To improve the instance segmentation accuracy of mango fruits and peduncles in complex mountainous orchard scenes and provide visual decision support for robotic harvesting, this study proposes a model-driven perception and picking-point localization method. Specifically, an RGB-D mango dataset was constructed under natural orchard conditions, covering strong illumination, shadows, backlighting, fruit overlap, branch and leaf occlusion, and peduncle crossing. An improved lightweight instance segmentation model named SHS-YOLOv8n-seg was then developed based on YOLOv8n-seg. StarNet_s1 was introduced as the backbone to enhance feature extraction under complex backgrounds. Furthermore, a high-frequency and spatial perception feature pyramid network was adopted to strengthen multi-scale feature fusion and improve the representation of slender peduncles. The SPPF module was used to expand the receptive field, and the parameter-free SimAM attention mechanism was introduced to enhance target responses while suppressing background interference. In the single-run comparison, the proposed model achieved Precision, Recall, mAP@50, and mAP@50:95 values of 89.62%, 88.17%, 90.19%, and 67.94%, respectively. Compared with the baseline YOLOv8n-seg model, these values increased by 2.42, 2.90, 2.75, and 2.57 percentage points, respectively. Moreover, fruit–peduncle matching, geometric constraints, RGB-D depth information, and PCA-based local direction estimation were combined to infer the picking point and recover its 3D coordinates from the segmentation masks. In the evaluation of 57 RGB-D images, the picking point position accuracy reached 98.2%, while the local peduncle direction accuracy reached 91.2%. Overall, the proposed method can accurately segment mango fruits and peduncles in complex natural environments and convert the segmentation results into picking point positions, 3D coordinates, and local direction information, thereby providing theoretical and technical support for intelligent mango harvesting robots.
Why it matches plant phenotyping methodsマンゴー果実・果梗の画像セグメンテーションと3D形状情報の抽出手法を開発・評価しており、単なる収穫対象の位置検出を超えて、再利用可能な植物器官の形態情報を取得する方法が中心である。
abstractfruit–peduncle matching, geometric constraints, RGB-D depth information, and PCA-based local direction estimation were combined to infer the picking point and recover its 3D coordinates from the segmentation masks
- Enhancing agricultural productivity and attaining sustainable crop management depend on the early and precise identification of leaf disease. Using state-of-the-art technologies in precision agriculture like machine learning (ML) and image processing greatly increases the effectiveness of disease detection and facilitates well-informed decision-making. But conventional manual inspection techniques are still tedious, unpredictable, and prone to errors. In order to overcome these constraints, this research offers YOLOv12-CropNet, an innovative deep learning-based system for multi-crop leaf disease diagnosis in real time. The proposed YOLOv12-CropNet approach makes use of the Convolutional Block Attention Module (CBAM) for adaptive attention, the Content-Aware Reassembly of Features (CARAFE) up-sampling module to preserve fine-grained disease characteristics, the YOLOv12 architecture improved with Ghost Convolution for effective feature extraction, and Involution layers to capture spatially specific patterns. Inspection techniques are still laborious, arbitrary, and prone to mistakes. A substantial set of data of 38 classes of both healthy and sick leaves from a variety of crops, including tomato, potato, apple, grape, corn, mango and sugarcane, was put together for training and evaluation. Experimental results show that YOLOv12-CropNet finds a suitable balance between computational speed and accurate detection. Accuracy, F1-score, recall, and precision are important performance metrics that verify the model's resilience in challenging environmental and visual circumstances. The suggested technique provides a scalable and field-deployable way to assist effective identification of diseases and precision agricultural decision-making. The proposed YOLOv12-CropNet model exhibits better performance than the other evaluated models, attaining a 98.45% peak accuracy, 98.10% precision ,98.20 % sensitivity and a 98.18% F1 score, thereby highlighting its efficacy in multi-crop leaf disease detection.
Why it matches plant phenotyping methods複数作物の葉の病徴を画像から検出・分類する深層学習手法を開発し、データセットと性能評価を伴うため、植物病害状態のフェノタイピング手法が中心である。
abstractthis research offers YOLOv12-CropNet, an innovative deep learning-based system for multi-crop leaf disease diagnosis in real time.
Reproduction assets foundThe paper uses public Kaggle datasets as its phenotyping image inputs: the PlantVillage dataset (38 crop-disease classes) and the Sugarcane Leaf Disease dataset, both cited with explicit public URLs. No author code, models, or checkpoints are reported as publicly available.Dataset · public[37] PlantVillage Dataset. Available online: https://www.kaggle.com/datasets/abdallahalidev/plantvillage-datasetOpen asset ↗pdf-page:22 lines:1-61Dataset · public[39] Sugarcane leaf Disease Dataset available online: https://www.kaggle.com/datasets/nirmalsankalana/sugarcane-Open asset ↗pdf-page:22 lines:1-61Plant phenotyping relevance match · UnverifiedCrossref · checked 14 Sept 2026
Mango (Mangifera indica L.) is among the most important commercial fruits grown throughout the world in the tropical and subtropical areas. Even though mangoes are economically important, their cultivation is continuously threatened by a wide variety of leaf and fungi diseases, resulting in crop losses of up to 15-30% annually. [1]. Conventional disease identification depends heavily on expert visual inspection—a process that is inherently slow, subjective, and largely impractical for smallholder farmers operating in remote areas with limited access to agronomic specialists. This paper provides an end-to-end deep learning-based approach towards automatic mango leaf disease detection along with an Android application for real-time deployment in the field. This work is built upon MangoLeafBD [1], an openly accessible dataset that consists of a total of 4,000 images of RGB color space for seven different disease categories and one healthy class, with each category having 500 samples collected from four separate orchards in Bangladesh. Three types of transfer learning models including VGG16 [2], MobileNetV2 [3], and DenseNet121 [4] were considered after applying two-phase fine-tuning based on pre-trained ImageNet weights. For each of the three types of neural network models tested, a series of preprocessing steps consisting of bilinear resizing to size 224 x 224, channel-wise normalization, and image augmentation (rotation, zoom, brightness adjustment, horizontal flip, and shear) was used to increase model accuracy for diverse real-world images. The training model was deployed using TensorFlow Lite (TFLite), which allowed it to be run in offline mode on mid-end Android phones, without needing internet connectivity. The app lets farmers upload leaf images or take images and get a diagnosis of the leaf diseases, along with possible treatments for them.This work contributes a replicable pipeline linking state-of-the-art deep learning research with practical precision agriculture, particularly for rural communities that currently lack access to timely agronomic advisory services.
Why it matches plant phenotyping methodsマンゴー葉画像から病害状態を推定する深層学習パイプラインとモバイル実装が研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractThis paper provides an end-to-end deep learning-based approach towards automatic mango leaf disease detection along with an Android application for real-time deployment in the field.
Fruit diseases caused by fungal, bacterial, and viral pathogens cause devastating pre- and post-harvest losses to Indian agriculture, particularly in Maharashtra where Guava, Mango, Orange, Papaya, and Pomegranate are major horticulture crops. Accurate, early-stage disease identification directly impacts farmer income, food security, and precision crop management. Conventional manual inspection by agronomists is subjective, time-consuming, and not scalable across thousands of orchard acres. Automated deep learning-based image analysis has emerged as a transformative solution, offering high accuracy, speed, and field deployability. Existing deep learning models for plant disease detection are predominantly trained on the real time database, which inadequately represents Indian fruit crop species. Furthermore, published systems focus on binary disease presence detection and lack disease severity grading — a critical requirement for treatment decision-making. The absence of expert-verified datasets and the visual overlap among disease classes (such as Phytophthora vs. Scab in Guava, and Powdery Mildew vs. Ring-spot in Papaya) present significant classification challenges. Limited training data for rare classes such as Stylerandroot (Guava, 310 samples) and Bacterial Blight (Pomegranate, 304 samples) further compounds model generalization. This study presents an end-to-end deep learning framework using VGG-16 and VGG-19 architectures trained on a novel, expert-verified dataset of 7,372 approved images spanning 22 disease/healthy classes across 5 fruit types, after on filed validation. The pipeline includes image preprocessing (CLAHE, Gaussian denoising, normalization), hybrid multi-feature extraction (CNN features, GLCM, LBP, Color Histograms), transfer learning with progressive fine-tuning, and a cascaded severity estimation module. Three algorithms are designed: (1) a Transfer Learning Classification Algorithm using VGG-16/VGG-19 backbone with softmax multi-class head, (2) a Hybrid Feature Fusion Algorithm combining CNN deep features with handcrafted descriptors for improved minority-class performance, and (3) a Cascaded Rule-CNN Severity Estimation Algorithm classifying disease progression into Healthy, Mild, Moderate, and Severe categories. VGG-19 achieved 96.1% overall accuracy, 95.1% precision, 94.5% recall, and a macro F1-score of 0.942, significantly outperforming VGG-16 (94.5% accuracy, F1: 0.918). Mango classification achieved the highest accuracy at 97.8%, while severity estimation reached 91.2% overall accuracy with the Mild category being the most challenging at 86.4%.The proposed system demonstrates that expert-verified, domain-specific datasets combined with transfer learning and hybrid feature fusion significantly advance the state of fruit disease detection for Indian agriculture. This framework provides a scalable, interpretable, and practically deployable solution for precision horticulture.
Why it matches plant phenotyping methods植物画像から病害状態と重症度を推定する深層学習手法、データセット、評価を中心的に開発しており、植物表現型計測の方法論的研究に該当する。
abstractThis study presents an end-to-end deep learning framework using VGG-16 and VGG-19 architectures trained on a novel, expert-verified dataset of 7,372 approved images spanning 22 disease/healthy classes across 5 fruit types
MangoTobaccoMultispectral / hyperspectralPhysiological trait estimationWater status / transpiration
This study proposed a novel multimodal hierarchical fusion framework integrating a convolutional neural network (CNN) and a transformer. The approach enhanced model performance by fusing spectral features with some auxiliary factors of the samples, such as the locality of growth (region), type of produce (cultivar), and sample temperature (temp). Spectral data were extracted using one-dimensional CNN to capture local spectral features, while auxiliary factors underwent sine-cosine or label encoding before being embedded into the same feature space as spectral data via a fully connected network. Ultimately, a transformer was employed to achieve global interaction and fusion between spectral features and auxiliary factors rather than merely concatenating different feature types. The fusion strategy was validated using the ultraviolet (UV)-visible (vis)-near-infrared (NIR) spectra of mango and tobacco data sets. Compared to single-modal models using spectra only, the multimodal model using spectra coupled with the auxiliary factors achieved improved prediction performance on both validation and test sets for the mango dry matter content (DMC). The RMSE decreased from 0.984 and 1.03 to 0.577 and 0.613, respectively. These results outperformed those of the other 11 machine learning models. SHAP analysis revealed that the CNN-transformer framework successfully captured the underlying relationships between auxiliary factors (region, temp, and cultivar) and spectral features near 960 nm (due to the O-H absorption signal) with DMC, with the former contributing more significantly to the model than the latter. Similar observations were obtained in the tobacco data set. The results demonstrated the advantages of the CNN-transformer multimodal model in overcoming the limitations of single-modal information, providing novel technical support for quantitative analysis.
Why it matches plant phenotyping methodsCNN-Transformerによるスペクトルと補助情報の融合モデルを開発・検証し、マンゴーの乾物含量という植物器官の形質を定量推定しているため、方法が中心的である。
abstractThis study proposed a novel multimodal hierarchical fusion framework integrating a convolutional neural network (CNN) and a transformer.
Plant diseases are one of the major challenges faced in modern agriculture, as they directly impact crop productivity, food quality, and the overall economic stability of farmers. Various environmental factors such as climate change, excessive moisture, poor soil conditions, and pest attacks contribute to the rapid spread of plant diseases. Traditional methods of disease identification mainly rely on manual inspection by agricultural experts, which is time-consuming, costly, and often inaccurate during the early stages of infection. Therefore, there is a growing need for an automated, fast, and reliable plant disease detection system that can assist farmers in identifying diseases at an early stage and taking appropriate preventive actions. This project presents an intelligent plant disease detection system that utilizes image processing, Machine learning, and machine learning techniques for accurate disease identification in crops such as Onion, Brinjal, Mango, Papaya, and Guava. The system is designed to analyze images of plant leaves captured through cameras or mobile devices. Using advanced image preprocessing methods, the captured leaf images are enhanced and processed to extract important features such as color, texture, and disease patterns. These features are then analyzed using a Convolutional Neural Network (CNN) model, which classifies the plant as either healthy or diseased with high accuracy. If a disease is detected, the system further identifies the specific type of disease affecting the plant and provides suitable recommendations for treatment and prevention. These recommendations include appropriate fertilizers, pesticides, organic supplements, and preventive agricultural practices customized for each crop type. The system also helps farmers understand the severity of the disease and suggests measures to minimize its spread to nearby plants. By providing real-time analysis and accurate predictions, the proposed solution reduces dependency on manual monitoring and expert consultation. The main objective of this project is to support precision agriculture by enabling early disease diagnosis, improving crop management efficiency, and increasing agricultural productivity. The automated detection process saves time, reduces crop losses, minimizes excessive pesticide usage, and promotes sustainable farming practices. Furthermore, this system can be integrated into smart farming applications and mobile-based agricultural support systems, making it accessible and beneficial for farmers in rural and urban areas alike
Why it matches plant phenotyping methods植物葉画像から健康・罹病状態と病害種を推定する画像処理・CNN手法が研究の中心であり、植物病害状態の表現型取得に該当する。ただし処置推奨は付随的である。
abstractThis project presents an intelligent plant disease detection system that utilizes image processing, Machine learning, and machine learning techniques for accurate disease identification in crops such as Onion, Brinjal, Mango, Papaya, and Guava.
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.
The presence of soil hydrocarbon parameters (SHPs), including total petroleum hydrocarbons (TPHs), total organic carbon (TOC; %), and soil toxicity (EC50; mg L−1), can affect vegetation in several ways. This study assessed the impact of SHPs on vegetation in the Niger Delta using field-measured, leaf-scale hyperspectral data acquired across the region. Red-edge position (REP) and four hyperspectral vegetation indices (HVIs)—mND705, photochemical reflectance index (PRI), Normalised Difference Vegetation Vigour Index (NDVVI844,447; a vegetation vigour index), and modified DATT (MDATT; a chlorophyll-sensitive red-edge index)—were used to quantify chlorophyll content in the vegetation types of Awolowo grass, elephant grass, mango trees, oil palm trees, and mangrove vegetation and to explore their variation with SHPs. The results show that mangrove vegetation was the most impacted by TPHs (R = −0.683), while mango vegetation was the most impacted by TOC (R = −0.725), based on Pearson correlation coefficients derived from the mND705 index. Similarly, mango and mangrove vegetation showed the strongest responses to soil toxicity (EC50; mg L−1), based on Spearman correlation coefficients (rs = 0.657 and rs = 0.870, respectively) using the MDATT index. These findings highlight species-specific physiological responses to soil hydrocarbon contamination and demonstrate the applicability of red-edge-based hyperspectral techniques for assessing vegetation stress in complex coastal ecosystems such as the Niger Delta.
