Abstract The classification of banana leaf disease has a large impact on agricultural output and relies heavily on timely early detection, with reliability as a fundamental component of effective crop management. The framework proposed in this study comprises a Hamiltonian Full Node Coverage Graph Attention Network (HFNC-GAT) and Fuzzy C-Means super pixel graph learning to explain the classification of banana leaf diseases. The HFNC-GAT allows for the representation of segmented leaf areas as the nodes in a graph. This framework also makes optimal use of an attention learning model to represent the spatial dependence of diseased leaf regions, allowing it to leverage both local and global spatial dependencies. The HFNC-GAT was demonstrated through observed experiments to achieve high performance with 96.11 and 94.19 accuracy, 0.9111 Cohen's Kappa, 0.9111 MCC, 0.9344 F2-score, and 0.9939 ROC-AUC compared to the performance of conventional CNN, GCN, and baseline GAT models.
Why it matches plant phenotyping methodsバナナ葉の病斑領域を画像から抽出・分類するグラフ学習手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として適格です。
abstractThe framework proposed in this study comprises a Hamiltonian Full Node Coverage Graph Attention Network (HFNC-GAT) and Fuzzy C-Means super pixel graph learning to explain the classification of banana leaf diseases.
Reproduction assets foundThe paper's Dataset Availability statement explicitly declares two public Kaggle banana leaf image datasets used for the phenotyping/classification experiments: Banana Leaf Disease Dataset V4 and BananaLSD. No author analysis code or trained model is reported as publicly available.Dataset · publicThe Banana Leaf Disease
Dataset V4 is available at https://www.kaggle.com/datasets/rayhanarlistya/banana-leaf-disease-dataset-v4.Open asset ↗Kaggle · banana-leaf-disease-dataset-v4pdf-page:19 lines:1-55Dataset · publicThe Banana Leaf Spot Diseases (BananaLSD) dataset is available at
https://www.kaggle.com/datasets/shifatearman/bananalsdOpen asset ↗Kaggle · bananalsdpdf-page:19 lines:1-55Plant phenotyping relevance match · UnverifiedEurope PMC · checked 5 Sept 2026
Introduction Banana production is increasingly threatened by fungal diseases such as Fusarium wilt and Black Sigatoka, posing severe risks to food security and agricultural economies. Recent image-based approaches using deep learning have shown high predictive capacity for plant disease recognition; however, their limited transparency, calibration uncertainty, and sensitivity to domain shifts can restrict their use in decision-support workflows that require auditability. Methods This study proposes an interpretable and calibrated Artificial Intelligence framework for multiclass banana disease-pattern characterization based on radiomic feature analysis of RGB leaf images. Radiomic features were extracted from HSV-segmented banana leaf regions, resulting in a dataset of 14,763 samples characterized by 103 quantitative descriptors and labeled as Healthy, Sigatoka, or Fusarium wilt race 1. Results Among the evaluated radiomics classifiers, the calibrated Random Forest achieved accuracy = 0.85, balanced accuracy = 0.84, macro-F1 = 0.84, and macro ROC-AUC OvR = 0.95 on the held-out test set. Bootstrap analysis yielded 95% confidence intervals of [0.8406, 0.8691] for accuracy and [0.8306, 0.8606] for balanced accuracy. Three deep learning baselines trained on the same partition achieved higher predictive performance: MobileNetV3 with accuracy = 0.96, macro-F1 = 0.95, and macro ROC-AUC OvR = 0.97; EfficientNet with accuracy = 0.96, macro-F1 = 0.97, and macro ROC-AUC OvR = 0.96; and ResNet-18 with accuracy = 0.96, macro-F1 = 0.96, and macro ROC-AUC OvR = 0.96. Discussion The CNNs produced strong classification performance on the evaluated repositories, and the radiomics approach demonstrated to be a complementary interpretable and explainable calibrated reference model. SHAP, LIME, permutation importance, accumulated local effects, calibration curves, and Brier score decomposition supported feature-level inspection of the final model.
Why it matches plant phenotyping methodsバナナ葉のRGB画像から病害状態を抽出・分類する画像解析および説明可能な機械学習手法が研究の中心であり、植物病害表現型の評価性能も検証している。
abstractThis study proposes an interpretable and calibrated Artificial Intelligence framework for multiclass banana disease-pattern characterization based on radiomic feature analysis of RGB leaf images.
ABSTRACT Banana diseases impose severe production losses in tropical smallholder farming systems, yet accurate in-field visual diagnosis remains difficult: symptom expression varies across cultivars and growth stages, and several diseases produce morphologically overlapping foliar signs. We developed a probabilistic image-recognition framework for detecting five economically important banana diseases — Xanthomonas Wilt, Banana Bunchy Top Disease, Fusarium Wilt (Panama disease), Yellow Sigatoka, and Black Sigatoka — from in-field photographs, without any disease-specific fine-tuning of the vision backbone. The approach extracts frozen 1,152-dimensional embeddings from the DINOv3 vision foundation model and couples them with a conditional normalizing flow, trained on four publicly available datasets spanning diseased banana plants, healthy tissue, non-banana vegetation, and general natural imagery. On an independent test set the model achieved F1 scores exceeding 0.98, average precision values of 0.968–0.999, and AUROC values of 0.997–1.000 across all five diseases evaluated as binary detection problems. Multi-class accuracy was near-perfect, with limited confusion between Yellow Sigatoka and Black Sigatoka — a biologically plausible ambiguity attributable to overlapping early-infection foliar symptoms. Because the normalizing flow estimates explicit conditional probability densities rather than decision boundaries, two complementary log-likelihood ratios can be derived: a disease ratio comparing each disease class against healthy banana, and a plant ratio comparing banana against non-banana imagery. Together these define an interpretable two-dimensional diagnostic space that simultaneously quantifies evidence for disease presence and image relevance, cleanly separating diseased plants, healthy plants, and out-of-distribution images while flagging uncertain predictions for confirmatory testing. Inference on frozen embeddings is lightweight and compatible with smartphone deployment, providing a scalable, uncertainty-aware diagnostic tool for smallholder farming systems and disease surveillance programmes.
Why it matches plant phenotyping methodsバナナ葉の病徴を圃場画像から直接推定する確率的画像認識手法を開発し、独立テストセットで性能検証しているため、植物病害状態のフェノタイピング手法が中心である。
abstractWe developed a probabilistic image-recognition framework for detecting five economically important banana diseases
Leaf diseases in banana plants and ornamental crops can significantly reduce productivity and product quality, highlighting the need for accurate early detection methods. This study proposes an image-based classification approach utilizing texture features extracted from the Gray Level Co-occurrence Matrix (GLCM) combined with a Hybrid Stacking model that integrates Random Forest (RF) and Support Vector Machine (SVM). The preprocessing stage involves image resizing and noise reduction, followed by feature extraction using energy, contrast, homogeneity, and correlation parameters. The dataset consists of eight classes of healthy and diseased leaves, collected from both field documentation and secondary sources. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics under a cross-validation scheme. Experimental results show that SVM achieved 89.2% accuracy, RF 88.5%, while the stacking model yielded the best performance with 91.7% accuracy, effectively reducing misclassification among visually similar disease classes. This study demonstrates the effectiveness of combining GLCM features and hybrid stacking models for leaf disease classification, with potential applications in automated plant monitoring systems to support precision agriculture.
Why it matches plant phenotyping methods植物葉の病害状態を画像から分類する手法の開発・評価が研究の中心であり、GLCM特徴量とRF/SVMの性能を交差検証しているため、植物フェノタイピング手法として収録する。
abstractThis study proposes an image-based classification approach utilizing texture features extracted from the Gray Level Co-occurrence Matrix (GLCM) combined with a Hybrid Stacking model that integrates Random Forest (RF) and Support Vector Machine (SVM).
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
High-fidelity 3D reconstruction and precise phenotypic parameter extraction of banana plants are critical for crop growth monitoring and yield estimation in precision agriculture. However, traditional methods encounter significant bottlenecks: LiDAR systems are cost-prohibitive for widespread adoption, while traditional photogrammetry often fails to handle the complex canopy structures, severe occlusions, and weak texture features characteristic of banana leaves. To address these limitations, this article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones. We introduce BN-NeRF, an enhanced Neural Radiance Field method built upon Instant-NGP. Specifically, we integrate three key technical improvements: (1) frame-level geometric calibration to correct camera pose drift caused by handheld motion; (2) sparse geometric anchoring to explicitly constrain depth and scale using sparse point clouds; and (3) thin-leaf prior regularization to suppress artifacts and improve the geometric accuracy of leaf surfaces. Building on this reconstruction, we establish a complete pipeline to recover explicit metric geometry from implicit radiance fields. By combining mesh topological analysis with geodesic algorithms, we achieve automated and precise extraction of key morphological parameters. Extensive experiments were conducted on a dataset of 90 banana plants in a real-world orchard. The results demonstrate that BN-NeRF achieves superior rendering quality (PSNR of 32.4 dB, SSIM of 0.951, and LPIPS of 0.152) while maintaining inference speeds comparable to Instant-NGP. Furthermore, the extracted phenotypic parameters showed strong agreement with manual ground truth across both leaf-level and structural traits. In addition to trait-specific regression performance, the evaluation also includes normalized completeness analysis, calibration-cube-based scale validation, and Bland-Altman agreement analysis, supporting the measurement reliability of BN-NeRF for field phenotyping. This study demonstrates that low-cost smartphone-based acquisition, combined with BN-NeRF, can support accurate field phenotyping of banana plants. In addition, an implemented mobile-cloud system was functionally validated through repeated end-to-end runs on an iPhone 13 client and a cloud workstation.
Why it matches plant phenotyping methodsスマートフォン画像からの3D再構成と植物形態形質抽出を中核とするBN-NeRF手法を開発し、圃場データで精度・再現性を検証しているため。
abstractthis article proposes a novel framework for 3D reconstruction and automatic phenotyping based on multi-view images captured by mobile phones
The existing methods of callose quantification from plant tissues include epifluorescence microscopy, fluorescence spectrophotometry, immunofluorescence microscopy, and indirect assessment of both callose synthase and β-(1,3)-glucanase activities. However, some of these methods have significant limitations, which include being time-consuming, non-specific to callose, labor-intensive, subjective, high autofluorescence, low sensitivity, being more qualitative rather than quantitative, and requiring the acquisition of software resources and technical skills. Therefore, there is a pressing need to explore alternative methods for callose quantification in plant tissues. It was hypothesized that immunofluorescence spectrophotometry or enzyme-linked immunosorbent assay (ELISA) that uses callose-specific antibodies could overcome some of the limitations of the current callose quantification methods. Biotic stress was administered by inoculating tissue culture-derived banana plantlets with Xanthomonas vasicola pv. musacearum (Xvm) bacteria which induced callose production. Banana corm tissue samples were collected at 14 days post-inoculation (dpi) for callose quantification using the new immunofluorescence spectrophotometry method. Callose production in the corms of Xvm-inoculated and control groups varied significantly in both the banana genotypes (independent sample t-test, p < 0.05). The immunofluorescence spectrophotometry method described here could be applied for the quantification of callose in different plant tissues with high specificity to callose, sensitivity, reliability, and reproducibility. Additionally, the use of a 96-well plate makes this method suitable for high throughput callose quantification studies with minimal sampling and analysis biases.
Why it matches plant phenotyping methods植物組織中のカロース量という生理状態を定量する新規免疫蛍光分光法・ELISA法の開発と性能評価が研究の中心であり、ハイスループット化や再現性も検討している。
abstractTherefore, there is a pressing need to explore alternative methods for callose quantification in plant tissues.
Bananas are among the most widely cultivated fruits worldwide and constitute a major staple crop in many developing regions. Banana production is highly vulnerable to foliar diseases, which significantly reduce yield and economic sustainability. Early and accurate disease diagnosis plays a central role in mitigating crop losses. This study presents an image-based artificial intelligence framework for banana leaf disease diagnosis using advanced segmentation and deep learning techniques. Gaussian Kernel Fuzzy C-Means (GKFCM) clustering is employed for pixel-level segmentation, enabling precise isolation of diseased regions by preserving nonlinear boundary characteristics and minimizing intra-cluster variance. For disease classification, a Correlation Weight Bayesian SqueezeNet (CWBSN) model is introduced, integrating Bayesian optimization and correlation-aware feature weighting to enhance discriminative representation and classification robustness. The framework is evaluated using RGB images obtained from the BananaLSD dataset and field-acquired imagery collected at Bangabandhu Sheikh MujiburRahman Agricultural University (BSMRAU), Bangladesh. Performance is assessed using accuracy, precision, recall, F1-score, Matthews Correlation Coefficient (MCC), and Receiver Operating Characteristic (ROC) analysis. Experimental results demonstrate that the proposed GKFCM–CWBSN framework achieves reliable and consistent disease recognition based solely on visual symptom analysis. The study establishes a computational foundation for image-driven plant disease diagnosis, with potential adaptability to future multimodal agricultural sensing systems.
Why it matches plant phenotyping methodsバナナ葉の病斑領域を画像から抽出・分類する手法の開発が研究の中心であり、植物病害状態の表現型推定に該当する。
abstractThis study presents an image-based artificial intelligence framework for banana leaf disease diagnosis using advanced segmentation and deep learning techniques.
Crop diseases significantly reduce agricultural output and are a serious problem, especially in the parts of the world where diagnostic experts are not readily available. Deep learning has recently shown us that it is possible for a computer to identify plant diseases directly from images of the leaves. Nevertheless, to make such solutions available on the web or mobile devices one has to really think about how heavy the calculations will be, how easy the user interface should be, and also the limit on the data used. Here is a paper on a web-based applied deep learning system for disease detection in multiple crops. The system detects disease in eight crops Apple, Banana, Grape, Mango, Cauliflower, Tomato, Potato, and Corn with each crop having several disease classes and healthy samples. Three transfer-learning-based CNN architectures MobileNetV3, EfficientNetB4, and ResNet50 were compared for classification performance on the public datasets collected from PlantVillage, Kaggle, and Mendeley. Considering class-wise accuracy, prediction time, and deployment scenarios, MobileNetV3 was picked as the main model to be integrated into the system. To compensate for the differences in image quality often found in pictures taken by users, an optional super-resolution preprocessing step with Real-ESRGAN is added and quantitatively assessed. Disease prediction with spectral activation maps (Grad-CAM) enhances the model's interpretability by highlighting image areas where the disease is detected. The resulting model is embedded in a multilingual Progressive Web Application (PWA). The platform enables users to submit their crop images and receive predicted disease names and treatment options, which are generated by a Large Language Model (LLM) using structured disease metadata. The research acknowledges dataset bias and limitations in extrapolating from curated datasets to the general real-world setting although it reports very good performance of the method on the test sets. In summary, the system proposed here is intended as a practical digital agriculture decision-support tool that demonstrates deployment feasibility and raises a few issues for future validation at the field level and improvement.
Why it matches plant phenotyping methods葉画像から植物病害を推定するCNN手法の比較・前処理評価・実装を中心とした研究であり、植物の病害状態を直接評価するため、植物フェノタイピング手法として中心的です。
abstractDeep learning has recently shown us that it is possible for a computer to identify plant diseases directly from images of the leaves.
Banana is a key staple crop in the world, but its yield is very desperately affected by the leaf diseases like Sigatoka, Fusarium Wilt, and Cordana, which bring serious losses of yield and economy. The manual disease detection methods deployed in the past are time and labor-intensive and cannot be effective in the field, so powerful automated solutions are sought. In this paper, we have presented a hybrid deep learning architecture which combines MobileNetV2 and ResNet101 with attention, dilated convolutions, multi-scale feature pooling and PCA-based feature fusion to classify banana leaf disease accurately and efficiently. The Banana and Banana Leaf, Banana Disease Recognition, and Banana LSD three benchmark datasets have been trained and evaluated following an innovative preprocessing pipeline that consists of illumination correction, background suppression, denoising, and hi-tech data augmentation. The experimental findings have shown that the presented hybrid model is always better than ten state-of-the-art deep learning models, such as VGG16, ResNet50, DenseNet121, EfficientNet-B0, and Vision Transformer (ViT). It has reached an optimal accuracy of 98.28, precision of 98.18, recall of 98.77 and F1-score of 98.43, with only 12.7 M trainable parameters and convergence rate of only 15 epochs, which makes the model both high-accuracy and computationally efficient.
Why it matches plant phenotyping methodsバナナ葉画像から病害状態を推定する深層学習手法の開発・評価が中心であり、植物病害フェノタイピングに該当する。
titleAttention enhanced hybrid deep learning architecture with PCA-based feature fusion for banana leaf disease detection.
CONTEXT: The rapid advancement of digital technologies alongside increased global commitment to sustainability has intensified the need for efficient crop management solutions. Across agricultural systems, delayed detection and misclassification of plant diseases remain major contributors to reduced yields, threatening food security and undermining progress toward Sustainable Development Goals such as Zero Hunger, No Poverty, Good Health and Well-being, Climate Action, and Life on Land. Plant pests, diseases, and excessive chemical use further exacerbate these challenges. Early, automated visual detection offers a pathway to environmentally responsible and economically viable agricultural practices. OBJECTIVE: This study aims to develop and evaluate an automated, real-time plant disease classification framework using Vision Transformers (ViT) and hybrid ViT–CNN architectures, with the goal of supporting farmers and agronomists in early decision-making and sustainable crop protection. METHODS: The research employs deep learning techniques, including ViT and a combined ViT–CNN model built on ResNet-9, trained and evaluated using four publicly available datasets: the Turkey Plant Pests and Diseases (TPPD) dataset (15 classes), the Namibia Maize Image Dataset (3 classes), the Banana Image Dataset (3 classes), and the Tanzania Maize Dataset (3 classes). SHapley Additive exPlanations (SHAP) were applied to generate saliency maps for interpretability. Comparative analyses assessed performance, accuracy, and classification speed across attention-based and hybrid architectures. RESULTS AND CONCLUSIONS: The proposed model achieved strong performance with 97.4% accuracy, 96.4% precision, 97.09% recall, a 95.7% F1-score, and high agreement measured by Cohen’s Kappa, outperforming existing benchmark models. SHAP visualizations highlighted that the model leverages high-activation areas, edge features, color patterns, texture, shape, and contextual cues in its predictions. While attention-based models improved accuracy, they also caused reduced classification speed. However, integrating attention blocks with CNN layers effectively compensated for this slowdown, achieving both high accuracy and efficient inference. To evaluate the interpretability and deployment feasibility of the proposed model, we considered several parameters are considered to have an integration of faithfulness, localization quality, sparsity, latency, and energy of the models, including pointing accuracy, localization IoU, Centroid Localization Error (pixels), Attribution Sparsity (%), Insertion AUC, Deletion AUC, Time per Explanation (ms/image), Energy Consumption (J/image), Memory Footprint (MB). SIGNIFICANCE: This research provides a transparent and high-performing deep learning solution for plant disease classification, promoting sustainable agricultural development. By reducing reliance on excessive pesticide and herbicide use, enhancing early diagnosis, and improving decision-making, the model supports environmentally responsible farming practices and contributes to global efforts toward food security and ecological resilience. The significance of this evaluation is that it comprehensively assesses the model’s interpretability and real-world deployability by measuring explanation reliability (faithfulness), spatial precision (localization quality), efficiency (latency and memory), and sustainability (energy consumption), ensuring the model is not only accurate but also transparent, efficient, and practical for deployment.
Why it matches plant phenotyping methods植物病害を画像から直接分類する深層学習フレームワークの開発・評価が研究の中心であり、病害状態という植物表現型を推定するため、対象範囲に含める。
abstractThis study aims to develop and evaluate an automated, real-time plant disease classification framework using Vision Transformers (ViT) and hybrid ViT–CNN architectures
Abstract Abstract. Accurate and timely identi cation of plant diseases is essential for improving crop productivity and ensuring sustainable agricultural practices. This paper presents a comprehensive image dataset of fruit and leaf diseases covering six economically important horticultural crops: Apple, Banana, Citrus, Guava, Mango, and Papaya. The dataset comprises high-quality RGB images representing both healthy and diseased samples, with disease symptoms including spots, lesions, discoloration, blight, rot, and fungal and bacterial infections captured under diverse real-world conditions. Variations in illumination, background complexity, viewing angles, growth stages, and symptom severity are intentionally included to enhance the robustness and generalizability of learning models developed using this data. The dataset is structured in a class-wise manner and preprocessed to support direct integration with deep learning frameworks. It is extensively used to train, validate, and evaluate deep learning based plant disease classi cation models, enabling automatic feature learning from raw images without manual intervention. Experimental usage demonstrates that the dataset is well suited for convolutional neural networks and attentionbased architectures, facilitating e ective discrimination between multiple disease categories across di erent crops and plant organs. By providing a uni ed multi-crop, multi-disease benchmark, this dataset aims to accelerate research in automated crop disease diagnosis, precision agriculture, and intelligent decision-support systems for sustainable farming.
Why it matches plant phenotyping methods植物の葉・果実の病徴画像を収録したデータセット／ベンチマークであり、病害状態の画像ベース推定を中心的に扱うため。
abstractThis paper presents a comprehensive image dataset of fruit and leaf diseases covering six economically important horticultural crops
Reproduction assets foundThe paper's core asset is the ABCGMP fruit and leaf disease image dataset, publicly deposited on Mendeley Data, with author analysis code also stated to be available on GitHub. Both are paper-specific, public, and actionable.Dataset · publicData is available on Mendeley:1Open asset ↗pdf-page:33 lines:1-56Code · publicCode availability: Code is available on GitHub 2Open asset ↗GitHubpdf-page:33 lines:1-56Plant phenotyping relevance match · UnverifiedCrossref · checked 5 Sept 2026
Crop monitoring over large land extensions represents a central challenge in precision agriculture, especially in polyculture contexts where species with different nutritional needs are combined. This study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level. The images are efficiently stored and retrieved using a Hilbert Curve, which reduces the complexity of the search process from O(n2) to O(log(n)) where n represents the number of indexed data points). The system connects to a distributed Structured Query Language (SQL) database, allowing for fast image retrieval based on GPS coordinates and other metadata. Additionally, the Normalized Difference Vegetation Index (NDVI) is calculated using reflectance data from the red and near-infrared channels, adjusted by semantic segmentation masks generated with a U-Net model, which allows for species-specific evaluations. The methodology was evaluated on a 20,000 m2 polyculture farm with coffee, avocado, and plantain crops, using a dataset of 270 aerial images partitioned into 70% for training and 30% for validation. The results show improvements in retrieval speed and precision with the Hilbert Space-Filling Curve (HSFC) approach, and an accuracy of 82.3% and an the Mean Intersection over Union (MIoU) of 68.4% in species detection with the U-Net model. Overall, this integrated framework demonstrates a scalable potential for precision agriculture in complex polyculture systems, facilitating efficient data management and targeted crop interventions.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像、セマンティックセグメンテーション、NDVIを統合した植物レベルの健康状態評価手法が研究の中心であり、手法の構築と検証も行っている。
abstractThis study presents a methodology to manage and analyze large volumes of multispectral images captured by unmanned aerial vehicles (UAVs) in order to identify and monitor crops at the plant level.
