Test Accuracy 97.59%, VGG-16 have Test accuracy 82.75%, ResNet-50 have Test Accuracy 98.73%, DenseNet have Test Accuracy 99.81%, Inception V4 have Test Loss 0.0586%, VGG-16 have Test Loss 0.64%, ResNet-50 have Test Loss 0.027%, DenseNet have Test Loss 0.0154% . CONCLUSION: conclusion summarizes the key findings and highlights the potential of deep learning as a valuable tool for accurate and efficient identification of Malvaceae plant diseases.
Why it matches plant phenotyping methodsマルバ科植物の病徴を画像から深層学習で識別し、複数CNNの精度を比較評価しており、植物病害状態の表現型取得・推定手法が中心である。
abstractto investigate the utilization of deep learning techniques in identifying various Malvaceae plant diseases.
This study constructed a high-throughput method for the acquisition and analysis of three-dimensional phenotypes of cotton, and proposes a method for evaluating the degree of wilting of cotton varieties based on phenotype. The upgraded version of the self-developed data acquisition platform MVS-Pheno V2 was used to continuously collect point cloud data. PointSegAt deep learning network model was used to establish plant stem and leaf segmentation and leaf overlap distinction models, realising the segmentation of cotton plant stems and leaves and the distinction of leaf overlap. In addition, an algorithm called "Active Boundary Segmentation" has been developed, which achieved automatic segmentation of overlapping cotton leaves. Based on point cloud technology, the automation of plant height, leaf count, and wilted leaf area based on voxels was realised, and a set of wilt measurement methods for cotton plants was designed. The results show that the PointSegAt model proposed has good performance in stem and leaf segmentation, with a segmentation accuracy of 0.995 and mean intersection over union of 0.924. In terms of single leaf segmentation, the average accuracy reached 0.95, and the average f1-score reached 0.94. Compared with manual measurements of plant height, leaf count, leaf area, and canopy area, the correlation coefficients were 0.99, 0.96, 0.90, and 0.99, respectively, and the root mean square errors were 0.01, 0.04, 0.19, and 0.02, respectively. Finally, the proposed method was used to perform wilting quantification experiments on two different varieties of cotton plants, and quantitative analysis of drought resistance of different varieties was conducted.
Why it matches plant phenotyping methods綿花の3D表現型を取得・解析する高スループット手法、分割モデル、萎れ定量法を開発し、手測定との比較検証も行っており、植物表現型取得が中心である。
abstractThis study constructed a high-throughput method for the acquisition and analysis of three-dimensional phenotypes of cotton
Crop seeding rate is one of the crucial factors that affect crop production. However, acquiring adequate crop data in multiple growing environments is time-consuming and challenging in large fields. This study aimed to develop and evaluate an efficient method using an unmanned aerial vehicle (UAV) imaging system and deep learning to assess cotton emergence spacing uniformity at different seeding rates. The study was conducted on a 3.27-hectare research field planted with two cotton cultivars at five seeding rates (56 k, 74 k, 91 k, 108 k, and 123 k seeds ha⁻¹), with each treatment containing four rows with three replicates in a random block design. A UAV imaging system collected RGB images at 10 m and 15 m flight height above the ground level at two and six weeks after planting. Orthomosaic images from the two days were segmented into small blocks that were processed using the object detection algorithm YOLOv7 to identify cotton plants. Hough transform and polynomial regression were used to identify each cotton row and remove weeds. The number of plants in each 5-m row segment (i.e., stand count) was calculated to correlate with soil electrical conductivity (ECₐ) and field elevation. Results show that the research could detect cotton plants with the mean average precision of 96.9 % at the 50 % intersection over the union threshold (mAP@50) for the two-week dataset and 92.7 % mAP@50 for the six-week dataset. The results also show that plant uniformity was closely correlated with field elevation and ECₐ, with an average R² of 0.62 using the Random Forest model. The coefficient of variation was used to evaluate the spacing uniformity of each seeding rate and demonstrated that the seed rates of 108 k and 123 k seeds ha⁻¹ tended to exhibit better spacing uniformity than others under various environmental conditions. This study provides valuable insights by developing a pipeline for early-stage cotton stand count using high-resolution remote sensing techniques to evaluate the uniformity of different seeding rates for cotton, ultimately improving the efficiency of crop management.
Why it matches plant phenotyping methodsUAV画像と深層学習・画像処理によりワタ個体を検出し、出芽数と株間均一性を定量化するパイプラインの開発・評価が中心であるため、植物表現型計測手法として採用する。
abstractThis study aimed to develop and evaluate an efficient method using an unmanned aerial vehicle (UAV) imaging system and deep learning to assess cotton emergence spacing uniformity at different seeding rates.
The use of drones to evaluate crops has become increasingly common. Among these uses, the drone helps in the cultivation of cotton a crop that is highly prone to intense attacks by pests and diseases. This study aimed to evaluate the use of drones in evaluating trials in cotton cultivation in the Southwest of Goiás, Brazil. A Phantom 4 Pro drone was used to carry out the mapping and WebODM was used to carry out photogrammetry and obtain the ExG vegetation index. ExG proved to be efficient in detecting differences between blocks. The use of drones provided a series of benefits in the assessment process in cotton cultivation. The ability to fly over the area quickly and accurately allowed detailed, and punctual images to be obtained which was essential for monitoring plant health and identifying problem areas such as pest or disease infestations.
Why it matches plant phenotyping methods綿花試験区の評価を目的に、ドローン画像、フォトグラメトリ、ExG植生指数を用いてブロック差や植物健全性を評価する方法を中心的に適用しているため、植物フェノタイピング研究に該当する。
abstractA Phantom 4 Pro drone was used to carry out the mapping and WebODM was used to carry out photogrammetry and obtain the ExG vegetation index.
CottonGreenhouseThermalLeafClassificationPhotosynthesis / fluorescenceStress response / toleranceWater status / transpiration
Introduction Drought detection, spanning from early stress to severe conditions, plays a crucial role in maintaining productivity, facilitating recovery, and preventing plant mortality. While handheld thermal cameras have been widely employed to track changes in leaf water content and stomatal conductance, research on thermal image classification remains limited due mainly to low resolution and blurry images produced by handheld cameras. Methods In this study, we introduce a computer vision pipeline to enhance the significance of leaf-level thermal images across 27 distinct cotton genotypes cultivated in a greenhouse under progressive drought conditions. Our approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features (e.g., min and max temperature, median value, quartiles, etc.). These features were then utilized to develop machine learning algorithms capable of assessing leaf hydration status and distinguishing between well-watered (WW) and dry-down (DD) conditions. Results Two different classifiers were trained to predict the plant treatment-random forest and multilayer perceptron neural networks-finding 75% and 78% accuracy in the treatment prediction, respectively. Furthermore, we evaluated the predicted versus true labels based on classic physiological indicators of drought in plants, including volumetric soil water content, leaf water potential, and chlorophyll a fluorescence, to provide more insights and possible explanations about the classification outputs. Discussion Interestingly, mislabeled leaves mostly exhibited notable responses in fluorescence, water uptake from the soil, and/or leaf hydration status. Our findings emphasize the potential of AI-assisted thermal image analysis in enhancing the informative value of common heterogeneous datasets for drought detection. This application suggests widening the experimental settings to be used with deep learning models, designing future investigations into the genotypic variation in plant drought response and potential optimization of water management in agricultural settings.
Why it matches plant phenotyping methods葉の熱画像からマスクと熱特徴量を抽出し、機械学習で水分状態・乾燥処理を判定する画像解析パイプラインが中心であり、植物表現型の取得・推定手法に該当する。
abstractOur approach involved employing a customized software pipeline to process raw thermal images, generating leaf masks, and extracting a range of statistically relevant thermal features
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicSupplementary Table S3
Single measurements of volumetric soil water content across all collected images.Open asset ↗lines:440-465Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Introduction The study addresses challenges in detecting cotton leaf pests and diseases under natural conditions. Traditional methods face difficulties in this context, highlighting the need for improved identification techniques. Methods The proposed method involves a new model named CFNet-VoV-GCSP-LSKNet-YOLOv8s. This model is an enhancement of YOLOv8s and includes several key modifications: (1) CFNet Module. Replaces all C2F modules in the backbone network to improve multi-scale object feature fusion. (2) VoV-GCSP Module. Replaces C2F modules in the YOLOv8s head, balancing model accuracy with reduced computational load. (3) LSKNet Attention Mechanism. Integrated into the small object layers of both the backbone and head to enhance detection of small objects. (4) XIoU Loss Function. Introduced to improve the model's convergence performance. Results The proposed method achieves high performance metrics: Precision (P), 89.9%. Recall Rate (R), 90.7%. Mean Average Precision (mAP@0.5), 93.7%. The model has a memory footprint of 23.3MB and a detection time of 8.01ms. When compared with other models like YOLO v5s, YOLOX, YOLO v7, Faster R-CNN, YOLOv8n, YOLOv7-tiny, CenterNet, EfficientDet, and YOLOv8s, it shows an average accuracy improvement ranging from 1.2% to 21.8%. Discussion The study demonstrates that the CFNet-VoV-GCSP-LSKNet-YOLOv8s model can effectively identify cotton pests and diseases in complex environments. This method provides a valuable technical resource for the identification and control of cotton pests and diseases, indicating significant improvements over existing methods.
Why it matches plant phenotyping methods綿花葉の病害・害虫を自然条件下の画像から識別する新規YOLOベースモデルを開発し、精度・速度・メモリを比較検証しており、植物の病害状態の取得・推定が中心である。
abstractThe proposed method involves a new model named CFNet-VoV-GCSP-LSKNet-YOLOv8s.