Why it matches plant phenotyping methods葉面ハイパースペクトルデータとレッドエッジ指標により植物のクロロフィル量・生理的ストレスを定量化する手法を中心的に適用しており、植物状態の測定方法として実質的です。
abstractfield-measured, leaf-scale hyperspectral data acquired across the region
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 15 Sept 2026
Published10 Mar 2026International Journal of Scientific Research in Engineering and ManagementCited by 0 · OpenAlex ↗
Abstract - Mango (Mangifera indica) is one of the most commercially significant and nutritious tropical fruit crops. However, yield and fruit quality are significantly reduced by several leaf diseases, such as powdery mildew, sooty mold, anthracnose, bacterial canker, and gall midge infestation. Traditional method of visually diagnosing these diseases is laborious, prone to mistakes, time consuming, and often unavailable to many small holder farmers, this research used deep learning-based approaches that use convolutional neural networks (CNN) to classify mango leaf diseases. In order to evaluate this research method, a dataset of 4000 images were taken over eight classes, including a class representing healthy and diseased leaves. The images in the dataset were also increased to help improve the CNN’s ability to generalize from a limited set of images. Using TensorFlow, the EfficientNetB0 architecture with pre-trained weights, achieved a validation accuracy of approximately 98% and produced high precision, recall, and f1-scores throughout testing. In order to make the CNN model practical to apply, it was converted to TensorFlow Lite and then incorporated into a mobile application developed using Flutter, which enables real time classification of diseases on mango leaves. The proposed system demonstrates the potential of CNN-based models to provide accessible, scalable and field-ready solutions for early detection of mango diseases, ultimately leading to better crop management and productivity. Keywords: Mango disease detection, deep learning, Convolutional Neural Networks, image classification, EfficientNet, mobile deployment.
Why it matches plant phenotyping methodsマンゴー葉の画像から病害状態を分類するCNN手法の開発・評価が中心であり、植物の病徴を直接推定するため、植物フェノタイピング手法として採用。
abstractthis research used deep learning-based approaches that use convolutional neural networks (CNN) to classify mango leaf diseases.
A critical challenge in agricultural automation is the precise detection of mango leaf diseases that compromise crop quality and yield. To address the limitation of existing heavy models in resource-constrained agricultural environments, this study proposes MangoLeafNet-XAI, a novel lightweight deep learning architecture. The model synergistically integrates Efficient Channel Attention (ECA) modules with a DenseNet-121 backbone to adaptively refine features and capture subtle pathological patterns with high precision. The proposed framework was rigorously evaluated using a 5-fold cross-validation and soft-voting ensemble strategy across three public datasets (MLDID, Mango Leaf Disease, and Harumanis). These datasets encompass diverse environmental conditions and distinct disease classes, including Anthracnose, Bacterial Canker, Die Back, Gall Midge, Powdery Mildew, Sooty Mould, and Cutting Weevil. MangoLeafNet-XAI achieved state-of-the-art accuracies of 98.83% on MLDID, 98.09% on the Mango Leaf Disease Dataset, and 98.76% on the Harumanis dataset. A primary contribution of this work is the optimal balance between performance and computational efficiency, utilizing only 6.9 million parameters, making it highly suitable for deployment on edge devices. Moreover, the interpretability of AI methods, such as Grad-CAM and LIME, that are used to explain the rationale behind predictions to offer pathological explanations, also validate the focus on clinically important aspects of the model. The results discuss the key limitations of existing methods, such as computational complexity, inability to interpret the findings, and dataset-dependent overfitting, and demonstrate a high level of resilience and generalizability on diverse datasets. MangoLeafNet-XAI will be a new benchmark of reliable, deployable, as well as accurate disease diagnosis systems, in smart agriculture.
Why it matches plant phenotyping methodsマンゴー葉画像から病害状態を推定する軽量・解釈可能な深層学習手法を開発し、複数データセットで交差検証・性能評価しており、植物フェノタイピング手法が中心である。
abstractthis study proposes MangoLeafNet-XAI, a novel lightweight deep learning architecture.
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.
Amidst rising global food security challenges, early and precise detection of plant diseases has become essential-particularly for high-value crops such as mangoes. This study introduces a novel deep learning-based framework for the classification of mango leaf pathologies using advanced convolutional and hybrid neural architectures. A curated dataset of 800 high-resolution mango leaf images, collected from the Rajshahi region of Bangladesh, was preprocessed using extensive data augmentation and color space transformations to enhance generalization. Multiple models, including K-Nearest Neighbors, AlexNet, VGG16, VGG19, and EfficientNet-B7, were evaluated and compared against two proposed architectures: a custom Convolutional Neural Network (CNN) and a hybrid model integrating EfficientNet-B7, Long Short-Term Memory, and attention mechanisms. The proposed CNN model achieved 100% accuracy, precision, recall, and F1-score, outperforming all baseline models. The hybrid model achieved comparable results, demonstrating the effectiveness of combining spatial and temporal feature extraction for plant disease detection. Additionally, Grad-CAM visualizations provided interpretable diagnostic heatmaps, reinforcing the transparency and reliability of the model's predictions. The proposed framework advances state-of-the-art agricultural diagnostics by offering a scalable, interpretable, and high-performing solution for real-time disease monitoring in mango cultivation. These findings hold strong potential for improving crop surveillance and addressing food scarcity in mango-producing regions.
Why it matches plant phenotyping methodsマンゴー葉の病害状態を画像から分類するCNN・ハイブリッドモデルを開発・比較し、データセット、性能評価、Grad-CAM解釈まで含むため、植物表現型取得手法が中心である。
abstractThis study introduces a novel deep learning-based framework for the classification of mango leaf pathologies using advanced convolutional and hybrid neural architectures.
Abstract Size is one of the important external quality criteria of fruit and is often used in grading and sorting processes. Computer vision offers an effective solution for quickly and non-destructively estimating fruit volume. Several approaches involving three-dimensional (3D) reconstruction models of irregularly shaped fruit from multiple side-view images have been employed to enhance estimation accuracy. However, these approaches tend to be expensive and computationally complex. This study proposes a low-cost multi-view system for the rapid and accurate estimation of fruit volume. Using a top-mounted mirror-based setup, the system captures multiple fruit surfaces with a single camera, eliminating the need for rotation or multiple cameras. The captured multi-view image is analyzed using a global thresholding technique to calculate the multi-view area. A simple linear regression model is then built to estimate the fruit’s volume based on multi-view area, without requiring 3D model reconstruction. The proposed system was successfully tested for estimating the volume of two irregularly shaped fruits (pomelo and mango), achieving high coefficients of determination (0.986 and 0.988, respectively). Additionally, the system was tested with different fruit orientations, and the results showed that orientation did not affect volume estimation. These results demonstrate that this approach has strong potential not only for fruit quality assessment but also for other irregularly shaped solid objects where rapid and accurate volume estimation is needed.
Why it matches plant phenotyping methods果実の体積という植物器官形質を、鏡面マルチビュー画像と画像解析・回帰で推定する手法を開発・検証しており、形質取得法が研究の中心である。
abstractThis study proposes a low-cost multi-view system for the rapid and accurate estimation of fruit volume.
Early diagnosis of pests and diseases in smart-agriculture environments is essential for improving productivity. In particular, apple mango—a subtropical crop cultivated in Korea—is highly vulnerable to pests and diseases, necessitating reliable automated diagnostic methods. Although transfer learning–based deep learning classifiers have recently attracted significant attention in agricultural image analysis, conventional stepwise approaches suffer from inherent instability during optimization. This study proposes an end-to-end training strategy that combines Layer-wise Learning Rate Decay (LLRD) with a One-Cycle learning-rate schedule. Using this strategy, we effectively adapted pretrained convolutional neural networks (ResNet50, VGG16, and MobileNetV2) for classifying apple-mango pests and diseases. We employed a publicly available AI-Hub dataset comprising 5,400 images labeled as Healthy, Thrips, and Sooty Mold, and applied data augmentation to simulate variations in real agricultural imaging conditions and mitigate overfitting. Experimental results confirm that the proposed method achieves superior performance compared to our prior stepwise transfer-learning baseline across all backbone architectures. In particular, ResNet50 achieved the best performance, with 90.71% accuracy and a Macro-F1 score of 89.21%. These results indicate that the proposed training strategy overcomes the limitations of standard transfer learning and enhances fine-grained discrimination among disease classes. Moreover, its efficiency suggests practical applicability in resource-constrained environments such as drones and smart-farm devices. In the future, we plan to expand the range of crops and classes and explore multimodal fusion to further strengthen field robustness.
Why it matches plant phenotyping methods植物画像から病害状態を推定する分類手法の開発・比較が研究の中心であり、植物フェノタイピング手法として適格です。
abstractThis study proposes an end-to-end training strategy that combines Layer-wise Learning Rate Decay (LLRD) with a One-Cycle learning-rate schedule.
Abstract Visible and near‐infrared (VIS‐NIR) spectral imaging is vital for agriculture, food safety, and biomedical applications. Conventional spectral imaging relies on precision optical components with limited environmental adaptability, whereas computational spectral imaging employs advanced processing algorithms to simplify hardware architecture while improving system flexibility. However, developing compact, high‐performance, low‐cost snapshot systems remains challenging, especially in mask fabrication and real‐time imaging algorithms. In this work, a low‐cost snapshot VIS‐NIR spectral imager based on an on‐chip all‐dielectric weak‐confined Fabry–Pérot filter array and a deep learning‐based reconstruction approach is presented. Using single‐exposure patterning and the Cumulative Attention Transformer with Random Mask (CATRM) algorithm, the manufacturing process is streamlined while the reconstruction accuracy is enhanced. The system achieves high spatial resolution (100.17 lp mm −1 ) and maintains isotropic imaging fidelity, while reconstructing full‐field (2048 × 2048 × 61) hyperspectral data at 12.35 fps. The spectral accuracy of the imager is confirmed by spectral imaging of two traditional Chinese medicinal herbs, Astragalus membranaceus and Coptis chinensis , which shows over 99.1% cosine similarity between the system and a commercial scanning hyperspectral imager. Moreover, the broad application prospects are validated by non‐destructive sugar content prediction in mangoes. The integrated imager design enables compact, cost‐effective VIS‐NIR spectral imaging for diverse application scenarios.
Why it matches plant phenotyping methodsVIS-NIRスナップショット型ハイパースペクトル撮像装置と再構成手法の開発・性能検証が中心で、植物試料のスペクトル測定およびマンゴー糖度という植物器官形質の非破壊推定に応用している。
abstractThe spectral accuracy of the imager is confirmed by spectral imaging of two traditional Chinese medicinal herbs, Astragalus membranaceus and Coptis chinensis
Abstract Mango diseases and pest infestations represent a major challenge to agricultural productivity, making early and accurate diagnosis crucial for reducing crop losses. This study presents a security-preserving ensemble convolutional neural network (CNN) framework for the automated identification and classification of mango leaf diseases using image-based analysis. The proposed system is designed to work with images captured under real field conditions, ensuring its suitability for practical agricultural applications. The dataset includes mango leaf images affected by various diseases and pests such as Gall Midge, Powdery Mildew, Sooty Mould, Die Back, Cutting Weevil, and Anthracnose, each characterized by distinct visual symptoms including discoloration, necrotic spots, fungal growth, leaf deformation, and edge damage. Traditional manual diagnosis of these conditions is often time-consuming, labor-intensive, and susceptible to human error. To overcome these limitations, the proposed framework employs an ensemble of transfer-learning-based CNN models to extract meaningful features related to texture, color distribution, shape, and lesion patterns. A security-preserving learning mechanism is integrated to ensure the safe handling of agricultural image data, minimizing data exposure risks while maintaining high model performance. Additionally, data augmentation techniques are utilized to improve model robustness, reduce overfitting, and address class imbalance commonly found in agricultural datasets. The system is capable of multi-class classification, reflecting real-world scenarios where multiple diseases may exhibit visually similar characteristics. Experimental results indicate that the ensemble CNN framework achieves high classification accuracy and demonstrates strong generalization across varying lighting conditions and complex backgrounds. By effectively capturing disease-specific visual features, the proposed approach enhances detection reliability in real-world field environments. Overall, this system offers a scalable, non-invasive, and security-aware solution for early mango leaf disease detection, contributing to precision agriculture and informed decision-making. The findings highlight the potential of deep learning and computer vision technologies in developing intelligent, secure, and efficient plant health monitoring systems.
Why it matches plant phenotyping methodsマンゴー葉の病徴を画像から分類するCNNフレームワークの開発が研究の中心であり、植物の病害状態を直接推定する画像ベース表現型解析に該当する。
abstractThis study presents a security-preserving ensemble convolutional neural network (CNN) framework for the automated identification and classification of mango leaf diseases using image-based analysis.
Reproduction assets foundThe paper's plant-phenotyping input is the public Kaggle Mango Leaf Disease Dataset of mango leaf images (Gall Midge, Powdery Mildew, Sooty Mould, Die Back, Cutting Weevil, Anthracnose, Healthy), explicitly declared as publicly available with a link. No author code, models, or checkpoints are shared.Dataset · publicdation.
Zahra Maryam handled data curation and resources.
Muhammad Haseeb Zia conducted the formal analysis. All
authors reviewed and approved the final manuscript for
submission.
Funding This research did not receive funding.
Data Availability The dataset used in this study is
publicly available on Kaggle. The dataset link is:
https://www.kaggle.com/datasets/aryashah2k/mango-leaf-disease-dataset.Declarations
Conflict of interest The authors declare that they
have no conflict of interest.
Ethical approval This study utilizes a publicly
available benchmark dataset from Kaggle (Mango
Leaf Disease Dataset:
https://www.kaggle.com/datasets/aryashah2k/mang
o-leaf-disease-dataset ). The dataset is Open asset ↗Kaggle · aryashah2k/mango-leaf-disease-datasetpdf-raw-page:11 lines:1-91Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Abstract Convolutional Neural Networks (CNNs) are widely used for image classification, yet their performance strongly depends on dataset complexity and deployment constraints. This study presents a comparative evaluation of custom-designed CNN architectures and popular pre-trained models on five real-world image datasets from Bangladesh, spanning agricultural and infrastructural applications. The tasks include mango variety classification (15 classes), paddy disease clas-sification (35 classes), and three binary classification problems: road damage, footpath encroachment, and auto-rickshaw detection. In addition to task-specific CNNs, VGG16 and ResNet50 are evaluated using fixed feature extraction and transfer learning strategies. The results show that transfer learning, particularly with ResNet50, achieves the highest accuracy on complex multi-class datasets, while custom CNNs deliver competitive performance on binary tasks with sub-stantially lower computational cost. These findings emphasize the trade-off between accuracy and efficiency and highlight the importance of selecting model architectures based on dataset characteristics and deployment requirements.
Why it matches plant phenotyping methods複数CNNアーキテクチャを実データセットで比較評価することが研究の中心であり、イネ病害画像分類は植物の病態を画像から推定するフェノタイピングに該当する。
abstractThis study presents a comparative evaluation of custom-designed CNN architectures and popular pre-trained models on five real-world image datasets from Bangladesh, spanning agricultural and infrastructural applications.
MangoLeafPhysiological trait estimationGrowth / development / phenology
This study proposes a non-destructive method for estimating the relative age of mango leaves using electrical impedance spectroscopy (EIS) and a modified double-shell equivalent circuit model. Impedance measurements were conducted on six mango varieties using five leaves per variety representing different positions along the twig. The Nyquist plots showed increasing circular arc diameters with leaf position, indicating higher charge transfer resistance in older leaves. Fitting results revealed that R1 increased while C1 decreased systematically with leaf age. Spearman correlation analysis confirmed a strong positive correlation between R1 and leaf position and a strong negative correlation for C1, whereas n1 showed no significant relationship. Linear regression yielded high coefficients of determination for most varieties. The selection of R1 and C1 as electrical indicators was supported by their low fitting errors, strong correlations, and consistent regression performance. These results demonstrate that EIS provides a rapid and reliable non-destructive approach for assessing the physiological age of mango leaves.