The objective of this study is to create a highly accurate and interpretable deep learning (DL) model for the multi-class classification of fruit using convolutional and transformer architectures. The classification performance can be enhanced by making sure that the used technique is explainable and interpretable. This research data was obtained from Kaggle which contains images of banana, grape, lemon, mango, and strawberry fruit classes. The total data was divided into 70:15:15 for training, validating and testing. To ensure consistent size and quality, all images were pre-processed before use. This study considered four pretrained models namely RegNetY-B3-GE, DarkNet53-SCSE, BEiT, and PVTv2 for performance assessment. We proposed a lightweight hybrid (convolution plus attention-based) CoAT-AgriLite model for fruit disease classification which extracts local lesion features and global context. Transferring training and data augmentation technique was utilized during training for better performance. To ensure interpretability of model decisions, Gradient-weighted Class Activation Mapping (Grad-CAM) which captures the discriminative regions from the input images for model predictions. Among all evaluated models, the proposed model achieved the highest classification accuracy of 99.37% on the testing dataset. Comparative results demonstrated that the proposed model outperformed other pretrained models in terms of precision, recall, and F1-score, confirming its robustness and effectiveness in real-world agricultural classification tasks. The experimental findings validate that the proposed model not only achieves superior classification accuracy but also provides interpretability through Grad-CAM visualizations. This hybrid framework offers a promising solution for intelligent and transparent fruit classification systems, with potential applications in precision agriculture and automated sorting systems.
Why it matches plant phenotyping methods果実病害を画像から分類する深層学習フレームワークを開発・比較し、病斑特徴の抽出とGrad-CAMによる説明性を評価しており、植物の病害状態の画像計測が中心である。
titleA hybrid convolution and attention-based framework with visual explanation for fruit disease identification.
Banana and plantain (Musa spp.) production in Sub-Saharan Africa is severely constrained by multiple diseases, with Banana bunchy top virus (BBTV) representing the most devastating viral threat. Inadequate diagnostic infrastructure limits effective management, particularly for asymptomatic infections disseminated through informal planting material exchange. This study presents an integrated diagnostic framework combining Loop-Mediated Isothermal Amplification (LAMP) molecular diagnostics with deep learning-based computer vision for rapid, scalable disease detection under field conditions. A LAMP assay targeting the BBTV DNA-S coat protein gene was developed using conserved sequences from diverse African isolates and validated with a simplified alkaline extraction protocol eliminating conventional DNA purification. The assay achieved 100% specificity and concordant detection with PCR and qPCR, reducing diagnostic time from 4 to 6 hours to 60 minutes. In-house recombinant Bst LF polymerase production demonstrated comparable enzymatic performance to commercial alternatives, with projected per-reaction cost reductions of 70 to 80%. Concurrently, an SSDLite MobileNetV2 object detection model was developed through 19 iterative training cycles on 19,914 field-collected images spanning 22 disease and physiological stress classes. The final model achieved recall rates of 92.5% for BBTV, 91.0% for Banana Xanthomonas Wilt, and 98.1% for healthy leaf classification, deployed via the PlantVillage mobile application for real-time offline diagnostics. A QR code-based metadata system integrates phenotypic AI assessments with molecular confirmation for comprehensive surveillance. This complementary framework addresses broad-scale phenotypic screening and molecular confirmation of pre-symptomatic infections, providing accessible tools to safeguard food security across Sub-Saharan Africa.
Why it matches plant phenotyping methods植物病害の表現型を画像から推定するコンピュータビジョン手法の開発・評価・アプリ展開が中心であり、LAMPによる分子診断も補完的に統合されている。
abstractan SSDLite MobileNetV2 object detection model was developed through 19 iterative training cycles on 19,914 field-collected images spanning 22 disease and physiological stress classes
Productivity and quality of food are crucial for populations around the world. However, food faces challenges due to the threats of fruit diseases, which lead to poor food quality. Therefore, early detection and classification of fruit diseases are important to help farmers detect and overcome these diseases, thereby improving food quality and productivity. One of the biggest challenges in the agriculture field is classifying and detecting fruit diseases using traditional manual visual grading. As a result, deep learning and computer vision models have emerged as new methods for visual grading, offering higher accuracy in classification and detection. This study proposes deep learning models for fruit disease detection and classification in the early stages. Five deep learning models are used: Convolutional Neural Network (CNN), DenseNet121, EfficientNetB3, Xception, and ResNet50. These models are applied to detect six types of fruit diseases, including orange, grape, mango, guava, apple, and banana plant diseases. Image preprocessing and data augmentation techniques were employed for image processing. The results show accuracies of 96.25%, 99.14%, 96.17%, 94.06%, 96.72%, and 99.33% for the CNN, EfficientNetB3, ResNet50, DenseNet121, ResNet50, and EfficientNetB3 models, respectively, for detecting orange, grape, mango, banana, guava, and apple plant diseases. We compared our models with other deep learning models, and the model that utilized image preprocessing and data augmentation techniques demonstrated higher accuracy and performance. We recommend the EfficientNetB3 model for fruit disease detection based on these results.
Why it matches plant phenotyping methods果実植物の病害状態を画像から検出・分類する深層学習手法の開発と比較が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThis study proposes deep learning models for fruit disease detection and classification in the early stages.
Why it matches plant phenotyping methodsバナナの感染状態をSWIRハイパースペクトルで取得し、バンド選択と深層メトリック学習により無症状段階の病害状態を推定する手法が研究の中心であるため。
abstractTo address this, we explored the potential of short-wave infrared (SWIR) hyperspectral sensing combined with deep metric learning for early-stage disease diagnosis.
Banana (Musa spp.) fruit morphology is a key determinant of yield and quality, yet modeling its 3D structural dynamics across genotypes remains difficult. To address this challenge, we developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology. Field experiments were conducted over two growing seasons in Hainan, China, using three representative genotypes. Morphological traits, including outer and inner arc length, circumference, and pedicel length, along with dry (Wd) and fresh weight (Wf), were measured every 10 days after flowering until 110 days. Quantitative relationships between morphological traits and Wf, as well as between Wd and Wf, were fitted using linear or Gompertz functions with genotype-specific parameters. Based on these functions, a parameterized 3D reconstruction method was implemented in Python, combining biomass-driven growth equations, curvature geometry, and cross-sectional interpolation to simulate the fruit’s bending, tapering, and volumetric development. The resulting dynamic 3D models accurately reproduced genotype-specific differences in curvature, length, and shape with average fitting R2 > 0.95. The proposed biomass-driven 3D structural model provides a methodological framework for integrating banana fruit morphology into functional–structural plant models.
Why it matches plant phenotyping methodsバナナ果実の形態形質を推定・再現するバイオマス駆動型3D構造モデルを開発し、遺伝子型間の形状を検証しており、フェノタイピング手法が中心である。
abstractwe developed a generic, biomass-driven 3D structural model for banana fruit fingers that quantitatively links growth and morphology.
Reproduction assets foundThe paper explicitly states that the source code of the Banana Morphology Simulation System and the datasets are publicly available on GitHub at the authors' URL, which matches an allowed URL. This covers the paper's phenotyping datasets and analysis/3D modeling code.Code · publicData analysis was performed using a custom-developed software platform, the Banana
Morphology Simulation System. The source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Dataset · publicThe source code and datasets are publicly available
on GitHub (https://github.com/Interstingsun/SimBanana, accessed on 4 January 2026).Open asset ↗Interstingsun/SimBananapdf-page:5 lines:1-24Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Published1 Jan 2026IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingCited by 0 · OpenAlex ↗
Banana plant canopies exhibit pronounced three-dimensional heterogeneity due to their large, sparse, and overlapping leaves. Under dense planting and severe occlusion, conventional single-modality approaches commonly suffer from insufficient information and limited recognition accuracy, highlighting the necessity of cross-modal complementarity and collaborative modeling. This study proposes a 3D-2D dual-modal collaborative framework based on low-cost UAV oblique imagery to enable end-to-end estimation of canopy volume and porosity in banana plantations. The framework integrates a task-oriented YOLO-SPES model to improve the detection of irregular and overlapping canopies and combines it with the SoftGroup instance segmentation model for three-dimensional structural extraction. At the data level, a cross-modal coordinate interaction strategy (PCI-LLCM) is introduced to achieve precise alignment between point clouds and orthomosaics. At the structural level, a consistent indexing scheme between 3D instances and 2D detection boxes is established, upon which a 3D-2D collaborative modeling algorithm (SGP-YS DMCA) and a multi-scale volume differencing algorithm (MSVDA) are developed for canopy volume and porosity estimation. At the decision level, an adaptive canopy volume completion module (CVC-AOML) leverages 2D detection information to correct and supplement errors and omissions in 3D segmentation, thereby ensuring the accuracy and completeness of large-scale automated measurements. In addition, the coupling performance of multiple geometric algorithms and collaborative models is systematically evaluated. Experimental results demonstrate that the proposed dual-modal collaborative framework achieves a coefficient of determination (R$^{2}$) of 0.885 for canopy volume estimation, representing an average improvement of 0.15 over single-modality baseline methods, with a corresponding mean absolute percentage error (MAPE) of 6.4%. For canopy porosity estimation, an R$^{2}$of 0.65 is obtained with a MAPE of 1.05%. These results not only overcome the limitations of single-modality approaches in phenotypic analysis of complex banana canopies but also provide a low-cost and scalable solution for large-scale agricultural monitoring and precision management of tropical fruit crops.
Why it matches plant phenotyping methodsUAV画像・点群を用いてバナナの樹冠体積と多孔性という明示的な植物形質を自動推定する3D–2D手法を開発し、ベースライン比較と精度評価を行っているため、フェノタイピング手法が中心である。
abstractThis study proposes a 3D-2D dual-modal collaborative framework based on low-cost UAV oblique imagery to enable end-to-end estimation of canopy volume and porosity in banana plantations.
Early and accurate detection of diseases is very important for the health of crops and ensuring sustainable agricultural productivity. This paper proposes OptiNet-B3, a novel approach and an efficient deep model for the multiclass classification of fruit and leaf diseases for apples, bananas, and oranges. Through two diverse and comprehensive image datasets, the model performs well for both fruit 13,602 images and leaf 11,199 images classification. OptiNet-B3 optimizes learning in low computational budget by integrating Mish activation, Convolutional Block Attention Module (CBAM), Group Normalization, and knowledge distillation. Great care in preprocessing and augmenting data was taken to improve generalization. Comparison with state-of-the-art models-including DenseNet121, ResNet50, MobileNetV3, and InceptionV3-based models-reveals that OptiNet-B3 substantially outperforms in terms of accuracy, with 98.12% and 99.23% on the fruit and leaf datasets, respectively. Due to its light-weight architecture, real-time deployment for in-field diagnosis on mobile and edge devices is much more feasible. The results underscore the potential of explainable, AI-driven tools in transforming plant disease management practices.
Why it matches plant phenotyping methods果実・葉の画像から植物病害を分類するモデルを開発し、複数データセットと既存モデルとの比較で性能検証しているため、植物状態の画像ベース表現型推定が中心です。
abstractThis paper proposes OptiNet-B3, a novel approach and an efficient deep model for the multiclass classification of fruit and leaf diseases for apples, bananas, and oranges.
Abstract - The global agricultural sector faces significant challenges due to plant diseases that threaten food security and sustainable agriculture. Traditional methods of disease detection are often labour-intensive, time-consuming, and require specialized expertise. This research presents a comprehensive machine learning framework for automated plant disease detection, leveraging both traditional machine learning and deep learning approaches. We implemented and evaluated multiple models including VGG19, Inception v3, Support Vector Machines (SVM), and k-Nearest Neighbors (kNN) on four distinct datasets: Banana Leaf, Custard Apple Leaf and Fruit, Fig Leaf, and Potato Leaf. Our experimental results demonstrate remarkable performance variations across different crops, with the highest achievement of 99.1% accuracy using VGG19 with kNN on the Custard Apple dataset, while the Potato Leaf dataset presented the greatest challenges with 62.6% accuracy using Inception v3 with SVM. The study provides valuable insights into model selection for specific agricultural applications and highlights the importance of customized solutions based on crop-specific characteristics. We also address critical challenges including dataset limitations, computational requirements, and implementation barriers in real-world agricultural settings. Keywords - Plant disease detection, machine learning, deep learning, convolutional neural networks, agricultural technology, precision agriculture.
Why it matches plant phenotyping methods植物病害状態を対象に、機械学習・深層学習による自動検出フレームワークを提示し、複数モデルとデータセットで性能評価しているため、病害表現型の取得・判定手法が中心である。
abstractThis research presents a comprehensive machine learning framework for automated plant disease detection, leveraging both traditional machine learning and deep learning approaches.
Precision agriculture using Unmanned Aircraft Spraying Systems (UASS) enables targeted pesticide delivery. However, structural changes in the banana canopy during growth complicate pesticide application, particularly in achieving uniform coverage of the lower canopy. This study investigates the impact of canopy porosity and UASS flight height on droplet penetration. The study focused on three growth stages (BBCH-35, BBCH-49, and BBCH-63) of banana plants. Using 3D reconstruction techniques, including LiDAR scanning and point cloud voxel processing, volumetric and optical porosity within the specified area were extracted for each stage. The mean volumetric porosity of the banana canopy was found to be 78.72 %, while the mean optical porosity was 4.16 %. In the lower canopy, the mean volumetric porosity was 73.33 %, and the mean optical porosity was 13.68 %. Additionally, the relationship between volumetric porosity and optical porosity of the banana canopy was fitted using a Boltzmann sigmoid function, achieving a Coefficient of Determination (R²) value of 0.708. Unmanned Aerial Vehicle (UAV) tests revealed that UASS spraying at a height of 4m achieved the optimal balance between deposition, deposition uniformity, and ground loss ratio. Additionally, a BP neural network model demonstrated a strong correlation between canopy porosity and lower-layer droplet penetration during the BBCH-63 stage, with an R² value of 0.883. The study found lower UASS flight heights optimal for banana plants and highlighted canopy porosity's role in enhancing droplet penetration, especially in later growth stages, providing insights for improving spraying efficiency.
Why it matches plant phenotyping methods3D LiDAR再構成と点群ボクセル処理により、バナナ樹冠の孔隙率という構造形質を定量抽出しており、植物表現型取得手法が研究の中心である。
abstractUsing 3D reconstruction techniques, including LiDAR scanning and point cloud voxel processing, volumetric and optical porosity within the specified area were extracted for each stage.
To enhance crop yield, detecting leaf diseases has become a crucial research focus. Deep learning and computer vision excel in digital image processing. Various techniques grounded in deep learning have been utilized for detecting plant leaf diseases; however, achieving high accuracy remains a challenge. Basic convolutional neural networks (CNNs) in deep learning struggle with issues such as the abnormal orientation of images, rotation, and others, resulting in subpar performance. CNNs also need extensive data covering a wide range of variations to deliver strong performance. CapsNet is an innovative deep-learning architecture designed to address the limitations of CNNs. It performs well without needing a vast amount of data in various variations. CapsNets have their limitations, such as the encoder network considering every element in the image and the crowding issue. Due to this, they perform well on simple image recognition tasks but struggle with more complex images. To address these challenges, we introduced a new CapsNet model known as CCFM-CapsNet. This model incorporates CLAHE to reduce image noise and CDH to extract crucial features. Also, max-pooling and dropout layers are incorporated in the original CapsNet model for identifying and classifying diseases in apples, bananas, grapes, corn, mangoes, pepper, potatoes, rice, tomato and also for classifying fashion-MNIST and CIFAR-10 datasets. The proposed CCFM-CapsNet demonstrates significantly high validation accuracies, achieving 99.53%, 95.24%, 99.75%, 97.40%, 99.13%, 100%, 99.77%, 100%, 98.54%, 93.48%, and 82.34% with corresponding parameters in millions(M) 4.68M, 4.68M, 4.68M, 4.68M, 4.79M, 4.63M, 4.66M, 4.68M, 4.84M, 2.39M, and 4.84M for the datasets aforementioned respectively, outperforming the traditional CapsNet and other advanced CapsNet models. Consequently, the CCFM-CapsNet model can be utilized effectively as a smart tool for identifying plant diseases and also in achieving Sustainable Development Goal 2 (Zero Hunger), which aims to end global hunger by the year 2030.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習モデルを提案・評価しており、画像取得・分類手法が研究の中心であるため。
abstractTo address these challenges, we introduced a new CapsNet model known as CCFM-CapsNet.
Abstract The growth and productivity of banana crops are critically affected by micronutrient deficiencies, which are often difficult to detect at early stages. Lightweight deep learning models, optimized through neural architecture search (NAS) and attention mechanisms, are hypothesized to provide accurate and efficient classification of such deficiencies for real-time agricultural applications. In this study, multiple convolutional neural networks (CNNs) and mobile-friendly architectures, including ResNet50, VGG16, NASNetMobile, and MobileNet variants (V1, V2, V3), were evaluated using transfer learning on a curated banana leaf deficiency dataset. To improve robustness and prediction accuracy, modified classification layers and ensemble strategies–initially average ensembling and later a NAS-guided dynamic attention weighting mechanism were employed. This optimization resulted in a novel lightweight model, NASMobV2 (NASNetMobile + MobileNetV2), capable of both classifying nutrient deficiencies and assessing their severity levels. The proposed model achieved a validation accuracy of 98.57%, outperforming baseline and state-of-the-art counterparts in precision, recall, and F1 score. To improve generalization, banana crop diseases along with an additional Coffee crop dataset were included for evaluation. Finally, the practical utility of the model was demonstrated by deploying the trained system in both mobile and web applications, enabling farmers and agronomists to perform fast and accurate diagnostics directly in the field.
Why it matches plant phenotyping methodsバナナ葉画像から栄養欠乏と重症度を推定する軽量深層学習モデルを開発・評価し、実運用アプリにも展開しており、植物状態の取得・推定手法が中心である。
titleA neural architecture search optimized lightweight attention ensemble model for nutrient deficiency and severity assessment in diverse crop leaves.
Reproduction assets foundThe paper's banana leaf nutrient-deficiency image dataset is publicly available on Mendeley Data and was directly used for the phenotyping/classification measurements. No author analysis code or trained model checkpoints are explicitly deposited; the GitHub/Streamlit links are deployment apps rather than deposited codeDataset · publiclidation and editing in addition to overall supervision.
Funding
Open access funding provided by Vellore Institute of Technology. We thank our Management “Vellore Institute of Technology, Vellore” for open access funding support.
Data availability
An openly available repository (Mendeley dataset) was used to perform this study;(https://data.mendeley.com/datasets/7vpdrbdkd4/1), Request for any data or materials shall be addressed to the author(sudhakar.m2020@vitstudent.ac.in).
Declarations
Competing interests
The authors declare that they have no competing interests.
References
1.
Sherefu A Zewide I
Review paper on effect of micronutrients for crop production
J. Nutr. Food Process. 2021
10.31Open asset ↗Mendeley · 7vpdrbdkd4lines:1245-1307Plant phenotyping relevance match · UnverifiedCrossref · checked 6 Sept 2026
This paper presents a novel hybrid and hierarchical architecture of a Convolutional Neural Network (CNN), based on MobileNetV2 and Support Vector Machines (SVM) for the classification of crop diseases (SVMobileNetV2). The system feeds from multispectral images captured by Unmanned Aerial Vehicles (UAVs) alongside data from IoT nodes. The primary objective is to improve classification performance in terms of both accuracy and precision. This is achieved by integrating contemporary Deep Learning techniques, specifically different CNN models, a prevalent type of artificial neural network composed of multiple interconnected layers, tailored for the analysis of agricultural imagery. The initial layers are responsible for identifying basic visual features such as edges and contours, while deeper layers progressively extract more abstract and complex patterns, enabling the recognition of intricate shapes. In this study, different datasets of tropical crop images, in this case banana crops, were constructed to evaluate the performance and accuracy of CNNs in detecting diseases in the crops, supported by transfer learning. For this, multispectral images are used to create false-color images to discriminate disease through spectra related to the blue, green and red colors in addition to red edge and near-infrared. Moreover, we used IoT nodes to include environmental data related to the temperature and humidity of the environment and the soil. Machine Learning models were evaluated and fine-tuned using standard evaluation metrics. For classification, we used fundamental metrics such as accuracy, precision, and the confusion matrix; in this study was obtained a performance of up to 86.5% using current deep learning models and up to 98.5% accuracy using the proposed hybrid and hierarchical architecture (SVMobileNetV2). This represents a new paradigm to significantly improve classification using the proposed hybrid CNN-SVM architecture and UAV-based multispectral images.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から作物病害を分類するCNN-SVM手法を開発・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 paper presents a novel hybrid and hierarchical architecture of a Convolutional Neural Network (CNN), based on MobileNetV2 and Support Vector Machines (SVM) for the classification of crop diseases (SVMobileNetV2).
ABSTRACT: In order to detect vitamin deficiencies in banana crops, this project presents an advanced convolutional neural network (CNN) model that analyses leaf images. Proper nutrition is essential for optimal crop development and yield, and deficiencies in critical nutrients can have a detrimental impact on plant health and production. To address this problem, we have developed a bespoke CNN model that recognizes and classifies various nutrient deficits using detailed leaf pictures. The study used a sizable dataset of banana leaves with a variety of insufficiency indicators to train and evaluate the algorithm. The CNN architecture was carefully modified to enhance feature extraction and classification abilities and facilitate precise diagnosis of nutrient-related illnesses. The outcomes demonstrate the model's ability to distinguish between distinct nutrient deficiencies, which makes it a valuable tool for precision farming. This approach aims to improve nutrient management and crop health monitoring methods, highlighting the significant role that machine learning technology plays in advancing agricultural research and practices.
Why it matches plant phenotyping methodsバナナ葉画像から栄養欠乏という植物の状態をCNNで分類する手法を開発・評価しており、表現型取得・推定が研究の中心である。
abstractwe have developed a bespoke CNN model that recognizes and classifies various nutrient deficits using detailed leaf pictures
Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.
Why it matches plant phenotyping methodsバナナ苗の密度・個体数という植物状態を、UAV画像と改良Faster R-CNN、切り出し・再結合・重複除去により定量化する手法を開発・評価しており、フェノタイピング手法が中心である。
abstractconstructed a counting method based on the improved Faster R-CNN algorithm
Leaves are the key organs in photosynthesis and nutrient production, and leaf counting is an important indicator of banana plant health and growth rate. However, in complex orchard environments, leaves often overlap, the background is cluttered, and illumination varies, making accurate segmentation and detection challenging. To address these issues, we propose a lightweight banana leaf detection and counting method deployable on embedded devices, which integrates a space–depth-collaborative reasoning strategy with multi-scale feature enhancement to achieve efficient and precise leaf identification and counting. For complex background interference and occlusion, we design a multi-scale attention guided feature enhancement mechanism that employs a Mixed Local Channel Attention (MLCA) module and a Self-Ensembling Attention Mechanism (SEAM) to strengthen local salient feature representation, suppress background noise, and improve discriminability under occlusion. To mitigate feature drift caused by environmental changes, we introduce a task-aware dynamic scale adaptive detection head (DyHead) combined with multi-rate depthwise separable dilated convolutions (DWR_Conv) to enhance multi-scale contextual awareness and adaptive feature recognition. Furthermore, to tackle instance differentiation and counting under occlusion and overlap, we develop a detection-guided space–depth position modeling method that, based on object detection, effectively models the distribution of occluded instances through space–depth feature description, outlier removal, and adaptive clustering analysis. Experimental results demonstrate that our YOLOv8n MDSD model outperforms the baseline by 2.08% in mAP50-95, and achieves a mean absolute error (MAE) of 0.67 and a root mean square error (RMSE) of 1.01 in leaf counting, exhibiting excellent accuracy and robustness for automated banana leaf statistics.