Reproduction assets foundThe paper's cotton pest/disease detection model was trained on images aggregated from two public Kaggle datasets, both explicitly linked in the text and data availability statement. No author code or trained model is deposited.Dataset · publicThe data used in this study were sourced from six publicly available cotton pest and disease datasets on KAGGLE ( https://www.kaggle.com/datasets/saeedazfar/customized-cotton-disease-datasetOpen asset ↗Kaggle · saeedazfar/customized-cotton-disease-datasetlines:340-382Dataset · publicThis data can be found here: https://www.kaggle.com/datasets/paridhijain02122001/cotton-crop-disease-detection and https://www.kaggle.com/datasets/saeedazfar/customized-cotton-disease-datasetOpen asset ↗Kaggle · paridhijain02122001/cotton-crop-disease-detectionlines:635-688Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published16 Feb 20242024 IEEE International Conference for Women in Innovation, Technology & Entrepreneurship (ICWITE)Cited by 15 · OpenAlex ↗
India is the largest contributor of cotton production in the world but at recent times the cotton production has been decreased significantly due to various cotton plant diseases. The bacterial and fungal diseases affect the growth of the cotton at early stages and cause decrease in cotton production. Manual monitoring of these plant diseases is not possible as cotton plants are cultivated in huge acres of land. Early detection of cotton plant diseases prevents the rapid spread of disease to the whole cotton field. This project deals with the implementation of a deep learning model to detect the type of cotton disease that affects the cotton plant and to classify whether it is a fresh cotton leaf or diseased cotton leaf at earlier stages of plant growth. MobileNetV2, a CNN based model is implemented with the real time dataset. When MobileNetV2 is compared to other CNN models, it performs better in terms of model size, accuracy, and validation speed, demonstrating its superiority in the classification and identification of diseases affecting cotton plants. Using drones for real time field monitoring is an efficient technique. It also reduces time consumption. In this project STM32 Discovery board and the camera module is integrated with the drone and the developed deep learning model has been deployed to it for real time classification of cotton plant diseases. The farmer can take appropriate preventive measures when plant diseases are detected at the earlier stage. Accuracy, loss, precision and recall of the model were analyzed and considered as the evaluation metrics of the developed model. Final output such as type of cotton disease affected and the accuracy of disease prediction is displayed to the user. The location of infected plant is tracked using the GPS module integrated with the microcontroller board.
Why it matches plant phenotyping methodsドローン画像と深層学習によって綿植物の疾病状態を直接分類・検出する手法を開発・評価しており、植物表現型（病徴・疾病状態）の取得が中心である。
abstractThis project deals with the implementation of a deep learning model to detect the type of cotton disease that affects the cotton plant and to classify whether it is a fresh cotton leaf or diseased cotton leaf at earlier stages of plant growth.
Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.
Why it matches plant phenotyping methods実圃場画像から植物葉の病害状態を検出する深層学習モデルを開発・評価しており、植物の病害表現型取得が研究の中心である。
abstractThis work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions.
Abstract Senescence is a degenerative biological process that affects most organisms. Timing of senescence is critical for annual and perennial crops and is associated with yield and quality. Tracking time-series senescence data has previously required expert annotation and can be laborious for large-scale research. Here, a convolutional neural network (CNN) was trained on unoccupied aerial system (UAS, drone) images of individual plants of cotton (Gossypium hirsutum L.), an early application of single-plant analysis (SPA). Using images from 14 UAS flights capturing most of the senescence window, the CNN achieved 71.4% overall classification accuracy across six senescence categories, with class accuracies ranging between 46.8–89.4% despite large imbalances in numbers of images across classes. For example, the number of images ranged from 109 to 1,129 for the lowest-performing class (80% senesced) to the highest-performing class (fully healthy). The results demonstrate that minimally pre-processed UAS images can enable translatable implementations of high-throughput phenotyping using deep learning methods. This has applications for understanding fundamental plant biology, monitoring orchards and other spaced plantings, plant breeding, and genetic research.
Why it matches plant phenotyping methodsCNNとドローン時系列画像により個体ごとの綿花の老化状態を推定する手法が研究の中心であり、精度評価も実施しているため。
abstracta convolutional neural network (CNN) was trained on unoccupied aerial system (UAS, drone) images of individual plants of cotton
Reproduction assets foundThe authors state that all CNN analysis code, evaluation metrics, figure generation scripts, and the raw single-plant UAS images are publicly available in their GitHub repository, directly reproducing this paper's phenotyping analysis.Code · publicAll of the code used to assess the CNN, calculate evaluation metrics, and generate figures are available at the GitHub repository associated with this manuscript (55): https://github.com/ajdesalvio/cotton-chronology/tree/main. All files necessary to run the script, including the raw images, are available in the repository.Open asset ↗github.com/ajdesalvio/cotton-chronology · cotton-chronologylines:106-141Plant phenotyping relevance match · UnverifiedOpenAlex · Crossref · Europe PMC · checked 7 Sept 2026
CottonField / plotFlowerObject detectionVisualization / data management
In this paper, we present the development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning. Specifically, we use a cluster of several Raspberry Pis in a primary-replica distributed architecture using the Apache Hadoop ecosystem and a pre-trained Tiny-YOLOv4 model for cotton bloom detection from our past work. We feed cotton image data collected from a research field in Tifton, GA, into our cluster's distributed file system for robust file access and distributed, parallel processing. We then submit job requests to our cluster from our client to process cotton image data in a distributed and parallel fashion, from pre-processing to bloom detection and spatio-temporal map creation. Additionally, we present a comparison of our four-node cluster performance with centralized, one-, two-, and three-node clusters. This work is the first to develop a distributed computing pipeline for high-throughput cotton phenotyping in field-based agriculture.
Why it matches plant phenotyping methods綿花の花の検出を対象とする高スループット表現型解析用の分散計算パイプラインを開発し、異なるクラスタ構成の性能比較も行っており、表現型取得・処理手法が中心である。
abstractthe development of a low-cost distributed computing pipeline for cotton plant phenotyping using Raspberry Pi, Hadoop, and deep learning
Leaf gas exchange measurements are an important tool for inferring a plant's photosynthetic biochemistry. In most cases, the responses of photosynthetic CO 2 assimilation to variable intercellular CO 2 concentrations (A/C i response curves) are used to model the maximum (potential) rate of carboxylation by ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco, V cmax ) and the rate of photosynthetic electron transport at a given incident photosynthetically active radiation flux density (PAR; J PAR ). The standard Farquhar-von Caemmerer-Berry model is often used with default parameters of Rubisco kinetic values and mesophyll conductance to CO 2 (g m ) derived from tobacco that may be inapplicable across species. To study the significance of using such parameters for other species, here we measured the temperature responses of key in vitro Rubisco catalytic properties and g m in cotton (Gossypium hirsutum cv. Sicot 71) and derived V cmax and J 2000 (J PAR at 2000 µmol m -2 s -1 PAR) from cotton A/C i curves incrementally measured at 15°C-40°C using cotton and other species-specific sets of input parameters with our new automated fitting R package 'OptiFitACi'. Notably, parameterisation by a set of tobacco parameters produced unrealistic J 2000 :V cmax ratio of cmax above 15°C, up to 2.3-fold higher estimates of J 2000 and more variable estimates of V cmax and J 2000 , for our cotton data compared to model parameterisation with cotton-derived values. We determined that errors arise when using a g m,25 of 2.3 mol m -2 s -1 MPa -1 or less and Rubisco CO 2 -affinities in 21% O 2 (K C 21%O2 ) at 25°C outside the range of 46-63 Pa to model A/C i responses in cotton. We show how the A/C i modelling capabilities of 'OptiFitACi' serves as a robust, user-friendly, and flexible extension of 'plantecophys' by providing simplified temperature-sensitivity and species-specificity parameterisation capabilities to reduce variability when modelling V cmax and J 2000 .
Why it matches plant phenotyping methods植物のガス交換から光合成形質を推定する新規Rパッケージを開発し、種特異的パラメータによる推定性能を検証しているため、方法が研究の中心である。
abstractwith our new automated fitting R package 'OptiFitACi'
Reproduction assets foundThe paper's authors publicly released the OptiFitACi R package containing the fitacis4 function used for all A/Ci curve fitting analyses in this study, with an explicit GitHub URL. The phenotype data (A/Ci response measurements) are stated to be in the article's Supporting Information, which is part of the article and,Code · public(Walker et al., 2013). KC
21%O2
and Γ* were calculated as
described above for tobacco. Equation (2) in Walker et al. (2013) was
used to calculate the gm of antirbcS Arabidopsis at each temperature.
2.6 | Design and implementation of function
fitacis4 in R package ‘OptiFitACi’
A new R function fitacis4 (in package ‘OptiFitACi’; https://github.com/jsamthor/OptiFitACi/tree/master/R) was designed to enhance the A/
Ci analysis capabilities of functions fitaci, fitacis, fitacis2 in the packages
‘plantecophys’ (Duursma, 2015) and ‘plantecowrap’. The function
fitacis4 is used for the batch analysis of leaf photosynthetic gas
exchange data to estimate Vcmax and J2000 using the FvCB C3 model of
leaf pOpen asset ↗jsamthor/OptiFitACi · OptiFitACipdf-raw-page:5 lines:1-114Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Abstract Background The lint percentage of seed cotton is one the most important parameters in evaluation the seed cotton quality, which affects the price of the seed cotton during the purchase and sale. The traditional method of measuring lint percentage is labor-intensive and time-consuming, and thus there is a need for an efficient and accurate method. In recent years, classification-based machine learning and computer vision have shown promise in solving various classification tasks. Results In this study, we propose a new approach for detecting lint percentage using MobileNetV2 and transfer learning. The model is deployed on the Lint Percentage detection instrument, which can rapidly and accurately determine the lint percentage of seed cotton. We evaluated the performance of the proposed approach using a dataset of 66924 seed cotton images from different regions of China. The results from the experiments showed that the model achieved an average accuracy of 98.43% in classification with an average precision of 94.97%, an average recall of 95.26%, and an average F1-score of 95.20%. Furthermore, the proposed classification model also achieved an average ac-curacy of 97.22% in calculating the lint percentage, showing no significant difference from the performance of experts (independent-samples t test, t = 0.019, p = 0.860). Conclusions This study demonstrates the effectiveness of the MobileNetV2 model and transfer learning in calculating the lint percentage of seed cotton. The proposed approach is a promising alternative to the traditional method, offering a rapid and accurate solution for the industry.