Why it matches plant phenotyping methodsEISを用いてマンゴー葉の相対葉齢を非破壊推定する方法を提案し、電気的指標の相関・回帰性能・誤差を検証しており、植物表現型取得法が研究の中心である。
abstractThis study proposes a non-destructive method for estimating the relative age of mango leaves using electrical impedance spectroscopy (EIS) and a modified double-shell equivalent circuit model.
Timely and precise detection of diseases on plants is crucial for minimizing losses during crop production in order to sustain food supply demands worldwide. In this work, deep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants using two publicly available datasets, the PlantVillage Potato Leaf Disease (2,152 images) dataset and the Kaggle Mango Leaf Disease dataset (4,000 images). Images were pre-processed, augmented, and split into training and testing datasets (80:20), to enable better model generalization. Four deep learning architectures, namely Convolutional Neural Networks (CNN), AlexNet, Residual Networks (ResNet), and EfficientNet, were evaluated in the context of multi-class disease classification. The baseline CNN achieved a training accuracy of 93.67% and a testing accuracy of 92.61%, with balanced precision and recall (92.5%), thus providing a very strong feature extraction and classification capability. AlexNet showed moderate performance (91.3% training, 90.2% validation), and a very small overfitting was observed. ResNet had an efficient convergence, and attained 96.7% validation accuracy in just a few epochs, thus pointing out the advantage of residual connections in the context of deeper learning. EfficientNet surpassed all the other architectures, since it reached a training accuracy of 98.2% and a validation accuracy of 97.8%, with very small loss (≈ 0.015) and no overfitting, thus proving to have the best generalization ability. The models demonstrated stability and discriminative ability with the support of confusion matrices and accuracy and loss plots produced on an epoch-wise basis. Therefore, the findings indicate that DL models can be adapted for real-time and accurate plant disease diagnosis, establishing a pathway for early remediation, and supporting precision agriculture. The research establishes the opportunity for EfficientNet to be considered a promising solution for scalable smart farming.
Why it matches plant phenotyping methods葉画像から植物病害を分類する深層学習手法を開発・比較し、複数データセットで精度を検証しており、植物の病害状態の取得・推定が研究の中心である。
abstractdeep learning (DL) was used to develop an automatic disease identification system for the leaves of potato and mango plants
Reproduction assets foundThe paper uses two public Kaggle leaf-image datasets (PlantVillage potato, mango leaf disease) and states that all code, preprocessing scripts, dataset splits, and model artifacts are publicly available in a GitHub repository (also archived on Zenodo). All three are paper-specific, public, and actionable.Dataset · publicThe datasets analyzed during the current study are available in (https://www.kaggle.com/datasets/aarishasifkhan/plantvillage-potato-disease-dataset)Open asset ↗html-lines:473-503Code · publicAll code, preprocessing scripts, dataset splits, and model artifacts used in this study are publicly available in the GitHub repository at: [https://github.com/logeswarig/PROJECT_1].Open asset ↗logeswarig/PROJECT_1html-lines:473-503Plant phenotyping relevance match · UnverifiedOpenAlex · Europe PMC · checked 6 Sept 2026
Accurate measurement of key phenotypic traits, including the horizontal and vertical diameters, the weights of both fruit and pit, is essential for the selection of elite litchi cultivars and the advancement of breeding research. Manual measurement, however, is laborious, inefficient, and subjective, highlighting the urgent need for automated and precise phenotyping tools. Unlike apples, mangoes, and grapes, litchi combines a spiny, highly variable pericarp (heterogeneous areoles/tubercles across cultivars) with diverse seed morphology (including irregular, wrinkled aborted seeds), thereby increasing the difficulty of semantic segmentation and biasing diameters and weight estimation. This study presents LitchiPhenoNet, a multimodal learning framework for litchi phenotypic analysis that employs a dual-branch architecture integrating RGB (color/texture) and depth (spatial/structural) information. Experiments were conducted on an RGB-D dataset comprising 1,198 image pairs (1280×720) across 10 cultivars, using a stratified train/test split of 958/240 pairs by cultivar. To address inherent semantic and scale inconsistencies between modalities, the framework incorporates the RD-Fusion module for precise cross-modal feature extraction, improving robustness under complex and variable pericarp surfaces. Comparative experiments show that LitchiPhenoNet consistently outperforms leading YOLO-based models, achieving millimeter-level diameter estimation with coefficients of determination approaching 0.98 and mean errors within 2 mm. For weight estimation, gram-level precision is attained across whole fruit, pit, and pulp, with coefficients of determination up to 0.98 and mean errors comparable to repeated manual measurements. By handling fine-scale surface relief and cross-cultivar variability, the framework is readily extensible to other textured fruits and scalable for high-throughput phenotyping in breeding programs. Collectively, these results demonstrate that LitchiPhenoNet provides an efficient, reliable, and accurate solution for quantifying litchi phenotypic traits, substantially advancing the objectivity and efficiency of phenotypic analysis and breeding selection.
Why it matches plant phenotyping methodsRGB-D画像を用いてライチ果実・種子・果肉の径と重量を自動推定する専用フレームワークを開発し、複数品種・比較実験で性能検証しているため、植物表現型取得法が中心である。
abstractThis study presents LitchiPhenoNet, a multimodal learning framework for litchi phenotypic analysis that employs a dual-branch architecture integrating RGB (color/texture) and depth (spatial/structural) information.
Published1 Dec 2025Journal of food composition and analysis : an official publication of the United Nations University, International Network of Food Data Systems
MangoRaman / spectroscopyFruitPhysiological trait estimationWater status / transpiration
Accurate, rapid, and online determination of mango dry matter content (DMC) holds great significance for the mango industry. The integration of near-infrared spectroscopy and deep learning theory offers an opportunity to enhance determination accuracy. In this paper, we propose a spectral data augmentation method based on the sparse autoencoder and establish a one-dimensional convolutional model to predict mango DMC. The test results indicate that the model performs optimally when trained on a training set comprising 80 % of the augmented data. The root mean square error (RMSE) of the test set was 0.4073, and the coefficient of determination (R²) was 0.9782. The prediction accuracy of our model surpasses that of models such as Gaussian Process Regression, Support Vector Machines, and Partial Least Squares Regression. This study can assist in fruit quality inspection, processing optimization, variety selection, and breeding, as well as storage and preservation, and has a wide range of application potential and value. It also provides novel insights into data augmentation techniques for near-infrared spectral regression modeling.
Why it matches plant phenotyping methodsマンゴー果実の乾物含量という植物器官形質をNIRSと1-D CNNで推定する手法を開発・評価しており、形質取得・抽出法が研究の中心である。
abstractwe propose a spectral data augmentation method based on the sparse autoencoder and establish a one-dimensional convolutional model to predict mango DMC.
Background: Mango leaf diseases reduce fruit yield and quality, requiring early detection for effective management. Traditional methods rely on manual inspection, which is slow, subjective and error-prone. Deep learning, especially Convolutional Neural Networks (CNNs), offers automation but faces challenges. These include class imbalance, poor dataset generalization and limited real-world scalability. This study develops a robust CNN model to improve mango leaf disease classification. Methods: A dataset of 2,494 mango leaf images from the Mendeley database was used. Images were categorized into anthracnose, bacterial canker, cutting weevil, dieback and healthy. Preprocessing involved image resizing, normalization and data augmentation to enhance model performance. The dataset was split into 80% training, 10% validation and 10% testing. A six-layer CNN with ReLU activation, max-pooling, dropout (0.5) and fully connected layers was trained for 25 epochs. The model used Adam optimizer and categorical cross-entropy loss. Result: The model achieved 98.03% training accuracy and 97.77% validation accuracy over 25 epochs. It had a low validation loss (0.0485), indicating good generalization. The confusion matrix showed high precision and recall across all classes. The overall classification accuracy was 96.53%, with a macro-average F1-score of 96.57%. Anthracnose and Dieback were perfectly classified. Bacterial canker had a lower precision (0.8500), suggesting minor misclassifications. AUC analysis showed good disease separation, with Cutting Weevil achieving the highest AUC (0.72). This CNN model can automate mango disease detection, reducing reliance on manual inspections. It can be useful for smart farming systems and mobile applications for real-time disease diagnosis. Future work will focus on expanding the dataset, optimizing for mobile use and integrating environmental factors for better disease prediction.
Why it matches plant phenotyping methodsCNNによるマンゴー葉画像からの病害状態分類が研究の中心であり、植物の病徴を直接推定する画像ベースのフェノタイピング手法を開発・評価している。
abstractThis study develops a robust CNN model to improve mango leaf disease classification.
To enhance fruit yield and quality, this study focuses on precise pre-harvest ripeness assessment and early disease detection. Addressing the limitations of conventional methods, we propose a multimodal flexible sensing and deep learning-based evaluation framework. The developed flexible optoelectronic in-situ sensing system integrates spectral (410-940 nm, 18 channels) and impedance (100 Hz-10 kHz) detection, allowing conformal attachment to mango surfaces for nondestructive monitoring throughout the growth cycle while collecting spectral, impedance, and physicochemical data. The proposed 1DCNN-ATT-BiLSTM-ATT network employs independent branches to extract local features from each modality, followed by attention mechanisms and temporal modelling for comprehensive feature fusion, achieving 97.5 % accuracy on test sets. Field experiments reveal systematic variations in soluble solid content (SSC), moisture content (MC), and optoelectronic signals during ripening. Correlation and Granger causality analyses underscore the necessity of multimodal fusion. This system supports intelligent harvesting and precision monitoring, advancing agricultural practices toward greater efficiency and sustainability while establishing a technical paradigm for precision agriculture. Future work will focus on improving environmental robustness and cross-cultivar applicability.
Why it matches plant phenotyping methodsマンゴー果実に装着する分光・インピーダンス統合センシングと深層学習による成熟度・品質状態推定を開発しており、植物状態の取得・抽出手法が研究の中心である。
abstractThe developed flexible optoelectronic in-situ sensing system integrates spectral (410-940 nm, 18 channels) and impedance (100 Hz-10 kHz) detection, allowing conformal attachment to mango surfaces for nondestructive monitoring throughout the growth cycle
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.
Mango is a fruit of great economic importance in India. India is the top mango-producing nation in the world, accounting for over half of global mango output. In order to determine the production capability of the insured orchards, a complete inventory is carried out in situ every three years. The inventory includes counting number of trees, grouping them into yield categories, and assessing damaged ones. Satellite Remote Sensing proves to be a vital tool for estimating ecological parameters such as population density, tree health, volume, biomass, and carbon sequestration rates. The significance of tree counting extends beyond orchard evaluations, playing a vital role in environmental protection, agricultural planning, and crop yield forecast. unfortunately, conventional tree counting methods often require very expensive feature engineering, which leads to more errors as well as lower overall optimization. In order to overcome these obstacles, deep learning-based methods have been used to count trees, exhibiting cutting-edge results in this crucial activity. This paper introduces a novel approach employing deep learning for Image-Based Mango Tree counting in high-resolution satellite imagery data. The proposed model, named Bi-directional Feature Pyramid Network (BiFPN)-YOLOv8m an improved version of YOLOv8, employs object detection to effectively separate, locate, and count mango trees with in orchards. A dataset of 1700 training and 300 testing images of mango orchards with trees of various ages is used to evaluate the various YOLOv8 variants, YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, YOLOv8x, including YOLOv9, YOLOv10, and BiFPN-YOLOv8m, with a focus on computational efficiency, accuracy, and speed. Experimental findings show that, even under difficult circumstances, the proposed method continuously outperforms state-of-the-art techniques.
Why it matches plant phenotyping methods衛星画像からマンゴー樹木を分離・位置推定・計数する深層学習手法を開発・評価しており、植物個体数という観測可能な形態・構造形質の抽出が中心である。
abstractThis paper introduces a novel approach employing deep learning for Image-Based Mango Tree counting in high-resolution satellite imagery data.
Reproduction assets foundThe paper's satellite remote sensing image dataset used for mango tree counting is publicly deposited on GitHub per the Data Availability Statement. No separate analysis code or trained model checkpoint is explicitly deposited.Dataset · publicRemote Sensing Image Data that support the findings of this study have been deposited in the GitHub. The url to the data uploaded is https://github.com/lbirla/Mango_tree_satellite_data.Open asset ↗https://github.com/lbirla/Mango_tree_satellite_datahtml-lines:497-525Plant phenotyping relevance match · UnverifiedEurope PMC · checked 6 Sept 2026
Mango is a crop of vital agronomic and commercial importance, particularly in tropical and subtropical regions. Accurate and timely identification of foliar diseases is essential for maintaining plant health and ensuring sustainable agricultural productivity. This study proposes MangoLeafCMDF-FAMNet (cross-modal dynamic fusion with feature attention module (FAM) network), an advanced, hybrid, deep-learning framework designed for the multi-class classification of mango leaf diseases. The model combines two state-of-the-art feature extractors, ConvNeXt and Vision Transformer, to capture local fine-grained textures and global contextual semantics simultaneously. To further improve feature discrimination, a FAM inspired by squeeze-and-excitation networks is integrated into each stage of the backbone. This module adaptively recalibrates channel-wise feature responses to highlight disease-relevant cues while suppressing irrelevant background noise. A novel cross-modal dynamic fusion strategy unifies the complementary strengths of both branches, resulting in highly robust and discriminative feature embeddings. The proposed model was rigorously evaluated using comprehensive metrics such as classification accuracy (CA), recall, precision, Matthews correlation coefficient (MCC) and Cohen's kappa score on three benchmark datasets: MangoLeafDataset1 (8 classes), MangoLeafDataset2 (5 classes) and MangoLeafDataset3 (8 classes). The experimental results consistently demonstrate the superiority of MangoLeafCMDF-FAMNet over the existing baseline models. It achieves exceptional CA values of 0.9978, 0.9988 and 0.9943 across the respective datasets, alongside strong MCC and Cohen's kappa scores. These results highlight the effectiveness and generalizability of the proposed framework for automated mango leaf disease diagnosis and contribute to advancing deep learning applications in precision plant pathology.
Why it matches plant phenotyping methodsマンゴー葉の病徴を画像から分類する深層学習手法の開発と複数データセットでの技術評価が中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用する。
abstractThis study proposes MangoLeafCMDF-FAMNet (cross-modal dynamic fusion with feature attention module (FAM) network), an advanced, hybrid, deep-learning framework designed for the multi-class classification of mango leaf diseases.
Accurate fruit disease classification is critical for agricultural productivity, yet traditional manual methods remain labour-intensive and error-prone. This study proposes a hybrid model that synergistically integrates multi-architecture convolutional neural network models such as ResNet, DenseNet, and EfficientNet to address challenges such as intra-class variability and dataset imbalances. The model has Mobile Inverted Bottleneck Convolution with Squeeze-and-Excitation blocks that help it prioritize useful features. It also has residual pathways for gradient optimization and data augmentation that makes it easier to generalize. The results are evaluated on 9714 apple disease images (four classes), 3403 guava disease images (three classes), and 5000 mango disease images (five classes), the hybrid model achieves perfect performance, with 99.99% accuracy, precision, recall, and F1-score for apple diseases, 99.60% accuracy for guava diseases, and 99.68% accuracy for mango diseases, surpassing state-of-the-art methods. Key innovations include dense connectivity for feature reuse, channel-wise attention mechanisms, and computational efficiency through global average pooling. The results underscore the model’s robustness and scalability, offering a practical solution for real-time disease detection in smart farming systems.