Why it matches plant phenotyping methodsバナナ葉の検出・計数という植物形態・生育指標を対象に、複雑環境での画像解析手法を開発し、精度評価しているため、植物フェノタイピング手法が中心である。
abstractwe propose a lightweight banana leaf detection and counting method deployable on embedded devices
Accurate banana yield prediction is essential for optimizing agricultural management and ensuring food security in tropical regions, yet traditional estimation methods remain labor-intensive and error - prone. This study developed a predictive model for banana yield in Buena Fé, Ecuador, using Random Forest integrated with phenological data, soil properties, spectral technology, and UAV imagery. Data were collected from a 75.2 ha banana farm divided into 26 lots, combining multispectral drone imagery, soil physicochemical analyses, and banana agronomic measurements (height, diameter, bunch weight). A rigorous variable selection process identified six key predictors: NDVI, plant height, plant diameter, soil nitrogen, porosity, and slope. Three machine learning algorithms were compared through 5-fold cross-validation with systematic hyperparameter optimization. Random Forest demonstrated superior performance with R²=0.956 and RMSE=1164.9 kg ha⁻¹, representing only 2.79% of mean production. NDVI emerged as the most influential predictor (importance=0.212), followed by slope (0.184) and plant structural variables. Local sensitivity analysis revealed distinct response patterns between low and high production scenarios, with plant diameter showing greatest impact (+74.9 boxes ha-1) under limiting conditions, while NDVI dominated (-140.4 boxes ha-1) under optimal conditions. The model provides a robust tool for precision agriculture applications in tropical banana production systems.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と植物形態・生育データを統合し、収量という植物状態を推定する機械学習ワークフローが研究の中心であり、交差検証とアルゴリズム比較による技術評価も行っている。
abstractThis study developed a predictive model for banana yield in Buena Fé, Ecuador, using Random Forest integrated with phenological data, soil properties, spectral technology, and UAV imagery.
Diagnosis of banana plant disease is a crucial aspect of sustaining the harvest of crops and their quality. Visual inspection of certain diseases like Black Sigatoka, Panama disease, and aphids is not easy and can lead to misjudgments. Generally, traditional deep learning approaches have been previously used but they have not performed well in addressing issues of class imbalance, sensitive disease differentiation and noisy images obtained in the field. Furthermore, most models are based on a collection of predetermined preprocessing methods and single-path networks that limit their ability to generalize to a wide variety of environments. Current methods of deep learning tend to achieve reasonable overall performance but fail to perform well on key performance indicators such as recall and F1-score when considering underrepresented and overlapping classes, such as Yellow and Black Sigatoka. Such constraints impede efficient field implementation, as diseases of minority classes are often falsely classified. To overcome these deficiencies, we develop a novel Duel-Path Attention Fusion Network (DPAFNet) that is trained utilizing adaptive quantum monarch butterfly optimization (AQMBO). The concept behind the proposed model is to feed MaxViT and HorNet-S two feature extractors to deliver global contextual details and minute-scale textural features. The traditional filters which do a reasonable job in handling dynamic noise and contrast are replaced by a learnable preprocessing unit. The cross-layer fusion attention encourages interclass discriminative learning of diseased plants. The suggested model has been trained and tested on an open-source dataset of Mendeley banana disease, which includes 5,170 images in 7 disease categories and 1 control condition. The accuracy, F1-score and MCC of 98.6% and 0.93 and 0.87 respectively (achieved experimentally) demonstrate the superiority of DPAFNet over baseline models such as EfficientNetB0 (accuracy 95.0%), DenseNet121 and ResNet50 (accuracy 93.50% and 92.0% respectively). As can be seen, the model had a 0.26-0.48 increase in F1-score in the challenging Panama disease category. These results prove that the proposed architecture can be successfully used to achieve high-accuracy disease classification in smart agriculture that is robust and prepared for field implementation.
Why it matches plant phenotyping methodsバナナ植物の病徴画像から病害状態を推定する深層学習手法を開発し、データセットとベースラインで性能検証しており、植物フェノタイピング手法が中心である。
abstractTo overcome these deficiencies, we develop a novel Duel-Path Attention Fusion Network (DPAFNet) that is trained utilizing adaptive quantum monarch butterfly optimization (AQMBO).
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe suggested model has been trained and tested
on an open-source dataset of Mendeley banana disease, which includes 5,170 images in 7
disease categories and 1 control condition.Open asset ↗pdf-page:1 lines:1-55Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
Banana / plantainField / plotLiDAR / point cloudFruit2D/3D reconstructionSegmentation
Accurate 3D reconstruction provides essential spatial information for orchard robots and serves as a critical foundation for crop phenotypic analysis. However, most existing studies have focused on industrial scenarios and are typically conducted in interference-free indoor environments. In this study, we propose a novel 3D reconstruction method called BBNeuS, which achieves accurate reconstruction of banana bunches in real-world orchard conditions. To accurately separate banana bunches from orchards, this study proposes a multi-view extraction framework (MVExt), which alleviates occlusion and interference caused by the complex banana orchard environment by combining multiple views and point cloud projection. BBNeuS combines Signed Distance Field (SDF) supervision with bias consistency, which not only reduces deviations in volumetric rendering but also alleviates viewpoint discrepancies caused by unstable lighting conditions. We conducted segmentation experiments across various scenarios, with all evaluation metrics showing improvement, achieving up to a 12 % increase. In reconstruction experiments, the evaluation scores for PSNR, SSIM, and LPIPS reached 21.17, 0.89, and 0.27, respectively, representing improvements of 21.4 %, 20.3 %, and 20.6 % compared to baseline methods. The MAE metric was well balanced. These results demonstrate that BBNeuS can accurately extract banana-related information and significantly enhance the reconstruction of banana bunches. This research provides valuable insights for 3D phenotypic analysis of banana bunches and holds great significance for the development of intelligent banana orchards.
Why it matches plant phenotyping methodsバナナ房のセグメンテーションと3D再構成手法を開発・評価し、3D表現型解析への応用を明示しているため、植物表現型取得手法が中心である。
abstractwe propose a novel 3D reconstruction method called BBNeuS, which achieves accurate reconstruction of banana bunches in real-world orchard conditions.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 6 Sept 2026
Abstract The key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves. The performance of multiple pre-trained models is compared against a newly presented model.The experiments used a publicly released dataset of healthy and unhealthy leaves from banana, cherry, and tomato plants. This dataset was uniformly split into training, validation, and test sets to obtain consistent and unbiased model evaluations. The data pre-processing also involved pre-processing steps suitable for DL architectures to keep the input the same among all the models.We use several state-of-the-art pre-trained ConvNets models for the baselines, such as EfficientNetV2, ConvNeXt, Swin Transformer, and Vi-Transformer (ViT), to have an outlook on the performance. A new ConvNet-ViT hybrid model combines the ConvNet and ViT layers for local feature extraction and maintaining the global context. The classifier’s performance was reinforced by a 5-fold cross-validation mechanism to avoid overfitting.The proposed Hybrid ConvNet-ViT model outperformed all the compared models evaluated, achieving a testing classification accuracy of 99.29%, which outperforms all the pre-trained models. This finding shows that combining ConvNets’ local feature learning with the capability of global representation of the ViT is effective.The result shows that the Hybrid ConvNet-ViT model is an effective and accurate solution in detecting and classifying plant leaf diseases. Its outstanding performance of the state-of-the-art pre-trained top models positions itself as a solid model for practical agricultural use. Fusing the ConvNet and transformer frameworks jointly is beneficial for improving classification performance in image-based disease detection work.
Why it matches plant phenotyping methods植物葉の病徴を画像から検出・分類する深層学習手法の開発と比較検証が研究の中心であり、植物の病害状態を推定するフェノタイピング手法に該当する。
abstractThe key objective of this study is to propose an effective and accurate deep learning (DL) framework to detect and classify diseases in banana, cherry, and tomato leaves.
One of the most widely grown commercial commodities in India is the banana tree, which has important cultural and gastronomic significance in tropical and subtropical areas where banana leaves are widely used for food delivery and packaging in a variety of cultures. Regrettably, the incidence of diverse ailments that damage banana leaves present a significant risk to total output, therefore having an instant effect on the country’s economy. To meet this issue, more efficient monitoring systems must be put in place, and control techniques for early illness and pest detection must be developed. Using pest indicators makes this proactive strategy easier. With the successful use of these approaches in a variety of industries, recent advances in agricultural technology have seen the incorporation of deep convolutional neural networks (DCNN) for disease identification in numerous crops. This study’s main goal is to put into practice a DCNN that is especially designed to anticipate various illnesses and pest occurrences in banana leaves. Through the use of DCNN, farmers may get vital insights to apply fertilizers sparingly during the early phases, hence preventing the advent of leaf diseases. Remarkably, the suggested approach, which uses a convolutional neural network (CNN) for accurate banana leaf disease detection, exhibits an astounding 99% accuracy when compared to other deep learning techniques. By offering a reliable and precise technique for predicting pest and disease in banana crops, this study advances agricultural practices. The use of state-of-the-art technologies, like CNN and DCNN, highlights the potential revolutionary influence on disease control in banana farming, promoting increased yield and sustainable farming methods. Received: 12 August 2023 | Revised: 20 May 2024 | Accepted: 11 August 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement Data sharing is not applicable to this article as no new data were created or analyzed in this study. Author Contribution Statement N. R. Rajalakshmi: Conceptualization, Software, Investigation, Data curation, Writing - original draft. S. Saravanan: Conceptualization, Software, Investigation, Data curation, Writing - original draft. J. Arunpandian: Validation, Formal analysis. Sandeep Kumar Mathivanan: Methodology, Writing - review & editing, Supervision, Project administration. Prabhu Jayagopal: Software, Investigation, Resources. Saurav Mallik: Methodology, Writing - review & editing, Supervision, Project administration. Guimin Qin: Resources, Data curation, Visualization, Supervision, Project administration.
Why it matches plant phenotyping methodsバナナ葉の病害状態を画像からCNNで推定する手法が研究の中心であり、植物病害の表現型判定に該当する。
abstractThis study’s main goal is to put into practice a DCNN that is especially designed to anticipate various illnesses and pest occurrences in banana leaves.
In agricultural scenarios, the images obtained are often affected by factors such as weather and environmental conditions, which can introduce varying levels of noise to the images. This requires computer vision models to possess a degree of robustness. Generally, model with strong robustness comes with higher computational complexity and model size, which place greater demands on hardware computing resources during deployment. Hence, this research proposes PConv BiFormer (PCBT) based on the BiFormer architecture for the purpose of identifying crop diseases. Prior to inputting images into the network, an additional convolutional layer is introduced to use feature maps generated through convolutional operations as the model’s input. Furthermore, the Depthwise Convolution in BiFormer is replaced with Partial Convolution (PConv) to encode relative positional information. The Convolutional Gated Linear Unit was introduced as the model’s channel mixer to filter global information, aiming to enhance the model’s robustness. PCBT-small has classification accuracies of 0.998, 0.878, and 0.919 on three datasets with computational load of 2.16G FLOPs and had a parameter count of 10.20M. PCBT maintains accuracy of above 0.711 even when detecting photos with various random noise. Compared to Biformer, PCBT reduced the parameter count by 22.4%. Additionally, it achieved improvements in recognition accuracy on the noiseless validation sets for cucumber, banana, and grape by 3.2%, 10.8%, and 1.5%, respectively. On the validation sets with 0-200 random pixel noise, the recognition accuracy also increased by 13.2%, 7.8%, and 17.3%, respectively. When compared to other lightweight models mentioned in the experiments, such as MobileNet, EfficientNet, and MobileFormer, PCBT demonstrates superior robustness. Furthermore, in comparison to more robust models like Swin Transformer, ConvNeXtV2, and DeepViT, PCBT not only maintains excellent robustness but also has fewer model parameters and lower FLOPs. Our proposed model aligns better with the practical requirements of agricultural applications.
Why it matches plant phenotyping methods作物画像から病害を識別する新規Transformerモデルを開発し、複数データセットおよびノイズ条件で精度・頑健性を比較検証しており、植物病害状態の画像ベース表現型取得が中心である。
titlePartial convolutional biformer: A transformer architecture for diagnosing crop diseases under complex backgrounds
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.
ABSTRACT This study offers a sophisticated convolutional neural network (CNN) model that uses leaf image analysis to identify micronutrient deficits in banana crops. For the best crop growth and output, proper nutrition is necessary, and deficits in important nutrients can negatively affect the productivity and health of plants. In order to tackle this issue, we have created a customized CNN model that uses detailed leaf photos to identify and categorize different nutrient shortages. A large dataset of banana leaves displaying various deficiency signs was employed in the study to train and assess the model. In order to improve feature extraction and classification skills and enable accurate identification of nutrient-related disorders, the CNN architecture was meticulously adjusted. Keywords: Banana Crop Micronutrient Deficiency, Convolutional Neural Network (CNN), Leaf Image Analysis, Image-Based Nutrient Diagnosis, Deep Learning in Agriculture, Plant Nutrient Classification, Precision Agriculture, Agricultural Image Processing, CNN-Based Deficiency Detection, AI-Driven Crop Management
Why it matches plant phenotyping methodsバナナ葉画像から栄養欠乏という植物状態を推定するCNN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 study offers a sophisticated convolutional neural network (CNN) model that uses leaf image analysis to identify micronutrient deficits in banana crops.
Abstract Plant diseases initiate major agricultural challenges because they lead to 16\% of worldwide crop loss in farms. The vulnerability of bananas to diseases including Xanthomonas Wilt and Sigatoka leaf spot puts severe threats to food security because of their extensive damage potential. These diseases possess the risk of damage to the complete harvest so their impact can reach 100%. While deep learning models, particularly YOLO-based architecture, have demonstrated success in plant disease identification, key research gaps remain. One major challenge is the lack of large-scale, annotated datasets for banana leaf diseases, limiting the development and evaluation of robust AI models. Addressing these challenges is crucial, and this study aims to do so by creating a large dataset and developing a robust disease detection model. The dataset comprises more than 5000 samples categorized into three classes: Healthy, Xanthomonas Wilt infected, and Sigatoka leaf spot infected. This study examines a novel framework by employing advanced optimization techniques such as Krill Herd Optimization (KHO) for YOLOv8 and its variants. Our research findings highlight the exceptional performance of the KHO-YOLOv8 model, achieving an impressive accuracy of 96.47%.
Why it matches plant phenotyping methodsバナナ葉の病害状態を画像から分類するデータセット構築とYOLOv8検出モデル開発・評価が研究の中心であり、植物の病徴を直接推定するため対象範囲に該当する。
abstractthis study aims to do so by creating a large dataset and developing a robust disease detection model.
Reproduction assets foundThe paper states its banana leaf disease dataset (5,000 annotated images) and code are publicly available on GitHub, but no concrete repository URL or identifier is provided in the supplied text, and the only allowed URL is an unrelated cited reference. The claim of public availability is explicit, but the asset is notDataset · publicMore than 5000 banana leaf images were acquired from different areas of Arba Minch
Zuria. Five plant pathologists meticulously verified the image classifications twice to
maintain accuracy. Code and dataset is publicly available on GitHub.Open asset ↗GitHubpdf-page:3 lines:1-44Plant phenotyping relevance match · UnverifiedOpenAlex · checked 13 Sept 2026
Orchard yield estimation is one of the key indicators of precision agriculture. The traditional random sampling yield estimation method has strict requirements for the laborer experience and scale of orchards. Intelligent orchard management enables growers to use resources more effectively and make wiser decisions to optimize orchard inputs. This study proposes a banana bunch counting and yield estimation method based on the DeepSORT tracking algorithm. This method involves obtaining RGB-D images and calculating the weight of an individual bunch of bananas, which was promoted in our previous work. Building on this, the DeepSORT was used to solve the repeated counting based on the Hungarian algorithm and Kalman filtering. Three constraints were set to improve the statistical accuracy, and a yield estimation system was designed for orchard management monitoring. This system provides managers with bunch weight predictions and statistical plant information to achieve real-time yield estimations for banana orchards. The experimental results showed that the accuracy of the yield estimations reached 97.25% and that banana bunch counting had a success rate of 96.82%. This demonstrates that the effective integration of RGB-D technology and the DeepSORT algorithm can be successfully applied to the intelligent management and harvesting of banana orchards.
Why it matches plant phenotyping methodsRGB-D画像とDeepSORTによるバナナ房の計数・重量推定という、植物の収量形質を抽出する画像ベース手法が研究の中心であり、精度評価も実施しているため。
abstractThis study proposes a banana bunch counting and yield estimation method based on the DeepSORT tracking algorithm.
In deep learning, Semi-Supervised Learning is a highly effective technique to enhances neural network training by leveraging both labeled and unlabeled data. This process involves using a trained model to generate pseudo labels to the unlabeled samples, which are then incorporated to further train the original model, resulting in a new model. However, if these pseudo labels contain substantial errors, the resulting model's accuracy may drop, potentially falling below the performance of the initial model. To tackle the problem, we propose an Ambiguity-Aware Semi-Supervised Learning method for Leaf Disease Classification. Specifically, we present a per-disease ambiguity rejection algorithm that eliminates ambiguous results, thereby enhancing the precision of pseudo labels for the subsequent semi-supervised training step and improving the precision of the final classifier. The proposed method is evaluated on two public leaf disease datasets of coffee and banana across various data scenarios, including supervised and semi-supervised settings, with varying proportions of labeled data. The results indicate that our semi-supervised method reduces the reliance for fully labeled datasets while preserving high accuracy by utilizing the ambiguity rejection algorithm. Additionally, the rejection algorithm significantly boosts precision of final classifier on both coffee and banana datasets, achieving rates of 99.46% and 100.0%, respectively, while using only 50% labeled data. The study also presents a thorough set of experiments and analyses to validate the effectiveness of the proposed method, comparing its performance against state-of-the-art supervised approaches. The results demonstrate that our method, despite using only 50% of the labeled data, achieves competitive performance compared to fully supervised models that use 100% of the labeled data.
Why it matches plant phenotyping methods葉画像から植物病害状態を分類する半教師あり学習法と曖昧性除去アルゴリズムを開発し、コーヒー・バナナの公開データセットで評価しており、病害表現型の抽出手法が研究の中心である。
abstractwe propose an Ambiguity-Aware Semi-Supervised Learning method for Leaf Disease Classification.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 14 Sept 2026
Abstract Conventional techniques for identifying plant leaf diseases can be labor-intensive and complicated. This research uses artificial intelligence (AI) to propose an automated solution that improves plant disease detection accuracy to overcome the difficulty of the conventional methods. Our proposed method uses deep learning (DL) to extract features from photos of plant leaves and machine learning (ML) for further processing. To capture complex illness patterns, convolutional neural networks (CNNs) such as VGG19 and Inception v3 are utilized. Four distinct datasets—Banana Leaf, Custard Apple Leaf and Fruit, Fig Leaf, and Potato Leaf—were used in this investigation. The experimental results we received are as follows: for the Banana Leaf dataset, the combination of Inception v3 with SVM proved good with an Accuracy of 91.9%, Precision of 92.2%, Recall of 91.9%, F1 score of 91.6%, AUC of 99.6% and MCC of 90.4%, FFor the Custard Apple Leaf and Fruit dataset, the combination of VGG19 with kNN with an Accuracy of 99.1%, Precision of 99.1%, Recall of 99.1%, F1 score of 99.1%, AUC of 99.1%, and MCC of 99%, and for the Fig Leaf dataset with Accuracy of 86.5%, Precision of 86.5%, Recall of 86.5%, F1 score of 86.5%, AUC of 93.3%, and MCC of 72.2%. The Potato Leaf dataset displayed the best performance with Inception v3 + SVM by an Accuracy of 62.6%, Precision of 63%, Recall of 62.6%, F1 score of 62.1%, AUC of 89%, and MCC of 54.2%. Our findings explored the versatility of the amalgamation of ML and DL techniques while providing valuable references for practitioners seeking tailored solutions for specific plant diseases.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定する深層学習・機械学習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 uses artificial intelligence (AI) to propose an automated solution that improves plant disease detection accuracy
Banana is an important cash and food crop worldwide. Recent outbreaks of banana diseases are threatening the global banana industry and smallholder livelihoods. Remote sensing data offer the potential to detect the presence of disease, but there is a need for formal analysis to compare inferred with observed disease data. Here we use Landsat-8 data to investigate the detection of two banana diseases: banana bunchy top disease (BBTD) and Fusarium wilt Tropical Race 4 (TR4). We use satellite imagery to develop meteorology-driven predictive models for vegetation phenology, specifically based on healthy crops. Machine learning is then applied to identify anomalies associated with diseased plants by comparing the predicted vegetation indices of healthy crops with the observed indices from published data when disease is present. Our results show a correlation between changes in vegetation indices and the number of infected cases, highlighting the potential of this approach for large-scale disease surveillance.
Why it matches plant phenotyping methods衛星画像と機械学習により、バナナの病害に伴う植生指数の異常を推定する手法を開発・適用しており、感染植物の状態を大規模に評価することが中心です。
abstractWe use satellite imagery to develop meteorology-driven predictive models for vegetation phenology, specifically based on healthy crops.
Reproduction assets foundThe paper's Data Availability Statement explicitly deposits analysis code and data in a public GitHub repository by the authors, covering the remote-sensing/ML phenotyping analysis.Code · publicn, data collection and analysis, decision to publish, or preparation of the
manuscript. We thank Australian Banana Growers’ Council (ABGC) and banana inspectors for the
original data collecting.
Data Availability Statement: Analysis code and data used in this study can be accessed at the
following URL: (accessed on 3 July 2025) https://github.com/rretkute/BananaDiseasesRS.
Conflicts of Interest: The authors declare no conflicts of interest.
References
1. Voora, V.; Larrea, C.; Bermudez, S. Global Market Report: Bananas; International Institute for Sustainable Development:
Winnipeg, MB, Canada, 2020.
2. Ploetz, R.C. Management of Fusarium wilt of banana: A review with special reference to troOpen asset ↗rretkute/BananaDiseasesRS · rretkute/BananaDiseasesRSpdf-layout-page:15 lines:1-58Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Chlorophyll levels are a key indicator of plant nitrogen status, which plays a critical role in optimizing agricultural yields. This study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement. Measurements were taken from a diverse set of five leaf types, including smooth, uniform leaves (banana and mango), textured leaves (jasmine and sugarcane), and narrow leaves (rice). Partial least squares regression models were used to fit sensor spectra to chlorophyll levels, using nested cross-validation to ensure robust model evaluation. Sensor performance was assessed using R2 and mean absolute error (MAE) scores. The AS7265x demonstrated the best performance on smooth, uniform leaves with validation R2 scores of 0.96-0.95. Its performance decreased for the other leaves, with R2 scores of 0.75-0.85. The AS7262 and AS7263 sensors, while slightly less accurate, achieved reasonable R2 scores ranging from 0.93 to 0.86 for smooth leaves, and from 0.85 to 0.73 for the other leaves. All sensors, particularly the AS7265x, show potential for non-destructive chlorophyll measurement in agricultural applications. Their low cost and reasonable accuracy make them suitable for agricultural applications such as monitoring plant nitrogen levels.