Why it matches plant phenotyping methods種子綿のリント率という植物器官・収量関連形質を、画像と深層学習で非破壊推定する手法を開発し、専門家およびデータセットで性能評価しており、表現型取得法が研究の中心である。
abstractwe propose a new approach for detecting lint percentage using MobileNetV2 and transfer learning.
Reproduction assets foundThe authors explicitly state their LPOSC dataset of 66,924 seed cotton images in six categories is available online via a Baidu Netdisk link, which matches an allowed URL. This is a paper-specific public phenotype image dataset. No code or model checkpoint availability is stated; the declarations say data available on,Dataset · publicThe proposed dataset for LPOSC, which consists of 66924 seed cotton images and six distinct categories, was collected in a real-life scenario. This dataset is unique in its scarcity of available data sets for the study of lint percentage, making it a valuable resource for the development of algorithms for the calculation of lint percentage and a potential stimulus for further research in this area. The dataset is available online at the following link:
https://pan.baidu.com/s/12pnAShYJbaFxMItiF6KdQw?pwd=juq9Open asset ↗pan.baidu.comlines:548-820Plant phenotyping relevance match · UnverifiedOpenAlex · checked 14 Sept 2026
Many advanced image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but the ability to recognize growth stages, often with stark differences in appearance, is uncertain. Palmer amaranth (Amaranthus palmeri) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production; due to high genetic diversity, it exhibits highly variable plant morphology across growth stages over a growing season, as well as between plants at a given growth stage. This paper investigates eight-class growth stage recognition of A. palmeri in cotton as a challenging detection case study for You Only Look Once (YOLO) architectures. In total, 26 architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 are compared on an eight-class growth stage dataset of A. palmeri. The highest mean average precision (mAP@[0.5:0.95]) for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar classes. With all growth stages grouped as a single class, performance increased, with a maximum mAP@[0.5:0.95] of 67.05% achieved by v7-Original. Single-class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures. Additionally, the first open-access, benchmarking growth stage dataset ‘Palmer amaranth growth stages – 8 (PAGS8)’ is presented, which is available here: https://weed-ai.sydney.edu.au/datasets/5c78d067-8750-4803-9cbe-57df8fae55e4.
Why it matches plant phenotyping methodsCNN物体検出により雑草の生育段階という植物状態を推定し、複数YOLOモデルの比較評価とベンチマークデータセット公開を中心的に扱うため、植物フェノタイピング手法研究として含める。
abstractThis paper investigates eight-class growth stage recognition of A. palmeri in cotton as a challenging detection case study for You Only Look Once (YOLO) architectures.
Due to its widespread cultivation and large yields by most farmers, cotton is another vital cash crop. However, a number of illnesses lower the quantity and quality of cotton harvests, which causes a large loss in output. Early diagnosis detection of these illnesses is essential. This study employs a thorough methodology to solve the crucial job of cotton leaf disease identification by utilising the "Cotton-Leaf-Infection" dataset. Preprocessing is the first step, in which noise is removed from the dataset using a Prewitt filter, which improves the signal-to-noise ratio. Next, a state-of-the-art process for image classification errands called Vision Transformer (ViT) model is used to carry out the disease categorization. Additionally, the study presents the African Buffalo Optimisation (ABO) method, which optimises weight during the classification procedure. The African buffalo's cooperative behaviour served as the model's inspiration for the ABO algorithm, which is remarkably effective at optimising the model's parameters. By integrating ABO, the problems caused by the dynamic character of real-world agricultural datasets are addressed and improved model resilience and generalisation are facilitated. The suggested ViT-based categorization model shows remarkable effectiveness, with a remarkable 99.3% accuracy rate. This performance is higher than current models.
Why it matches plant phenotyping methods綿花葉の画像から病害状態を分類するViT・ABO手法が研究の中心であり、データセットを用いた性能評価も行っているため、植物病害フェノタイピング手法として含める。
abstractThis study employs a thorough methodology to solve the crucial job of cotton leaf disease identification by utilising the "Cotton-Leaf-Infection" dataset.
The accurate assessment of nitrogen (N) status is important for N management and yield improvement. The N status in plants is affected by plant densities and N application rates, while the methods for assessing the N status in drip-irrigated cotton under reduced nitrogen treatment and different plant densities are lacking. Therefore, this study was conducted with four different N treatments (195.5, 299, 402.5, and 506 kg N ha−1) and three sowing densities (6.9 × 104, 13.8 × 104, and 24 × 104 plants ha−1) by using a low-cost Unmanned Aerial Vehicle (UAV) system to acquire RGB imagery at a 10 m flight altitude at cotton main growth stages. We evaluated the performance of different ground resolutions (1.3, 2.6, 5.2, 10.4, 20.8, 41.6, 83.2, and 166.4 cm) for image textures, vegetation indices (VIs), and their combination for leaf N concentration (LNC) estimation using four regression methods (stepwise multiple linear regression, SMLR; support vector regression, SVR; extreme learning machine, ELM; random forest, RF). The results showed that combining VIs (ExGR, GRVI, GBRI, GRRI, MGRVI, RGBVI) and textures (VAR, HOM, CON, DIS) yielded higher estimation accuracy than using either alone. Specifically, the RF regression models had a higher accuracy and stability than SMLR and the other two machine learning algorithms. The best accuracy (R2 = 0.87, RMSE = 3.14 g kg−1, rRMSE = 7.00%) was obtained when RF was applied in combination with VIs and texture. Thus, the combination of VIs and textures from UAV images using RF could improve the estimation accuracy of drip-irrigated cotton LNC and may have a potential contribution in the rapid and non-destructive nutrition monitoring and diagnosis of other crops or other growth parameters.
Why it matches plant phenotyping methodsUAV画像から植生指数・テクスチャを抽出し、回帰モデルで綿葉の窒素濃度を推定する手法の性能比較・検証が研究の中心である。
abstractusing a low-cost Unmanned Aerial Vehicle (UAV) system to acquire RGB imagery
A novel Modified Deep Fuzzy Clustering (MDFC) based classification model involves four major phases. They are preprocessing, segmentation, feature extraction and finally, detection and classification phase. To reduce noise and smooth the edges of the input image of the cotton crop, bilateral filtering is first used as a preprocessing approach. Next, a modified deep fuzzy clustering is suggested for the segmentation procedure that creates a collection of segments from the preprocessed image. The segmented image is then processed to extract relevant characteristics by using an enhanced Pyramid of Histogram Orientation Gradient (PHOG), Local Directional Ternary Pattern (LDTP), and statistical‐based features. In order to detect and classify cotton crop diseases more effectively, this paper proposes a hybrid system. Here, the features are put through a detection phase, after which the extracted features are trained in the Bidirectional Gated Recurrent Unit (Bi‐GRU) model to determine whether or not the cotton crop is infected. Once it is detected to be diseased, the type of disease is classified via an improved Recurrent Neural Network (RNN). In terms of several performance metrics, the proposed model is validated in comparison with the traditional approaches. The MDFC‐based classification model outperforms existing models with a specificity of 0.9687 at a learning rate of 90. In contrast, other models achieve lower specificities: Bi‐GRU (0.8436), RNN (0.8359), CNN (0.8654), LSTM (0.8769), SVM (0.7983), VGG16 (0.8619), DCNN (0.8725), BI‐RNN + BI‐LSTM (0.7869), and NN + CNN (0.85478).
Why it matches plant phenotyping methods綿花の病害状態を画像から推定する画像処理・特徴抽出・分類手法を開発し、既存手法と性能比較しており、植物フェノタイピング手法が中心である。
abstractA novel Modified Deep Fuzzy Clustering (MDFC) based classification model involves four major phases.
Currently, deep learning has achieved remarkable success in estimating plant disease from unmanned aerial system (UAS) images. However, two critical challenges remain unexplored: spatiotemporal variations in disease symptoms and the domain shift between source and target datasets. To overcome these challenges, this paper proposes an approach that incorporates temporal aspects of disease progression using time series analysis. Spatiotemporal information is integrated by combining convolutional neural networks and bidirectional long-short term memory (CNN-BiLSTM) to classify the disease into five severity levels. Various feature extraction methods, including both handcrafted and CNN-based feature extractors, are evaluated. Furthermore, to tackle the problem of domain shift, a feature-level domain adaptation method is proposed. This method aims to learn transferable feature representations that remain consistent despite variations between source and target datasets. This approach enhances the spatiotemporal transferability of the CNN-BiLSTM model, enabling the effective utilisation of historical datasets. The study demonstrates that the CNN-BiLSTM model outperforms traditional time-independent machine-learning methods that rely on handcrafted features. Specifically, the Resnet101-BiLSTM model achieves the highest overall classification accuracy of 89.7% among all tested models evaluated on a one-year dataset. Moreover, it shows superior generalisation with 72.7% accuracy for cross-spatiotemporal disease severity classification using domain adaptation, as demonstrated through two-year experiments. By reducing the domain shift of source and target datasets and harnessing time series high-resolution images obtained throughout the crop growing season, this hybrid approach has substantial potential to advance the assessment of crop disease severity in field conditions.
Why it matches plant phenotyping methodsUAS画像からワタの病害重症度を推定するCNN-BiLSTMとドメイン適応手法を開発・評価しており、植物状態の取得・推定が研究の中心であるため。
abstracta feature-level domain adaptation method is proposed.
Abstract Abstract. Senescence is a highly ordered biological process involving resource redistribution away from ageing tissues that affects yield and quality in annuals and perennials. Images from 14 unmanned/unoccupied/uncrewed aerial system/vehicle (UAS, UAV and drone) flights captured the senescence window across two experiments while functional principal component analysis effectively reduced the dimensionality of temporal visual senescence ratings (VSRs) and two vegetation indices: the red chromatic coordinate (RCC) index and the transformed normalized difference green and red (TNDGR) index. Convolutional neural networks trained on temporally concatenated, or ‘sandwiched’, UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis. The first functional principal component scores (FPC1) served as the regression target across six CNN models (M1–M6). Model performance was strongest for FPC1 scores from VSRs (R2 = 0.857 and 0.886 for M1 and M4), strong for TNDGR (R2 = 0.743 and 0.745 for M3 and M6), and strong-to-moderate for RCC index (R2 = 0.619 and 0.435 for M2 and M5), with deep learning attention of each model confirmed by activation of plant pixels within saliency maps. Single-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis. This has applications for fundamental plant biology, monitoring orchards or other spaced plantings, plant breeding, and genetic research.