Why it matches plant phenotyping methods果実の病害状態を画像から分類するCNN手法の開発と評価が中心であり、植物の病徴・病害状態を直接推定するため、植物フェノタイピング手法として収録する。
abstractThis study proposes a hybrid model that synergistically integrates multi-architecture convolutional neural network models such as ResNet, DenseNet, and EfficientNet
Objective The primary aim of this research is to develop an effective and robust model for identifying and classifying diseases in general fruits, particularly apples, guavas, mangoes, pomegranates, and oranges, utilizing computer vision techniques. Material An open-source collection of fruit disease images, comprising both diseased and healthy samples from the first five fruit types, was used in this study. The data was split into 70% training, 15% validation, and 15% testing. A 5-fold cross-validation was used to maintain the generalizability and stability of the model's performance. Models For performance comparisons of these models on the dataset, we benchmarked state-of-the-art pre-trained convolutional neural network (ConvNet) models, including Swin Transformer (ST), EfficientNetV2, ConvNeXt, YOLOv8, and MobileNetV3. A new model, the Dual-Branch Attention-Guided Vision Network (DBA-ViNet), was introduced. A hybrid with two branches of DBA-ViNet can efficiently integrate global and local features for improved disease identification accuracy. Grad-CAM was used to visualize the regions that contributed to each prediction, helping to interpret the model. These heatmaps verified that DBA-ViNet can correctly direct its attention to disease-specific symptoms, thereby increasing trust and transparency in the classification results. Results The proposed DBA-ViNet achieved a high testing classification accuracy of 99.51%, specificity of 99.42%, recall of 99.61%, precision of 99.30% and F1 score of 99.45% outperforming baseline models in all evaluation metrics. While the improvements were consistent, statistical significance testing was not performed and will be explored in future work. Conclusion These results confirm the effectiveness of the proposed DBA-ViNet architecture in fruit disease detection, suggesting that incorporating both global and local feature extraction into the design of the double-branch attention mechanism for classification can achieve high accuracy and reliability. It is potentially practical in smart agriculture and the automated crop health monitoring system.
Why it matches plant phenotyping methods果実画像から植物の病害状態を推定する深層学習モデルを開発し、複数モデルとの性能比較・検証を行っており、植物フェノタイピング手法が中心である。
abstractThe primary aim of this research is to develop an effective and robust model for identifying and classifying diseases in general fruits
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Dataset · publicThe dataset used in this study consists of 7,639 images representing healthy and diseased samples of five common fruits: apple, guava, mango, orange, and pomegranate https://www.kaggle.com/datasets/saravanansri/apple-guava-mangoe-pomegranate-orange-datasetOpen asset ↗Kagglelines:110-130Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published1 Jul 2025Indonesian Journal of Electrical Engineering and Computer ScienceCited by 1 · OpenAlex ↗
Agriculture has become simply to feed ever-growing populations. The tomato is arguably the most well-known vegetable in agricultural areas and plays a significant role in the growth of vegetables in our daily lives. However, because this tomato has multiple diseases, image segmentation of the diseased leaf shows a key role in classifying the disease by the leaf's symptoms. Therefore, in this paper, an efficient plant disease segmentation using an adaptive radial basis function neural network (ARBFNN) classifier. The proposed radial basis function (RBF) neural network is enhanced by using the flower pollination algorithm (FPA). Firstly, the noise is detached by an adaptive median filter and histogram equalization. Then, from every leaf image, different kind of color features is extracted. After the extraction of features, those are fed to the segmentation phase to section the disease serving from the input image. The efficiency of the suggested method is analyzed based on various metrics and our technique attained a better accuracy of 97.58%.
Why it matches plant phenotyping methods植物葉画像から病徴領域を自動抽出する画像セグメンテーション手法の開発・評価が中心であり、植物病害状態の表現型推定に該当する。
abstractimage segmentation of the diseased leaf shows a key role in classifying the disease by the leaf's symptoms.
AppleMangoPeachPearPlumField / plotNeRF / 3D Gaussian SplattingRGB / grayscaleFruitWhole plant / canopy / plot / field
FruitNeRF++: A Generalized Multi-Fruit Counting Method Utilizing Contrastive Learning and Neural Radiance Fields We introduce FruitNeRF++, a novel fruit-counting approach that combines contrastive learning with neural radiance fields to count fruits from unstructured input photographs of orchards. Our work is based on FruitNeRF [6], which employs a neural semantic field combined with a fruit-specific clusteringapproach. The requirement for adaptation for each fruit type limits the applicability of the method, and makes it difficult to use in practice. To lift this limitation, we design a shape-agnostic multi-fruit counting framework, that complements the RGB and semantic data with instance masks predicted by a vision foundation model. The masks are used to encode the identity of each fruit as instance embeddings into a neural instance field. By volumetrically sampling the neural fields, we extract apoint cloud embedded with the instance features, which can be clustered in a fruit-agnostic manner to obtain the fruit count. We evaluate our approach using a synthetic dataset containing apples, plums, lemons, pears, peaches, and mangoes, as well as a real-world benchmark apple dataset. Our results demonstrate that FruitNeRF++ is easier to control and compares favorably to other state-of-the-art methods.
Why it matches plant phenotyping methods果実を対象とした画像ベースの汎用カウント手法を開発し、合成および実データで評価しているため、植物形質（果実数）の取得・推定が研究の中心です。
abstractWe introduce FruitNeRF++, a novel fruit-counting approach that combines contrastive learning with neural radiance fields to count fruits from unstructured input photographs of orchards.
Maintaining optimal yield plays a crucial role in the prosperity of agriculture and in turn the economy of the country. One way to optimize this yield is by early and accurate detection and diagnosis of crop diseases. Traditional methods that involve manual inspection or the like tend to be tedious and often inaccurate. Hence the use of machine learning and convolutional neural networks have proven to be of great advantage in terms of accuracy, reliability, ease of implementation etc. This paper explores various deep learning models such as AlexNet, ResNet, Swin Transformer, Vgg-16, vit model for plant leaf disease detection and classification on a dataset of mango leaves and compares aspects such as accuracy and loss. Further the models have been combined using feature fusion, and their accuracies compared. Finally, a combination of ResNet and AlexNet has been proposed with an impressive accuracy of 99.97%. Further, Grad-CAM (Gradient-weighted Class Activation Mapping) has been implemented to highlight important regions in the leaf images which improves visualization. This can potentially provide an accurate identification and classification of plant diseases based on leaf images.
Why it matches plant phenotyping methodsマンゴー葉画像から植物病害を検出・分類する深層学習手法を比較・融合し、Grad-CAMで病徴領域を可視化しており、植物の病害状態を画像から推定する方法が中心である。
abstractThis paper explores various deep learning models such as AlexNet, ResNet, Swin Transformer, Vgg-16, vit model for plant leaf disease detection and classification on a dataset of mango leaves and compares aspects such as accuracy and loss.
Precision agriculture is an approach that uses information technologies to improve and optimize agricultural production. It is based on the collection and analysis of agricultural data to support decision making in agricultural processes. In recent years, Artificial Neural Networks (ANNs) have demonstrated significant benefits in addressing precision agriculture needs, such as pest detection, disease classification, crop state assessment, and soil quality evaluation. This article aims to perform a systematic literature review on how ANNs with an emphasis on image processing can assess if fruits such as mango, apple, lemon, and coffee are ready for harvest. These specific crops were selected due to their diversity in color and size, providing a representative sample for analyzing the most commonly employed ANN methods in agriculture, especially for fruit ripening, damage, pest detection, and harvest prediction. This review identifies Convolutional Neural Networks (CNNs), including commonly employed architectures such as VGG16 and ResNet50, as highly effective, achieving accuracies ranging between 83% and 99%. Additionally, it discusses the integration of hardware and software, image preprocessing methods, and evaluation metrics commonly employed. The results reveal the notable underuse of vegetation indices and infrared imaging techniques for detailed fruit quality assessment, indicating valuable opportunities for future research.
Why it matches plant phenotyping methods果実の成熟・損傷・病害などを画像処理とANNで評価する方法を体系的にレビューしており、植物表現型取得・推定手法が中心である。
abstractThis article aims to perform a systematic literature review on how ANNs with an emphasis on image processing can assess if fruits such as mango, apple, lemon, and coffee are ready for harvest.
Rapid quality assessment of fruit is important to ensure consistent-quality production and supply. This study explored hyperspectral imaging (HSI) as a method to predict °Brix, acidity, and mineral nutrient concentrations using skin or flesh images of two lychee and two mango cultivars. Partial least squares regression (PLSR) models were developed using each cultivar. Spectral data across two cultivars were then pooled and these models compared with the models developed using individual cultivars. Artificial neural network (ANN) and support vector machine regression (SVMR) models were also developed for predicting Brix, acidity and Brix/acid ratio. Both the skin and flesh images were useful for developing PLSR models that predicted Brix and the Ca, Cu, Fe and Mn concentrations of lychee and mango flesh, with R² from 0.50 to 0.89. Pooled-cultivar datasets were useful for developing PLSR models that predicted Brix of lychee flesh, and Brix, acidity and Brix/acid ratio of mango flesh, with R² ≥ 0.60. The prediction accuracies were improved using ANN to estimate Brix and acidity of lychee flesh, while the prediction accuracies were improved using both ANN and SVMR to estimate Brix, acidity and Brix/acid ratio of mango flesh. The results demonstrate that skin images can be used for non-destructive assessment, and that HSI can predict fruit quality even among mixed-cultivar consignments. Advanced machine learning techniques further improve the prediction capacity. HSI provides a rapid method for predicting flesh quality of lychee and mango fruit, facilitating the timely scheduling of harvesting and allowing the grading of fruit into consistent-quality batches.
Why it matches plant phenotyping methods果実画像から糖度・酸度・ミネラル濃度を非破壊推定するハイパースペクトル画像法と回帰モデルが研究の中心であり、果実品質という植物器官形質の取得・推定手法に該当する。
abstractThis study explored hyperspectral imaging (HSI) as a method to predict °Brix, acidity, and mineral nutrient concentrations using skin or flesh images of two lychee and two mango cultivars.
Abstract – Agricultural yield is highly dependent on timely disease management and optimal growing conditions. In mango cultivation, especially for the Alphonso variety, diseases such as anthracnose and rust cause significant damage. This paper introduces an IoT-based system that combines environmental monitoring with machine learning-driven leaf disease detection. Temperature, humidity, and soil moisture are tracked using DHT11 and soil moisture sensors interfaced with a NodeMCU ESP8266. This data is visualized on ThingSpeak. For disease diagnosis, a trained Convolutional Neural Network (CNN) model classifies mango leaves into healthy, rust-infected, or fungal-infected categories. The model is deployed via a Streamlit web application, offering users an intuitive interface for image upload and result display. The integrated system supports precision agriculture through timely alerts and remedies, reducing manual inspection and promoting sustainable farming. Keywords: Alphonso mango, Convolutional Neural Network (CNN), IoT-based monitoring, Leaf disease detection, Smart agriculture, Internet of Things (IoT).
Why it matches plant phenotyping methodsマンゴー葉の画像から病害状態をCNNで推定する手法と、IoTセンサーを統合したモニタリングシステムが中心であり、植物の病害表現型を直接評価している。
abstractThis paper introduces an IoT-based system that combines environmental monitoring with machine learning-driven leaf disease detection.
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 14 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 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.
The development of unmanned aerial spraying systems (UASSs) has significantly transformed pest and disease control methods of crop plants. Precisely adjusting pesticide application rates based on the target conditions is an effective method to improve pesticide use efficiency. In orchard spraying, the structural characteristics of the canopy are crucial for guiding the pesticide application system to adjust spraying parameters. This study selected mango trees as the research sample and evaluated the differences between UAV aerial photography with a Structure from Motion (SfM) algorithm and airborne LiDAR in the results of extracting canopy parameters. The maximum canopy height, canopy projection area, and canopy volume parameters were extracted from the canopy height model of SfM (CHMSfM) and the canopy height model of LiDAR (CHMLiDAR) by grids with the same width as the planting rows (5.0 m) and 14 different heights (0.2 m, 0.3 m, 0.4 m, 0.5 m, 0.6 m, 0.8 m, 1.0 m, 2.0 m, 3.0 m, 4.0 m, 5.0 m, 6.0 m, 8.0 m, and 10.0 m), respectively. Linear regression equations were used to fit the canopy parameters obtained from different sensors. The correlation was evaluated using R2 and rRMSE, and a t-test (α = 0.05) was employed to assess the significance of the differences. The results show that as the grid height increases, the R2 values for the maximum canopy height, projection area, and canopy volume extracted from CHMSfM and CHMLiDAR increase, while the rRMSE values decrease. When the grid height is 10.0 m, the R2 for the maximum canopy height extracted from the two models is 92.85%, with an rRMSE of 0.0563. For the canopy projection area, the R2 is 97.83%, with an rRMSE of 0.01, and for the canopy volume, the R2 is 98.35%, with an rRMSE of 0.0337. When the grid height exceeds 1.0 m, the t-test results for the three parameters are all greater than 0.05, accepting the hypothesis that there is no significant difference in the canopy parameters obtained by the two sensors. Additionally, using the coordinates x0 of the intersection of the linear regression equation and y=x as a reference, CHMSfM tends to overestimate lower canopy maximum height and projection area, and underestimate higher canopy maximum height and projection area compared to CHMLiDAR. This to some extent reflects that the surface of CHMSfM is smoother. This study demonstrates the effectiveness of extracting canopy parameters to guide UASS systems for variable-rate spraying based on UAV oblique photography combined with the SfM algorithm.
Why it matches plant phenotyping methodsUAV-SfMとLiDARによる樹冠形状パラメータ抽出を中心に、抽出精度の比較・検証を行っており、植物形質取得法が主要な貢献である。
abstractevaluated the differences between UAV aerial photography with a Structure from Motion (SfM) algorithm and airborne LiDAR in the results of extracting canopy parameters.
Purpose: Mango production can be severely impacted by diseases affecting mango plants. By leveraging artificial intelligence, the agricultural sector can automate the analysis of mango leaves to monitor plant health. The goal of this research is to improve the early detection of diseases in mango leaves to allow early treatment to minimize damage to the crops. Methods: This study employs an approach of combining two pre-trained CNN architectures, namely MobileNetV2 and DenseNet201 through concatenation method. To enhance the model’s generalization ability, various image augmentation techniques were applied during the training phase. Result: The model developed in this study achieved great performance in classifying mango leaf diseases with a testing accuracy of 99.25%. This result indicates the effectiveness of the concatenation method by outperforming the accuracy of either MobileNetV2 or DenseNet201 when implemented separately. Novelty: This research introduces a novel strategy by concatenating two pre-trained CNN architectures for mango leaf disease classification, a method not previously explored in this context. The model developed from this study has the potential to serve as a tool for the early detection and treatment of mango leaf diseases.
Why it matches plant phenotyping methodsマンゴー葉の病害状態を画像から分類するCNN手法の開発・性能評価が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThis research introduces a novel strategy by concatenating two pre-trained CNN architectures for mango leaf disease classification
The health and productivity of plants, particularly those in agricultural and horticultural industries, are significantly affected by timely and accurate disease detection. Traditional manual inspection methods are labor-intensive, subjective, and often inaccurate, failing to meet the precision required by modern agricultural practices. This research introduces an innovative deep transfer learning method utilizing an advanced version of the Xception architecture, specifically designed for identifying plant diseases in roses, mangoes, and tomatoes. The proposed model introduces additional convolutional layers following the base Xception architecture, combined with multiple trainable dense layers, incorporating advanced regularization and dropout techniques to optimize feature extraction and classification. This architectural enhancement enables the model to capture complex, subtle patterns within plant leaf images, contributing to more robust disease identification. A comprehensive dataset comprising 5491 images across four distinct disease categories was employed for the training, validation, and testing of the model. The experimental results showcased outstanding performance, achieving 98% accuracy, 99% precision, 98% recall, and a 98% F1-score. The model outperformed traditional techniques as well as other deep learning-based methods. These results emphasize the potential of this advanced deep learning framework as a scalable, efficient, and highly accurate solution for early plant disease detection, providing substantial benefits for plant health management and supporting sustainable agricultural practices.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習手法の開発・性能評価が中心であり、植物病害フェノタイピング手法に該当する。
abstractThis research introduces an innovative deep transfer learning method utilizing an advanced version of the Xception architecture, specifically designed for identifying plant diseases in roses, mangoes, and tomatoes.