Why it matches plant phenotyping methods低コストマルチスペクトルセンサーによる葉のクロロフィル測定法を評価・比較し、交差検証で性能を検証しているため、植物フェノタイピング手法が中心です。
abstractThis study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement.
Reproduction assets foundThe authors publicly release raw sensor data, analysis scripts, firmware, and GUI in the GitHub repository KyleLopin/asm_chloro_test, plus supplementary information including extracted chlorophyll reference measurements (S2) at the MDPI supplement URL.Code · publicRaw data, scripts to generate the data and figures used in the manuscript, programs to run the sensors, and GUI used to collect the data are available at https://github.com/KyleLopin/asm_chloro_test (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:187-200Code · publicThe microcontroller code to operate the sensor and a GUI for data collection are available at https://github.com/KyleLopin/asm_chloro_test/tree/master/source (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:155-167Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s25072198/s1 . Supplementary Information S1: Device Electrical Characterization. Supplementary Information S2: Extracted Chlorophyll Reference Measurements.Open asset ↗lines:176-186Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
In this work, we present a dataset of banana leaf imagery, both with and without diseases. The dataset consists of 11,767 images, categorized as follows: 3,339 healthy images, 3,496 images of leaves affected by Black Sigatoka and 4,932 images of leaves affected by Fusarium Wilt Race 1. This data was collected to support machine learning diagnostics for disease detection. The data collection process involved farmers, researchers, agricultural experts and plant pathologists from the northern and southern highland regions of Tanzania. To ensure unbiased representation, farms were randomly selected from the Rungwe, Mbeya, Arumeru, and Arusha districts, based on the presence of banana crops and the targeted diseases. The dataset offers a comprehensive collection of images captured from November 2022 to January 2023, using a high-resolution smartphone camera across a wide geographical area. Researchers and developers can use this dataset to build machine learning solutions that automatically detect diseases in images, potentially enabling agricultural stakeholders, including farmers, to diagnose Fusarium Wilt Race 1 and Black Sigatoka early and take timely action.
Why it matches plant phenotyping methodsバナナ葉の病徴画像を収録した再利用可能なデータセットで、植物の病害状態を画像から推定するフェノタイピング基盤として中心的です。
abstractwe present a dataset of banana leaf imagery, both with and without diseases.
Chlorophyll levels are a key indicator of plant nitrogen status, which plays a critical role in optimizing agricultural yields. This study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement. Measurements were taken from a diverse set of five leaf types, including smooth, uniform leaves (banana and mango), textured leaves (jasmine and sugarcane), and narrow leaves (rice). Partial Least Squares regression models were used to fit sensor spectra to chlorophyll levels, using nested cross-validation to ensure robust model evaluation. Sensor performance was assessed using R2 and mean absolution error (MAE) scores. The AS7265x demonstrated the best performance on smooth, uniform leaves with R2 scores of 0.96-0.95. Its performance decreased for the other leaves, with R2 scores of 0.75-0.85. The AS7262 and AS7263 sensors, while slightly less accurate, achieved reasonable R2 scores ranging from 0.93 to 0.86 for smooth leaves, and 0.85 to 0.73 for the other leaves. All sensors, particularly the AS7265x, show potential for non-destructive chlorophyll measurement in agricultural applications. Their low cost and reasonable accuracy make them suitable for agricultural applications such as monitoring plant nitrogen levels.
Why it matches plant phenotyping methods低コスト分光センサーによる葉のクロロフィル量推定法を比較評価し、交差検証と精度指標で性能を検証しているため、植物表現型取得法が中心です。
abstractThis study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement.
Radopholus similis severely damages banana roots causing significant yield losses. Field screening for resistance is labor intensive and inconsistent due to environmental variation and mixed nematode populations. The screenhouse offers a controlled environment but is limited by the time needed for root development and variation in plant growth. We developed and validated a high-throughput in vitro method for phenotyping banana resistance to R. similis using sand-Murashige and Skoog (MS) media. Tissue culture plantlets grown in sterilized sand-MS were inoculated with 50 female R. similis after root development and nematodes extracted eight weeks after inoculation to calculate the reproduction factor (RF). Although RF values were higher for in vitro than in the screenhouse, accession responses showed similar trends under both conditions. The in vitro method was rapid, cost-effective with higher throughput, accelerating phenotyping and enabling rapid assessment of banana accessions for breeding programs. Some accessions responded differently to the two methods indicating that additional methods, such as root necrosis scores are important to confirm resistance. This study is the first in vitro-based demonstration of phenotyping for nematode resistance using modified sand-MS media with improved root development and pathogen interactions.
Why it matches plant phenotyping methodsバナナの線虫抵抗性という植物状態を評価する高スループットin vitroフェノタイピング法を開発・検証し、既存のスクリーンハウス法と比較しているため、方法が研究の中心である。
abstractWe developed and validated a high-throughput in vitro method for phenotyping banana resistance to R. similis using sand-Murashige and Skoog (MS) media.
Reproduction assets foundThe paper's Data Availability statement explicitly deposits the plant entry list, raw datasets, and generated/analyzed datasets (Extended data) for this banana R. similis resistance phenotyping study on Figshare under CC-BY 4.0, matching an allowed URL. No author analysis code was deposited.Dataset · publicThe list of all plant entries, raw datasets, and datasets generated during and/or analyzed during the current study (Extended data) referred to in the manuscript text as supplementary materials are publicly available in Figshare: High-throughput resistance phenotyping of banana ( Musa spp.) against Radopholus similis . https://doi.org/10.6084/m9.figshare.28787480.v3 . The dataset has a CC-BY 4.0 license applied.Open asset ↗Figshare · 10.6084/m9.figshare.28787480.v3lines:139-144Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Bananas (Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease monitoring and detection are often time-consuming, labor-intensive, and prone to inaccuracies. This study introduces an AI-powered multiplatform georeferenced surveillance system designed to enhance the detection and management of banana wilt diseases. We developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms. Our results demonstrate the superior performance of YOLOv9 in detecting healthy, Fusarium Wilt and Xanthomonas Wilt diseased plants in aerial images, achieving high mAP@50, precision and recall metrics ranging from 55 to 86%. In terms of ground level images, we organized the dataset based on disease occurrence in Africa, Latin America, India, Asia and Australia. For this platform, YOLOv8 outperforms the rest and achieves mAP@50, precision and recall between 65 and 99% depending on the plant part and region. Additionally, we incorporated Explainable AI techniques, such as Gradient-weighted Class Activation Mapping, to enhance model transparency and trustworthiness. Human in the Loop Artificial Intelligence was also utilized to enhance the ground level model's predictions.
Why it matches plant phenotyping methodsバナナ個体の健全・萎凋病状態を航空・地上画像から推定する深層学習モデルを開発・評価しており、植物病害状態の画像ベースフェノタイピング手法が中心です。
abstractWe developed and evaluated several deep learning foundation models, including YOLO-NAS, YOLOv8, YOLOv9, and Faster-RCNN to perform accurate disease detection on both platforms.
Bananas are the most commonly eaten and significant fruit in global trade. Bananas are produced using a variety of methods and environments. In addition to regularly updating farmers on problems in banana plant leaves, this system aims to discover, diagnose, and treat banana leaf diseases. Customers' wants and lifestyles have changed significantly during the past few decades. These modifications provide additional difficulties for farmers whose output must satisfy consumer needs. Both the farmer and the consumer will benefit from the capacity to categorize agricultural products according to size and quality. In this case, the system gets its input in the form of standard photos of banana leaves taken using various image capture devices. It will then process those photos to identify any diseases and alert the farmer. Additionally, the system will advise the farmer on what to do next, including which fertilizers, herbicides, and agricultural practices to employ in order to prevent illnesses from harming neighboring crops. In this systematic review, useful and efficient methods for identification are presented in works that fall under the categories of image classification, AI/ML, deep learning, and mobile applications.
Why it matches plant phenotyping methodsバナナ葉の画像から病害を識別する画像分類・AI/ML・深層学習手法を体系的にレビューしており、植物の病害状態を観測するフェノタイピング手法が中心です。
abstractthis system aims to discover, diagnose, and treat banana leaf diseases
ABSTRACT Implementation of context‐specific solutions, including cultivation of varieties adapted to current and future climatic conditions, have been found to be effective in establishing resilient, climate‐smart agricultural systems. Gene banks play a pivotal role in this. However, a large fraction of the collections remains neither genotyped nor phenotyped. Hypothesizing that significant genotypic diversity in Musa temperature responses exists, this study aimed to assess the diversity in the world's largest banana gene bank in terms of base temperature ( T base ) and to evaluate its impact on plant performance in the East African highlands during a projected climate scenario. One hundred and sixteen gene bank accessions were evaluated in the BananaTainer, a tailor‐made high throughput phenotyping installation. Plant growth was quantified in response to temperature and genotype‐specific T base were modelled. Growth responses of two genotypes were validated under greenhouse conditions, and gas exchange capacity measurements were made. The model confirmed genotype‐specific T base , with 30% of the accessions showing a T base below the reference of 14°C. The Mutika/Lujugira subgroup, endemic to the East African highlands, appeared to display a low T base , although within subgroup diversity was revealed. Greenhouse validation further showed low temperature sensitivity/tolerance to be related to the photosynthetic capacity. This study, therefore, significantly advances the debate of within species diversity in temperature growth responses, while at the same time unlocking the power of gene banks. Moreover, with this case study on banana, we provide a high throughput method to reveal the existing genotypic diversity in temperature responses, paving the way for future research to establish climate‐smart varieties.
Why it matches plant phenotyping methodsバナナ遺伝資源の温度応答を高スループットに定量化し、遺伝子型別の基底温度をモデル化・温室で検証しており、表現型取得法と検証が研究の中心です。
abstractOne hundred and sixteen gene bank accessions were evaluated in the BananaTainer, a tailor‐made high throughput phenotyping installation.
Crop productivity and food security in modern agriculture are largely dependent on efficient monitoring and early identification of plant diseases. With a focus on the identification of leaf diseases, this study investigates the use of machine learning algorithms in the context of plant health monitoring. To extract pertinent characteristics from photos of plant leaves, the study makes use of sophisticated image processing techniques. This results in a large dataset that can be used to train and assess machine learning models. The proposed approach makes use of a comparison study to assess how well different machine learning algorithms recognise and categorise distinct leaf disease kinds. High-resolution photos of plant leaves displaying disease signs are collected as part of the approach, which also include preprocessing the data to improve feature extraction and utilising labelled datasets to train machine learning models. After that, the models are evaluated on hypothetical data to see how well they generalise and perform in actual situations. In this paper an Ensembled CNN Leaf Disease Detection Classifier (ECNNLDD) used to predict the healthiness of a leaf which classifies whether is healthy or unhealthy which is a 2-class problem and compared with the existing classifier like Decision Tree and SVM algorithm in which the proposed classifier Ensembled CNN Leaf Disease Detection Classifier outperformed when compared with the existing classifiers. The findings of proposed classifier gave the best accuracy in agriculture by offering an intelligent and automated solution for early detection and diagnosis of plant leaf diseases. The integration of Deep learning into plant health monitoring systems holds the potential to revolutionize farming practices, enabling farmers to adopt timely and targeted interventions, thereby minimizing crop losses, and promoting sustainable agriculture.
Why it matches plant phenotyping methods植物葉の病徴を画像から抽出し、深層学習で健康状態・疾病を分類する手法が研究の中心であるため、植物表現型計測法として含める。
titleA Novel Classifier for Plant Health Monitoring: A Focus on Banana Leaf Disease Detection Using Deep Learning
Accurately assessing weevil damage is critical when evaluating banana germplasm to identify genotypes resistant to the banana weevil (Cosmopolites sordidus), for use as elite parents in the banana breeding pipeline or evaluating breeding products. Visual observation remains the most common phenotyping approach but limited by individual bias. This study investigated the potential of image analyses as precise and objective alternatives for assessing weevil damage on the banana corm. Phenotyping trials were set up as partially replicated (P-rep) designs with 22 tissue culture-generated genotypes raised in pots and infested with banana weevils. At termination, the percentage score of weevil damage to the corms was evaluated by visual observation and image analysis using ImageJ and machine learning. In total, 370 high-quality images were assessed for weevil damage using ImageJ and machine learning. On average, damage scores from visual observation were 5.52% and 3.88% higher than ImageJ and machine learning respectively. There was a proportional trend with visual observation agreeing closely to image analyses for smaller scores and but less for larger scores. The results show that both ImageJ and machine learning exhibited a strong level of agreement and are interchangeable with consistent, reliable, and repeatable measurements. In conclusion, to avoid individual bias and subjectivity arising from visual observation, we recommend the use of either ImageJ or machine learning when scoring weevil damage in the banana corm.
Why it matches plant phenotyping methodsバナナ果茎のゾウムシ被害という植物状態を、画像解析と機械学習で定量化し、目視法との一致度・再現性を検証した方法研究である。
abstractThis study investigated the potential of image analyses as precise and objective alternatives for assessing weevil damage on the banana corm.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Abstract Implementation of context-specific solutions, including cultivation of varieties adapted to current and future climatic conditions, were found to be effective in establishing resilient, climate-smart agricultural systems. Gene banks play a pivotal role in this. However, a large fraction of the collections remains neither genotyped nor phenotyped. Hypothesising that significant genotypic diversity in Musa temperature responses exists, this study aimed to assess the diversity in the world’s largest banana gene bank in terms of base temperature (T base ) and to evaluate its impact on plant performance in the East African highlands during a projected climate scenario. 116 gene bank accessions were evaluated in the BananaTainer, a tailor-made high throughput phenotyping installation. Plant growth was quantified in response to temperature and genotype-specific T base were modelled. Growth response of two genotypes was validated under greenhouse conditions, and gas exchange capacity measurements were made. The model revealed genotype-specific T base , with 30 % of the accessions showing a T base below the reference of 14 °C. The Mutika/Lujugira subgroup, endemic to the East African highlands, appeared to display a low T base , although within subgroup diversity was revealed. Greenhouse validation further showed low T sensitivity/tolerance to be related to the photosynthetic capacity. This study, therefore, significantly advances the debate of within species diversity in temperature growth responses, while at the same time unlocking the power of gene banks. Moreover, we provide a high throughput method to reveal the existing genotypic diversity in temperature responses, paving the way for future research to establish climate-smart varieties.
Why it matches plant phenotyping methodsバナナの温度応答と成長を定量化する高スループット表現型計測設備・手法を提示し、温室条件で検証しているため、表現型取得法が中心的です。
abstract116 gene bank accessions were evaluated in the BananaTainer, a tailor-made high throughput phenotyping installation.
Modern, automated quality control systems for speciality crops utilise computer vision together with a machine learning paradigm exploiting large datasets for learning efficient crop assessment components. To model anomalous visuals, data augmentation methods are often developed as a simple yet powerful tool for manipulating readily available normal samples. State-of-the-art augmentation methods embed arbitrary “structural” peculiarities in normal images to build a classifier of these artefacts (i.e., pretext task), enabling self-supervised representation learning of visual signals for anomaly detection (i.e., downstream task). In this paper, however, we argue that learning such structure-sensitive representations may be suboptimal for agricultural anomalies (e.g., unhealthy crops) that could be better recognised by a different type of visual element like “colour”. To be specific, we propose Channel Randomisation (CH-Rand)—a novel data augmentation method that forces deep neural networks to learn effective encoding of “colour irregularities” under self-supervision whilst performing a pretext task to discriminate channel-randomised images. Extensive experiments are performed across various types of speciality crops (apples, strawberries, oranges, and bananas) to validate the informativeness of learnt representations in detecting anomalous instances. Our results demonstrate that CH-Rand’s representations are significantly more reliable and robust, outperforming state-of-the-art methods (e.g., CutPaste) that learn structural representations by over 43% in Area Under the Precision–Recall Curve (AUC–PR), particularly for strawberries. Additional experiments suggest that adopting the L∗a∗b∗ colour space and “curriculum” learning in the pretext task — gradually disregarding channel combinations for unrealistic outcomes — further improves downstream-task performance by 16% in AUC–PR. In particular, our experiments employ Riseholme-2021, a novel speciality crop dataset consisting of 3.5K real strawberry images gathered in situ from the real farm, along with the Fresh & Stale public dataset. All our code and datasets are made publicly available online to ensure reproducibility and encourage further research in agricultural technologies.
Why it matches plant phenotyping methods作物画像から異常・不健全状態を検出する画像解析手法を開発し、複数作物で検証しているため、植物状態の取得・推定が研究の中心である。
abstractwe propose Channel Randomisation (CH-Rand)—a novel data augmentation method that forces deep neural networks to learn effective encoding of “colour irregularities” under self-supervision
Most of the work done in image processing-based crop disease detection focuses on images with plain background. This paper presents a technique for crop disease detection for complex real field background images. A segmentation technique is presented to extract leaf patches from the entire image. Transform domain cepstral analysis is proposed for obtaining cepstral coefficients, to attain two level classifications. The first level classifies the crop species while the second level classifies the species into healthy leaf or leaf with specific type of disease. The work is tested on three crops Banana, Soybean and Grape and is checked on plain background laboratory images and on complex real field images. Suggested technique give species level accuracy of 94.33 %, 94.11 % and 98.44 % and disease level average accuracy of 97.75 %, 96.66 % and 97.95 % for Banana, Soybean and Grape, respectively. Comparison with standard features like texture and shape indicate that the presented technique gives the best results for both plain and complex background images suggesting its utilization in crop disease detection to reduce the agricultural and economic losses.
Why it matches plant phenotyping methods複雑な野外画像から葉を抽出し、画像特徴により健全葉と病害葉を分類する手法が中心で、植物の病害状態を直接推定している。
abstractThis paper presents a technique for crop disease detection for complex real field background images.
Published20 Oct 20242024 IEEE International Conference on Automation/XXVI Congress of the Chilean Association of Automatic Control (ICA-ACCA)Cited by 2 · OpenAlex ↗
The banana is a crucial crop in tropical regions, facing challenges from diseases such as black Sigatoka, which affect its production and quality due to defoliation. This research proposes the use of drones equipped with high-resolution RGB cameras to capture images of banana plantations, employing a hybrid deep learning model that combines detection and semantic segmentation to accurately identify and count banana leaves. Additionally, the metadata from the images provided the geographical coordinates of each plant, exported in shapefiles compatible with Geographic Information Systems (GIS). The results show high accuracy in detection (98.5%) and leaf counting (93.45%), surpassing previous, more costly methods. This facilitates the identification of areas affected by diseases, evidenced in the detection of potential black Sigatoka outbreaks. The ability to make informed decisions based on this data improves agricultural management, promoting sustainable practices and optimizing crop quality and productivity.
Why it matches plant phenotyping methodsドローン画像と深層学習・セマンティックセグメンテーションによりバナナ葉を検出・計数する手法が中心で、葉数という植物形質を定量化しているため。
abstractemploying a hybrid deep learning model that combines detection and semantic segmentation to accurately identify and count banana leaves
Banana leaf diseases pose significant challenges to agriculture, reducing crop yields and threatening food security. Traditional manual disease detection is often labor-intensive and prone to errors. This study proposes an ensemble deep learning classifier, RF-XG-ResNet50, for automated banana leaf disease identification, combining multiple deep learning architectures to improve accuracy. Data augmentation was employed to enhance training and generalization. The proposed classifier, known as RF- XG-ResNet50 Model, is proposed and compared with two base classifiers like Decision Tree and SVM models, to discriminate between healthy and sick leaves. By automating the disease detection process, this model can potentially reduce human error, save time, and provide realtime insights to farmers, contributing to improved crop management and sustainability. Finally, the proposed classifier gave the highest accuracy of 95% when compared with DT and SVM.
Why it matches plant phenotyping methodsバナナ葉の病徴を画像から分類する深層学習手法を開発・比較しており、植物の病害状態推定が研究の中心であるため。
abstractThis study proposes an ensemble deep learning classifier, RF-XG-ResNet50, for automated banana leaf disease identification, combining multiple deep learning architectures to improve accuracy.
In this research, plant pathogens are considered as big data because of the numerical counts for high intensity pixels in the images. The research presents an automated approach for early detection of plant diseases using image processing techniques. By analyzing the color features of leaf areas, the k-means algorithm for color segmentation and the Gray-Level Co-Occurrence Matrix (GLCM) are used for disease classification. A novelty of this research is that it illustrates four categories of plants to analyze and compare: (1.) Grain, represented by Rice Plant Leaf Data; (2.) Fruit, represented by banana plant leaf data, (3.) Flower, represented by sunflower plant leaf data; and (4.) Vegetable, represented by potato plant leaf data. Six stages of image processing are applied to real data for diseases of leaf smut for rice, black sigatoka for banana, leaf scars for sunflower, and late blight for potato. Finally, a comparison of the image processing for each of the four plant types, conclusions, and future research directions are presented.
Why it matches plant phenotyping methods葉画像から植物病害を自動検出・分類する画像処理手法が研究の中心であり、植物の病害状態を直接推定しているため、植物フェノタイピング手法として含める。
abstractThe research presents an automated approach for early detection of plant diseases using image processing techniques.
One of the most popular fruits worldwide is the banana. Accurate identification and categorization of banana diseases is essential for maintaining global fruits security and stakeholder profitability. Four different types of banana leaves exist Healthy, Cordana, Sigatoka, and Pestalotiopsis. These types can be analyzed using four types of vision: RGB, night vision, infrared vision, and thermal vision. This paper presents an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision. Each vision consisted of 1600 images with a size of (224 × 224). The training-testing approach was used to evaluate the performance of the hybrid model on Kaggle dataset, which was justified by various methods and metrics. The proposed model achieved a remarkable mean accuracy rate of 99.16% for RGB vision, 98.02% for night vision, 96.05% for infrared vision, and 96.10% for thermal vision for training and testing data. Microscopy employed in this research as a validation tool. The microscopic examination of leaves confirmed the presence and extent of the disease, providing ground truth data to validate and refine the proposed model. RESEARCH HIGHLIGHTS: The model can be helpful for internet of things -based drones to identify the large scale of banana leaf-disease detection using drones for images acquisition. Proposed an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision. The model detected banana leaf disease with a 99.16% accuracy rate for RGB vision, 98.02% accuracy rate for night vision, 96.05% accuracy rate for infrared vision, and 96.10% accuracy rate for thermal vision The model will provide a facility for early disease detection which minimizes crop loss, enhances crop quality, timely decision making, cost saving, risk mitigation, technology adoption, and helps in increasing the yield.
Why it matches plant phenotyping methodsバナナ葉の病徴・病害状態を画像から分類する深層学習モデルを開発し、複数の撮像方式と顕微鏡による検証を含むため、植物病害フェノタイピング手法が中心である。
abstractThis paper presents an intelligent deep augmented learning model composed of VGG19 and passive aggressive classifier (PAC) to classify the four diseases types of bananas under each type of vision.