Why it matches plant phenotyping methods単一個体の綿花について、時系列UAS画像とCNN・機能的データ解析を用いて老化表現型を推定する方法が研究の中心であり、性能評価も実施している。
abstractConvolutional neural networks trained on temporally concatenated, or ‘sandwiched’, UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis.
Reproduction assets foundThe paper's DATA AVAILABILITY section states that all code for FPCA, ANOVA and CNN regression, plus all files needed to run the scripts including the raw single-plant UAS images, are publicly available in the authors' GitHub repository. This is a paper-specific, public, actionable asset covering both the phenotyping (cCode · public)
graduate program. OGR and SMD were partially supported
by Cotton Incorporated Awards 18-201 and 20-724, and NSF
Award 1739092.
DATA AVAILABILITY
All of the code used for FPCA, ANOVA and CNN regres-
sion is available at the GitHub repository [see Supporting
Information—Notes S1] associated with this manuscript
(DeSalvio 2024): https://github.com/ajdesalvio/cotton-sand-wiches. All files necessary to run the scripts, including the raw
images, are available in the repository.
NSF STATEMENT
Any opinion, findings and conclusions or recommendations
expressed in this material are those of the authors(s) and do not
necessarily reflect the views of the National Science Foundation.
REFERENCES
Adak A,Open asset ↗ajdesalvio/cotton-sand-wichespdf-raw-page:15 lines:1-93Plant phenotyping relevance match · UnverifiedCrossref · OpenAlex · checked 15 Sept 2026
High-throughput phenotyping (HTP) has become a powerful tool for gaining insights into the genetic and environmental factors that affect cotton ( Gossypium spp.) growth and yield. With the recent advances in the field of computer vision, namely the integration of deep learning algorithms, the accuracy and efficiency of HTP systems have improved dramatically, enabling them to automatically quantify such fundamental phenotypic traits as fruit identification and enumeration. However, there is currently no HTP system available for counting all the reproductive phases of cotton crop that can be deployed in agronomic field conditions throughout the growing season. This study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll. Consequently, CottonSense enhances agronomic management through increased opportunities for data collection and analysis. Using RGB-D cameras, it captures and processes both two and three-dimensional data, facilitating a wider range of phenotypic trait extractions such as crop biomass and plant architecture. To segment the cotton fruits, a Mask-RCNN model is trained and optimized for faster inference using TensorRT. The model yields an average AP score of 79% in segmentation across the four fruit categories. Moreover, the model's accuracy in estimating total fruit count per image is validated by a strong agreement with the counts given by ten domain experts, as reflected by an R 2 value of 0.94. Furthermore, to accurately count the segmented fruits over large populations of plants, an enumeration algorithm based on a tracking strategy is developed that achieves an R 2 value of 0.93 when compared to hand-counted fruits in the field. The proposed HTP system, which is implemented entirely on an edge computing device, is cost-effective and power-efficient, making it an effective tool for high-yield cotton breeding and crop improvement. The code for CottonSense is publicly available at https://github.com/FeriBolour/CottonSense .
Why it matches plant phenotyping methods綿花の果実を画像から分割・列挙し、専門家および手作業カウントで検証する高スループット表現型解析システムの開発が中心である。
abstractThis study presents CottonSense, an advanced HTP system that overcomes the challenges of deployment across multiple growth periods by effectively segmenting and enumerating cotton fruits at four stages of growth, including square, flower, closed boll, and open boll.
Early and accurate detection of verticillium wilt (VW), the most common and devastating disease of cotton, is essential to prevent the spread of VW. However, it remains challenging to achieve accurate detection of VW in cotton before symptoms appear after infection with Verticillium dahliae (asymptomatic phase). This study evaluated the feasibility of detection of VW in the asymptomatic phase based on cotton main stem leaf chlorophyll fluorescence parameters (CFPs) and spectral features extracted based on continuous wavelet transform (CWT) in two different environments. The aim was to achieve accurate detection of cotton VW in the asymptomatic period by convenient methods. Hyperspectral data of cottons inoculated with V. dahliae were collected at different times, and the CFPs of main stem leaves were measured simultaneously. After preprocessing the hyperspectral data with CWT, common wavelet features for all spectral acquisition days and sensitive CFPs were extracted based on the results of ANOVA. Then, the variance inflation factor combined with least absolute shrinkage and selection operator (LASSO-VIF) was used to select the optimal wavelet features. Finally, the support vector machine, logistic regression, and k-nearest neighbors (KNN) were used to construct the models for detecting VW in asymptomatic leaves based on CFPs and optimal wavelet features, and the accuracy of the models were compared. The results showed that the CFPs were significantly affected 24 h after V. dahliae infection. V. dahliae infection reduced the maximum quantum yield (Pm') of photosystem II (PSII) and increased non-photochemical quenching (NPQt) in cotton leaves. Compared with the raw spectrum, the spectral features in the near-infrared region (800–1350 nm) extracted based on CWT could accurately reflect the subtle changes of leaves in the asymptomatic phase. Besides, compared with CFPs, the 4–5 wavelet features selected based on the LASSO-VIF were more helpful to accurately identify asymptomatic cotton leaves infected with V. dahliae, with an accuracy greater than 80 % and a Kappa coefficient higher than 0.6. Among them, the average accuracy of the logistic regression model based on wavelet features was as high as 90.62 %. The results of this study confirm the changes in CFPs in cotton leaves in the VW-asymptomatic period and the feasibility of accurate identification by using wavelet features. This study will provide a reliable reference for accurate large-scale identification of V. dahliae infection in cotton in the asymptomatic phase.
Why it matches plant phenotyping methods綿花の無症状植物に対する病害状態を、葉のクロロフィル蛍光とハイパースペクトル特徴から抽出・分類する手法を開発し、複数環境で精度比較しており、植物表現型取得が研究の中心である。
abstractThis study evaluated the feasibility of detection of VW in the asymptomatic phase based on cotton main stem leaf chlorophyll fluorescence parameters (CFPs) and spectral features extracted based on continuous wavelet transform (CWT) in two different environments.
Upland cotton is a leading natural fiber crop; and an important source of vegetable oil and protein. The protein content and fatty acid composition are important nutritional indicators of cottonseed. In order to establish rapid, high throughout, nondestructive and low-cost method for cottonseed nutrition evaluation, phenotypic data of seed kernel protein, oil and fatty acid content were combined with preprocessed near-infrared spectra of shell-intact cottonseed from 179 upland cotton genotypes to establish calibration models for these cottonseed nutrients. A total of 17 kinds of fatty acids were stably detected and quantitatively determined using Gas Chromatography-Mass Spectrometry (GC-MS) technology, including 7 kinds of fatty acids which were detected in cottonseed, to the best of our knowledge, for the first time. Finally, 19 NIR calibration models were established through partial least squares regression analysis, and evaluated by cross-validation and external verification. Results indicated that 16 models for seed protein, oil, and 14 fatty acids (capric acid, lauric acid, myristic acid, pentadecanoic acid, palmitic acid, heptadecanoic acid, linoleic acid, arachidic acid, cis-11-eicosenoic acid, linolenic acid, heneicosanoic acid, docosanoic acid, tridecanoic acid, and lignoceric acid) achieved acceptable or satisfactory prediction ability. Three calibration models for palmitoleic acid, stearic acid and oleic acid obtained moderate prediction ability with R²cₐₗ above 0.7 which could be used in rough screening. Nine fatty acids in cottonseed were modeled for the first time. These results broadened our understanding of the fatty acid profile of cottonseed oil and laid a foundation for fast, nondestructive evaluation of cottonseed nutrient quality for cotton breeding, processing and marketing.
Why it matches plant phenotyping methods綿実のタンパク質・油・脂肪酸という種子形質を、近赤外スペクトルから非破壊推定する校正モデルを開発し、交差検証と外部検証で評価しており、表現型取得・推定法が中心である。
abstractIn order to establish rapid, high throughout, nondestructive and low-cost method for cottonseed nutrition evaluation, phenotypic data of seed kernel protein, oil and fatty acid content were combined with preprocessed near-infrared spectra of shell-intact cottonseed from 179 upland cotton genotypes to establish calibration models for these cottonseed nutrients.
Synthetic auxin herbicide movement onto sensitive cotton (Gossypium hirsutum L.) cultivars has impacted many US cotton hectares. The spatial scope and severity of auxin damage in‐season is typically estimated by an agronomist. The use of remote sensing technology has the potential to objectively quantify the spatial scope and severity of auxin damage. Experiments were conducted in 2019, 2020, and 2021 in Grand Junction, TN, to determine: (1) the effect of reflectance data collection timing; (2) the effect of auxin exposure timing; (3) the value of near infrared and red‐edge (RE) reflectance versus reflectance within the visible spectrum data; and (4) if/how visual injury relates to aerial reflectance data. Applications of 2,4‐D or dicamba were made to susceptible cotton cultivars at 1X, 1/4X, 1/16X, 1/64X, 1/256X, and 1/1024X rates at either matchhead square (MHS) or 2 weeks after first bloom (FB+2WK). Non‐treated controls were also included for each application timing. Aerial reflectance data were collected 7, 14, 21, and 28 days after application. Unsupervised classification of images into pixels with and without vegetation did not increase correlations between vegetation indices (VIs) and application rate. Although Vis, which generated the strongest correlations with application rate, visual injury, and relative lint yield, were generally RE based, similar correlations were also noted with visible spectrum VIs. Correlations were greater when auxin injury occurred at MHS than FB+2WK. Results suggest reflectance measured within the visible spectrum can quantify the scope and severity of auxin injury if the injury occurs early during the growing season.
Why it matches plant phenotyping methods航空反射画像を用いて綿花の除草剤傷害の範囲・重症度を定量化し、測定時期や波長帯、視覚評価・収量との相関を検証しており、植物状態の取得手法が中心である。
abstractThe use of remote sensing technology has the potential to objectively quantify the spatial scope and severity of auxin damage.