Using chemical laboratory procedures to estimate the fruit quality parameters (biochemical parameters) of mango "Succarri" and strawberry "Florida" as indicators of ripening degrees in a large area presents challenges such as low throughput, labor intensity, time consumption, and the need for multiple samples. So, using spectral reflectance-based proximal remote sensing to quickly and accurately measure biochemical parameters in different fruits is important to find the best time to harvest, make food ripen faster, and the processing of food easier. This has significant economic and ecological advantages. The objective of this study was to evaluate the biochemical parameters of mango and strawberry fruits at various ripening stages. This was done by utilizing a combination of established and newly developed spectral reflectance indices (SRIs) in conjunction with machine learning (ML) models, including artificial neural networks (ANN), random forests (RF), and decision trees (DT). For mango fruit, the parameters estimated were chlorophyll content, total soluble solids (TSS), and firmness, whereas for strawberry fruit, the parameters were L*, b*, TSS, and firmness. These results revealed significant differences in SRI values across various ripening stages, indicating variances in the fruit's biochemical parameters. The newly developed SRIs showed superior efficacy in evaluating these parameters. The integration of SRIs with diverse ML models proved to be a successful strategy for precisely estimating biochemical parameters. For mango's biochemical parameter prediction, the ANN models demonstrated R2 values ranging from 0.92 to 1.00 and from 0.93 to 0.98 for training and testing, respectively. On the other hand, the RF models exhibited R2 values ranging from 0.98 to 1.00 and from 0.93 to 0.99 during training and testing, respectively. The DT models showed high performance, with R2 values ranging from 0.95 to 1.00 and from 0.88 to 0.99 for the training and testing phases. For strawberry's biochemical parameter prediction, the ANN models achieved R2 values between 0.75 and 0.91 and between 0.58 and 0.91 during training and testing phases, respectively. On the other hand, RF models showed R2 values between 0.85 and 0.91 during training and between 0.74 and 0.86 during testing. The DT models demonstrated excellent results, with R2 values ranging from 0.75 to 0.91 for the training set and 0.74 to 0.81 for the testing set. It can be concluded that combining SRIs with ML models, such as ANN, RF, and DT, can accurately predict the biochemical properties of mango and strawberry fruits.
Why it matches plant phenotyping methods果実の成熟関連形質を対象に、分光反射センシング、独自スペクトル指数、機械学習モデルによる非破壊推定法を開発・評価しており、表現型取得・抽出が中心である。
abstractusing spectral reflectance-based proximal remote sensing to quickly and accurately measure biochemical parameters in different fruits
The portable X-ray fluorescence (pXRF) spectrometry has been very useful for the characterization of different earth materials, and its application for foliar analysis is really promising. The performance of pXRF for foliar analysis depends on several factors such as concentration of the elements, fluorescence yield which is influenced by atomic number, spectral interference, and water content. Mn is one of the elements that present a prominent fluorescence peak. In this sense, it was hypothesized that pXRF can directly determine the Mn concentration on foliar samples, even when used on intact leaves (fresh or dry) being a useful tool for agronomic and environmental purposes. Thus, the objective was to assess the performance of a pXRF to determine Mn concentration in two different foliar datasets from Brazil/South America and Mali/Africa. In the Brazilian dataset, leaves from eight crops (common bean, castor plant, coffee, eucalyptus, guava tree, maize, mango, and soybean) were scanned via pXRF at the following conditions: intact and fresh leaves, intact and dry leaves, and powdered samples). In the Malian dataset, powdered samples from cotton and maize were analyzed via pXRF. For comparison, Mn concentration was also determined after nitro-perchloric digestion followed by quantification via inductively coupled plasma optical emission spectroscopy (ICP-OES). After descriptive statistics, linear regressions were performed for all sample preparation conditions in both datasets, using Mn concentrations obtained through pXRF and the acid digestion method. The data quality level of all linear regressions was considered quantitative with high R (0.93 to 0.98) and R 2 (0.87 to 0.96) values. The direct analysis of Mn via pXRF on intact and fresh leaves yielded R of 0.93, R 2 of 0.87, and a low relative standard deviation (< 10%). The manufactured pXRF calibration used in this work allowed an accurate direct Mn determination in plant leaves. Considering the importance of Mn as a plant micronutrient and its potential toxicity depending on soil redox conditions, the fast, in situ, non-destructive, and eco-friendly determination via pXRF has a tremendous agronomic and environmental application worldwide.
Why it matches plant phenotyping methods植物葉のMn濃度という生理・元素形質を、携帯型XRFで非破壊測定する方法の性能評価と検証が中心であり、単なる生物学的実験での routine 測定ではない。
abstractThe direct analysis of Mn via pXRF on intact and fresh leaves yielded R of 0.93, R 2 of 0.87, and a low relative standard deviation (< 10%).
Indian agriculture is vital sector in the country's economy, providing employment and sustenance to millions of farmers. However, Plant diseases are a serious risk to crop yields and farmers' livelihoods. Traditional plant disease diagnosis methods rely heavily on human expertise, which can lead to inaccuracies due to the invisible nature of early disease symptoms and the labor-intensive process, making them inefficient for large-scale agricultural management. To recover from this and, address these challenges, this study explores deep learning, specifically Convolutional Neural Networks (CNN), as a means to enhance the accuracy and efficiency of plant disease detection. Deep learning architectures, like convolutional neural network, can autonomously learn and extract complicated characteristics and patterns from huge datasets. Our research, conducted on mango and groundnut leaves collected during field visits in western Maharashtra and supplemented by online datasets, demonstrates a CNN model that achieves an impressive 96% accuracy as compared to machine learning techniques that follow tedious feature extraction. Furthermore, image processing contributes to enhancing the dataset through normalization, resizing, and augmentation for better classification results. Overall, CNN can continuously improve and adapt its performance through iterative training, resulting in higher accuracy rates and reduced false positives in contrast to conventional machine learning methods.
Why it matches plant phenotyping methodsCNNによる植物葉の病害状態分類と画像前処理・性能比較が研究の中心であり、植物病害表現型の取得・推定手法に該当する。
abstractthis study explores deep learning, specifically Convolutional Neural Networks (CNN), as a means to enhance the accuracy and efficiency of plant disease detection.
Agriculture sector play a vital role in economy of India where the crop of mangoes is also considered as major fruit crop as it contributes significantly to the country's agricultural economy. Mango cultivation provides livelihood to millions of farmers across the country. One of the main barriers to increased food production is the diseases of the plants. Mango trees are prone to a variety of diseases and addressing them effectively can be quite challenging. This paper presents an ensemble-based classification of mango tree leaf diseases. Ensemble based classification makes use of multiple classifiers in order to make an efficient decision about the crop disease. In this paper, homogeneous VGG-19 CNN architecture is employed in bagging manner which proves the validity of the system by providing the accuracy of 95%, precision of 97%, recall of 97% and F-score of 97%.This system will be useful for Ministry of agricultural and farmer welfare for taking preventive measures to make Mango trees disease free.
Why it matches plant phenotyping methodsマンゴー葉の画像に基づく病害分類を、アンサンブルCNNで開発・検証しており、植物の病害状態を推定する方法が中心である。
abstractThis paper presents an ensemble-based classification of mango tree leaf diseases.
The Mango leaf diseases significantly restrain mango output as they affect yield and tree health. Mango leaf sooty mould disease is one of the diseases which affects the tree’s photosynthesis and general vigor quite severely. This research study looks into the use of deep learning models like the Residual Network with 50 stacks and ResNext50 for assessing that severity classification of mango leaf sooty mould disease. The model evaluates severity based on the dataset of 25,000 images obtained from different mango fields, as per the study. The overall accuracy achieved is 94.61% for the ResNext50 architecture through layer-wise parameter analysis, performance metrics, and confusion matrices. Model comparisons in the field reveal advantages across and between models. This research not only proves the use of DL in disease management but also paves the way for more applications in farming use. Automated mango leaf disease assessment is always bright white.
Why it matches plant phenotyping methodsマンゴー葉の画像から病害の重症度を推定する深層学習手法を開発・評価しており、植物の病害状態を直接定量化するフェノタイピング手法が中心である。
abstractThis research study looks into the use of deep learning models like the Residual Network with 50 stacks and ResNext50 for assessing that severity classification of mango leaf sooty mould disease.
The economic stability of many countries is closely tied to agriculture, where crop quality and yield are heavily affected by weather conditions and disease control. Unpredictable climate patterns and plant diseases remain major challenges, emphasizing the need for more reliable disease detection methods. Traditionally, plant disease identification has relied on visual examination, a method that is often inaccurate. To address this, our study proposes a two-stage model for improved disease detection in mango leaves. In the first stage, we implement an innovative, block-based feature extraction technique using Local Directional Patterns (LDP) and Local Directional Pattern variance (LDPv) on a comprehensive dataset, MangoLeafBD, consisting of 4000 images, achieving satisfactory results in terms of detection accuracy, sensitivity, specificity, and false negative rate. In the second stage, we introduce a Knowledge Distillation (KD) process to further enhance model performance by transferring knowledge from a larger teacher model to a smaller student model. Our results demonstrate significant advancement, with the KD-enhanced model achieving an improvement in detection accuracy from 89.2% to 95.6%, sensitivity from 7.8% to 4.1%, and specificity from 71.2% to 88.9% for Anthracnose disease. Similar improvements were observed in detecting other diseases, making our approach a more robust and efficient solution for mango plant disease detection.
Why it matches plant phenotyping methodsマンゴー葉画像から病害状態を推定する特徴抽出・知識蒸留モデルを開発し、精度等を評価しており、植物病害フェノタイピング手法が研究の中心です。
abstractour study proposes a two-stage model for improved disease detection in mango leaves
In the agricultural sector, pesticides are used to prevent disease transmission and protect crop yields. However, due to the diverse range of diseases, the human observation can often lead to misidentification. It is essential for a timely and precise disease classification approach without human intervention. Classifying the plant leaf diseases with an automated system is the significant need in this scenario. In this work, a hybrid classification model for the categorisation of plant leaf diseases is presented. Preprocessing, segmentation, feature extraction and classification of leaf diseases are the four steps in this method. In this work, crops such as grapes and mango are considered. Primarily, preprocessing the input image by utilising Gaussian filtering methods, which enhances the quality of image. The filtered image is then put through a segmentation process using the MBIRCH framework. The segmented image is then used to extract a number of features, including GLCM, ILGBHS, colour, shape and deep features using the VGG16 and AlexNet networks. Following the procedure, the hybrid model—which combines Bi‐GRU and DCNN with TL—is applied to the acquired features, and the final classified result is determined by the enhanced fusion score method.
Why it matches plant phenotyping methods植物葉の病害状態を画像から抽出・分類する前処理、分割、特徴抽出、分類のワークフローが研究の中心であり、植物病害フェノタイピング手法に該当する。
abstractIn this work, a hybrid classification model for the categorisation of plant leaf diseases is presented.
Purpose Diagnosing the crop diseases by farmers accurately with the naked eye can be challenging. Timely identification and treating these diseases is crucial to prevent complete destruction of the crops. To overcome these challenges, in this work a light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset. Design/methodology/approach The proposed system is divided into four phases: image acquisition and preprocessing, data augmentation, feature extraction and classification. In the first phase, data have been collected using a drone in real time, and preprocessing has been performed to improve the images. In the second phase, four data augmentation techniques have been applied to increase the size of the real-time dataset. In the third phase, feature extraction has been done using two deep convolutional neural network (DCNN)-based models, individually, ResNet49 and ResNet41. In the last phase, four machine learning classifiers random forest (RF), support vector machine (SVM), logistic regression (LR) and eXtreme gradient boosting (XGBoost) have been employed, one by one. Findings These proposed systems have been trained and tested using our own real-time dataset that consists of healthy and unhealthy leaves for six crops such as corn, grapes, okara, mango, plum and lemon. The proposed combination of Resnet49-SVM and ResNet41-SVM has achieved accuracy of 99 and 97%, respectively, for the images that have been collected from the city of Kurukshetra, India. Originality/value The proposed system makes novel contribution by using a newly proposed real time dataset that has been collected with the help of a drone. The collected image data has been augmented using scaling, rotation, flipping and brightness techniques. The work uses a novel combination of machine learning methods based classification with ResNet49 and ResNet41 based feature extraction.
Why it matches plant phenotyping methodsドローン画像から植物の健康状態・病害を推定する画像解析システムの開発が研究の中心であり、特徴抽出、分類、データセット構築と性能評価を含むため。
abstracta light-weight automatic crop disease detection system has been developed, which uses novel combination of residual network (ResNet)-based feature extractor and machine learning algorithm based classifier over a real-time crop dataset.
PURPOSE: Fruit detection and counting represent one of the most important steps toward yield estimation and a well-known practice for farmers, on which they base the management of the harvesting, storage, and distribution phases of agricultural products. In the era of precision agriculture, yield estimation, which was previously performed only by human operators, is currently being re-designed through the employment of Artificial Intelligence and Computer Vision techniques. Despite the impressive results that AI has demonstrated in fruit detection systems, they rely on large image datasets, whose availability is still limited if compared to the great number of crop typologies. For this reason, great interest has recently been devoted to weakly supervised algorithms, which can reduce the dataset annotation effort required by using simple image-level labels. METHOD: Based on these considerations, this work proposes a new method relying on a sample-efficient weakly supervised approach. The proposed system, named MangoDetNet, is trained through a two-stage curriculum learning approach, first involving an image reconstruction task, and secondly an image binary classification task for heatmap generation. In particular, during the first stage, the network is trained in an unsupervised manner for the image reconstruction task, in order to promote the learning of robust feature extractors that are customized for the fruit scenarios. The second stage of training, instead, is performed to achieve image binary classification, employing presence/absence binary labels. This phase further refines the feature extractor from the previous stage and favors the computation of more refined and precise activation maps. CONCLUSION: As demonstrated through the experimental campaign, performed on a mango orchard image dataset, MangoDetNet is able to outperform the state-of-the-art weakly supervised approaches, providing an F1 score equal to 0.861, which is on par with those of fully supervised methods, and an F1 score equal to 0.856 when halving the number of labeled samples needed for training.