In India, one of the primary agricultural practices is the production of bananas. A prevalent issue in farming is that the crop has been impacted by multiple illnesses. Disease identification in bananas has been shown to be more difficult in the field because the fruit is prone to various diseases and causes farmers to suffer significant losses. Consequently, this study aimed at developing an automatic system for the early detection and classification of banana plant diseases using deep learning. Three pre-trained convolutional neural network models MobileNet, VGG16, and InceptionV3 are used to classify banana disease images. The banana disease images dataset from the PSFD-Musa Dataset is utilized for training, validation, and testing. The proposed system is developed and checked to classify banana plant disease photographs into one of seven categories. The MobileNet achieved an accuracy of 96.72%, VGG16 an accuracy of 55.68%, and InceptionV3 an accuracy of 63.65%.
Why it matches plant phenotyping methodsバナナ葉などの病徴画像から植物の病害状態を分類する深層学習システムの開発・評価が中心であり、植物表現型（病害状態）の画像ベース取得・推定に該当する。
abstractthis study aimed at developing an automatic system for the early detection and classification of banana plant diseases using deep learning.
Plant leaf diseases are a significant concern in agriculture due to their detrimental impact on crop productivity and food security. Effective disease management depends on the early and accurate detection and diagnosis of these conditions, facilitating timely intervention and mitigation strategies. In this study, we address the pressing need for accurate and efficient methods for detecting leaf diseases by introducing a new architecture called DenseNet201Plus. DenseNet201 was modified by including superior data augmentation and pre-processing techniques, an attention-based transition mechanism, multiple attention modules, and dense blocks. These modifications enhance the robustness and accuracy of the proposed DenseNet201Plus model in diagnosing diseases related to plant leaves. The proposed architecture was trained using two distinct datasets: Banana Leaf Disease and Black Gram Leaf Disease. Through extensive experimentation, we evaluated the performance of DenseNet201Plus in terms of various classification metrics and achieved values of 0.9012, 0.9012, 0.9012, and 0.9716 for accuracy, precision, recall, and AUC for the banana leaf disease dataset, respectively. Similarly, the black gram leaf disease dataset model provides values of 0.9950, 0.9950, 0.9950, and 1.0 for accuracy, precision, recall, and AUC. Compared to other well-known pre-trained convolutional neural network (CNN) architectures, our proposed model demonstrates superior performance in both utilized datasets. Last but not least, we combined the strength of Grad-CAM++ with our proposed model to enhance the interpretability and localization of disease areas, providing valuable insights for agricultural practitioners and researchers to make informed decisions and optimize disease management strategies.
Why it matches plant phenotyping methods植物葉の病害領域を画像から分類・局在化する深層学習手法を開発し、複数データセットで性能評価しているため、病害状態のフェノタイピング手法が中心です。
abstractwe address the pressing need for accurate and efficient methods for detecting leaf diseases by introducing a new architecture called DenseNet201Plus.
Reproduction assets foundThe paper used two publicly available Mendeley leaf image datasets (banana leaf disease and black gram leaf disease) as its phenotyping inputs; both have explicit public URLs in the Data availability statement. No author analysis code or trained model was deposited.Dataset · public+: generalized gradient-based visual explanations for deep convolutional networks
2018 IEEE Winter Conference on Applications of Computer Vision (WACV) 2018
IEEE
839 847
Data availability
This study utilized publicly accessible datasets for analysis, which can be accessed at the following links: the Banana leaf disease dataset: https://data.mendeley.com/datasets/rjykr62kdh/1
[30] and the Black Gram leaf disease dataset: https://data.mendeley.com/datasets/zfcv9fmrgv/3
[31] .Open asset ↗lines:560-673Dataset · publicnter Conference on Applications of Computer Vision (WACV) 2018
IEEE
839 847
Data availability
This study utilized publicly accessible datasets for analysis, which can be accessed at the following links: the Banana leaf disease dataset: https://data.mendeley.com/datasets/rjykr62kdh/1
[30] and the Black Gram leaf disease dataset: https://data.mendeley.com/datasets/zfcv9fmrgv/3
[31] .Open asset ↗lines:560-673Plant phenotyping relevance match · UnverifiedCrossref · checked 7 Sept 2026
Abstract Bananas and plantains are vital for food security and smallholder livelihoods in Africa, but diseases pose a significant threat. Traditional disease surveillance methods, like field visits, lack accuracy, especially for specific diseases like Xanthomonas wilt of banana (BXW). To address this, the present study develops a Deep-Learning system to detect BXW-affected stems in mixed-complex landscapes within the Eastern Democratic Republic of Congo. RGB (Red, Green, Blue) and multispectral (MS) images from unmanned aerial vehicles UAVs were utilized using pansharpening algorithms for improved data fusion. Using transfer learning, two deep-learning model architectures were used and compared in our study to determine which offers better detection capabilities. A single-stage model, Yolo-V8, and the second, a two-stage model, Faster R-CNN, were both employed. The developed system achieves remarkable precision, recall, and F1 scores ranging between 75 and 99% for detecting healthy and BXW-infected stems. Notably, the RGB and PAN UAV images perform exceptionally well, while MS images suffer due to the lower spatial resolution. Nevertheless, specific vegetation indexes showed promising performance detecting healthy banana stems across larger areas. This research underscores the potential of UAV images and Deep Learning models for crop health assessment, specifically for BXW in complex African systems. This cutting-edge deep-learning approach can revolutionize agricultural practices, bolster African food security, and help farmers with early disease management. The study’s novelty lies in its Deep-Learning algorithm development, approach with recent architectures (Yolo-V8, 2023), and assessment using real-world data, further advancing crop-health assessment through UAV imagery and deep-learning techniques.
Why it matches plant phenotyping methodsUAV画像と深層学習により、バナナの健全・感染茎および植物病害状態を直接推定する手法を開発・比較・評価しており、フェノタイピング手法が中心である。
abstractthe present study develops a Deep-Learning system to detect BXW-affected stems in mixed-complex landscapes within the Eastern Democratic Republic of Congo.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. Hence, continuous health monitoring of plant is very crucial for handling plant stress. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. Furthermore, a GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called P lant N utrition Deficiency and D isease Net work (PND-Net), has been evaluated on two public datasets for nutrition deficiency, and two for disease classification using four backbone CNNs. The best classification performances of the proposed PND-Net are as follows: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40 $$\times $$ × : 95.50%, and BreakHis 100 $$\times $$ × : 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, the proposed method has been evaluated using five-fold cross validation and achieved improved performances on these datasets. Clearly, the proposed PND-Net effectively boosts the performances of automated health analysis of various plants in real and intricate field environments, implying PND-Net’s aptness for agricultural growth as well as human cancer classification.
Why it matches plant phenotyping methods葉の視覚症状から植物の栄養欠乏・病害状態を分類する画像解析手法を新規開発し、複数データセットで評価しており、植物フェノタイピング手法が中心である。
abstractThis article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN).
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 7 Sept 2026
Abstract The Indian economy is greatly influenced by the Banana Industry, necessitating advancements in agricultural farming. Recent research emphasizes the imperative nature of addressing diseases that impact Banana Plants, with a particular focus on early detection to safeguard production. The urgency of early identification is underscored by the fact that diseases predominantly affect banana plant leaves. Automated systems that integrate machine learning and deep learning algorithms have proven to be effective in predicting diseases. This manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies. The study makes a contribution by proposing two approaches for improved performance and suggesting future research directions. In summary, the objective is to advance understanding and stimulate progress in the prediction and detection of diseases in banana leaves. The need for enhanced disease identification processes is highlighted by the results of the survey. Existing models face a challenge due to their lack of rotation and scale invariance. While algorithms such as random forest and decision trees are less affected, initially convolutional neural networks (CNNs) is considered for disease prediction. Though the Convolutional Neural Network models demonstrated impressive accuracy in many research but it lacks in invariance to scale and rotation. Moreover, it is observed that due its inherent design it cannot be combined with feature extraction methods to identify the banana leaf diseases. Due to this reason two alternative models that combine ANN with scale-invariant Feature transform (SIFT) model or histogram of oriented gradients (HOG) combined with local binary patterns (LBP) model are suggested. The first model ANN with SIFT identify the disease by using the activation functions to process the features extracted by the SIFT by distinguishing the complex patterns. The second integrate the combined features of HOG and LBP to identify the disease thus by representing the local pattern and gradients in an image. This paves a way for the ANN to learn and identify the banana leaf disease. Moving forward, exploring datasets in video formats for disease detection in banana leaves through tailored machine learning algorithms presents a promising avenue for research.
Why it matches plant phenotyping methodsバナナ葉の病害状態を画像から機械学習で検出・分類する手法が研究の中心であり、植物病害表現型の取得・推定に直接該当する。
abstractThis manuscript examines the prediction and detection of diseases in banana leaves, exploring various diseases, machine learning algorithms, and methodologies.
Abstract This study focuses on the vital task of detecting Banana Black Sigatoka in banana plants using a cutting-edge method that combines deep learning algorithms with Unmanned Aerial Vehicles (UAVs). The research includes building a detailed dataset that features images of both healthy and infected banana plants. A variety of deep learning algorithms, such as convolutional neural networks and residual networks, are thoroughly tested to select the most effective model for analyzing this dataset. The selected algorithm is then integrated into a UAV-based system for the real-time detection of Black Sigatoka within banana plantations. This proactive strategy allows for the quick detection and localization of affected plants, making it possible to intervene promptly and improve overall crop management. The proposed method marks a significant step forward in using technology for precision agriculture, aiming to enhance the resilience and productivity of banana farming.
Why it matches plant phenotyping methodsバナナ葉の病害状態を画像から推定する深層学習モデルを開発・比較し、UAVシステムへ統合して実地検出する研究であり、植物病害フェノタイプの取得手法が中心です。
abstractdetecting Banana Black Sigatoka in banana plants using a cutting-edge method that combines deep learning algorithms with Unmanned Aerial Vehicles (UAVs)
Precise detection and localization are prerequisites for intelligent harvesting, while fruit size and weight estimation are key to intelligent orchard management. In commercial banana orchards, it is necessary to manage the growth and weight of banana bunches so that they can be harvested in time and prepared for transportation according to their different maturity levels. In this study, in order to reduce management costs and labor dependence, and obtain non-destructive weight estimation, we propose a method for localizing and estimating banana bunches using RGB-D images. First, the color image is detected through the YOLO-Banana neural network to obtain two-dimensional information about the banana bunches and stalks. Then, the three-dimensional coordinates of the central point of the banana stalk are calculated according to the depth information, and the banana bunch size is obtained based on the depth information of the central point. Finally, the effective pixel ratio of the banana bunch is presented, and the banana bunch weight estimation model is statistically analyzed. Thus, the weight estimation of the banana bunch is obtained through the bunch size and the effective pixel ratio. The R2 value between the estimated weight and the actual measured value is 0.8947, the RMSE is 1.4102 kg, and the average localization error of the central point of the banana stalk is 22.875 mm. The results show that the proposed method can provide bunch size and weight estimation for the intelligent management of banana orchards, along with localization information for banana-harvesting robots.
Why it matches plant phenotyping methodsRGB-D画像からバナナ房のサイズと重量という植物器官形質を推定する手法を開発・評価しており、収穫ロボット用の局在化にとどまらないため。
abstractwe propose a method for localizing and estimating banana bunches using RGB-D images
Volatile organic compounds (VOCs) released from plants have been correlated with disease-status. Analysis of VOCs using GC-MS is time-consuming, laboratory-based, and requires specialist training. Electronic nose devices (E-nose) provide a portable alternative. Three different E-nose devices were compared to assess how accurately they could detect Mango Twig Tip Dieback and Panama disease in banana. The devices were initially trained on known volatiles, then pure cultures of Pantoea sp., Staphylococcus sp., and Fusarium odoratissimum, and finally, on infected and healthy mango leaves and field-collected, infected banana pseudo-stems. The experiments were repeated three times with six replicates for each host-pathogen pair. The variation between healthy and infected host materials was evaluated by principal component and linear discriminant analysis, cross-validation and chemometric data analysis. GC-MS analysis was conducted contemporaneously and identified an 80% similarity between healthy and infected plant material. The portable C 320 was 100% successful in discriminating known volatiles but had a low capability in differentiating healthy and infected plant substrates. The advanced devices (PEN 3 / MSEM 160) successfully detected healthy and diseased samples with a high variance. The results suggest that E-nose devices are more sensitive and accurate in detecting changes of VOCs between healthy and infected plants compared to headspace GC-MS.
Why it matches plant phenotyping methods植物の健全・感染状態をVOCsで識別する電子鼻センサー手法を比較・評価し、交差検証とケモメトリクスで性能を検証しているため、植物フェノタイピング手法が中心である。
abstractThree different E-nose devices were compared to assess how accurately they could detect Mango Twig Tip Dieback and Panama disease in banana.
Abstract Due to widespread usage of banana as a staple food crop and susceptibility to numerous illnesses. Bananas require sophisticated detection techniques to support sustainable agricultural practices. Bananas are particularly susceptible to various stem and leaf spot diseases, resulting in significant economic losses within the banana cultivation sector. In this paper, a new XAI framework for banana disease detection and classification is introduced. Our framework uses state-of-the-art AI methods to analyze photos of banana plants. With great precision, it can detect a variety of illnesses like Cordana, Black Sigatoka, Pestalotiopsis, and fusarium wilt. The outcomes show that the framework performs better than current techniques in precisely identifying and categorizing banana diseases. The research employed a Convolutional Neural Networks (CNNs) to detect diseases in banana plants using RGB images of banana leaves. We used pre-trained model called EfficientnetB0 model to evaluate using two datasets BLSD and BDT. For BLSD, the model achieved an accuracy of 99.22%. Next for BDT, on the other hand, demonstrated improved performance with an accuracy of 99.63%.
Why it matches plant phenotyping methodsバナナ葉のRGB画像から病害を検出・分類するCNN/XAIフレームワークが研究の中心であり、植物の病害状態を画像から推定するフェノタイピング手法に該当する。
abstractIn this paper, a new XAI framework for banana disease detection and classification is introduced.
Banana / plantainLeafTissuePhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
Direct and non-destructive measurements of plant-water relations of plants exhibiting the Crassulacean acid metabolism (CAM) photosynthetic pathway are seldom addressed, with most findings inferred from gas exchange measurements. The main focus of this paper was to study how the water exchange between the chlorenchyma and the hydrenchyma depends on and follows the CAM photosynthetic diel pattern using non-invasive and continuous methods. Gas exchange and leaf patch clamp pressure probe (LPCP) measurements were performed on Aloe vera (L.) Burm f., a CAM species, and compared to measurements on banana (Musa acuminata Colla), a C 3 species. The LPCP output pressure, P p , of Aloe vera plants follows its diel CAM photosynthetic cycle, reversed to that observed in banana and other C 3 species. The four phases of CAM photosynthesis can also be identified in the diel LPCP output pressure, P p , cycle. The P p values in Aloe vera are determined by the hydrenchyma turgor pressure, with both parameters being reversely related. A non-invasive and continuous assessment of the water exchange between the chlorenchyma and the hydrenchyma in CAM plants, namely, by following the changes in the hydrenchyma turgor pressure, is presented. However, showing once more how the LPCP output pressure, P p , depends on the leaf structure, such an approach can be used to study plant-water relations in other CAM species with a leaf structure similar to Aloe vera, with the hydrenchyma composing most of the leaf volume.
Why it matches plant phenotyping methodsLPCPを用いた非侵襲・連続的な葉内水交換および水分状態の測定法を中心に提示しており、植物生理形質の取得方法が研究の主要目的である。
abstractDirect and non-destructive measurements of plant-water relations of plants exhibiting the Crassulacean acid metabolism (CAM) photosynthetic pathway are seldom addressed
Banana / plantainLeafTissuePhysiological trait estimationPhotosynthesis / fluorescenceWater status / transpiration
Direct and non‐destructive measurements of plant‐water relations of plants exhibiting the Crassulacean acid metabolism (CAM) photosynthetic pathway are seldom addressed, with most findings inferred from gas exchange measurements. The main focus of this paper was to study how the water exchange between the chlorenchyma and the hydrenchyma depends on and follows the CAM photosynthetic diel pattern using non‐invasive and continuous methods. Gas exchange and leaf patch clamp pressure probe (LPCP) measurements were performed on Aloe vera (L.) Burm f., a CAM species, and compared to measurements on banana (Musa acuminata Colla), a C₃ species. The LPCP output pressure, Pₚ, of Aloe vera plants follows its diel CAM photosynthetic cycle, reversed to that observed in banana and other C₃ species. The four phases of CAM photosynthesis can also be identified in the diel LPCP output pressure, Pₚ, cycle. The Pₚ values in Aloe vera are determined by the hydrenchyma turgor pressure, with both parameters being reversely related. A non‐invasive and continuous assessment of the water exchange between the chlorenchyma and the hydrenchyma in CAM plants, namely, by following the changes in the hydrenchyma turgor pressure, is presented. However, showing once more how the LPCP output pressure, Pₚ, depends on the leaf structure, such an approach can be used to study plant‐water relations in other CAM species with a leaf structure similar to Aloe vera, with the hydrenchyma composing most of the leaf volume.
Why it matches plant phenotyping methodsLPCPを用いた非侵襲・連続的な葉内水分交換／ハイドレンキマの膨圧測定を中心に、CAM植物の水分状態を評価する方法を提示・適用しているため。
abstractA non‐invasive and continuous assessment of the water exchange between the chlorenchyma and the hydrenchyma in CAM plants, namely, by following the changes in the hydrenchyma turgor pressure, is presented.
Identify banana plant diseases using machine learning with the CNN method to make it easier to identify diseases in banana plants through leaf images. It employs the CNN method, incorporating ResNet50 because ResNet50 is one of the best models and a suitable model for the data set used, and the VGG-19 model is used because VGG-19 was one of the winning models of the 2014 ImageNet Challenge and is a model that also fits the data set used. The research objectives encompass data set processing, model architecture development, evaluation, and result reporting, all aimed at improving disease identification in banana plants. The ResNet50 model achieved impressive 94% accuracy, with 88% precision, 91% recall, and an F1 score of 89%, while the VGG-19 model demonstrated strong performance with 91% accuracy, surpassing previous research and highlighting the effectiveness of these models in identifying banana plant diseases through leaf images. In conclusion, the exceptional accuracy positions it as the preferred model for CNN-based disease identification in banana plants, offering significant advances and insights for agricultural practices. Future research opportunities include exploring alternative CNN models, architectural variations, and more extensive training datasets to improve disease identification accuracy.
Why it matches plant phenotyping methodsバナナ葉画像から植物病害状態を推定するCNN手法を開発・評価しており、植物表現型（病害）の取得・分類が研究の中心です。
abstractIdentify banana plant diseases using machine learning with the CNN method to make it easier to identify diseases in banana plants through leaf images.
In recent years plant disease detection and classification is finding a lot of scope in the field of agriculture. The use of image pre-processing along with deep learning techniques is making the role of farmers easy in the process of plant leaf disease detection. In this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower. Images for the training and testing purpose are collected by visiting the farms and from village dataset for normal, leaf spot, leaf blight, powdery mildew, bunchy top, sigatoka, panama wilt. Pre-processing is done to remove eliminate the noise in the image by converting the RGB input to HSV image. Binary pictures are retrieved to separate the diseased and unaffected portions based on the hue and saturation components. A clustering method is utilized to separate the diseased region from the normal portion and the background. Classification of the disease is carried out using ResNet-50 algorithm. The experimental results obtained are compared with CNN, machine learning algorithms like SVM, KNN, DT and Ensemble algorithm like RF and XG booster. The proposed algorithm provided maximum efficiency compared to other algorithms.
Why it matches plant phenotyping methods植物葉の画像から病変領域を抽出し、深層学習で病害を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対象範囲に含める。
abstractIn this paper we propose a deep learning technique, ResNet-50 for the identification and classification of leaf diseases mainly in banana and sunflower.
During the 1950s, the Gros Michel species of bananas were nearly wiped out by the incurable Fusarium Wilt, also known as Panama Disease. Originating in Southeast Asia, Fusarium Wilt is a banana pandemic that has been threatening the multi-billion-dollar banana industry worldwide. The disease is caused by a fungus that spreads rapidly throughout the soil and into the roots of banana plants. Currently, the only way to stop the spread of this disease is for farmers to manually inspect and remove infected plants as quickly as possible, whereas it is a time-consuming process. The main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify fusarium wilt infections on banana crop leaves. We chose to use the ResNet50 architecture as the base CNN model for our transfer learning approach owing to its remarkable performance in image classification, which was demonstrated through its victory in the ImageNet competition. After its initial training and fine-tuning on a data set consisting of 300 healthy and diseased images, the CNN model achieved near-perfect accuracy of 0.99 and was fine-tuned to adapt the ResNet base model. ResNet50’s distinctive residual block structure could be the reason behind these results. To evaluate this CNN model, 500 test images, consisting of 250 diseased and healthy banana leaf images, were classified by the model. The deep CNN model was able to achieve an accuracy of 0.98 and an F-1 score of 0.98 by correctly identifying the class of 492 of the 500 images. These results show that this DCNN model outperforms existing models such as Sangeetha et al., 2023’s deep CNN model by at least 0.07 in accuracy and is a viable option for identifying Fusarium Wilt in banana crops.
Why it matches plant phenotyping methodsバナナ葉の画像からFusarium萎凋病の感染状態をCNNで推定する手法を開発・評価しており、植物の病徴状態の取得が研究の中心である。
abstractThe main purpose of this study is to build a deep Convolutional Neural Network (CNN) using a transfer learning approach to rapidly identify fusarium wilt infections on banana crop leaves.
Reproduction assets foundThe paper publicly shares the banana leaf image dataset used to evaluate its Fusarium wilt detection CNN via a Google Drive link in the Data Availability Statement. No author analysis code or trained model checkpoints are explicitly deposited; other URLs are generic libraries or cited prior work.Dataset · publicve Program, Agriculture Economics and Rural Communities, under grant no. 2023-69006-40213.
Data Availability Statement: This study utilizes a modified data set from a previous work (Medhi and Deb, 2022)
for training neural networks. The data set used in evaluating the trained neural network is accessible via the
following link: https://drive.google.com/drive/folders/17wONO9e_goGjfTl-wSJp1SgIEthxzB1l?usp=share_link.
Supplementary information and any additional data not included in the main manuscript can be obtained from
the corresponding author upon reasonable request.
Conflicts of Interest: The authors have no known competing financial or non-financial interests that are directly
or indirecOpen asset ↗pdf-layout-page:11 lines:1-61Plant phenotyping relevance match · UnverifiedEurope PMC · checked 15 Sept 2026
Senescence is happening in every corner of the living organisms. β-galactosidase (β-gal) is one of the most important biomarkers during senescence in both plant and mammalian cells. Most β-gal fluorescent probes were focused on bio-imaging, only a few probes were developed for the detection of β-gal in fruit, and the probes that could detect β-gal in both fruits and living cells were even less. Here, two β-gal probes (TNap-βGal and TBNap-βGal) were synthesized, which can not only image the increase of β-gal during both fruits softening and cellular senescence, but also prove that bananas are not suitable for storage in refrigerator and the subsequent accumulation of β-gal still in lysosome of mammalian cells. In addition, TNap-βGal was successfully applied to two-photon imaging of endogenous β-gal in both hDPMSCs and tissues of human dental pulp for the first time.