In India, agriculture serves as the primary income source for the majority of the population. Identifying crop diseases is a critical factor in mitigating production losses. To address this, deep learning techniques, specifically utilizing pre-trained Convolutional Neural Network (CNN) models such as ResNet-50, VGG-16, MobileNetV2, and InceptionV3, are employed for the detection of plant diseases. This study involves different key stages including dataset creation, preprocessing, data augmentation, and classification. The dataset comprises 3725 images of cotton plant leaves distributed across 11 classes. Here, the model performance is assessed based on classification accuracy, with ResNet-50 achieving the highest accuracy at 99.8% among the four approaches.
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の比較・評価が研究の中心であり、植物の病害状態を直接推定するため、方法論文として採用する。
abstractdeep learning techniques, specifically utilizing pre-trained Convolutional Neural Network (CNN) models such as ResNet-50, VGG-16, MobileNetV2, and InceptionV3, are employed for the detection of plant diseases.
This study aimed to explore the feasibility of applying Generative Adversarial Networks (GANs) for the diagnosis of Verticillium wilt disease in cotton and compared it with traditional data augmentation methods and transfer learning. By designing a model based on small-sample learning, we proposed an innovative cotton Verticillium wilt disease diagnosis system. The system uses Convolutional Neural Networks (CNNs) as feature extractors and applies trained GAN models for sample augmentation to improve classification accuracy. This study collected and processed a dataset of cotton Verticillium wilt disease images, including samples from normal and infected plants. Data augmentation techniques were used to expand the dataset and train the CNNs. Transfer learning using InceptionV3 was applied to train the CNNs on the dataset. The dataset was augmented using GAN algorithms and used to train CNNs. The performances of the data augmentation, transfer learning, and GANs were compared and analyzed. The results have demonstrated that augmenting the cotton Verticillium wilt disease image dataset using GAN algorithms enhanced the diagnostic accuracy and recall rate of the CNNs. Compared to traditional data augmentation methods, GANs exhibit better performance and generated more representative and diverse samples. Unlike transfer learning, GANs ensured an adequate sample size. By visualizing the images generated, GANs were found to generate realistic cotton images of Verticillium wilt disease, highlighting their potential applications in agricultural disease diagnosis. This study has demonstrated the potential of GANs in the diagnosis of cotton Verticillium wilt disease diagnosis, offering an effective approach for agricultural disease detection and providing insights into disease detection in other crops.
Why it matches plant phenotyping methods綿花の萎凋病画像を対象に、GANによるデータ拡張とCNN診断モデルを開発・比較評価しており、植物病徴の画像ベース表現型推定が研究の中心である。
abstractThe performances of the data augmentation, transfer learning, and GANs were compared and analyzed.
Salt stress is considered one of the primary threats to cotton production. Although cotton is found to have reasonable salt tolerance, it is sensitive to salt stress during the seedling stage. This research aimed to propose an effective method for rapidly detecting salt stress of cotton seedlings using multicolor fluorescence-multispectral reflectance imaging coupled with deep learning. A prototyping platform that can obtain multicolor fluorescence and multispectral reflectance images synchronously was developed to get different characteristics of each cotton seedling. The experiments revealed that salt stress harmed cotton seedlings with an increase in malondialdehyde and a decrease in chlorophyll content, superoxide dismutase, and catalase after 17 days of salt stress. The Relief algorithm and principal component analysis were introduced to reduce data dimension with the first 9 principal component images (PC1 to PC9) accounting for 95.2% of the original variations. An optimized EfficientNet-B2 (EfficientNet-OB2), purposely used for a fixed resource budget, was established to detect salt stress by optimizing a proportional number of convolution kernels assigned to the first convolution according to the corresponding contributions of PC1 to PC9 images. EfficientNet-OB2 achieved an accuracy of 84.80%, 91.18%, and 95.10% for 5, 10, and 17 days of salt stress, respectively, which outperformed EfficientNet-B2 and EfficientNet-OB4 with higher training speed and fewer parameters. The results demonstrate the potential of combining multicolor fluorescence-multispectral reflectance imaging with the deep learning model EfficientNet-OB2 for salt stress detection of cotton at the seedling stage, which can be further deployed in mobile platforms for high-throughput screening in the field.
Why it matches plant phenotyping methods綿実生の塩ストレス状態を画像から検出する撮像プラットフォームと深層学習手法を開発しており、植物状態の取得・抽出が研究の中心である。
abstractThis research aimed to propose an effective method for rapidly detecting salt stress of cotton seedlings using multicolor fluorescence-multispectral reflectance imaging coupled with deep learning.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the authors' EfficientNet-OB2 code and training script on GitHub at the allowed URL. No public phenotype/image dataset is stated.Code · publication and technical support for the project. H.W., B.Z., and D.Y. provided suggestions on the experiment design and discussion sections.
Competing interests: The authors declare that they have no competing interests.
Data Availability
The code and training script of EfficientNet-OB2 has been hosted to GitHub and is available at https://github.com/foddcus/EfficientNetOB .
Supplementary Materials
Supplementary 1
Figs. S1 and S2
Tables S1 and S2
Click here for additional data file.
References
1. Noreen S, Ahmad S, Fatima Z, Zakir I, Iqbal P, Nahar K, Hasanuzzaman M. Abiotic stresses mediated changes in morphophysiology of cotton plant. In: Ahmad S, Hasanuzzaman M, editors. Cotton production andOpen asset ↗foddcus/EfficientNetOBlines:363-403Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 15 Sept 2026
The inner leaves of crop canopies are obscured by outer branches and leaves, leading to information loss and observation difficulty of the occluded canopy structure using modern crop monitoring techniques. It has restricted the development of phenotypic analysis and precision agriculture. In this paper, we propose a neural network approach to reconstruct the occluded structure of crop canopies with an RGB-D sensor. Taking the cotton plant as the object of study, we propose a novel Cascade Leaf Segmentation and Completion Network (CLSCN) to reconstruct the occluded leaf images and propose a Fragmental Leaf Point–cloud Reconstruction Algorithm (FLPRA) to complete the missing point clouds. By combining the Instance Segmentation Network (ISN), Generative Adversarial Network (GAN) and Point-cloud Reconstruction Algorithm (PRA), the three-dimensional models of cotton plants with both completed internal and external structures of the canopy are smoothly reconstructed. Firstly, we collect a large number of leaf images and point clouds of cotton plants using an RGB-D sensor with the top view and construct a manually labeled cotton leaf dataset for training and evaluation. Secondly, a network named CLSCN is cascading constructed with an Instance Segmentation Network (ISN) and a Generative Adversarial Network (GAN), and the two parts of CLSCN are separately trained with our constructed dataset to output complete cotton leaves. Thirdly, with the fusion of the completed RGB images output by cascaded network segmentation and the point clouds captured by RGB-D sensor, the proposed FLPRA is used to filter, reconstruct, fuse and register the cotton canopy leaf point clouds, and to obtain the whole cotton canopy point-clouds with inner occluded structure recovery. Finally, the CLSCN and FLPRA are validated using the validation dataset of cotton leaf. The test results indicate that the front-end ISN of the proposed CLSCN can generate high-quality cotton leaf masks, with FID scores less than 35 and mIoU up to 84.65%. Additionally, the back-end GAN of CLSCN can complete the occluded leaves with an accuracy of over 94%. The reconstruction accuracy of the final three-dimensional model of the cotton canopy is as high as 82.70%. Therefore, the proposed neural network and algorithm effectively solve the problem of incomplete canopy point cloud caused by the occlusion of outer leaves and provide an effective way to recover the complete three-dimensional structure of crop canopy with internal occlusion. It is a meaningful theoretical and technical support to realize real-time crop status observation and precise field management in agriculture production.
Why it matches plant phenotyping methodsRGB-D画像、ニューラルネットワーク、点群再構成を用いて、遮蔽されたワタ個体群の3次元構造を復元する手法を開発し、データセットと精度検証も行っており、植物表現型取得が中心である。
abstractwe propose a neural network approach to reconstruct the occluded structure of crop canopies with an RGB-D sensor.
Color of seed cotton is one of the key indexes of seed cotton quality, which greatly affects the price, grading, storage, and processing of seed cotton. Currently, there are shortage of mature color measurement methods and equipment specifically for seed cotton. Therefore, a color measurement method for seed cotton based on machine vision technology was proposed in this research. To solve the problem of color difference in images, a color difference correction algorithm based on multiple linear regression was proposed, which significantly reduced the color difference by 54.19%. To segment large impurities and large hard particles (cotton seeds, cotton stalks, and boll shells) that are easy to produce shadows from seed cotton images, a quadratic dynamic thresholding segmentation algorithm based on multi-channel fusion was proposed, which significantly improved the segmentation accuracy. The verification results showed that the average value of the intersection over union was 0.9. In the calculation of the color indexes of seed cotton, a correction algorithm based on the BP neural network was used to correct the indexes by taking standard tiles as a reference to reduce the difference caused by system error. The results of the machine vision method were compared with those of the detection of corresponding lint by HVI 1000 and spectrophotometer HX-410. The coefficients of determination (R²) of the Reflectance degree (Rd) and Yellowness (+b) measured by HVI 1000 were 0.790 and 0.865, respectively. The R² for Rd and +b measured by HX-410 were 0.809 and 0.879, respectively. In addition, the analysis results of the effect of impurities and shadows on seed cotton color showed that both impurities and shadows had a negative effect on Rd. However, the effect of shadows on +b was negative and the effect of impurities was positive. This study indicated that it was feasible to detect seed cotton color using machine vision method.
Why it matches plant phenotyping methods種子綿の色という植物由来試料の品質形質を、機械視覚で測定・補正・分割する手法を開発し、既存機器と比較検証しているため、フェノタイピング手法が中心である。
abstractTherefore, a color measurement method for seed cotton based on machine vision technology was proposed in this research.