Why it matches plant phenotyping methodsマンゴー果実の検出・計数による収量推定を目的とした画像解析手法を新規開発し、データセット上で性能評価しており、果実形質の取得が中心である。
abstractFruit detection and counting represent one of the most important steps toward yield estimation
Droughts are expected to intensify in the Mediterranean region due to climate change, yet the effect of these highly variable events on local trees is unknown. To study the particular effect of heatwaves in orchards, where soil-drought can be mitigated by irrigation, we propose a heatwave definition that focuses on atmospheric stress and its consequences, by relating the intensity of high VPD events to losses in tree stem-water storage (StWS). We found that the sensitivity and resilience of StWS to heatwaves is species-specific, and varies among species with different water-management strategies (e.g., isohydric orange and anisohydric mango trees, p < 10 −3 ). Navel orange trees were sensitive to heatwaves starting at the 80th percentile of VPD in early spring, and once irrigation began, despite the harsh Mediterranean summer temperatures, StWC increased to 0.57 g cm −3 , slightly greater than the StWC of the earlier wet season (approximately 0.55 g cm −3 ). Oppositely, there was a net reduction in StWC in Shelly mango trees from 0.75 to 0.69 g cm −3 between the two seasons, as sensitivity to heatwaves increased from the 90th to the 80th percentile in spring and summer, respectively. By first quantifying heatwaves and relating this new variable to changes in StWS, we were able to describe the sensitivity of each species according to the rarity of the heatwave events by VPD percentile, and their resilience to heatwaves over seasons based on the corresponding net changes in StWC. Though the experiment in this study was performed in a Mediterranean climate, hotter-droughts are rising globally and the framework developed here for quantifying and measuring the effect of heatwaves can be broadly applied across geographic locations.
Why it matches plant phenotyping methods熱波をVPD percentileで定量化し、樹幹水分貯蔵・含水量の変化から樹木の生理的応答を評価する汎用的フレームワークが研究の中心であり、単なる routine 測定ではない。
abstractwe propose a heatwave definition that focuses on atmospheric stress and its consequences, by relating the intensity of high VPD events to losses in tree stem-water storage (StWS).
Agricultural productivity is one of the critical factors towards ensuring food security across the globe. However, some of the main crops, such as potato, tomato, and mango, are usually infested by leaf diseases, which considerably lower yield and quality. The traditional practice of diagnosing disease through visual inspection is labor-intensive, time-consuming, and can lead to numerous errors. To address these challenges, this study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification. The Excess Green Index (ExG) is a novel approach that is a specified vegetation index that can further the ability of the model to distinguish and detect vegetative properties even in scenarios with minimal labeled data, demonstrating the tremendous potential for this application. AgirLeafNet demonstrates outstanding accuracy, with 100% accuracy for potato detection, 92% for tomato, and 99.8% for mango leaves, producing incredibly accurate results compared to the models already in use, as described in the literature. By demonstrating the viability of a deep learning/IoT system architecture, this study goes beyond the current state of multi-crop disease detection. It provides practical, effective, and efficient deep-learning solutions for sustainable agricultural production systems. The innovation of the model emphasizes its multi-crop capability, precision in results, and the suggested use of ExG to generate additional robust disease detection methods for new findings. The AgirLeafNet model is setting an entirely new standard for future research endeavors.
Why it matches plant phenotyping methods葉画像から植物病害状態を推定する深層学習手法の開発・比較が中心であり、植物表現型計測法として採用する。
abstractthis study evokes the AgirLeafNet model, a deep learning-based solution with a hybrid of NASNetMobile for feature extraction and Few-Shot Learning (FSL) for classification.
Reproduction assets foundThe paper's Data Availability Statement explicitly lists three public Kaggle leaf-image datasets (potato, tomato, mango) that constitute the phenotyping image inputs used for the study's disease-detection experiments. No author analysis code or trained model checkpoints are reported.Dataset · publicAgronomy 2024, 14, 2230 32 of 33
Data Availability Statement: These data were derived from the following resources available in
the public domain: Potato Plant Diseases Data (https://www.kaggle.com/datasets/hafiznouman
786/potato-plant-diseases-data) (accessed on 22 September 2024), Tomato Leaf Diseases Dataset
(https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf) (accessed on 22 September 2024),
Mango Leaf Disease Dataset (https://www.kaggle.com/datasets/aryashah2k/mango-leaf-disease-dataset) (accessed on 22 September 2024).
Conflicts ofOpen asset ↗Kagglepdf-raw-page:32 lines:1-53Dataset · publicAgronomy 2024, 14, 2230 32 of 33
Data Availability Statement: These data were derived from the following resources available in
the public domain: Potato Plant Diseases Data (https://www.kaggle.com/datasets/hafiznouman
786/potato-plant-diseases-data) (accessed on 22 September 2024), Tomato Leaf Diseases Dataset
(https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf) (accessed on 22 September 2024),
Mango Leaf Disease Dataset (https://www.kaggle.com/datasets/aryashah2k/mango-leaf-disease-dataset) (accessed on 22 September 2024).
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publica-
tion of this study.
References
1. Mohanty, S.P.; Hughes,Open asset ↗Kagglepdf-raw-page:32 lines:1-53Dataset · publicng resources available in
the public domain: Potato Plant Diseases Data (https://www.kaggle.com/datasets/hafiznouman
786/potato-plant-diseases-data) (accessed on 22 September 2024), Tomato Leaf Diseases Dataset
(https://www.kaggle.com/datasets/kaustubhb999/tomatoleaf) (accessed on 22 September 2024),
Mango Leaf Disease Dataset (https://www.kaggle.com/datasets/aryashah2k/mango-leaf-disease-dataset) (accessed on 22 September 2024).
Conflicts of Interest: The authors declare that there are no conflicts of interest regarding the publica-
tion of this study.
References
1. Mohanty, S.P.; Hughes, D.P.; Salathé, M. Using Deep Learning for Image-Based Plant Disease Detection. Front. Plant Sci. 2016,
Open asset ↗Kagglepdf-raw-page:32 lines:1-53Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published13 Sept 2024Tenth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2024)Cited by 1 · OpenAlex ↗
Precision agriculture is the application of correct amount of fertilizers and water pesticide to achieve higher agricultural productivity. Furthermore, under the framework of precision agriculture is the automated estimation of yield with advanced technologies including Artificial Intelligence (AI) and Remote Sensing (RS). The use of RS has advanced crop yield estimations and predictions in recent years. However, to validate RS-based models it is important to perform in-situ exercises such as fruit counting, which is a time-consuming task that increases the production costs. Drones, robots, and in-situ cameras in combination with AI algorithms are widely used to efficiently address these issues. The recent advancement in computational resources and power available has enabled the utilization of Deep Learning AI models. One of the best-performing models for object detection is the You-Only-Look-Once (YOLO). In this study, the YOLOv5s is used for object detection, which is the second smallest and fastest YOLOv5 architecture, on two different benchmark datasets collected from AgML. The first dataset consists of 1730 images of mango trees in Australia during night, and the second dataset consists of 6512 images of wheat heads collected from different regions around the world. The main objective of this work is to demonstrate the capabilities of light AI models for object detection and to evaluate their performance, which will serve as a benchmark for future comparison with the on-board environment.
Why it matches plant phenotyping methods植物器官の検出・カウントによる収量推定を対象とし、YOLOv5sの性能評価とベンチマーク化が主目的であるため、計算画像フェノタイピング手法として採用。
abstractIn this study, the YOLOv5s is used for object detection, which is the second smallest and fastest YOLOv5 architecture, on two different benchmark datasets collected from AgML.
Reproduction assets foundThe paper evaluates YOLOv5s on two public benchmark datasets. The MangoYOLO dataset is explicitly cited with public access URLs and was directly used for the paper's mango yield-estimation experiments, qualifying as a paper-specific public asset. The Global Wheat Head Detection dataset is also used but its Zenodo URL (Dataset · publicAnand Koirala, C McCarthy, Kerry Walsh, and Z Wang, ‘MangoYOLO data set’. Central Queensland University,
2021. Accessed: May 23, 2024. [Online]. Available: http://hdl.handle.net/10018/1261224,
https://researchdata.edu.au/mangoyolo-setOpen asset ↗pdf-page:7 lines:1-50Code / dataset availability confirmedarXiv · OpenAlex · checked 13 Sept 2026
AppleCitrusMangoPeachPearPlumField / plotLiDAR / point cloudRGB / grayscaleFruit
We introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D. Our framework takes an unordered set of posed images captured by a monocular camera and segments fruit in each image. To make our system independent of the fruit type, we employ a foundation model that generates binary segmentation masks for any fruit. Utilizing both modalities, RGB and semantic, we train a semantic neural radiance field. Through uniform volume sampling of the implicit Fruit Field, we obtain fruit-only point clouds. By applying cascaded clustering on the extracted point cloud, our approach achieves precise fruit count.The use of neural radiance fields provides significant advantages over conventional methods such as object tracking or optical flow, as the counting itself is lifted into 3D. Our method prevents double counting fruit and avoids counting irrelevant fruit.We evaluate our methodology using both real-world and synthetic datasets. The real-world dataset consists of three apple trees with manually counted ground truths, a benchmark apple dataset with one row and ground truth fruit location, while the synthetic dataset comprises various fruit types including apple, plum, lemon, pear, peach, and mango.Additionally, we assess the performance of fruit counting using the foundation model compared to a U-Net.
Why it matches plant phenotyping methods果実を対象に、画像・NeRF・点群クラスタリングを組み合わせて3D果実数を推定する手法を開発し、実データおよび合成データで評価しているため、植物表現型取得法が中心である。
abstractWe introduce FruitNeRF, a unified novel fruit counting framework that leverages state-of-the-art view synthesis methods to count any fruit type directly in 3D.
Reproduction assets foundThe paper's real-world apple tree image dataset with manual ground-truth counts and synthetic Blender fruit tree data are publicly released via the project website, and the FruitNeRF analysis code is open-source on GitHub. The Zenodo DOI refers to the third-party BlenderNeRF plugin (cited tool), not a paper-specific.Dataset · publicThe data has been made publicly available, and visualizations can be accessed on the project website.Open asset ↗lines:183-221Code · publicFruitNeRF code: https://github.com/meyerls/FruitNeRF has been made open-source.Open asset ↗meyerls/FruitNeRFlines:74-108Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
This research proposes a near-infrared hyperspectral imaging/two-branch convolutional neural network (NIR-HSI/2B-CNN) algorithmic scheme to detect mango anthracnose of the species Colletotrichum asianum at the early stages of disease development. In the algorithmic model development, root mean square propagation was used as the solver to train the neural network, given 150 epochs. In addition, spectral raw data was preprocessed to transform it into an understandable and efficient format. The optimal classification model was the 2B-CNN model with 1st-derivative preprocessing, achieving an accuracy of 0.94 for the calibration set and 0.71 for the prediction set. The proposed NIR-HSI/2B-CNN scheme could detect anthracnose mangoes since the the first day of inoculation of the spore suspension (i.e., day 0) through to day 3, achieving a moderate classification accuracy. Meanwhile, the accuracy of conventional convolutional neural networks (CNN) were within a range of 0.66-0.67 for the calibration set and 0.55-0.57 for the prediction set. The results indicated that incorporating spatial features in the 2B-CNN modeling enhanced the prediction performance of the algorithm. The proposed NIR-HSI/2B-CNN algorithmic scheme needs refinements to be able to reliably sort mango fruits into those suitable for premium fresh consumption and export without anthracnose and those for domestic consumption or processing. The novelty of this research lies in the use of NIR-HSI and 2B-CNN algorithm to detect plant pathogens at the early stages of disease development. In addition, the new method of natural simulation to deposit the fungal spores onto the mango surface by spraying spore suspension onto the mango surface where the conidia penetrated unaided into the underneath of the mango peel is proposed.
Why it matches plant phenotyping methodsマンゴー果実の病徴・病害状態をNIR-HSIとCNNで直接推定する手法の開発・比較・検証が研究の中心であり、植物病害フェノタイピングに該当する。
abstractThis research proposes a near-infrared hyperspectral imaging/two-branch convolutional neural network (NIR-HSI/2B-CNN) algorithmic scheme to detect mango anthracnose of the species Colletotrichum asianum at the early stages of disease development.
Deep learning techniques and computer vision systems offer effective fruit counting solutions for farm yield estimation. However, the performance of these solutions drops when identifying different cultivars of the same fruit species. This study clarified the differences between mango fruit detection and mango cultivar identification. An original double-threshold-based classification method for fruit cultivar identification, with estimation of the misidentification rate was proposed in order to significantly increase the performance of a specialised mango fruit detection method known as Faster R-CNN. This method was applied on images of mango trees of three cultivars taken in Senegalese orchards of different existing cropping systems, with varying tree features, planting patterns and acquisition contexts. Analysis of the results focused on the contributions of fruit detection errors and fruit cultivar confusion to the overall error of the network for fruit counts by cultivar class. The shift from fruit detection to cultivar identification resulted in a drop in the average prediction rate from 92 to 68%. With its explicitly independent fruit detection and cultivar identification steps, the double-threshold-based classification method increased the prediction rate to 86%, with a maximum identification error of 0.05%. This setting also led to relative equality between the recall and the precision of each cultivar class, making the network well suited for fruit counting by cultivar class. This work opened new perspectives for decision support tools for fruit growers that could provide more appropriate yield estimates per cultivar.
Why it matches plant phenotyping methodsマンゴー果実の検出・品種別カウントと収量推定に用いる画像分類手法を開発し、誤認識率や性能を評価しており、果実数・品種別収量という植物形質の取得が中心である。
abstractAn original double-threshold-based classification method for fruit cultivar identification, with estimation of the misidentification rate was proposed
The identification and categorization of plant diseases continue to be a major obstacle in the field of agricultural technology, especially for crops with substantial economic value such as apples and mangoes. In “Cross-Species Plant Disease Detection: Utilizing Neural Style Transfer on Mango and Apple Leaf Images,” a novel method is presented in this research study. It makes use of the powers of Neural Style Transfer (NST) combined with Convolutional Neural Networks (CNN). The goal of this innovative technique is to improve plant disease detection efficiency and accuracy in a variety of species. A number of well-known deep learning architectures, such as Xception Net, DenseNet-121, ResNet-50, VGGNet 16, AlexNet, and Efficient Net B2, were carefully compared to the suggested model. For this comparison analysis, important performance indicators like F1 Score, Accuracy, Precision, Recall/Sensitivity, and Specificity were employed. The proposed model outperformed the other models, with an accuracy of $\mathbf{9 1 . 9 \%}$ for apple leaves and $\mathbf{9 2 . 5 \%}$ for mango leaves. The findings were astounding. This work not only shows how well NST and CNNs work together to detect plant diseases, but it also shows how the model may help minimize false positives, which is an important feature for real-world agricultural applications. The effectiveness of this strategy points to a substantial development in precision agriculture and provides a dependable and scalable early disease detection method. This discovery has implications for improving crop sustainability and production, which will help ensure food security worldwide. This research establishes a new standard for AI-driven plant disease diagnosis and creates opportunities for more advancements in intelligent farming and precision agriculture.
Why it matches plant phenotyping methods植物葉画像から病害状態を推定する深層学習手法の開発・比較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。
abstracta novel method is presented in this research study. It makes use of the powers of Neural Style Transfer (NST) combined with Convolutional Neural Networks (CNN).
Abstract Plant disease severity is the ratio between the surface area of disease symptoms and the total surface area of the plant unit (e.g. fruit, leaf). It is related to plant disease diagnosis and has several advantages for farmers. It is therefore a key element in the protection and management of plant diseases. In the literature, there are three proposed categories of plant disease severity determination solutions: those based on segmentation algorithms, those based on classical ML algorithms and those based on DL algorgorithms. Despite their many advantages, these solutions have a number of limitations, including i) subjectivity in data labeling, ii) loss of information on disease lesion contours during (manual) data labeling, and iii) the proposed solutions have focused on estimating plant disease severity from leaves, although diseases can also affect other parts of the plant, such as fruits. In this paper, we present a solution for estimating the severity of four mango fruit diseases, namely alternaria, anthracnose, aspergillus rot and stem rot. This solution is based on ResNet50 CNN and uses a dataset automatically labeled by a proposed algorithm based on two segmentation algorithms such as image color space segmentation and image thresholding. The solution has achieved an accuracy and a F1_score of 97.82% and 97.79%, respectively, on test data. It is then deployed in a mobile application with a diagnostic solution we previously proposed. This mobile application will help mango growers, particularly those in Sahelian countries like Senegal, to manage their mango diseases earlier.