Why it matches plant phenotyping methods植物の果実軟化・老化に伴うβ-galactosidaseを可視化する蛍光プローブを開発し、果実状態の画像取得に適用しており、植物フェノタイピング手法が中心です。
abstractHere, two β-gal probes (TNap-βGal and TBNap-βGal) were synthesized, which can not only image the increase of β-gal during both fruits softening and cellular senescence
Knowledge on the health of banana trees is critical for farmers to profit from banana cultivation. Fusarium wilt and banana blood diseases (BDB), two significant diseases infecting banana trees, are caused by Fusarium oxysporum and Ralstonia syzygii, respectively. They have successfully caused a decline in crop yield as they destroy the trees, starting sequentially from the pseudostem to the fruits. The entire distribution of BDB and Fusarium on a plantation can be understood using advanced geospatial information obtained from multispectral aerial photographs taken using an unmanned aerial vehicle (UAV), combined with the reliable data field of infected trees. Vegetation and soil indices derived from a multispectral aerial photograph, such as normalized difference vegetation index, modified chlorophyll absorption ratio index, normalized difference water index (NDWI) and soil pH, may have to be relied on to explain the precise location of these two diseases. In this study, a random forest algorithm was used to handle a large dataset consisting of two models: the banana diseases multispectral model and the banana diseases spectral model. The results show that the soil indices, soil pH and NDWI are the most important variables for predicting the spatial distribution of these two diseases. Simultaneously, the plantation area affected by BDB is more extensive than that affected by Fusarium, if the variation of planted banana cultivars is not considered.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像と携帯型分光計、ランダムフォレストを組み合わせ、バナナ樹の病害状態の空間分布を推定する方法が研究の中心であるため、植物病害フェノタイピングとして収録する。
abstractThe entire distribution of BDB and Fusarium on a plantation can be understood using advanced geospatial information obtained from multispectral aerial photographs taken using an unmanned aerial vehicle (UAV), combined with the reliable data field of infected trees.
Banana is among major crops cultivated by most smallholder farmers in Tanzania and other parts of Africa. This crop is very important in the household economy as well as food security since it serves as both food and cash crops. Despite these benefits, the majority of smallholder farmers are experiencing low yields which are attributed to diseases. The most problematic diseases are Black Sigatoka and Fusarium Wilt Race 1. Black Sigatoka is a disease that produces spots on the leaves of bananas and is caused by an air-borne fungus called Pseudocercospora fijiensis , formerly known as Mycosphaerella fijiensis . Fusarium Wilt Race 1 disease is one of the most destructive banana diseases that is caused by a soil-borne fungus called Fusarium oxysporum f.sp. Cubense (Foc). The dataset of curated banana crop image is presented in this article. Images of both healthy and diseased banana leaves and stems were taken in Tanzania and are included in the dataset. Smartphone cameras were used to take pictures of the banana leaves and stems. The dataset is the largest publicly accessible dataset for banana leaves and stems and includes 16,092 images. The dataset is significant and can be used to develop machine learning models for early detection of diseases affecting bananas. This dataset can be used for a number of computer vision applications, including object detection, classification, and image segmentation. The motivation for generating this dataset is to contribute to developing machine learning tools and spur innovations that will help to address the issue of crop diseases and help to eradicate the problem of food security in Africa.
Why it matches plant phenotyping methodsバナナの健全・罹病状態を画像で記録した公開データセットが論文の中心であり、植物病害状態の画像ベース表現型解析に利用できる。
abstractThe dataset of curated banana crop image is presented in this article.
Reproduction assets foundThe paper's banana leaf/stem image dataset (16,092 images) is publicly deposited on Harvard Dataverse (doi:10.7910/DVN/LQUWXW), and annotation was done with the Makerere AI Lab public web annotation tool on GitHub. Both are paper-specific, public, and actionable.Dataset · publiccation
• Institution: The Nelson Mandela African Institution of Science and Technology (NM-AIST), The International Institute of Tropical Agriculture (IITA)
• City/Town/Region: Arusha
• Country: Tanzania
Data accessibility
Repository name: Harvard Dataverse
Data identification number: doi: 10.7910/DVN/LQUWXW
Direct URL to data: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/LQUWXW
Value of the Data
•
Machine learning models for early detection of Black Sigatoka and Fusarium Wilt Race 1 diseases that affect productivity can be trained using this dataset.
•
Researchers in the field of machine learning can use the collected imagery dataset of bananas to develop the endOpen asset ↗Harvard Dataverse · doi:10.7910/DVN/LQUWXWlines:1-53Code · publict and Remove Duplicate Pictures
2023 https://visipics.en.softonic.com (Accessed 10 February 2023)
3
Chen Q. Zobel J. Zhang X. Verspoor K.
Supervised learning for detection of duplicates in genomic sequence databases
PLoS ONE 11 2016 1 15
10.1371/journal.pone.0159644
PMC4973881
27489953
4
Makerere AI Lab
Web Annotation Tool
2023 https://github.com/AI-Lab-Makerere/web-annotation-tool (Accessed 5 March 2023)
5
Mduma N. Leo J. Loyani L. Jomanga K. Kamara A. Msaki I. Sanga S.
Banana Dataset Tanzania, Havard Dataverse
2022
10.7910/DVN/LQUWXW
Data Availability
Bananas Dataset Tanzania (Original data) (Dataverse).
Acknowledgments
The authors would like to extend their gratitude to Rockefeller FoundaOpen asset ↗github.com/AI-Lab-Makerere/web-annotation-toollines:89-126Plant phenotyping relevance match · UnverifiedEurope PMC · OpenAlex · checked 14 Sept 2026
Why it matches plant phenotyping methodsRTB品種の育種選抜を目的に、官能評価と物理化学測定を組み合わせて品質・受容性形質を取得する方法論が研究の中心であり、植物器官由来の品質形質のフェノタイピングに該当する。
abstractOur methodology is based on a combination of sensory approaches aiming to evaluate the sensory characteristics and user acceptability of root, tuber and banana (RTB) varieties.
Plantain tree is the most popular crop grown all over the world and banana (Musa spp.) is the most marketable fruit. It is the leading food in many countries, especially in developing countries. Plant diseases are significant aspects that result in a serious reduction in the quantity and quality of fruit crops. Plantain tree cultivation is affected by various diseases such as Black Sigatoka/Yellow sigatoka, Panama, Bunchy top, Moko, chlorosis, etc. Rapid and novel approaches for the apt discovery of diseases help farmers in developing better decisions and efficient control measures. Convolutional Neural Networks (CNN) and Recurrent Neural Network (RNN) have been proved their efficiency in several fields and it has recently moved in the field of crop disease classification and detection. The objective of this research work is to create a Deep Learning Model for the disease classification and its early prediction to support farmers in plantain tree cultivation. A new sequential image classification model is proposed to detect the diseases by combining RNN and CNN, which is named as Gated-Recurrent Convolutional Neural Network (G-RecConNN). The input to the proposed model is the sequences of plant images. The experiments are carried out in real-time datasets collected from the state named Tamil Nadu situated in the Southern part of India. This method aims at numerous advantages such as reduced pre-processing of the data, easy online performance evaluation and advancements with less real data, etc. The experimental results inspired the utilization of the G-RecConNN model with farmer support systems that will process continuous banana tree images as part or whole for the early detection of banana tree diseases.
Why it matches plant phenotyping methodsバナナ葉・樹体画像から病害状態を推定する深層学習画像分類法の開発が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThe objective of this research work is to create a Deep Learning Model for the disease classification and its early prediction to support farmers in plantain tree cultivation.
Abstract: India is primarily an agricultural country where a significant portion of the population depends on agriculture for their livelihood. However, plant diseases are a major issue for farmers, hindering their efforts to cultivate crops. Delayed detection of diseases can result in a significant loss of yield and income for farmers. To mitigate these negative impacts, we have created a project that utilizes machine learning and deep learning techniques such as image processing and Convolutional Neural Networks to detect various diseases in banana plants. Our machine learning model enables early detection of diseases, which can help minimize the loss of yield and enable farmers to take necessary preventive measures to halt the spread of diseases in their crops
Why it matches plant phenotyping methodsバナナ植物の病害状態を画像処理と深層学習で推定する手法が研究の中心であり、植物病害フェノタイピングに該当する。
abstractwe have created a project that utilizes machine learning and deep learning techniques such as image processing and Convolutional Neural Networks to detect various diseases in banana plants.
The determination of key phenological growth stages of banana plantations, such as flower emergence and plant establishment, is difficult due to the asynchronous growth habit of banana plants. Identifying phenological events assists growers in determining plant maturity, and harvest timing and guides the application of time-specific crop inputs. Currently, phenological monitoring requires repeated manual observations of individual plants’ growth stages, which is highly laborious, time-inefficient, and requires the handling and integration of large field-based data sets. The ability of growers to accurately forecast yield is also compounded by the asynchronous growth of banana plants. Satellite remote sensing has proved effective in monitoring spatial and temporal crop phenology in many broadacre crops. However, for banana crops, very high spatial and temporal resolution imagery is required to enable individual plant level monitoring. Unoccupied aerial vehicle (UAV)-based sensing technologies provide a cost-effective solution, with the potential to derive information on health, yield, and growth in a timely, consistent, and quantifiable manner. Our research explores the ability of UAV-derived data to track temporal phenological changes of individual banana plants from follower establishment to harvest. Individual plant crowns were delineated using object-based image analysis, with calculations of canopy height and canopy area producing strong correlations against corresponding ground-based measures of these parameters (R2 of 0.77 and 0.69 respectively). A temporal profile of canopy reflectance and plant morphology for 15 selected banana plants were derived from UAV-captured multispectral data over 21 UAV campaigns. The temporal profile was validated against ground-based determinations of key phenological growth stages. Derived measures of minimum plant height provided the strongest correlations to plant establishment and harvest, whilst interpolated maxima of normalised difference vegetation index (NDVI) best indicated flower emergence. For pre-harvest yield forecasting, the Enhanced Vegetation Index 2 provided the strongest relationship (R2 = 0.77) from imagery captured near flower emergence. These findings demonstrate that UAV-based multitemporal crop monitoring of individual banana plants can be used to determine key growing stages of banana plants and offer pre-harvest yield forecasts.
Why it matches plant phenotyping methodsUAVマルチスペクトル画像から個体別の樹冠形状・反射特性・生育段階・収量関連形質を抽出し、地上測定で検証する方法の開発・実証が中心である。
abstractIndividual plant crowns were delineated using object-based image analysis, with calculations of canopy height and canopy area producing strong correlations against corresponding ground-based measures of these parameters
Background: Sukali Ndizi quality traits such as Total soluble solid (TSS) content, pulp texture and sugar/acid (S/A) ratio are critical in quality assessment. Screening very large numbers of fruit genotypes has prompted the development of a high throughput method using Near Infrared spectrometry (NIRS). Results: The calibration procedure for the attributes of TSS, pulp texture and S/A ratio was optimized with respect to a reference sampling technique, scan averaging, spectral window, data pre-treatment and regression procedure. Calibration equations for all analytical characteristics were computed by NIR Software ISI Present WINISI using Modified Partial Least Squares (MPLS) and Partial Least Squares. The quality of calibration models were evaluated by Standard Error of Calibration and coefficient of determination parameters between the measured and the predicted values. The results obtained with FOSS NIR systems 2500 spectrometer (model DS 2500) using the 350-2500 nm range, showed good prediction of the quality traits TSS content, pulp texture and S/A ratio. The MPLS method produced satisfactory Calibration model performance for TSS, texture and S/A ratio, with typical Rc2 of 0.73%Brix, 0.69kgf and 0.7; and root mean squared standard error of calibration of 0.73%Brix, 0.25kgf and 5.36 respectively. This is a good set of quality traits predicting Sukali Ndizi quality with NIRS with robustness, as it was obtained by using diverse Ndizi populations. Conclusions: This can be a useful tool to phenotype large numbers of Ndizi hybrids per day, making it possible to reduce on the resources spent when utilizing organoleptic evaluation selection technique.
Why it matches plant phenotyping methodsバナナ果実の品質形質をNIRSで高速推定する方法を開発・最適化し、校正性能を評価しており、表現型取得法が研究の中心である。
abstractThe calibration procedure for the attributes of TSS, pulp texture and S/A ratio was optimized with respect to a reference sampling technique, scan averaging, spectral window, data pre-treatment and regression procedure.
Fruit protection is critical in the agricultural industry in the context of the global economy. The general public has recently learned that various diseases are wreaking havoc on fruit supplies. Agriculture economies all over the world are failing as a result of this. Computerized automatic methods for assessing the quality of fresh and rotting apples can relieve the burden of manually researching various apple fruit varieties. This study presents a novel method for comparing apple varieties based on fruit quality. The technique employs principal component analysis (PCA) early on to collect relevant characteristics. Additionally, the statistical, textual, and geometrical features are also extracted. The model is first tested by classifying apples from the Fruits-360 dataset as fresh or spoiled. Furthermore, we classified four different types of fresh and rotting fruits during the training and testing phases of the classification procedure (apple, avocado, banana, and orange). In addition, the k-NN algorithm, the linear support vector machine (LSVM), the kernel support vector machine (KSVM), and decision trees (DT) are classifiers to categorize the fruits according to quality. After evaluating the models’ performance, they are retrained using only the first two principal components. The results of using SVM and DT models for quality evaluation have been discovered to be more encouraging and comparable to those obtained using state-of-the-art methods.
Why it matches plant phenotyping methods果実画像から鮮度・腐敗状態を抽出・分類する機械学習手法が研究の中心であり、植物器官の状態を直接評価しているため採用。
abstractComputerized automatic methods for assessing the quality of fresh and rotting apples can relieve the burden of manually researching various apple fruit varieties.
Agriculture is the primary source in providing food for the entire nation. Consequently, agriculture is the fundamental origin of food supply. The contributions of agriculture include increase in employment opportunity and economy of the nation. According to IBEF, in India 58% of entire population depends on agriculture as their main occupation. Currently 81.1% of the total agricultural production is produced by livestock farmers. There will be 50% of loss in yield because of pest and disease. The disease in the plant excitants farmers to use unsuitable pesticide which causes unfavorable consequences. This may lead to the reduction in soil and food quality. Besides it has an adverse impact on human life. Nevertheless, farmers are heedless of these effects. The diseases which are naturally created will cause a serious impact on yields also it will bring down the quality of the food and soil. The symptoms which can cause the less yield are infinitesimal and because of the less human vision potentiality it is difficult to recognize the disease. Plant diseases will require genuine identification and proper categorization of the crops. The developed advanced methodology will identify the diseases, percentage of spread area, pesticide name with the quantity of the pesticide require to heal particular disease using image processing technique.
Why it matches plant phenotyping methods画像処理によりバナナ葉の病害を識別し、病害の広がり面積という植物の病徴・重症度を推定する方法が研究の中心であるため、農薬助言を含むが植物病害フェノタイピングとして採用する。
abstractThe developed advanced methodology will identify the diseases, percentage of spread area, pesticide name with the quantity of the pesticide require to heal particular disease using image processing technique.
Deep learning is a branch of artificial intelligence.With the benefits of autonomous learning and feature extraction, it has received a lot of attention in recent years from both academic and professional circles.The latest improvements in computer vision formulated through deep learning have paved the method for how to detect and diagnose disease in plants by using a camera to capture image as a basis for recognizing several types of plant disease This system provides an efficient solution for detecting multiple disease in several plants The system is designed to recognize several plant leaf diseases in plants like Maize, Mango, Chickpea, Rice, Cotton, Banana, Watermelon etc.
Why it matches plant phenotyping methods植物葉の画像を用いて深層学習で病害を検出・診断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に該当します。
abstractcomputer vision formulated through deep learning have paved the method for how to detect and diagnose disease in plants by using a camera to capture image as a basis for recognizing several types of plant disease
Abstract The Internet of Things (IoT) solutions for agriculture are rapidly growing and have the potential to transform agriculture in many aspects. In particular, the plant disease detection devices play a vital role in improving the agriculture. The visual monitoring of plants for the onset of diseases is a tedious and time-consuming task for farmers and at the same time it is less accurate. Hence an automated system with environmental data and camera sensors can serve as an alternative and effective solution for manual monitoring of plants. In this paper, a novel and efficient compressed sensing inbuilt plant disease detection device is developed which uses a foreground-based segmentation method and two step feature extraction technique to detect and classify two of the major banana diseases. A database is created for banana bunchy top and sigatokaleaf spot diseases by collecting images in real time from the fields of southern parts of Tamilnadu namely Thadiyankudisai and Thandikudi of Dindigul district, KC Patti, Muthalapuram, Suruli Patti and Kambam of Theni district and ICAR NRCB, Tiruchirapalli. The suggested device's effectiveness has been assessed in terms of the proportion of infected areas, detection accuracy, percentage of feature reduction, and classification accuracy. The prototype of the proposed device is developed and validated using the Raspberry pi board. The findings demonstrate that the suggested device achieves classification accuracy of 97.33% and detection accuracy of 96.75%.
Why it matches plant phenotyping methodsバナナ葉の病徴を画像から検出・分類する装置と画像解析手法を開発し、感染領域・検出精度・分類精度で検証しており、植物病害表現型の取得が中心である。
abstracta novel and efficient compressed sensing inbuilt plant disease detection device is developed which uses a foreground-based segmentation method and two step feature extraction technique to detect and classify two of the major banana diseases.
In development economics of many countries, banana has been thought of as a key factor, however, banana trees suffer from susceptible to the main climatic conditions such as temperature, humidity, or solar radiation, resulting in a decrease in canopy and leaf area, which has an impact in bunch size and quality. In recent years, teledetection has emerged as a method to analyze the state of the plantation, where multispectral images captured by Unmanned Aerial Vehicle (UAV) are the main information source. This study set out to canopy extraction and estimation of a banana plantation from multispectral images taken at 35 meters height, which are transformed to HSV color field by red-edge band reflectance (REG) and near infrared (NIR). This allowed segmentation to separate the canopy from objects such as dry leaves, ground, and other elements. Finally, the results were compared with manual technique, resulting the proposed methodology has a significant accuracy and adjustment in the results..
Why it matches plant phenotyping methodsUAVマルチスペクトル画像からバナナの樹冠を抽出・推定し、手動法と精度比較しており、植物形態の取得手法が研究の中心である。
abstractThis study set out to canopy extraction and estimation of a banana plantation from multispectral images taken at 35 meters height
Current chemical methods used to control plant diseases cause a negative impact on the environment and increase production costs. Accurate and early detection is vital for designing effective protection strategies for crops. We evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging. We discuss the learning features of the techniques used, which will help researchers improve their understanding of the required data conditions and identify a method suitable for their research needs. A set of hyperspectral images of banana leaves inoculated with a conidial suspension of black sigatoka fungus ( Pseudocercospora fijiensis ) was used to train and validate machine learning models. Support vector machine (SVM), multilayer perceptron (MLP), neural networks, N-way partial least square-discriminant analysis (NPLS-DA), and partial least square-penalized logistic regression (PLS-PLR) were selected due to their high predictive power. The metrics of AUC, precision, sensitivity, prediction, and F1 were used for the models' evaluation. The experimental results show that the PLS-PLR, SVM, and MLP models allow for the successful detection of black sigatoka disease with high accuracy, which positions them as robust and highly reliable HSI classification methods for the early detection of plant disease and can be used to assess chemical and biological control of phytopathogens.
Why it matches plant phenotyping methodsバナナ葉の病徴をハイパースペクトル画像と複数の機械学習モデルで早期検出し、モデル性能を評価する手法研究であり、植物病害状態の取得・推定が中心です。
abstractWe evaluate advanced distributed edge intelligence techniques with distinct learning principles for early black sigatoka disease detection using hyperspectral imaging.
Reproduction assets foundThe paper's hyperspectral banana-leaf training/validation datasets and the authors' analysis source code (PLS-PLR, NPLS-DA, SVM, MLP) are explicitly stated as publicly available on the authors' GitHub repository.Code · publicThe source programs are available at the following link: https://github.com/JUG2019/Sigatoka-detect (accessed on 21 August 2022).Open asset ↗JUG2019/Sigatoka-detectlines:34-65Dataset · publicThe two datasets used in this study (i.e., the training dataset and validation dataset are available at: https://github.com/JUG2019/Sigatoka-detect (accessed on 21 August 2022).Open asset ↗JUG2019/Sigatoka-detectlines:234-247Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
This study used Kinect V2 sensor to collect the three-dimensional point cloud data of banana pseudostem and developed an automatic measurement method of banana pseudostem width. The banana plant was selected as the research object in a banana plantation in Fusui, Guangxi. The mobile measurement of banana pseudostem was carried out at a distance of 1 m from the banana plant using the field operation platform with Kinect V2 as the collection equipment. To eliminate the background data and improve the processing speed, a cascade classifier was used to recognize banana pseudostems from the depth image, extract the region of interest (ROI), and transform the ROI into a color point cloud combined with the color image; secondly, the point cloud was sparse by down-sampling; then, the point cloud noise was removed according to the classification of large-scale and small-scale noise; finally, the stem point cloud was segmented along the y -axis, and the difference between the maximum and minimum values in the x -axis direction of each segment was calculated as its horizontal width. The center point of each segment point cloud was used to fit the slope of the stem centerline, and the average horizontal width was corrected to the stem diameter. The test results show that the average measurement error is only 2.7 mm, the average relative error was 1.34%, and the measurement time is only about 300 ms. It could provide an effective solution for the automatic and rapid measurement of stem width of banana plants and other similar plants.
Why it matches plant phenotyping methodsKinect深度センサーと点群処理により、バナナ偽茎幅を自動・高速測定する手法を開発し、誤差検証も行っており、表現型取得手法が研究の中心である。
abstractdeveloped an automatic measurement method of banana pseudostem width
Introduction. Remote sensors offer the ability to observe an object without being in contact with it. They are widely used in agricultural applications and have large development potential in banana (Musa AAA) plantations. During the past decades, the research in remote sensing and agriculture has increased through the availability of high-resolution satellite images (spatial, spectral, and temporal) and the use of remotely piloted vehicles that generate base information for research. Objective. To carry out a general review on the applications of the use of remote sensors for banana plantations in three specific aspects: determination of the cultivation area, productivity estimation, and disease diagnosis. Development. The extension of land covered by commercial banana plantations can be detected visually or easily by means of remote image classifications, such as the Synthetic Aperture Radar (SAR) sensor, which hve resulted in classification accuracies of around 95%. This is due to the high backscattering of the large leaves of the plant. However, the studies on productivity are scarce for banana cultivation and have been limited to the use of vegetation index, showing poor results in their correlations. As for the identification of diseases, work has been done on the main diseases affecting production with correlation levels above 90 % for some diseases. Conclusion. This review shows that banana plantations can be detected through the use of remote sensors and, likewise, these allow the identification of the main diseases in the crop. However, the results obtained to determine productivity are scarce and with little precision.