Crop growing information is important for precision agriculture. Instance segmentation can be employed to extract not only the location and quantity information of objects from images, but also the individual phenotype information of each target. However, when it is applied on real in-field images, complex environment increases the cost of annotating. Besides, it is hard for instance segmentation network with complicated structure to be deployed in real application scenarios. To cost-efficiently extract data used for precision management from field images, this paper proposes a novel model based on dense prediction and conditionally parameterized convolution termed Box-supervised dynamical regression instance segmentation net(BDRISNet) for segmenting individuals from image in a simple way, using box-level supervision. By formulating segmentation into dense regression from each pixel to the location of its instance indicators, mask features can be generated without redundant convolution branch. The compact construction of proposed network achieves efficient inference for segmentation. Robustness and accuracy of proposed method are evaluated on an in-field cotton boll image dataset we provide. Comparison experiments with other state-of-the-arts demonstrate that our method has an average improvement of 3.13%, 5.55%, 7.08% in AP, AP50 and AP75. Furthermore, predictions of proposed method have desirable and balanced recall and precision.
Why it matches plant phenotyping methods綿花の個体（boll）を画像から分割し、個体数・表現型情報を抽出する新規インスタンスセグメンテーション手法を開発・評価しており、植物フェノタイピング手法が中心である。
abstractInstance segmentation can be employed to extract not only the location and quantity information of objects from images, but also the individual phenotype information of each target.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Machine vision and machine learning for plant phenotyping and precision agriculturePlant phenotyping (PP) describes the physiological and biochemical properties of plants affected by both genotypes and environments.It is an emerging research field assisting the breeding and cultivation of new crop varieties to be more productive and resilient to challenging environments.Precision agriculture (PA) uses sensing technologies to observe crops and then manages them optimally to ensure that they grow in healthy conditions, have maximum productivity, and have minimal adverse effects on the environment.Traditionally, the observation of plant traits heavily relies on human experts, which is labour-intensive, time-consuming, and subjective.Although PP and PA are two different fields, they share similar sensing and data processing technologies in many respects.Recently, driven by computer and sensor technologies, machine vision (MV) and machine learning (ML) have contributed to accurate, high-throughput and nondestructive sensing and data processing technologies to PP and PA.However, these technologies are still in their infant stage, and many challenges and questions related to them still need to be addressed.This Research Topic aims to share the latest research results on applying MV and ML to PP and PA.It demonstrates cutting-edge technologies, bottle-necks and future research directions for MV and ML in crop breeding, crop cultivation, and disease or pest management.This Research Topic of Frontiers in Plant Sciences published a total of 28 peer-reviewed research articles, including one review paper for the phenotyping of Prunoideae fruits (Liu et al.).These articles reveal the latest research trends regarding different crop species, data types and algorithms.The summary of the published reports shows that cotton (Gossypium), canola or oilseed rape (Brassica napus), wheat (Triticum) and maize (Z.mays) are the most important crops for study in PP and PA (Figure 1A).Cotton stands out as the most frequently examined crop, with a total of five articles dedicated to it.Yan et al. developed a leaf segmentation method in the field environments.Tang et al. investigated early detection
Why it matches plant phenotyping methods植物フェノタイピングにおける機械視覚・機械学習技術を中心に扱う編集論文であり、方法論の動向と応用を概説しているため。
titleEditorial: Machine vision and machine learning for plant phenotyping and precision agriculture
Crop height is a vital indicator of growth conditions. Traditional drone image-based crop height measurement methods primarily rely on calculating the difference between the Digital Elevation Model (DEM) and the Digital Terrain Model (DTM). The calculation often needs more ground information, which remains labour-intensive and time-consuming. Moreover, the variations of terrains can further compromise the reliability of these ground models. In response to these challenges, we introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height. Our method enables the model to recognize the relation between crop height, elevation, and growth stages, eliminating reliance on DTM and thereby mitigating the effects of varied terrains. We also introduce a data preparation process to handle the unique DEM and multispectral image. Upon evaluation using a cotton dataset, our G-DMD method demonstrates a notable increase in accuracy for both maximum and average cotton height measurements, achieving a 34% and 72% reduction in Root Mean Square Error (RMSE) when compared with the traditional method. Compared to other combinations of model inputs, using DEM and multispectral drone images together as inputs results in the lowest error for estimating maximum cotton height. This approach demonstrates the potential of integrating deep learning techniques with drone-based remote sensing to achieve a more accurate, labour-efficient, and streamlined crop height assessment across varied terrains.
Why it matches plant phenotyping methodsドローンのDEM・マルチスペクトル画像から作物高を推定する手法を開発し、従来法と精度比較・検証しており、植物表現型取得が中心である。
abstractwe introduce G-DMD, a novel method based on Gated Recurrent Units (GRUs) using DEM and multispectral drone images to calculate the crop height.
Reproduction assets foundThe paper's crop-height phenotyping analysis is built on a public cotton UAV multispectral/DEM dataset deposited by Xu et al. on Figshare, which qualifies as a paper-specific, publicly actionable phenotyping input. The authors' own G-DMD code and processed data are only available upon request, so that component is not公Dataset · public47. Xu, R.; Li, C.; Paterson, A.H. UAV Multispectral. Figshare. Dataset. 2018. Available online: https://figshare.com/articles/Open asset ↗Figsharepdf-page:22 lines:1-20Plant phenotyping relevance match · UnverifiedEurope PMC · checked 7 Sept 2026
Cotton plays a significant role in people's lives, and cottonseeds serve as a vital assurance for successful cotton cultivation and production. Premium-quality cottonseeds can significantly enhance the germination rate of cottonseeds, resulting in increased cotton yields. The vitality of cottonseeds is a crucial metric that reflects the quality of the seeds. However, currently, the industry lacks a non-destructive method to directly assess cottonseed vitality without compromising the integrity of the seeds. To address this challenge, this study employed a hyperspectral imaging acquisition system to gather hyperspectral data on cottonseeds. This system enables the simultaneous collection of hyperspectral data from 25 cottonseeds. This study extracted spectral and image information from the hyperspectral data of cottonseeds to predict their vitality. SG, SNV, and MSC methods were utilized to preprocess the spectral data of cottonseeds. Following this preprocessing step, feature wavelength points of the cottonseeds were extracted using SPA and CARS algorithms. Subsequently, GLCM was employed to extract texture features from images corresponding to these feature wavelength points, including attributes such as Contrast, Correlation, Energy, and Entropy. Finally, the vitality of cottonseeds was predicted using PLSR, SVR, and a self-built 1D-CNN model. For spectral data analysis, the 1D-CNN model constructed after MSC+CARS preprocessing demonstrated the highest performance, achieving a test set correlation coefficient of 0.9214 and an RMSE of 0.7017. For image data analysis, the 1D-CNN model constructed after SG+CARS preprocessing outperformed the others, yielding a test set correlation coefficient of 0.8032 and an RMSE of 0.9683. In the case of fused spectral and image data, the 1D-CNN model built after SG+SPA preprocessing displayed the best performance, attaining a test set correlation coefficient of 0.9427 and an RMSE of 0.6872. These findings highlight the effectiveness of the 1D-CNN model and the fusion of spectral and image features for cottonseed vitality prediction. This research contributes significantly to the development of automated detection devices for assessing cottonseed vitality.
Why it matches plant phenotyping methods綿実の活力という植物形質を、ハイパースペクトル画像から非破壊的に推定する取得・特徴抽出・予測ワークフローが研究の中心であり、性能評価も行っている。
abstractthe industry lacks a non-destructive method to directly assess cottonseed vitality without compromising the integrity of the seeds
Maturity is a major fiber trait that affects the processing and performance of cotton fiber. Rapid and accurate identification of fiber maturity phenotypes and genotypes is of importance to breeders. Previous studies showed that either conventional fiber measurements or attenuated total reflection Fourier transform infrared spectroscopy (ATR FT-IR) analysis discriminated the immature fiber ( im ) phenotype from the wild type (WT) mature fiber phenotype in a segregating F 2 population from a cross between two upland cotton lines differing in fiber maturity. However, both conventional fiber property measurement methods and FT-IR analyses with current algorithms could not detect the subtle differences among the WT fibers composed of two different genotypes, WT homozygosity (WT-homo) and WT heterozygosity (WT-hetero). This research explored the FT-IR method, in combination with soft independent modeling of class analogy of principal component analysis (SIMCA-PCA), for the discrimination of WT fiber phenotypes consisting of two different genotypes (WT-homo and WT-hetero). The new approach enabled the detection of IR spectral intensity differences between WT-homo and WT-hetero fibers. Successful classification originated from a distinctive spectral difference in the low-wavenumber region ( -1 ) between WT-hetero fibers and WT-homo fibers. This observation emphasized that ATR FT-IR with a SIMCA-PCA approach would be a sensitive tool for classifying the WT fibers demonstrating minor phenotypic differences. The improved sensitivity of the infrared method may provide a way of dissecting genotype-phenotype interactions of cotton fibers rapidly and efficiently.
Why it matches plant phenotyping methods綿繊維成熟度という植物形質の微細な表現型差を、ATR FT-IRとSIMCA-PCAで分類する手法の開発・検証が中心である。
abstractThis research explored the FT-IR method, in combination with soft independent modeling of class analogy of principal component analysis (SIMCA-PCA), for the discrimination of WT fiber phenotypes consisting of two different genotypes (WT-homo and WT-hetero).
Defoliation and artificial ripening are critical steps before mechanical harvesting of cotton. The defoliation effect is a key factor in determining the optimal harvesting time of cotton, as an improper machine-harvesting time can reduce the yield and quality of cotton. Therefore, rapid and accurate estimation of the defoliation rate and boll-opening rate is critical for evaluating the effect of defoliation on machine-harvested cotton. This study aimed to test whether high-resolution RGB images using UAVs, different characteristics screening methods, and modelling techniques can effectively monitor the defoliation rate and boll-opening rate for machine-harvested cotton. The hypothesis was that the combination of vegetation index, colour space component, and textural features, along with different methods, could improve the defoliation rate and boll-opening rate of machine-harvested cotton. Cotton defoliant treatment field experiments were conducted in Shihezi, Xinjiang, China, during 2019–2020, after defoliant treatment to acquire the defoliation rate and boll-opening rate. Correlation coefficient (Cor), maximum information coefficient (MIC), and random forest (RF) was used to screen the visible light vegetation indices, colour space parameters, and texture features. Multivariate stepwise regression (MSR), kernel ridge regression (KRR), extreme learning machine (ELM), and particle swarm optimized extreme learning machine (PSO–ELM) algorithms were used to construct monitoring models for the defoliation rate and boll-opening rate of machine-harvested cotton. Using the principal components analysis algorithm, comprehensive evaluation indices of defoliation effects were constructed to provide theoretical and methodological support for determining the time of machine-harvested cotton. The results showed that (1) a model based on RF_PSO-ELM achieved optimal assessment of the defoliation rate (R²=0.59, RMSE=19.37%, rRMSE=34.54%) and boll-opening rate (R²=0.73, RMSE=19.11%, rRMSE=46.40%), (2) a comprehensive evaluation index of the machine-harvested cotton defoliation effect was constructed based on the defoliation rate, boll-opening rate, and yield, which was named PCA1. (3) PCA1 was used as the standard to determine the harvesting time, when a PCA1 value of > 1.3225 indicated the optimal harvesting time. This index can accurately judge the best harvest time of machine-harvested cotton in large-scale production.