Why it matches plant phenotyping methodsマンゴー果実の病斑面積に基づく病害重症度を、画像セグメンテーションと深層学習で推定する方法が中心であり、植物状態の定量的フェノタイピングに該当する。
abstractIn this paper, we present a solution for estimating the severity of four mango fruit diseases
Reproduction assets foundThe paper uses the authors' own public SenMangoFruitDDS dataset of 862 mango fruit images, explicitly stated to be downloadable from Mendeley Data, as the image input for their severity estimation and automatic labeling pipeline. No code or trained model deposit is mentioned.Dataset · public2 Material and Method
2.1 Datataset used
In this work, we have used our dataset SenMangoFruitDDS presented in our paper [8].
It is downloadable from Mendeley data plateform via the url https://data.mendeley.com/datasets/jvszp9cbpw/3. This dataset contains 862 mango fruit images of four dis-
eases such as Anthracnose, Alternariose, aspergillus rot and Stem and rot. The infected
fruits in the images show different stages of severity. The dataset also contains, as
additionnal category, images of healthy mango fruits. Mango fruit images are gathered
from an orOpen asset ↗Mendeley · jvszp9cbpw/3pdf-raw-page:5 lines:1-26Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
MangoFruitClassificationSegmentationGrowth / development / phenology
Abstract Mangos are a significant fruit crop that is widely cultivated in tropical and subtropical regions. However, manual detection of mango crop maturity is time‐consuming and labor‐intensive. It is a vital agricultural commodity, and accurate assessment of the mango maturity stage is critical for harvesting and post‐harvest handling. Accurately detecting mango crop maturity is essential for ensuring optimal harvesting and quality. This study introduced a specialized Meta‐learning method for classifying mango crop images with limited data. The conventional approach to maturity classification, often hindered by limited labeled data, is significantly enhanced by meta‐learning's ability to adapt quickly to new tasks with minimal examples. The proposed approach works as initial image segmentation to isolate mango crop regions, comprehensive feature extraction to capture meaningful data representations, and a critical meta‐training. This phase involves refining a classifier within a metric space by utilizing cosine distance and an adaptable scale parameter, this is followed by a meta‐testing stage. where the adapted classifier is utilized for maturity prediction with minimal support samples. This research holds substantial promise for the agricultural sector, offering practical solutions to optimize harvest management practices and improve mango crop yield predictions. Practical applications Optimized Harvest Scheduling : Scenario: Farmers can use crop maturity estimation to schedule harvest times more precisely. Application: Knowing crop maturity optimizes harvest, minimizing losses. Resource Management : Scenario: Efficient use of resources such as water, fertilizers, and pesticides is essential for sustainable agriculture. Application: Crop maturity guides resource use, for example, reducing late‐stage water/nutrients prevents overuse, saves resources. Quality Assurance : Scenario: Crop quality is often linked to its maturity level. Application: Maturity estimation assesses crop quality, guiding post‐harvest planning and determining market value. Data‐Driven Decision Making : Scenario: Modern agriculture relies on data for decision‐making. Application: Maturity estimation promotes data‐driven farming, aiding trend analysis, optimizing planting, and enhancing farm management.
Why it matches plant phenotyping methodsマンゴー画像から成熟度という植物器官・果実の状態を推定する画像解析手法を、セグメンテーション、特徴抽出、メタ学習として中心的に開発しているため。
abstractThis study introduced a specialized Meta‐learning method for classifying mango crop images with limited data.
Abstract: Because food is a basic requirement for all living beings on Earth, agriculture is especially important in our daily life. Agriculture is our main source of food. In addition to plant diseases that impede the growth and quality of food crops, agriculture works to generate food to feed the growing population. We'll look at five plants: bitter gourd, mango tree, spinach, tomato, and hibiscus. This study proposes a CNN-based technique for early plant disease diagnosis. The approach consisted of three steps: image segmentation, feature extraction, and picture pre-processing. The results of these three processes are combined to form a Convolutional Neural Network (CNN) classifier. To research and analyse a plant, an input image of the damaged portions is obtained and compared to the desired dataset. The disease is then anticipated, along with therapeutic treatments. Once the disease has been recognized, the quantity of pesticides, recommended application place, and chemicals themselves will be displayed. It will also identify the nearest location where pesticides are available.
Why it matches plant phenotyping methods植物病害の症状画像から病害状態・重症度を推定するCNNベースの画像解析法が研究の中心であり、単なる生物学的実験のルーチン測定ではない。
titleA Deep Learning-based System for Automated Plant Disease Severity Detection and Fertilizer Optimization
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.
Fruit leaf disease segmentation is an essential foundation for achieving accurate disease diagnosis and identification. However, shadows caused by folded leaves and serrations on leaves can lead to difficulty in extracting edge features, affecting the accuracy of leaf segmentation. In addition, the varying shapes and blurred boundaries of disease spots can further lead to poor segmentation performance of spots. To address the above problems, this work proposes a method called EAIS-Former by combining the advantages of global modeling of Transformer, local processing and positional coding of convolutional neural network (CNN) for accurate segmentation in fruit leaf disease images. Dual scale overlap (DSO) patch embedding is designed to effectively extract multi-scale disease features by dual paths to alleviate omission of lesions. Ultra large convolution (ULC) Transformer block is customized for performing positional encoding and global modeling to efficiently extract global and positional features of leaves and diseases. Skip convolutional local optimization (SCLO) module is proposed to optimize the local detail and edge information and improve the pixel classification ability of the model so that the segmentation results of leaves and spots can be finer and more tiny spots can be extracted. Double layer upsampling (DLU) decoder is built to efficiently fuse the detail information with the semantic information and output the accurate segmentation results of leaves and spots. The experimental results show that the proposed method reach 99.04%, 98.64%, 99.24%, 99.42%, 98.59% and 98.58% intersection over union (IoU) for leaf segmentation on apple rust, pomegranate cercospora spot, mango anthracnose, jamun fungal disease, apple alternaria blotch and apple gray spot datasets, respectively. The IoU of lesion segmentation achieve 94.47%, 94.54%, 83.83%, 86.60%, 89.59% and 88.76%, respectively. In contrast to DeepLabv3+, the accuracy of disease segmentation is raised by 5.25%, 5.15%, 5.55%, 7.64%, 7.04% and 9.35%, respectively. Compared with U-Net, the proposed method improves the accuracy of disease spot segmentation by 4.3%, 4.44%, 5.26%, 9.42%, 5.87% and 6.53% under the six fruit leaf test sets, respectively. In addition, total parameters and FLOPs of the proposed method are only 18.44% and 8.47% of U-Net, respectively. Therefore, this study can provide an efficient and accurate method for the task of fruit leaf disease spot segmentation, which provides a sufficient basis for the accurate analysis of fruit leaves and diseases.
Why it matches plant phenotyping methods果樹葉および病斑の画像セグメンテーション手法を開発・比較評価しており、植物の病害状態を抽出する方法が研究の中心である。
abstractthis study can provide an efficient and accurate method for the task of fruit leaf disease spot segmentation
Using deep learning-based tools in the field of agriculture for the automatic detection of plant leaf diseases has been in place for many years. However, optimizing their use in the specific background of the agriculture field, in the presence of other leaves and the soil, is still an open challenge. This work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions. Experiments were carried out on a self-collected dataset containing 3305 real-field images of cotton, wheat, and mango (healthy and diseased) leaves. The results show that the proposed model outperformed many state-of-the-art and recent models, including the base YOLOv6s, in terms of detection accuracy. It was also found that this improvement was achieved without any significant increase in the computational cost. Hence, the proposed model stood out as an effective technique to detect plant leaf diseases in real-field conditions without any increased computational burden.
Why it matches plant phenotyping methods実圃場画像から植物葉の病害状態を検出する深層学習モデルを開発・評価しており、植物の病害表現型取得が研究の中心である。
abstractThis work presents a deep learning model based on YOLOv6s that incorporates (1) Gaussian error linear unit in the backbone, (2) efficient channel attention in the basic RepBlock, and (3) SCYLLA-Intersection Over Union (SIOU) loss function to improve the detection accuracy of the base model in real-field background conditions.
Anthracnose is the major plant disease causing an economic loss of mango fruit. Anthracnose symptom is not visible at a quiescent stage and the infected fruit often enters the food chain before the infection is known. Detection of a pre-symptomatic anthracnose infection is thus, crucial to prevent the infected fruit from entering the food chain. This research applied hyperspectral imaging (HSI) spectroscopy integrated with machine learning (ML) including principal component analysis (PCA) and support vector machine (SVM) for rapid identification of quiescent infection of anthracnose in mango fruit. Mango fruit (Nam Dok Mai Si Thong) was artificially infected with Colletotrichum gloeosporioides and stored at 20 °C and 90 % RH. The HSI was used to collect the spectral and spatial data of the samples. PCA and SVM were respectively performed to explore the hyperspectral data and to classify different symptom severities. The obtained spectral data can be recognized as fingerprints ascribing to the metabolites produced by C. gloeosporioides and the decomposed fruit tissues caused by the fungal infection. The HSI integrated with ML was able to not only detect the anthracnose infection at a latent stage before the onset of disease symptoms but also correctly classify different symptom severities. The symptom maps were also constructed using false-color image processing to simplify the data visualization of different symptom severities. The capability of detecting a pre-symptomatic anthracnose infection is a key advantage of the developed ML-assisted HSI.
Why it matches plant phenotyping methodsマンゴー果実の感染状態・症状重症度を、ハイパースペクトル画像と機械学習で検出・分類する方法が研究の中心であり、植物病害状態の表現型取得に該当する。
abstractThis research applied hyperspectral imaging (HSI) spectroscopy integrated with machine learning (ML) including principal component analysis (PCA) and support vector machine (SVM) for rapid identification of quiescent infection of anthracnose in mango fruit.
Plants exist all over the place; we live, as well as places without us. Plant disease is one of the essential causes that reduces quantity and degrades quality of the agricultural merchandises. Plant diseases have turned into a terrible as it can cause significant reduction in both quality and quantity of agricultural products. Images form important data and information in biological sciences. Until recently photography was the only method to reproduce and report such data. It is difficult to quantify or treat the photographic data mathematically. This project, classifies the plant leaves and stems at hand into infected and non-infected classes. The developing software provides a fast and accurate method in which the leaf diseases are detected and classified using k-means based segmentation and neural networks-based classification. Most common diseases seen in the leaves of Tapioca and Mango are discussed here for this approach. In this paper, respectively, the applications of K-means clustering and Neural Networks (NNs) have been formulated for clustering and classification of diseases that effect on plant leaves. Recognizing the disease is mainly the purpose of the proposed approach. Thus, the proposed Algorithm was tested on five diseases which influence on the plants; they are: Early scorch, Cottony mold, ashen mold, late scorch, tiny whiteness. The experimental results indicate that the proposed approach is a valuable approach, which can significantly support an accurate detection of leaf diseases in a little computational effort. This project gives 95% of efficiency using MATLAB simulation results.
Why it matches plant phenotyping methods植物葉の感染状態を画像から抽出・分類するソフトウェアと手法の開発が中心であり、植物病害状態の画像ベース表現型計測に該当する。
abstractThe developing software provides a fast and accurate method in which the leaf diseases are detected and classified using k-means based segmentation and neural networks-based classification.
To diminish disease transmission together with promoting effective management techniques, it is crucial to monitor plant health and detect pathogens earlier. The initial part in reducing losses sourced from plant diseases is to make an accurate and earlier identification. Thus, the usage of unmanned aerial vehicle (UAV) hyperspectral imaging (HSI) sensors for surveying and assessing crops, orchards, and forests has rapidly elevated over the last decade, particularly for the stress management like water, diseases, nutrition deficits, and pests. Using Minkowski Distance-based Fuzzy C Means (MD-FCM) clustering and Xavier initialization-adapted Cosine Similarity-induced Radial Bias Function Neural Network (XCS-RBFNN) techniques, a UAV HS imaging remote sensor for Spatial and Temporal Resolution (STR) of mango plant disease and pest identification is proposed in this scheme. Collecting the input UAV source (image or video) is eventuated initially along with the Region of Interest (ROI) calculated which is followed by preprocessing. Leaf segmentation is eventuated using Logistic U-net after preprocessing. Next, MD-FCM performs clustering to cluster the diseased leaves and pests individually. The disease and pest characteristics are then retrieved separately and classified further. The requisite features are then chosen from the retrieved features utilizing the Levy Flight Distribution-produced Butterfly Optimization Algorithm (LFD-BOA). Finally, the XCS-RBFNN classifier is utilized to categorize the various diseases together with pests found in the UAV input source using the chosen features. The proposed framework's experimental findings are then compared to some prevailing schemes, with the results revealing that the proposed work outperforms other benchmark techniques.
Why it matches plant phenotyping methodsUAVハイパースペクトル画像からマンゴー葉の病害状態を抽出・分類する画像解析ワークフローが研究の中心であり、植物状態の評価手法をベンチマーク比較しているため。
abstractUsing Minkowski Distance-based Fuzzy C Means (MD-FCM) clustering and Xavier initialization-adapted Cosine Similarity-induced Radial Bias Function Neural Network (XCS-RBFNN) techniques, a UAV HS imaging remote sensor for Spatial and Temporal Resolution (STR) of mango plant disease and pest identification is proposed in this scheme.
In fruit tree growth, pruning is an important management practice for preventing overcrowding, improving canopy access to light and promoting regrowth. In fruit with a high energy content, including avocado (Persea Americana), ensuring all parts of the canopy have sufficient exposure to light is of particular importance. Due to the slow nature of agriculture and the numerous parameters contributing to yield, decisions in pruning, particularly in selective limb removal, are typically made using tradition or rules of thumb rather than data-driven analysis. Many existing algorithmic, simulation-based approaches rely on high-fidelity digital captures or purely computer-generated fruit trees, and are unable to provide specific results on an orchard scale. We present a framework for suggesting pruning strategies on LiDAR-scanned commercial fruit trees using a scoring function with a focus on improving light distribution throughout the canopy. Due to the destructive nature of physical experimentation, this framework is presented using a three-stage approach where stages can be independently validated. Firstly, a scoring function to assess the quality of the tree shape based on its light availability and size was developed for comparative analysis between trees using observations from agricultural literature, and was validated against yield characteristics from an avocado and mango orchard. This demonstrated a reasonable correlation against fruit count, with an R2 score of 0.615 for avocado and 0.506 for mango. The second stage was to implement a tool for simulating pruning by algorithmically estimating which parts of a tree point cloud would be removed given specific cut points using structural analysis of the tree. This was validated experimentally using manually generated ground truth pruned tree models, showing good results with an average F1 score of 0.78 across 144 experiments. Finally, new pruning locations were suggested by discovering points in the tree which negatively impact the light distribution, and we used the previous two stages to estimate the improvement of the tree given these suggestions. These results were compared to a tree which was commercially pruned using existing wisdom. The light distribution was improved by up to 25.15%, demonstrating a 16% improvement over the commercial pruning, and certain cut points were discovered which improved light distribution with a smaller negative impact on tree volume. The final results suggest value in the framework as a decision making tool for commercial growers, or as a starting point for automated pruning since the entire process can be performed with little human intervention. Further development should be performed to improve the suggestion mechanism and incorporate more agricultural objectives and operations.