Why it matches plant phenotyping methodsバナナ作物の生産性推定と病害診断におけるリモートセンシング手法をレビューしており、植物状態の取得・推定方法が中心である。
abstractTo carry out a general review on the applications of the use of remote sensors for banana plantations in three specific aspects: determination of the cultivation area, productivity estimation, and disease diagnosis.
The recent advancement of big data technology causes the data from agriculture domain to enter into the big data. They are not conventional techniques in existence to process such a large volume of data. The processing of large datasets involves parallel computation and analysis model. Hence, it is necessary to use big data analytics framework to process a large image datasets. In this paper, an automated big data framework is presented to classify the plant disease condition. This framework consists of a series operations that leads into a final step. When the classification is carried out using novel image classifier. The image classifier is designed using a Convolutional Recurrent Neural Network Classifier (CRNN) algorithm. The classifier is designed in such a way that it provides classification between a normal leaf and an abnormal leaf. The classification of plant images over large datasets that includes banana plant, pepper, potato, and tomato plant. Which is compared with other existing big data plant classification techniques like convolutional neural network, recurrent neural network, and deep neural network, artificial neural network with forward and backward propagation. The result shows that the proposed method obtains improved detection and classification of diseased plants compared to other the convolutional neural network (94.14%), recurrent neural network (94.07%), deep neural network (94%), artificial neural network with forward (93.96%), and backward propagation method (93.66%).
Why it matches plant phenotyping methods植物画像から病害状態を分類するCRNNベースの画像解析フレームワークを開発・比較しており、病害状態という植物表現型の抽出が中心である。
abstractan automated big data framework is presented to classify the plant disease condition
In recent times, the classification and identification of different fruits and food crops have become a necessity in the field of agricultural science; for sustainable growth. Probable processes have been developed worldwide to improve the production of food crops. Problem-specific, clean and crisp datasets are also lagging in the sector. This article introduces an image dataset of varieties of banana plants and the diseases related to them. The varieties of Banana plants that we have considered in the dataset are the Malbhog ( Musa assamica ), Jahaji ( Musa chinensis ), Kachkol ( Musa paradisiaca L. ), Bhimkol ( M. Balbisiana Colla ). And the diseases and pathogens that we have considered here are the Bacterial Soft Rot, Banana Fruit Scarring Beetle, Black Sigatoka, Yellow Sigatoka, Panama disease, Banana Aphids, and Pseudo-Stem Weevil. A dataset of Potassium deficiency has been also considered in this article. A total of 8000+ processed images are present in the dataset. The purpose of this article is to provide the Researchers and Students in getting access to our dataset that would help them in their research and in developing some machine learning models.
Why it matches plant phenotyping methodsバナナ植物の器官・品種・病害・カリウム欠乏を画像化した大規模データセットを提供しており、植物の状態推定に再利用できるデータ基盤が中心である。
abstractThis article introduces an image dataset of varieties of banana plants and the diseases related to them.
Reproduction assets foundThe paper is a data descriptor for the PSFD-Musa banana image dataset, publicly deposited on Mendeley Data with an explicit URL and DOI.Dataset · publics of different backgrounds to train, test, and validate classification models.
Data source location
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BORTARI VILLAGE,
Chaygaon, Kukurmara, District – Kamrup (Rural), Assam, India.
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HAJO VILLAGE,
District – Kamrup (Rural), Assam, India.
Data accessibility
Data is available at Mendeley Data, under the DOI: 10.17632/4wyymrcpyz.1 https://data.mendeley.com/datasets/4wyymrcpyz/1
Value of the Data
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The dataset provided here is the collection of different varieties of banana plants, some common diseases that affect them, and their deficiency. These varieties of banana plants are indigenously found in Assam. The data can be useful in the way to classifying the different diseases and pathogens whicOpen asset ↗Mendeley Data · 10.17632/4wyymrcpyz.1lines:1-54Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
The existing methods of callose quantification include epifluorescence microscopy and fluorescence spectrophotometry of aniline blue-stained callose particles, immuno-fluorescence microscopy and indirect assessment of both callose synthase and β-(1,3)-glucanase enzyme activities. Some of these methods are laborious, time consuming, not callose-specific, biased and require high technical skills. Here, we describe a method of callose quantification based on Sandwich Enzyme-Linked Immunosorbent Assay (S-ELISA). Tissue culture-derived banana plantlets were inoculated with Xanthomonas campestris pv. musacearum ( Xcm ) bacteria as a biotic stress factor inducing callose production. Banana leaf, pseudostem and corm tissue samples were collected at 14 days post-inoculation (dpi) for callose quantification. Callose levels were significantly different in banana tissues of Xcm -inoculated and control groups except in the pseudostems of both banana genotypes. The method described here could be applied for the quantification of callose in different plant species with satisfactory level of specificity to callose, and reproducibility. Additionally, the use of 96-well plate makes this method suitable for high throughput callose quantification studies with minimal sampling and analysis biases. We provide step-by-step detailed descriptions of the method.
Why it matches plant phenotyping methods植物組織中のカロース量を定量するELISA法を開発・再現性評価し、高スループット測定への適用性を示した研究であり、植物状態の取得方法が中心です。
abstractHere, we describe a method of callose quantification based on Sandwich Enzyme-Linked Immunosorbent Assay (S-ELISA).
Reproduction assets foundThe paper's supplementary material (Table S1) publicly hosts the callose quantification measurements (concentrations in leaves, pseudostems, corms of Xcm-inoculated vs. control banana plantlets) underlying this study's analysis. No author analysis code, images, or trained models are deposited; the R statistical packageDataset · publicor up to 12 months).
Dissolve para-nitrophenyl phosphate (pNPP) in substrate buffer to a working concentration of 1 mg/mL.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/mps5040054/s1 , Table S1: Analysis of callose concentration in the leaves, pseudostems and corms of banana plants inoculated and non-inoculated (control) with Xcm (Independent sample t-test, α ≤ 0.05).
Click here for additional data file.
Author Contributions
Conceptualization, A.K.T.; methodology, A.S.M., A.K.T. and P.S.; validatiOpen asset ↗lines:167-297Plant phenotyping relevance match · UnverifiedEurope PMC · checked 8 Sept 2026
The monitoring of ethylene is of great importance to fruit and vegetable quality, yet routine techniques rely on manual and complex operation. Herein, a chemiresistive ethylene sensor based on reduced graphene oxide (rGO)/tungsten diselenide (WSe 2 )/Pd heterojunctions was designed for room-temperature (RT) ethylene detection. The sensor exhibited high sensitivity and quick p-type response/recovery (33/13 s) to 10-100 ppm ethylene at RT, and full reversibility and excellent selectivity to ethylene were also achieved. Such excellent ethylene sensing behaviors could be attributed to the synergistic effects of ethylene adsorption abilities derived from the negative adsorption energy and the promoted electron transfer across the WSe 2 /Pd and rGO/WSe 2 interfaces through band energy alignment. Furthermore, its application feasibility to banana ripeness detection was verified by comparison with routine technique through simulation experiments. This work provides a feasible methodology toward designing and fabricating RT ethylene sensors, and may greatly push forward the development of modernized intelligent agriculture.
Why it matches plant phenotyping methodsエチレンを検出してバナナの成熟状態を推定するセンサーの設計・性能評価が研究の中心であり、植物器官の状態を測定する実質的なセンシング手法である。
abstracta chemiresistive ethylene sensor based on reduced graphene oxide (rGO)/tungsten diselenide (WSe 2 )/Pd heterojunctions was designed for room-temperature (RT) ethylene detection.
Fusarium oxysporum f. sp. cubense (FOC) causes Fusarium wilt, one of the most concerning diseases in banana ( Musa spp.), compromising global banana production. There are limited curative management options after FOC infections, and early Fusarium wilt symptoms are similar with other abiotic stress factors such as drought. Therefore, finding a reliable and timely form of early detection and proper diagnostics is critical for disease management for FOC. In this study, Portable Raman spectroscopy (handheld Raman spectrometer equipped with 830 nm laser source) was applied for developing a confirmatory diagnostic tool for early infection of FOC on asymptomatic banana. Banana plantlets were inoculated with FOC; uninoculated plants exposed to a drier condition were also prepared compared to well-watered uninoculated control plants. Subsequent Raman readings from the plant leaves, without damaging or destroying them, were performed weekly. The conditions of biotic and abiotic stresses on banana were modeled to examine and identify specific Raman spectra suitable for diagnosing FOC infection. Our results showed that Raman spectroscopy could be used to make highly accurate diagnostics of FOC at the asymptomatic stage. Based on specific Raman spectra at vibrational bands 1,155, 1,184, and 1,525 cm -1 , Raman spectroscopy demonstrated nearly 100% accuracy of FOC diagnosis at 40 days after inoculation, differentiating FOC-infected plants from uninoculated plants that were well-watered or exposed to water deficit condition. This study first reported that Raman spectroscopy can be used as a rapid and non-destructive tool for banana Fusarium wilt diagnostics.
Why it matches plant phenotyping methodsラマン分光による無症状バナナのフザリウム萎凋感染状態の非破壊診断法を開発・評価しており、植物状態の取得が研究の中心である。
abstractPortable Raman spectroscopy (handheld Raman spectrometer equipped with 830 nm laser source) was applied for developing a confirmatory diagnostic tool for early infection of FOC on asymptomatic banana.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 8 Sept 2026
Abstract Background The number of banana plants is closely related to banana yield. The diameter and height of the pseudo-stem are important morphological parameters of banana plants, which can reflect the growth status and vitality. To address the problems of high labor intensity and subjectivity in traditional measurement methods, a fast measurement method for banana plant count, pseudo-stem diameter, and height based on terrestrial laser scanning (TLS) was proposed. Results First, during the nutritional growth period of banana, three-dimensional (3D) point cloud data of two measured fields were obtained by TLS. Second, the point cloud data was preprocessed. And the single plant segmentation of the canopy closed banana plant point cloud was realized furtherly. Finally, the number of banana plants was obtained by counting the number of pseudo-stems, and the diameter of pseudo-stems was measured using a cylindrical segmentation algorithm. A sliding window recognition method was proposed to determine the junction position between leaves and pseudo-stems, and the height of the pseudo-stems was measured. Compared with the measured value of artificial point cloud, when counting the number of banana plants, the precision,recall and percentage error of field 1 were 93.51%, 94.02%, and 0.54% respectively; the precision,recall and percentage error of field 2 were 96.34%, 92.00%, and 4.5% respectively; In the measurement of pseudo-stem diameter and height of banana, the root mean square error (RMSE) of pseudo-stem diameter and height of banana plant in field 1 were 0.38 cm and 0.2014 m respectively, and the mean absolute percentage error (MAPE) were 1.30% and 5.11% respectively; the RMSE of pseudo-stem diameter and height of banana plant in field 2 were 0.39 cm and 0.2788 m respectively, and the MAPE were 1.04% and 9.40% respectively. Conclusion The results show that the method proposed in this paper is suitable for the field measurement of banana count, pseudo-stem diameter, and height and can provide a fast field measurement method for banana plantation management.
Why it matches plant phenotyping methodsTLSによるバナナ個体数、偽茎径、偽茎高の自動取得法を開発・精度評価した研究であり、植物表現型の取得が中心的です。
abstracta fast measurement method for banana plant count, pseudo-stem diameter, and height based on terrestrial laser scanning (TLS) was proposed.
Crop wild relatives, the closely related species of crops, may harbour potentially important sources of new allelic diversity for (a)biotic tolerance or resistance. However, to date, wild diversity is only poorly characterized and evaluated. Banana has a large wild diversity but only a narrow proportion is currently used in breeding programmes. The main objective of this study was to evaluate genotype-dependent transpiration responses in relation to the environment. By applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential. We characterized and evaluated six (sub)species and discerned four phenotypic clusters. Significant differences were observed in leaf area, cumulative transpiration and transpiration efficiency. We confirmed a general stomatal-driven 'isohydric' drought avoidance behaviour, but discovered genotypic differences in the onset and intensity of stomatal closure. We pinpointed crucial genotype-specific soil water potentials when drought avoidance mechanisms were initiated and when stress kicked in. Differences between (sub)species were dependent on environmental conditions, illustrating the need for high-throughput dynamic phenotyping, modelling and validation. We conclude that the banana wild relatives contain useful drought tolerance traits, emphasising the importance of their conservation and potential for use in breeding programmes.
Why it matches plant phenotyping methods連続ハイスループット表現型解析により蒸散応答モデルを構築し、動的フェノタイピング・モデリング・検証を主要手法として実施しているため。
abstractBy applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential.
Banana cultivation is one of the main agricultural elements in India, while the common problem of cultivation is that the crop has been influenced by several diseases, while the pest indications have been needed for discovering the infections initially for avoiding the financial loss to the farmers. This problem will affect the entire banana productivity and directly affects the economy of the country. A hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed to overcome these issues guide the farmers through enabling fertilizers that have to be utilized for avoiding the disease in the initial stages, and the proposed technique shows 99% of accuracy that is compared with the related deep learning techniques.
Why it matches plant phenotyping methodsバナナ葉など植物の観察画像から病害状態を分類するCNN手法を中心に開発・評価しており、植物病害表現型の推定に該当する。
abstractA hybrid convolution neural network (CNN) enabled banana disease detection, and the classification is proposed
The measurement of banana pseudo-stem phenotypic parameters is a critical way to evaluate the growth status of bananas, and it can provide essential data support for mechanized cultivation operations such as fertilization and pesticide application. Existing studies mainly measure the diameter of banana pseudo-stem as its phenotypic parameter. The banana pseudo-stem cross section was closer to an ellipse other than a standard circle, so the diameter parameter cannot adequately represent the phenotypic characteristics of the banana plant. In this study, an automatic measuring device for banana pseudo-stem phenotypic parameters was developed. The device, which integrates three different types of sensors: a laser ranging sensor, a rotary encoder, and a digital camera, was used to obtain the point cloud and image data of banana pseudo-stem. A K-means point clouds clustering algorithm based on Euclidean distance was proposed. The point cloud of banana pseudo-stem was identified and extracted. A three-dimensional reconstruction algorithm based on the ellipse model was also proposed. The three-dimensional contour of the pseudo-stem was calculated to obtain three types of phenotypic parameters: the long axis length, the short axis length, and the perimeter. Further, a synchronous trigger image acquisition mechanism was used to take pictures of pseudo-stems during measurement. It can be utilized for manual assessment of the growth status of the banana. Field experimental results showed that the three banana phenotypic parameters had a high correlation with the manual measurement results, and R2 is always more significant than 0.95, the total average measurement error and relative error were only 6.16 mm and 4.38%, respectively, both are within the acceptable agronomy range. In general, this method has good universality for plant stem detection, and the stem phenotypic parameters can be obtained by means of a non-contact test, which is of great significance to the mechanized cultivation of the forest and fruit industry. Keywords: multi-sensor fusion, point cloud fitting, phenotypic parameter extraction, banana pseudo-stem, ellipse model DOI: 10.25165/j.ijabe.20221503.6614 Citation: Jiang Y L, Duan J L, Xu X, Ding Y H, Li Y, Yang Z. Measurement of the banana pseudo-stem phenotypic parameters based on ellipse model. Int J Agric & Biol Eng, 2022; 15(3): 195–202.
Why it matches plant phenotyping methodsバナナ偽茎の形態形質を非接触・マルチセンサーで取得する装置と、点群抽出および3D再構成アルゴリズムを開発し、手測定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。
abstractIn this study, an automatic measuring device for banana pseudo-stem phenotypic parameters was developed.
Banana is a fruit plant that is widely produced in Indonesia. Unfortunately, this plant is very susceptible to diseases which can reduce the quality and quantity of the crop. This paper proposes disease detection in banana plants using a thermal camera. The detection is carried out using image processing techniques with multilevel thresholding methods. The image is captured using a thermal camera, then the image is preprocessed to suit what is desired. After that, so that the position is the same as the image taken using a digital camera, the image produced by the thermal camera is carried out by an image registration process. The image processing result is compared with the ground truth image obtained from a digital camera to determine the effectiveness of the proposed method. The effectiveness of the proposed method is measured using the parameters Recall, Precision, F-measure, and Accuracy. The effectiveness of the proposed method is quite effective because it produces parameter values above 80%, namely the recall value of 86,59%, the Precision of 99,1%, the F-measure of 92%, and the accuracy of 89,78%.
Why it matches plant phenotyping methods熱画像とマルチレベル閾値処理によりバナナ植物の病徴を検出・評価する手法を提案し、デジタル画像を基準に性能検証しているため、植物フェノタイピング手法が中心である。
abstractThis paper proposes disease detection in banana plants using a thermal camera.
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 14 Sept 2026
Crop wild relatives, the closely related species of crops, may harbor potentially important sources of new allelic diversity for (a)biotic tolerance or resistance. However, to date wild diversity is only poorly characterized and evaluated. Banana has a large wild diversity but only a narrow proportion is currently used in breeding programs. The main objective of this work was to evaluate genotype-dependent transpiration responses in relation to the environment. By applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential. We characterized and evaluated 6 (sub)species and discerned four phenotypic clusters. Significant differences were observed in leaf area, cumulative transpiration and transpiration efficiency. We confirmed a general stomatal-driven ‘isohydric’ drought avoidance behavior, but discovered genotypic differences in the onset and intensity of stomatal closure. We pinpointed crucial genotype specific environmental conditions when drought avoidance mechanisms were initiated and when stress kicked in. Differences between (sub)species were more pronounced under certain environmental conditions, illustrating the need for high-throughput dynamic phenotyping, modelling and validation. We conclude that the banana wild relatives contain useful drought tolerance traits, emphasizing the importance of their conservation and potential for use in breeding programs.
Why it matches plant phenotyping methods連続的なハイスループット表現型計測とモデル構築により、遺伝型特異的な蒸散応答や乾燥耐性形質を抽出・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。
abstractBy applying continuous high-throughput phenotyping, we were able to construct genotype-specific transpiration response models in relation to light, VPD and soil water potential.
Solving many phenotyping problems involves not only automatic detection of objects in an image, but also counting the number of parts per object. We propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster. The suggested network incorporates object detection, object resizing, and part counting as modules in a single deep network, with several variants tested. The detection module is based on a Retina-Net architecture, whereas for the counting modules, two different architectures are examined: the first based on direct regression of the predicted count, and the other on explicit parts detection and counting. The results are promising, with the mean relative deviation between estimated and visible part count in the range of 9.2% to 11.5%. Further inference of count-based yield related statistics is considered. For banana bunches, the actual banana count (including occluded bananas) is inferred from the count of visible bananas. For spikelets-per-wheat-spike, robust estimation methods are employed to get the average spikelet count across the field, which is an effective yield estimator.
Why it matches plant phenotyping methods植物器官の可視パーツ数を画像から検出・計数する深層学習手法を開発し、複数作物データセットで評価しているため、表現型取得・推定法が中心です。
abstractWe propose a solution in the form of a single deep network, tested for three agricultural datasets pertaining to bananas-per-bunch, spikelets-per-wheat-spike, and berries-per-grape-cluster.
Reproduction assets foundThe paper's grape experiments use the public Embrapa WGISD dataset, extended by the authors with berry dot annotations that they state were made publicly available as part of that dataset extension. The banana and wheat datasets (Israel Phenomics Consortium) and the authors' code/models have no stated public release,; Dataset · publicThe dot annotations were made
publicly available as part of Embrapa WGISD dataset extension.Open asset ↗Embrapa WGISDpdf-page:5 lines:1-59Code / dataset availability confirmedOpenAlex · Europe PMC · checked 8 Sept 2026
Leaf counting in potted plants is an important building block for estimating their health status and growth rate and has obtained increasing attention from the visual phenotyping community in recent years. Two novel deep learning approaches for visual leaf counting tasks are proposed, evaluated, and compared in this study. The first method performs counting via direct regression but using multiple image representation resolutions to attend leaves of multiple scales. The leaf count from multiple resolutions is fused using a novel technique to get the final count. The second method is detection with a regression model that counts the leaves after locating leaf center points and aggregating them. The algorithms are evaluated on the Leaf Counting Challenge (LCC) dataset of the Computer Vision Problems in Plant Phenotyping (CVPPP) conference 2017, and a new larger dataset of banana leaves. Experimental results show that both methods outperform previous CVPPP LCC challenge winners, based on the challenge evaluation metrics, and place this study as the state of the art in leaf counting. The detection with regression method is found to be preferable for larger datasets when the center-dot annotation is available, and it also enables leaf center localization with a 0.94 average precision. When such annotations are not available, the multiple scale regression model is a good option.
Why it matches plant phenotyping methods植物画像から葉数を推定する2つの深層学習手法を開発・比較し、既存および新規データセットで評価しているため、表現型取得手法が中心である。
abstractTwo novel deep learning approaches for visual leaf counting tasks are proposed, evaluated, and compared in this study.
Reproduction assets foundThe paper's authors explicitly state their full leaf-counting code (MSR and DRN models) is freely available on GitHub. The banana leaf dataset is proprietary and not public; the LCC datasets are external community benchmarks, not paper-specific assets.Code · publicThe entire code is freely accessible at https://github.com/farjon/Leaf-Counting .Open asset ↗farjon/Leaf-Countinglines:300-310Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Published31 May 2021International Journal for Research in Applied Science and Engineering TechnologyCited by 3 · OpenAlex ↗
Agriculture and crop production play an important role in our everyday lives.The primary source of food and clothing is agriculture.Food and clothing are currently being lost due to contaminated crops that decrease production rates.There are a variety of diseases that affect the plant's leaves, fruits, and stem.Bacteria, fungi, viruses, and other microorganisms are the most common causes of plant disease.Diseases are often difficult to monitor, and observations made with the naked eye are unreliable in detecting them.If we can't detect the disease quickly, we won't be able to take the appropriate action.The quality and quantity of goods would improve if pesticides are used less in agriculture.The technique is now mostly used in image processing for the identification of plant diseases.The technology is used in this method for detecting and classifying leaf diseases using SVM classification.Image acquisition, image preprocessing, feature extraction, and classification are all steps in this technology.Banana, pepper, and rice were the three crops we used.A total of 400 leaf sample images were used.From there, 80% will be used for preparation and 20% for research.With an accuracy of 92.99 percent, this device can successfully diagnose and identify the disease.
Why it matches plant phenotyping methods植物葉の画像から病害状態を抽出・分類するSVM手法が研究の中心であり、画像取得から特徴抽出、分類、精度評価まで記述されているため、植物フェノタイピング手法として含める。
abstractThe technology is used in this method for detecting and classifying leaf diseases using SVM classification.
Plant phenotyping relevance match · UnverifiedOpenAlex · bioRxiv · Europe PMC · checked 8 Sept 2026
Abstract Irrigation and fertilization stress in plants are limitations for securing global food production. Sustainable agriculture is at the heart of global goals because threats of a rapidly growing population and climate changes are affecting agricultural productivity. Plant phenotyping is defined as evaluating plant traits. Traditionally, this measurement is performed manually but with advanced technology and analysis, these traits can be observed automatically and nondestructively. A high correlation between plant traits, growth, biomass, and final yield has been found. From the early stages of plant development, lack of irrigation and fertilization directly influence developing stages, thus the final crop yield is significantly reduced. In order to evaluate drought and fertilization stress, plant height, as a morphological trait, is the most common one used in precision-agriculture research. The present study shows that three-dimension volumetric approaches are more representative markers for alerting growers to the early stages of stress in young banana plants’ for fine-scale phenotyping. This research demonstrates two different group conditions: 1) Normal conditions; and 2) zero irrigation and zero fertilization. The statistical analysis results show a successfully distinguished early stress with the volumetric traits providing new insights on identifying the key phenotypes and growth stages influenced by drought stress.