Why it matches plant phenotyping methodsUAV RGB画像から綿花の落葉率・吐絮率という植物状態を推定し、特徴量選択と複数のモデルを比較・構築しているため、表現型取得・推定手法が研究の中心である。
abstractThis study aimed to test whether high-resolution RGB images using UAVs, different characteristics screening methods, and modelling techniques can effectively monitor the defoliation rate and boll-opening rate for machine-harvested cotton.
Estimating plant nitrogen concentrations (PNCs) with remote sensing technology is critical for ensuring precise field nitrogen (N) management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolutions that can be used to clearly identify soil and vegetation. Previously, many spectral indices were designed to remove soil effects to obtain optimal PNC predictions. Herein, we attempt to enhance the PNC prediction accuracy only by removing soil pixels in high-resolution images. Thus, we aimed to collect a dataset containing different crops and image types to investigate whether removing soil pixels to purify crop spectra can improve PNC estimations. For this purpose, N fertilizer experiments were conducted on cotton (Gossypium hirsutum L.), wheat (Triticum aestivum L.) and maize (Zea mays L.), and multispectral and hyperspectral UAV images and PNCs were collected at different growth stages. The multispectral images had actual high spatial resolutions, while the hyperspectral images had virtual high spatial resolutions constructed by fusing high resolution panchromatic images and coarse resolution hyperspectral images. These images represent two typical UAV image types. First, for each crop, the relative changes and driving forces associated with the purified and nonpurified spectra were analyzed under different growth stage, N treatment. Then, three commonly used methods, the spectral index (SI), partial least squares regression (PLSR) and artificial neural network (ANN) methods were used to design PNC prediction model using purified and nonpurified spectra respectively. The results showed the differences between purified and nonpurified spectra were affected by the proportion of crop pixel, sunlit soil pixel and sunshade soil pixel in image. This influence had various trends and magnitudes among different N treatment, growth stages and crop types. It is better to remove soil pixels in imagery, when designing PNC prediction model for plants across growth stages, crop types or even in a single growth stage. The results from actual high spatial resolution images demonstrated this point, with the best PNC prediction model from purified spectra. When considering virtual high spatial resolution image, as the spectrum obtained for each vegetation pixel still represented a mixed vegetation and soil spectrum, removing soil pixels showed no improved performance for PNC estimation. These results provide a reference for others to reasonably choose an optimal data-processing method for constructing PNC prediction models.
Why it matches plant phenotyping methodsUAVマルチスペクトル・ハイパースペクトル画像から植物窒素濃度を推定するための土壌画素除去と予測モデルを比較・評価しており、植物形質取得手法が中心である。
abstractwe attempt to enhance the PNC prediction accuracy only by removing soil pixels in high-resolution images
The main goal of wind-driven spraying is to use assisted airflow to disrupt the structure of branches and leaves and broaden the air delivery channel, so as to achieve uniform droplet deposition in the middle and lower parts of the canopy. Due to the complex branch and leaf structure inside the canopy, there is currently no effective method to express the dynamic changes of canopy porosity and the law of airflow attenuation under assisted airflow. In this study, based on the two-way fluid-structure interaction numerical simulation method, the relating between the assisted airflow and the structural parameters of the cotton canopy is analyzed, and a new method for predicting and simulating the dynamic porosity of the canopy is proposed. Firstly, a two-way fluid-structure interaction model based on Lattice Boltzmann (LB) solver and Finite Element (FE) solver is developed to simulate the deformation motion of cotton leaves and the spatial distribution of airflow field, and the correctness of the numerical simulation is verified based on indoor measurement data. Secondly, the post-processing method of Computational Fluid Dynamics (CFD) is used to obtain images of leaves at different canopy positions under assisted airflow, and the porosity changes are calculated and analyzed by image processing. The research results show that under different initial wind speeds (5 m·s -1 , 10 m·s -1 , 15 m·s -1 ), the maximum normalized mean absolute error (NMAE) between the simulated values and the measured values is 13.99%, 20.72% and 16.08%, respectively. The coefficient of determination (R 2 ) for linear fitting between simulated values and measured values is 0.9221. These validation results indicate the effectiveness of the numerical simulation method. The validated CFD model is applied to predict leaf deformation and porosity changes within the canopy under various wind loads and times. The application results have well revealed the interaction between crop leaves and airflow, and will be beneficial to make a better understanding of the effect of assisted airflow on droplet deposition.
Why it matches plant phenotyping methods綿花キャノピーの動的空隙率と葉変形を推定する数値・画像処理手法を開発し、実測値で検証しており、植物表現型の取得・推定が研究の中心である。
abstracta new method for predicting and simulating the dynamic porosity of the canopy is proposed
Image processing techniques are used to autonomously identify agricultural plant diseases can significantly reduce reliance on farmers for safeguarding crop yields. The classification of cotton leaf diseases presents a formidable challenge. This study introduces a novel approach for classifying Cotton Leaf Diseases, employing an Antlion Optimization (ALO)-enhanced Deep Neural Network (DNN) classifier. The dataset comprises 10,000 images, a combination of directly captured farm field images and downloaded samples, encompassing normal leaves, bacterial blight, Anthracnose, Cercospora leaf spot, and Alternaria diseases. Preprocessing incorporates Wiener filtering to eliminate image noise, while Fuzzy Rough C-Means (FRCM) clustering is employed for diseased and normal portion segmentation. The ALO-augmented DNN achieves an impressive 93.37% accuracy in classifying cotton leaf diseases.
Why it matches plant phenotyping methods綿花葉の病徴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、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用。
abstractThis study introduces a novel approach for classifying Cotton Leaf Diseases, employing an Antlion Optimization (ALO)-enhanced Deep Neural Network (DNN) classifier.
Cotton ( Gossypium hirsutum L.) seed morphological structure has a significant impact on the germination, growth and quality formation. However, the wide variation of cotton seed morphology makes it difficult to achieve quantitative analysis using traditional phenotype acquisition methods. In recent years, the application of micro-CT technology has made it possible to analyze the three-dimensional morphological structure of seeds, and has shown technical advantages in accurate identification of seed phenotypes. In this study, we reconstructed the seed morphological structure based on micro-CT technology, deep neural network Unet-3D model, and threshold segmentation methods, extracted 11 basics phenotypes traits, and constructed three new phenotype traits of seed coat specific surface area, seed coat thickness ratio and seed density ratio, using 102 cotton germplasm resources with clear year characteristics. Our results show that there is a significant positive correlation ( P P < 0.001). Comparison of changes in Chinese self-bred varieties showed that seed volume, seed surface area, seed coat volume, cavity volume and seed coat thickness increased by 11.39%, 10.10%, 18.67%, 115.76% and 7.95%, respectively, while seed kernel volume, seed kernel surface area and seed fullness decreased by 7.01%, 0.72% and 16.25%. Combining with the results of cluster analysis, during the hundred-year cultivation history of cotton in China, it showed that the specific surface area of seed structure decreased by 1.27%, the relative thickness of seed coat increased by 8.70%, and the compactness of seed structure increased by 50.17%. Furthermore, the new indicators developed based on micro-CT technology can fully consider the three-dimensional morphological structure and cross-sectional characteristics among the indicators and reflect technical advantages. In this study, we constructed a microscopic phenotype research system for cotton seeds, revealing the morphological changes of cotton seeds with the year in China and providing a theoretical basis for the quantitative analysis and evaluation of seed morphology.
Why it matches plant phenotyping methodsマイクロCT、3Dセグメンテーション、深層学習を用いて綿実の形態形質を抽出し、新規指標を開発した研究であり、表現型取得・解析法が中心である。
Cotton is one of the most important agricultural products and is closely linked to the economic development of Pakistan. However, the cotton plant is susceptible to bacterial and viral diseases that can quickly spread and damage plants and ultimately affect the cotton yield. The automated and early detection of affected plants can significantly reduce the potential spread of the disease. This paper presents the implementation and performance analysis of bacterial blight and curl virus disease detection in cotton crops through deep learning techniques. The automated disease detection is performed through transfer learning of six pre-trained deep learning models, namely DenseNet121, DenseNet169, MobileNetV2, ResNet50V2, VGG16, and VGG19. A total of 1362 images of local agricultural fields and 1292 images from online resources were used to train and validate the models. Image augmentation techniques were performed to increase the dataset diversity and size. Transfer learning was implemented for different image resolutions ranging from 32×32 to 256×256 pixels. Performance metrics such as accuracy, precision, recall, F1 Score, and prediction time were evaluated for each implemented model. The results indicate higher accuracy, up to 96%, for DenseNet169 and ResNet50V2 models when trained on the 256×256 pixels image dataset. The lowest accuracy, 52%, was obtained by the MobileNetV2 model when trained on low-resolution, 32×32, images. The confusion matrix analysis indicates the true-positive prediction rates higher than 91% for fresh leaves, 87% for bacterial blight, and 76% for curl virus detection for all implemented models when trained and tested on an image dataset of 128×128 pixels or higher resolution.
Why it matches plant phenotyping methods綿花葉の病害状態を画像から自動推定する深層学習手法を実装・比較評価しており、植物病害表現型の取得方法が研究の中心です。
abstractThis paper presents the implementation and performance analysis of bacterial blight and curl virus disease detection in cotton crops through deep learning techniques.
CottonPhysiological trait estimationWater status / transpiration
The measurement of seed cotton moisture regain (MR) during harvesting operations is an open and challenging problem. In this study, a new method for resistive sensing of seed cotton MR measurement based on pressure compensation is proposed. First, an experimental platform was designed. After that, the change of cotton bale parameters during the cotton picker packaging process was simulated through the experimental platform, and the correlations among the compression volume, compression density, contact pressure, and conductivity of seed cotton were analyzed. Then, support vector regression (SVR), random forest (RF), and a backpropagation neural network (BPNN) were employed to build seed cotton MR prediction models. Finally, the performance of the method was evaluated through the experimental platform test. The results showed that there was a weak correlation between contact pressure and compression volume, while there was a significant correlation ( p 2 ) of 0.986 and a root mean square error (RMSE) of 0.204%. The mean RMSE and mean coefficient of variation (CV) of the performance evaluation test results were 0.20% and 2.22%, respectively. Therefore, the method proposed in this study is reliable. In addition, the study will provide a technical reference for the accurate and rapid measurement of seed cotton MR during harvesting operations.
Why it matches plant phenotyping methods種子綿の水分戻りを抵抗式センシングと圧力補償で推定する方法を開発し、実験プラットフォームと複数の予測モデルで性能評価しており、植物試料の形質測定法が中心です。
abstracta new method for resistive sensing of seed cotton MR measurement based on pressure compensation is proposed.
Recent advances in phenotyping techniques have substantially improved the ability to mitigate type-II errors typically associated with high variance in phenotyping data sets. In particular, the implementation of automated techniques such as the High-Volume Instrument (HVI) and the Advanced Fiber Information System (AFIS) have significantly enhanced the reproducibility and standardization of various fiber quality measurements in cotton. However, micronaire is not a direct measure of either maturity or fineness, lending to limitations. AFIS only provides a calculated form of fiber diameter, not a direct measure, justifying the need for a visual-based reference method. Obtaining direct measurements of individual fibers through cross-sectional analysis and electron microscopy is a widely accepted standard but is time-consuming and requires the use of hazardous chemicals and specialized equipment. In this study, we present a simplified fiber histology and image acquisition technique that is both rapid and reproducible. We also introduce an automated image analysis program that utilizes machine learning to differentiate good fibers from bad and to subsequently collect critical phenotypic measurements. These methods have the potential to improve the efficiency of cotton fiber phenotyping, allowing for greater precision in unravelling the genetic architecture of critical traits such as fiber diameter, shape, areas of the secondary cell wall/lumen, and others, ultimately leading to larger genetic gains in fiber quality and improvements in cotton.
Why it matches plant phenotyping methods綿繊維の断面画像を取得し、機械学習で繊維を識別して直径・形状・細胞壁・ルーメンなどの形質を自動測定する手法の開発であり、表現型取得・抽出が中心です。
abstractwe present a simplified fiber histology and image acquisition technique that is both rapid and reproducible.
Humans’ basic needs include food, shelter, and clothing. Cotton is the foundation of the textile industry. It is also one of the most profitable non-food crops for farmers around the world. Different diseases have a significant impact on cotton yield. Cotton plant leaves are adversely affected by aphids, army worms, bacterial blight, powdery mildew, and target spots. This paper proposes an encoder decoder model for generating captions in English and Marathi language to describe health of cotton plant from aerial images. The cotton disease captions dataset (CDCD) was developed to assess the effectiveness of the proposed approach. Experiments were conducted using various convolutional neural network (CNN) models, such as VGG-19, InceptionResNetV2, and EfficientNetV2L. The quality of generated caption is evaluated on BiLingual evaluation understudy (BLEU) metrics and using subjective criteria. The results obtained for captions generated in English and Marathi language are comparable. The network combination of EfficientNetV2L and long short-term memory (LSTM) has outperformed the other combinations.
Why it matches plant phenotyping methods綿花の航空画像から植物の健康状態・病害を推定して説明文を生成する画像解析手法とデータセットを開発・評価しており、フェノタイピング手法が中心である。
abstractThis paper proposes an encoder decoder model for generating captions in English and Marathi language to describe health of cotton plant from aerial images.
Lint yield in cotton is governed by light intercepted by the canopy (IPAR), radiation use efficiency (RUE), and harvest index (HI). However, the conventional methods of measuring these yield-governing physiological parameters are labor-intensive, time-consuming and requires destructive sampling. This study aimed to explore the use of low-cost and high-resolution UAV-based RGB and multispectral imagery 1) to estimate fraction of IPAR (IPAR f ), RUE, and biomass throughout the season, 2) to estimate lint yield using the cotton fiber index (CFI), and 3) to determine the potential use of biomass and lint yield models for estimating cotton HI. An experiment was conducted during the 2021 and 2022 growing seasons in Tifton, Georgia, USA in randomized complete block design with five different nitrogen treatments. Different nitrogen treatments were applied to generate substantial variability in canopy development and yield. UAV imagery was collected bi-weekly along with light interception and biomass measurements throughout the season, and 20 different vegetation indices (VIs) were computed from the imagery. Generalized linear regression was performed to develop models using VIs and growing degree days (GDDs). The IPAR f models had R 2 values ranging from 0.66 to 0.90, and models based on RVI and RECI explained the highest variation (93%) in IPAR f during cross-validation. Similarly, cotton above-ground biomass was best estimated by models from MSAVI and OSAVI. Estimation of RUE using actual biomass measurement and RVI-based IPAR f model was able to explain 84% of variation in RUE. CFI from UAV-based RGB imagery had strong relationship (R 2 = 0.69) with machine harvested lint yield. The estimated HI from CFI-based lint yield and MSAVI-based biomass models was able to explain 40 to 49% of variation in measured HI for the 2022 growing season. The models developed to estimate the yield-contributing physiological parameters in cotton showed low to strong performance, with IPAR f and above-ground biomass having greater prediction accuracy. Future studies on accurate estimation of lint yield is suggested for precise cotton HI prediction. This study is the first attempt of its kind and the results can be used to expand and improve research on predicting functional yield drivers of cotton.
Why it matches plant phenotyping methodsUAV画像からIPAR分率、バイオマス、RUE、収量、収穫指数などの植物形質・生理状態を推定するモデルを開発・評価しており、表現型取得・抽出手法が研究の中心である。
abstractThis study aimed to explore the use of low-cost and high-resolution UAV-based RGB and multispectral imagery
Pigment glands in cotton store gossypol, which is a highly valuable substance in agriculture and medicine. Therefore, obtaining phenotype information on cotton pigment glands is essential for evaluating gossypol content. However, the current research on pigment glands in cotton leaves faces several challenges, including a small proportion of pigment glands in the leaf area, a small segmentation target, a large number of glands at the leaf edge, and the interference of leaf veins, making the whole-leaf phenotype detection difficult. To address these challenges, this study proposes a deep learning-based semantic segmentation model named GlandSegNet. The GlandSegNet uses a specific encoder architecture formed by fusing the upsampling process and feature-extraction networks. In the decoder part, pooling and skip-connection operations are performed, and the network is optimized by embedding the ECA attention module. The experimental results show that the GlandSegNet can achieve area accuracy rates of 0.9842 and 0.9510, with corresponding error areas of 0.6966 mm² and 4.1258 mm², on test set 1 consisting of intact leaves from a single species and test set 2 consisting of randomly selected leaves from multiple species, respectively. The results demonstrate that the GlandSegNet semantic segmentation method can exhibit excellent performance in the cotton leaf pigment gland area detection tasks. Compared with traditional microscopic observation methods, the GlandSegNet is characterized by high efficiency and the ability to quantitatively analyze the glandular phenotype. Thus, the GlandSegNet could provide an effective tool and technical support for large-scale cotton pigment gland research and could be of great significance for the phenotype evaluation of cotton pigment glands and gossypol content evaluation.
Why it matches plant phenotyping methods綿葉の色素腺面積という植物形質を画像セグメンテーションで定量する手法を開発・検証しており、フェノタイピング手法が研究の中心である。
abstractthis study proposes a deep learning-based semantic segmentation model named GlandSegNet
Recent progress in ecological remote sensing calls for a more rapid measurement and a closer assessment of crop drought tolerance traits under field conditions. This study addresses three main questions: (1) If leaf dry matter content (LDMC) is equally effective in indicating cotton drought tolerance as leaf osmotic potential at full turgor ( π o ); (2) if drought tolerance is inversely related to fiber yield/quality in line with the leaf economics spectrum; and (3) if a reliable statistical model can be developed to rank cotton drought tolerance. The values of π o , along with those of LDMC, of 2736 leaves obtained from cotton variety trials conducted during 2020-2022 in both dryland and irrigated regimes were measured using osmometry. The relationships between π o and LDMC, as well as those between traits and lint yield and fiber quality indices, were investigated using regression analysis. A Bayesian hierarchical linear model was developed to rank cotton drought tolerance based on differences (or adjustments) in π o and LDMC between dryland and irrigated sites. LDMC was not only shown to be an alternate and equally effective drought tolerance trait compared with π o obtained from the widely accepted osmometry method, its use is also estimated to lead to a tenfold increase in measuring speed. A stronger drought tolerance capacity of the tested cotton varieties correlated with a lower lint yield and quality, which is generally consistent with the prediction of the leaf economics spectrum. The drought tolerance rankings using the Bayesian hierarchical model help divide the selected 17 cotton varieties into three groups: (a) more-drought tolerant, (b) less-drought tolerant, and (c) intermediate. The ranking results are interpreted using field-measured data of root distribution and diurnal leaf gas exchange from selected cotton varieties. Our work provides new opportunities for a more rapid measurement and an unambiguous ranking of drought tolerance capacity for crop genotypes under various management regimes.
Why it matches plant phenotyping methods綿花の乾燥耐性形質を迅速に測定・統計的に順位付けする方法を開発し、LDMCを浸透ポテンシャルと比較検証しているため、方法が研究の中心である。
titleRapid measurement and statistical ranking of leaf drought tolerance capacity in cotton