Why it matches plant phenotyping methods若いバナナ植物の干ばつ・施肥ストレスを、3次元体積形質によって早期検出するフェノタイピング手法の適用が中心であり、単なる生物学的処理試験ではない。
abstractThe present study shows that three-dimension volumetric approaches are more representative markers for alerting growers to the early stages of stress in young banana plants’ for fine-scale phenotyping.
Boiled pulp is a major form of consumption for plantain. We assessed instrumental (puncture test and texture profile analysis) and sensory texture attributes of 13 plantain cultivars, two cooking hybrids and one dessert banana at different stages of ripeness after cooking in boiling water. Firmness, chewiness, stickiness, mealiness, sweetness and moistness described sensory variability, which was greater between stages of ripeness than between types of cultivars. Firmness and chewiness were well‐predicted by instrumental force and hardness (r² > 0.72), and by soluble solid and dry matter content (r² > 0.85). Complementary sensitivity analysis revealed that a pulp puncture force or a hardness of at least 2.1 N or of 0.3 N/mm² was needed before a difference in firmness or chewiness could be perceived; a Brix of 3.7 was required to ensure a detectable difference in sweetness. Rheological and biochemical predictors can be useful for breeders for high‐throughput phenotyping.
Why it matches plant phenotyping methods植物育種向けに、調理後プランテン果肉の食感・甘味を機器測定値や理化学指標から予測し、検出閾値も評価しており、表現型取得・推定手法が中心である。
abstractFirmness and chewiness were well‐predicted by instrumental force and hardness (r² > 0.72), and by soluble solid and dry matter content (r² > 0.85).
3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines.Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs.In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain, and training is supplemented with different unlabelled datasets from the target real domain.We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment (Blender).Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss, improving performance of 3D reconstruction on real images.Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images.We focus this work on the task of 3D banana reconstruction from a single image, representing a common task in plant phenotyping, but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.
Why it matches plant phenotyping methods果実の3D再構成と体積回帰を対象とする教師なしドメイン適応手法を開発しており、植物器官の形態・サイズ推定に用いるフェノタイピング手法が研究の中心である。
abstractIn this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction
Reproduction assets foundThe paper's synthetic banana image-volume dataset (25,000 image-volume pairs) is publicly deposited at the authors' project site, and the pipeline/training code is deposited on the authors' GitHub. Both are paper-specific, public, and actionable.Code · publicThe code used to create the dataset for this study has been deposited on github at https://github.com/zanehartley . The code for the neural networks used for this study has been deposited on github at https://github.com/zanehartley .Open asset ↗github.com/zanehartleylines:158-160Plant phenotyping relevance match · UnverifiedarXiv · checked 8 Sept 2026
Prediction of stress conditions is important for monitoring plant growth stages, disease detection, and assessment of crop yields. Multi-modal data, acquired from a variety of sensors, offers diverse perspectives and is expected to benefit the prediction process. We present several methods and strategies for abiotic stress prediction in banana plantlets, on a dataset acquired during a two and a half weeks period, of plantlets subject to four separate water and fertilizer treatments. The dataset consists of RGB and thermal images, taken once daily of each plant. Results are encouraging, in the sense that neural networks exhibit high prediction rates (over $90\%$ amongst four classes), in cases where there are hardly any noticeable features distinguishing the treatments, much higher than field experts can supply.
Why it matches plant phenotyping methodsRGB・熱画像からバナナ幼植物の非生物的ストレス状態を推定する画像解析手法とデータセットが研究の中心であり、植物状態のフェノタイピングに該当する。
abstractWe present several methods and strategies for abiotic stress prediction in banana plantlets
Abstract A method is described which uses cyclohexanediaminetetraacetic acid (CDTA) to produce numerous separated whole cells from plant tissue. CDTA chelates divalent cations that cross‐link the pectic polysaccharides of the middle lamella, allowing gentle separation of the cells without harsh physical treatments. These individual cells are ideal for observing starch granules in situ by microscopy without the requirement for fixation, embedding, sectioning, or prior starch extraction. Starch can easily be observed either unstained, or by polarizing optics, or after staining with iodide (I 2 /KI). Staining with I 2 /KI in combination with polarizing optics gives information on polarizing colors that indicate compositional differences within granules. Examples of the starch complement in developing, mature, and cooked rr wrinkled pea cells, and in banana and potato tissue are shown. The CDTA‐separation method is ideal for the survey of starch mutants and other cell components as it preserves cytoplasmic organization and prevents microbial degradation during storage.
Why it matches plant phenotyping methods植物組織から細胞を分離し、顕微鏡でデンプン顆粒を観察する方法自体が中心であり、デンプン変異体の調査など植物形質評価への再利用性が示されている。
abstractA method is described which uses cyclohexanediaminetetraacetic acid (CDTA) to produce numerous separated whole cells from plant tissue.
Crop or leaf disease detection using Python and Machine learning application is designed by using image processing technique for the purpose of farmers to identify, analyze and classify automatically through the computer vision and machine learning vision system for mainly banana leaf to find diseases and by plotting the graph for their pixel range of the affected areas. Leaf diseases are restricting the growth of the plants and it is also destroying the crop. Disease can be controlled by knowing which disease is destroying the plant. The symptom of the banana diseases will be noticed in the leaf, by change in color to yellowish and turning to a dark color and this can be observed between the fourth and fifth month of the plant. Causing reduction in the growth of the plant as well as rotting of the banana. The support vector machine (SVM) algorithm is used for extraction of color and texture features. The proposed work attains a high accuracy in identification of diseases and thereby controlling the spread in other plants.
Why it matches plant phenotyping methodsバナナ葉の画像から病徴・罹病状態を抽出し、色・テクスチャ特徴と機械学習で病害を識別する方法が研究の中心であり、植物病害状態の表現型計測に該当する。
abstractCrop or leaf disease detection using Python and Machine learning application is designed by using image processing technique
Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 15 Sept 2026
Plant phenotyping is the task of measuring plant attributes mainly for agricultural purposes. We term length phenotyping the task of measuring the length of a plant part of interest. The recent rise of low cost RGB-D sensors and accurate deep artificial neural networks provides new opportunities for length phenotyping. We present a general technique for length phenotyping based on three stages: object detection, point of interest identification, and a 3D measurement phase. We address object detection and interest point identification by training network models for each task, and develop a robust de-projection procedure for the 3D measurement stage. We apply our method to three real world tasks: measuring the height of a banana tree, the length and width of banana leaves in potted plants, and the length of cucumbers fruits in field conditions. The three tasks were solved using the same pipeline with minor adaptations, indicating the method’s general potential. The method is stagewise analyzed and shown to be preferable to alternative algorithms, obtaining error of less than 10% deviation in all tasks. For leaves’ length and width, the measurements are shown to be useful for further phenotyping of plant treatment and mutant classification.
Why it matches plant phenotyping methods植物部位の長さをRGB-Dセンサー、物体検出、関心点検出、3D計測で推定する汎用フェノタイピング手法の開発・評価が中心である。
abstractWe present a general technique for length phenotyping based on three stages: object detection, point of interest identification, and a 3D measurement phase.
Automatic vision-based picking in orchards and fields is a highly challenging task. The orchard banana central stock, which is large in size, low in color contrast, and falls within a complex background, was taken as the subject in this research. A measurement framework based on multi-vision technology was established, and a set of general methods were utilized to improve the comprehensive performance of multi-view-geometry-based vision modules in orchard picking tasks. Multiple cameras at different angles were deployed to maximize the perception range. The global geometric parameters of the cameras were calibrated and a robust semantic segmentation network was trained to achieve effective image pre-processing. A novel adaptive stereo matching strategy was designed to ensure that the robot reliably completes 3D triangulation at various depths as it moves across the target area. Global calibration errors were corrected via a high-accuracy point cloud stitching algorithm. Experimental results indicated that the proposed adaptive stereo matching strategy was accurate to different sampling depths and showed stable performance, and the proposed point cloud stitching algorithm accurately stitched multi-view point clouds. This work provides theoretical and practical references for the 3D sensing of banana central stocks in complex environments. The proposed technique was designed for adaptability of the multi-vision system for field perception, so it can be easily transferred to similar applications such as the 3D reconstruction of agricultural targets, 3D positioning of fruit clusters, and 3D robotic arm obstacle avoidance.
Why it matches plant phenotyping methodsバナナ株の3次元形状を取得・再構成するマルチビジョン計測法が研究の中心であり、単なる収穫対象の位置検出を超えた植物器官の形態計測手法に該当する。
abstractA measurement framework based on multi-vision technology was established
Monitoring pests and diseases is an extremely important activity for increasing productivity in agriculture. In this scenario, remote sensing, coupled with techniques of machine learning, offer new prospects for monitoring and identifying characteristic specific patterns, such as manifestations of diseases, pests, and water and nutritional stress. The aim was to use high spatial resolution aerial images to monitor the extent of an attack of yellow sigatoka in a banana crop, following the basic assumptions of identification, classification, quantification and prediction of phenotypic factors. Monthly flights were carried out on a commercial banana plantation using an unmanned aerial vehicle, equipped with a 16-megapixel RGB camera (GSD of 0.016781 m pixel⁻¹). Five classification algorithms were used to identify and quantify the disease while field evaluations were also made following traditional methodology. The results showed that, for September 2017, the Support Vector Machine algorithm achieved the best performance (99.28% overall accuracy and 97.13 Kappa Index), followed by the Artificial Neural Network and Minimum Distance algorithms. In quantifying the disease, the SVM algorithm was more effective than other algorithms compared to the conventional methodology used to estimate the extent of yellow sigatoka, demonstrating that the tools used for monitoring leaf spots can be handled by remote sensing, machine learning and high spatial-resolution RGB images.
Why it matches plant phenotyping methodsUAV RGB画像と機械学習を用いてバナナ葉の黄化斑点病の発生範囲・重症度を画像から定量化し、従来法と比較検証しており、植物病害表現型の取得手法が中心である。
abstractThe aim was to use high spatial resolution aerial images to monitor the extent of an attack of yellow sigatoka in a banana crop
The production of banana-one of the highly consumed fruits-is highly affected due to loss of certain number of banana plants in an early phase of vegetation. This affects the ability of farmers to forecast and estimate the production of banana. In this paper, we propose a deep learning (DL) based method to precisely detect and count banana plants on a farm exclusive of other plants, using high resolution RGB aerial images collected from Unmanned Aerial Vehicle (UAV). An attempt to detect the plants on the normal RGB images resulted less than 78.8% recall for our sample images of a commercial banana farm in Thailand. To improve this result, we use three image processing methods-Linear Contrast Stretch, Synthetic Color Transform and Triangular Greenness Index-to enhance the vegetative properties of orthomosaic, generating multiple variants of orthomosaic. Then we separately train a parameter-optimized Convolutional Neural Network (CNN) on manually interpreted banana plant samples seen on each image variants, to produce multiple results of detection on our region of interest. 96.4%, 85.1% and 75.8% of plants were correctly detected on three of our dataset collected from multiple altitude of 40, 50 and 60 meters, of same farm. Further discussion on results obtained from combination of multiple altitude variants are also discussed later in the research, in an attempt to find better altitude combination for data collection from UAV for the detection of banana plants. The results showed that merging the detection results of 40 and 50 meter dataset could detect the plants missed by each other, increasing recall upto 99%.
Why it matches plant phenotyping methodsUAV RGB画像からバナナ個体を検出・計数する画像解析手法の開発と性能評価が研究の中心であり、植物個体数という観測可能な植物形質を抽出している。
abstractwe propose a deep learning (DL) based method to precisely detect and count banana plants on a farm exclusive of other plants, using high resolution RGB aerial images collected from Unmanned Aerial Vehicle (UAV).
Reproduction assets foundThe paper's Data Availability statement deposits the paper-specific UAV-collected banana plant image datasets (the phenotyping inputs used for detection/counting) in a public Figshare repository with an authors' URL, making it directly actionable.Dataset · publichas-pdf yes pmc-prop-has-supplement no pmc-prop-pdf-only no pmc-prop-suppress-copyright no pmc-prop-is-real-version no pmc-prop-is-scanned-article no pmc-prop-preprint no pmc-prop-in-epmc yes pmc-license-ref CC BY Data Availability Data available from the Figshare Repository (DOIs: 10.6084/m9.figshare.7981547 ), and the URL is: https://figshare.com/s/62e391492b1be99515b4 .
Data Availability
Data available from the Figshare Repository (DOIs: 10.6084/m9.figshare.7981547 ), and the URL is: https://figshare.com/s/62e391492b1be99515b4 .
Introduction
Significance of counting banana plantsOpen asset ↗Figshare · 10.6084/m9.figshare.7981547lines:1-36Plant phenotyping relevance match · UnverifiedOpenAlex · checked 15 Sept 2026
In fruit crops, the ripening of fruit is expressed in terms of change in its physical, physiological & biochemical parameters. Some of the relevant parameters like Size & Shape, Colour, Hardness/Softness, Texture etc. can be treated as reference for its maturity. The final stage of fruit ripening is considered to attain a maturity level of these parameters as an indicator for harvesting the fruit crop or ready to use in ripening storage units/chambers. Development of sensor-based maturity indicators can serve as important technological aid to the farmers. The present paper envisages design and implementation of a portable sensor-based prototype for real time monitoring of fruit maturity in crop field and in storage. The sensing parameters in the proposed design are Colour; Softness; surrounding Temperature& Humidity. An embedded program is developed based on a decision-making algorithm which compares the process values of the sensor output with the reference value of fruit maturity, and the result is displayed and conveyed to the end user. The prototype design is tested for three types of fruits Musa acuminate (Banana- ‘Kela’), Psidium guajava (Gauva-‘Amrood’); Carica (papaya-‘Papita’) and the results are reported in the paper. The proposed design shows 99% accuracy for all three types of fruits.
Why it matches plant phenotyping methods果実の成熟度という植物器官の状態を、色・硬さ等のセンサーで推定する携帯型プロトタイプを設計・実装し、複数果実で検証しており、表現型取得法が中心である。
abstractDevelopment of sensor-based maturity indicators can serve as important technological aid to the farmers.
Background The development of leaf area is one of the fundamental variables to quantify plant growth and physiological function and is therefore widely used to characterize genotypes and their interaction with the environment. To date, analysis of leaf area often requires elaborate and destructive measurements or imaging-based methods accompanied by automation that may result in costly solutions. Consequently in recent years there is an increasing trend towards simple and affordable sensor solutions and methodologies. A major focus is currently on harnessing the potential of applications developed for smartphones that provide access to analysis tools to a wide user basis. However, most existing applications entail significant manual effort during data acquisition and analysis. Results With the development of Plant Screen Mobile we provide a suitable smartphone solution for estimating digital proxies of leaf area and biomass in various imaging scenarios in the lab, greenhouse and in the field. To distinguish between plant tissue and background the core of the application comprises different classification approaches that can be parametrized by users delivering results on-the-fly. We demonstrate the practical applications of computing projected leaf area based on two case studies with Eragrostis and Musa plants. These studies showed highly significant correlations with destructive measurements of leaf area and biomass from both ground truth measurements and estimations from well-established screening systems. Conclusions We show that a smartphone together with our analysis tool Plant Screen Mobile is a suitable platform for rapid quantification of leaf and shoot development of various plant architectures. Beyond the estimation of projected leaf area the app can also be used to quantify color and shape parameters of other plant material including seeds and flowers.
Why it matches plant phenotyping methodsスマートフォン画像と分類アルゴリズムによる葉面積・バイオマス推定アプリを開発し、破壊測定および既存システムと検証しており、植物表現型取得が中心である。
abstractWith the development of Plant Screen Mobile we provide a suitable smartphone solution for estimating digital proxies of leaf area and biomass in various imaging scenarios in the lab, greenhouse and in the field.
Reproduction assets foundThe authors deposited the plant image data and corresponding ground truth measurements from the banana and Eragrostis case studies in the e!DAL research data publication system (DOI 10.25622/FZJ/2018/1), a paper-specific public asset. The project homepage (fz-juelich.de/ibg/ibg-2/psm) hosts the app and manual but is a Dataset · publichave no competing interests.
Availability of data and materials
The app is accompanied by a detailed manual and checkerboard images for calibration, which can also be downloaded from the project homepage. The datasets generated and/or analyzed during the current study are available in the e!DAL research data publication system, http://dx.doi.org/10.25622/FZJ/2018/1 [ 30 ].
Availability and requirements
Project name: Plant Screen Mobile. Project home page: https://fz-juelich.de/ibg/ibg-2/psm . Operating system(s): Android OS 4.0 (Ice Cream Sandwich) or higher. Programming language: Java. Other requirements: OpenCV manager (will be installed during Plant Screen Mobile setup). License: GNU GOpen asset ↗e!DAL research data publication system · 10.25622/FZJ/2018/1lines:127-198Plant phenotyping relevance match · UnverifiedEurope PMC · checked 10 Sept 2026
Why it matches plant phenotyping methodsバナナ葉の病徴発生時期や病斑数を用いて、BLSDに対する薬剤効果を迅速に評価する方法そのものを開発しており、植物病害状態の取得が研究の中心です。
abstractDeveloping a faster methodology for assessment of the biological efficacy of a contact fungicide offers promising perspectives for testing current and new fungicides or application techniques.
In the agriculture farming business, plant diseases are the major reason for monetary misfortunes around the globe. It is an imperative factor, as it causes significant diminution in both quality and capacity of growing crops. Therefore, detection and taxonomy of various plants diseases is crucial, and it demands utmost attention. In plants, fruits act a major source of nutrients worldwide, however, various range of diseases adversely affect the production as well as the quality of the fruits. Therefore, utilization of an efficient machine vision technology not only detects the diseases at their early stages but also classify them accordingly. This research is primarily focusing on the detection and classification of various fruits diseases based on correlation coefficient and deep features (CCDF). The proposed technique incorporates two major steps of infected regions detection and finally feature extraction and classification. In the first step, initially contrast of input image is enhanced by utilizing a hybrid method - followed by proposed correlation coefficient-based segmentation method which separates the infected regions from the background. In the second step, two deep pre-trained models (VGG16, caffe AlexNet) are utilized for feature extraction of selected diseases (apple scab, apple rot, banana sigotka, banana cordial leaf spot, banana diamond leaf spot and deightoniella leaf and fruit spot). Parallel features fusion step is embedded to consolidate the extracted features prior to max-pooling step. Selection of most discriminant features are being performed using genetic algorithm before subjecting to the final stage of classification using mutli-class SVM. Experiments are being performed on publicly available datasets - plant village and CASC-IFW datasets to achieve the classification accuracy of 98.60%. Qualitative analysis of achieved results clearly shows that the proposed method outperforms several existing methods in terms of greater precision and improved classification accuracy.
Why it matches plant phenotyping methods植物の病変領域を画像から分割・抽出し、病害状態を分類する機械視覚手法が研究の中心であるため、植物病害フェノタイピング手法として含める。
abstractThis research is primarily focusing on the detection and classification of various fruits diseases based on correlation coefficient and deep features (CCDF).
UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.
Why it matches plant phenotyping methods果樹園のUASマルチスペクトル画像に対する放射補正手法を比較・評価し、樹体構造・状態や生産関連パラメータの測定に必要な校正条件を検討しているため、画像ベース植物フェノタイピング手法が中心です。
abstractThis study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards.
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · OpenAlex · checked 10 Sept 2026
Volume carving is a well established method for visual hull reconstruction and has been successfully applied in plant phenotyping, especially for 3d reconstruction of small plants and seeds. When imaging larger plants at still relatively high spatial resolution (≤1 mm), well known implementations become slow or have prohibitively large memory needs. Here we present and evaluate a computationally efficient algorithm for volume carving, allowing e.g., 3D reconstruction of plant shoots. It combines a well-known multi-grid representation called "Octree" with an efficient image region integration scheme called "Integral image." Speedup with respect to less efficient octree implementations is about 2 orders of magnitude, due to the introduced refinement strategy "Mark and refine." Speedup is about a factor 1.6 compared to a highly optimized GPU implementation using equidistant voxel grids, even without using any parallelization. We demonstrate the application of this method for trait derivation of banana and maize plants.
Why it matches plant phenotyping methods植物シュートの3D再構成と形質抽出を目的とする計算手法を開発・評価しており、フェノタイピング手法が研究の中心である。
abstractHere we present and evaluate a computationally efficient algorithm for volume carving, allowing e.g., 3D reconstruction of plant shoots.
Morphological traits are commonly used for characterizing plant genetic resources. Germplasm characterization should be based on distinctly identifiable, stable and heritable traits that are expressed consistently and are easy to distinguish by the human eye. Characterization and documentation of a representative sample of East African highland bananas (Lujugira-Mutika subgroup) was carried out following an internationally accepted standard protocol for bananas. Eleven cultivars were characterized using an existing set of minimum descriptors (31 qualitative and quantitative traits) with the aim of determining stable descriptors and the ability of these descriptors to distinguish among East African highland banana cultivars. There was variation in stability of these descriptors within cultivars and across the 11 cultivars. Only 10 (32%) out of 31 descriptors studied were stable in the 11 cultivars. However, they had similar scores and therefore are not suitable to distinguish between cultivars within this group. Nonetheless, these 10 descriptors may be useful for distinguishing the East African highland bananas as a group from other groups of bananas. A few descriptors were unique to the cultivar 'Tereza' and may be used to distinguish this cultivar from other 'matooke' cultivars. None of the quantitative descriptors were stable.
Why it matches plant phenotyping methodsバナナの形態形質記述子プロトコルを用い、記述子の安定性と品種識別能力を評価しており、植物形質の測定法の検証が研究の中心である。
abstractEleven cultivars were characterized using an existing set of minimum descriptors (31 qualitative and quantitative traits) with the aim of determining stable descriptors and the ability of these descriptors to distinguish among East African highland banana cultivars.
Ethene (C2H4), which is produced in plants as they mature, was used to study its photoacoustic properties using photoacoustic spectroscopy. Detection of trace amounts, with N2 gas, of C2H4 gas was also applied. The gas was tested in various conditions- temperature, concentration of the gas, gas cell length, and power of the laser to determine their effect on the photoacoustic signal, the ideal conditions to detect trace gas amounts, and concentration of C2H4 produced by an avocado and banana. A detection limit of 10 ppm was determined for pure C2H4. A detection of 5% and 13% (by volume) concentration of C2H4 produced for a ripening avocado and banana, respectively, in closed space.
Why it matches plant phenotyping methods植物由来エチレンを光音響分光で検出する測定法を条件検討・検出限界評価し、果実の成熟状態に関連するエチレン産生を測定しているため、植物状態の取得法が中心です。
abstractwas used to study its photoacoustic properties using photoacoustic spectroscopy