Why it matches plant phenotyping methodsLiDAR点群から樹形・樹冠内の光分布を評価し、剪定シミュレーションと剪定位置提案を行う技術を開発・検証しており、植物形態・構造の取得と解析が中心である。
abstractWe present a framework for suggesting pruning strategies on LiDAR-scanned commercial fruit trees using a scoring function with a focus on improving light distribution throughout the canopy.
Abstract This study suggests a Convolutional Neural Network (CNN) and Histogram Oriented Gradients (HOG)-based automatic detection and classification system for mango disease. Early detection is essential for efficient disease management since mango disease can have a major influence on fruit quality and yield. The suggested system makes use of the CNN algorithm for extracting features and the HOG technique for capturing shape and texture data. The extracted features are subsequently used to feed a disease classification model for disease detection. The efficiency of the proposed model is demonstrated by experimental findings, which achieve excellent accuracy in both disease detection and classification tasks. The CNN-HOG hybrid model outperforms CNN or HOG alone in terms of performance, demonstrating the complementary nature of these two methods for the detection and classification of mango disease. The system's performance is evaluated using measures for accuracy, precision, and recall and the accuracy of the proposed model is 98.80%. This research helps establish effective and trustworthy tools for managing mango disease by automating the detection and classification process. This enables prompt intervention and reduces crop losses.
Why it matches plant phenotyping methodsマンゴー葉・植物の病徴を画像から検出・分類するCNN/HOG手法が研究の中心であり、植物病害状態の表現型推定に該当する。
abstractThis study suggests a Convolutional Neural Network (CNN) and Histogram Oriented Gradients (HOG)-based automatic detection and classification system for mango disease.
The Part 1 prequel to this review evaluated the evolution of modelling techniques used in evaluation of fruit quality over the past three decades and noted a progression towards the use of artificial neural networks (ANNs) and convolutional neural networks (CNNs). In this review, Part 2, the use of CNNs for NIR fruit quality evaluation is explored, given the success of CNNs in various other fields, such as image, video, speech, and audio processing, and the availability of large (open source) datasets of fruit spectra and reference quality attribute, which is required for the training of CNN models. The review provides an overview of deep learning and the CNN architectures and techniques used in NIR spectroscopy for regression modelling, with advantages and disadvantages identified. Studies using CNN for NIR based fruit quality evaluation are then critically examined. Eight publications have presented on models using the same open-source mango dry matter calibration and test set, enabling inter-method comparisons. CNN models have been demonstrated to be accurate, precise and robust. Techniques of transfer learning for CNN models offer an alternative solution to model updating and calibration transfer methods applied in traditional chemometrics. The review has highlighted crucial areas that require resolution and exploration in this application through future research, including, (i) data requirements for training a CNN (ii) optimal spectral pre-processing for CNN (iii) CNN architecture and hyper-parameter selection and tuning for fruit quality evaluation (iv) CNN model interpretability and explainability. Future studies must conduct clearer comparison to partial least squares (PLS) regression and shallow ANNs to better assess the prospective benefit of using CNN, a more complex model. The potential for visualisation of spectra relevance to the CNN model using techniques such as GradCam, currently employed in visualising 2D-CNN models, remains to be explored.
Why it matches plant phenotyping methods果実品質という植物器官の形質を対象に、NIRスペクトルとCNNによる評価手法を体系的にレビューしており、方法論が中心である。
abstractThe review provides an overview of deep learning and the CNN architectures and techniques used in NIR spectroscopy for regression modelling
One of the primary issues with inside the agricultural region is crop sicknesses and automated detection is crucial for crop monitoring. Plant leaves generally display the maximum ailment symptoms, however professional laboratory leaf analysis is high priced and time-consuming. According to the Food and Agriculture Organization (FAO), agricultural pests lessen crop yields international via way of means of 20 to 40% according to year. Smart farming is good solution for farmers is to apply artificial intelligence techniques along with modern statistics and communication technologies to get rid of those dangerous insect infestations. Farmers can substantially lessen financial losses via way of means of treating plants directly and detecting sicknesses and pests in apple, mango and graphs leaves appropriately and timely. Image type accuracy has progressed notably because of latest advances in deep studying-primarily based totally convolutional neural networks (CNN). This article evolved strategies primarily based totally on deep studying to hit upon sicknesses and pests in apples, mango, and grape leaves. These strategies have been influenced via way of means of the achievement of CNNs in photograph type.
Why it matches plant phenotyping methods葉画像から植物の病害状態をCNNで推定する手法が中心であり、植物病害の画像ベース表現型評価に該当する。
abstractThis article evolved strategies primarily based totally on deep studying to hit upon sicknesses and pests in apples, mango, and grape leaves.
Estimation of fruit size on-tree is useful for yield estimation, harvest timing and market planning. Automation of measurement of fruit size on-tree is possible using RGB-depth (RGB-D) cameras, if partly occluded fruit can be removed from consideration. An RGB-D Time of Flight camera was used in an imaging system that can be driven through an orchard. Three approaches were compared, being: (i) refined bounding box dimensions of a YOLO object detector; (ii) bounding box dimensions of an instance segmentation model (Mask R-CNN) applied to canopy images, and (iii) instance segmentation applied to extracted bounding boxes from a YOLO detection model. YOLO versions 3, 4 and 7 and their tiny variants were compared to an in-house variant, MangoYOLO, for this application, with YOLO v4-tiny adopted. Criteria developed to exclude occluded fruit by filtering based on depth, mask size, ellipse to mask area ratio and difference between refined bounding box height and ellipse major axis. The lowest root mean square error (RMSE) of 4.7 mm and 5.1 mm on the lineal length dimensions of a population (n = 104) of Honey Gold and Keitt varieties of mango fruit, respectively, and the lowest fruit exclusion rate was achieved using method (ii), while the RMSE on estimated fruit weight was 113 g on a population weight range between 180 and 1130 g. An example use is provided, with the method applied to video of an orchard row to produce a weight frequency distribution related to packing tray size.
Why it matches plant phenotyping methodsRGB-D画像と物体検出・インスタンスセグメンテーションを用いて樹上マンゴー果実のサイズ・重量を推定し、複数手法を比較検証しているため、植物表現型取得法が研究の中心である。
abstractThree approaches were compared, being: (i) refined bounding box dimensions of a YOLO object detector; (ii) bounding box dimensions of an instance segmentation model (Mask R-CNN) applied to canopy images, and (iii) instance segmentation applied to extracted bounding boxes from a YOLO detection model.
Reproduction assets foundThe paper publicly releases the RGB-D image datasets (Dataset-B and Dataset-C) used for training/testing the Mask R-CNN and YOLO-based mango fruit sizing models via a DOI deposit. No author analysis code or trained model checkpoints are explicitly deposited; the GitHub links cited are third-party frameworks (Darknet, MDataset · publicAll images in Dataset B and Dataset C used in
this study are available at https://doi.org/10.25946/21655628 (accessed on 15 October 2022).Open asset ↗10.25946/21655628pdf-page:4 lines:1-58Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published24 Nov 20222022 IEEE 19th India Council International Conference (INDICON)Cited by 14 · OpenAlex ↗
The image processing technique is a method useful in agricultural processes for enhancing accuracy and uniformity of processes in farming while decreasing farmers’ manual observation. Leaf disease detection using deep learning applications can be helpful for farmers to analyze the affected leaves at an early stage which will in turn aid in the agricultural process. In this paper, we have used Convolution Neural Network (CNN), a deep learning algorithm mainly used for analyzing visual imagery, for the detection of various crop leaf diseases. The CNN-based model will help in differentiating between diseased and healthy leaves, which will improve farmers’ harvest quality. The main objective of the paper is to create a new dataset that contains three plant leaves that are cauliflower, tomato, and mango, and then compare the accuracy using various CNN models which are generally used for unstructured datasets i.e., images. Also analyzing the results on the basis of different experimental configurations such as choice of deep learning architecture, choice of dataset type, and Choice of training-testing set distribution. Results achieved from these experiments display the performance and precision of the model best fit for disease detection of plants.
Why it matches plant phenotyping methods植物葉の画像から病害状態を推定するCNN手法を開発・比較し、新規データセットも作成しているため、病害表現型の取得・推定が中心です。
abstractLeaf disease detection using deep learning applications can be helpful for farmers to analyze the affected leaves at an early stage
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
MangoTomatoFruitObject detectionGrowth / development / phenology
Fruits require different planting techniques at different growth stages. Traditionally, the maturity stage of fruit is judged visually, which is time-consuming and labor-intensive. Fruits differ in size and color, and sometimes leaves or branches occult some of fruits, limiting automatic detection of growth stages in a real environment. Based on YOLOV4-Tiny, this study proposes a GCS-YOLOV4-Tiny model by (1) adding squeeze and excitation (SE) and the spatial pyramid pooling (SPP) modules to improve the accuracy of the model and (2) using the group convolution to reduce the size of the model and finally achieve faster detection speed. The proposed GCS-YOLOV4-Tiny model was executed on three public fruit datasets. Results have shown that GCS-YOLOV4-Tiny has favorable performance on mAP, Recall, F1-Score and Average IoU on Mango YOLO and Rpi-Tomato datasets. In addition, with the smallest model size of 20.70 MB, the mAP, Recall, F1-score, Precision and Average IoU of GCS-YOLOV4-Tiny achieve 93.42 ± 0.44, 91.00 ± 1.87, 90.80 ± 2.59, 90.80 ± 2.77 and 76.94 ± 1.35%, respectively, on F. margarita dataset. The detection results outperform the state-of-the-art YOLOV4-Tiny model with a 17.45% increase in mAP and a 13.80% increase in F1-score. The proposed model provides an effective and efficient performance to detect different growth stages of fruits and can be extended for different fruits and crops for object or disease detections.
Why it matches plant phenotyping methods果実の生育段階という植物器官の状態を画像から推定する深層学習モデルを開発し、複数データセットで性能検証しており、フェノタイピング手法が中心である。
abstractthis study proposes a GCS-YOLOV4-Tiny model
Plants diseases is a major threat to agricultural production. Reduced yield due to plant diseases can lead to immeasurable economic losses. Therefore, the detection and classification of plant diseases are of great significance. Most of the existing plant disease detection methods focus on improving the identification accuracy. However, besides accuracy, real-time performance cannot be ignored. In this paper, a new module named 2-way residual dense layer is presented to effectively decrease the number of parameters in our network. In this module, depth separable convolution is introduced, which reduces the amount of parameter calculation and achieves a performance of over 98%. Our network is verified by an open dataset which includes 4503 images from four classes, including Mango, Arjun, Alstonia Scholaris, Guava, Bael, Jamun, Jatropha, Pongamia Pinnata, Basil, Pomegranate, Lemon, and Chinar. The leaf images of these plants have healthy and diseased condition. The experimental results showed that this method can be practically applied to the identification of plant leaf diseases and provide a basis for the identification of other leaf diseases.
Why it matches plant phenotyping methods植物葉画像から病害状態を分類する軽量CNN手法の開発・検証が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として含める。
abstractIn this paper, a new module named 2-way residual dense layer is presented to effectively decrease the number of parameters in our network.
Detection of plant diseases plays a crucial role in taking disease control measures to increase the quality and quantity of crops produced. Plant disease automation is beneficial because it eliminates surveillance work at significant farms. As plants are a food source, diagnosing leaf conditions early and accurately is essential. This work involves a detailed learning approach that automates leaf disease detection in mango plant species. This paper presents a detection system using Brightness Preserving Bi-Histogram Equalization (BBHE) and Convolutional Neural Network (CNN). The photographs of mango leaves were first flattened, then resized and translated to their threshold value, followed by feature extraction. CNN and BBHE have extensively been used for pattern recognition. The test images of affected leaves were subsequently uploaded to the system and then matched to the ailments being trained. Training data and test data were cross-validated to balance over-adjustment and under-adjustment problems. The proposed method correctly detects the mango leaves disease at the early stage with 99.21% maximum accuracy.
Why it matches plant phenotyping methodsマンゴー葉の病害状態を画像から推定するBBHEとCNNの検出手法が研究の中心であり、交差検証と精度評価も実施しているため、植物フェノタイピング手法として含める。
abstractThis work involves a detailed learning approach that automates leaf disease detection in mango plant species.
Reproduction assets foundThe paper's mango leaf disease detection experiments rely on a publicly available Kaggle mango leaf image dataset (265 diseased and 170 healthy images), explicitly cited as reference [30] with a public URL. No author analysis code or trained model is deposited.Dataset · publicIn the proposed method, mango leaf dataset considered
which is publicly available at [30]. The dataset comprises of
265 diseased and 170 healthy images.Open asset ↗pdf-raw-page:5 lines:1-112Plant phenotyping relevance match · UnverifiedEurope PMC · checked 14 Sept 2026
The color of plant leaves can be assessed qualitatively by color charts or after processing of digital images. This pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves. In order to assess its performance, a color-dependent parameter (SPAD index) was used as the dependent variable, since there is a strong correlation between SPAD index and greenness of plant leaves. A total of 1,872 fresh and intact leaves from 13 crops were analyzed using a SPAD-502 meter and scanned using the Nix™ Pro color sensor. The color was assessed via RGB and CIELab systems. The full dataset was divided into calibration (70% of data) and validation (30% of data). For each crop and color pattern, multiple linear regression (MLR) analysis and multivariate modeling [least absolute shrinkage and selection operator (LASSO), and elastic net (ENET) regression] were employed and compared. The obtained MLR equations and multivariate models were then tested using the validation dataset based on r, R 2 , root mean squared error (RMSE), and mean absolute error (MAE). In both RGB and CIELab color systems, the Nix™ Pro color sensor was able to differentiate crops, and the SPAD indices were successfully predicted, mainly for mango, quinoa, peach, pear, and rice crops. Validation results indicated that ENET performed best in most crops (e.g., coffee, corn, mango, pear, rice, and soy) and very close to MLR in bean, grape, peach, and quinoa. The correlation between SPAD and greenness is crop-dependent. Overall, the Nix™ Pro color sensor was a fast, sensible and an easy way to obtain leaf color directly in the field, constituting a reliable alternative to digital camera imagery and associated image processing.
Why it matches plant phenotyping methods植物葉色を定量取得する携帯型センサーを開発・評価し、SPAD指数（葉の緑色状態）の推定モデルを校正・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 pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves.
MangoField / plotFruitObject detectionGrowth / development / phenology
Real-time detection of agricultural growth stages is one of the key steps of estimating yield and intelligent spraying in commercial orchards. However, due to considerable degree of occultation in surrounding leaves, significant overlapping between neighboring fruits, differences in size, color, cluster density, and other growth characteristics, traditional detection methods have the limitation in the accuracy of detecting different growth phases. The current work proposes a real-time object detection framework Dense-YOLOv4 based on an improved version of the YOLOv4 algorithm by including DenseNet in the backbone to optimize feature transfer and reuse. Furthermore, a modified path aggregation network (PANet) has been implemented to preserve fine-grain localized information. The model has been applied to detect different growth stages of mango with high degree of occultation in a complex orchard scenario. At a detection rate of 44.2 FPS, the mean average precision (mAP) and F1-score of the proposed model have reached up to 96.20% and 93.61%, respectively. The proposed Dense-YOLOv4 has outperformed the state-of-the-art YOLOv4 with 7.94%,13.10%,10.47%, and 4.73% increase in precision, recall, F1-score, and mAP, respectively. The present work provides an effective and efficient framework to detect different growth stages under a complex orchard scenario and can be extended to different fruit and crop detection, disease detection, and different automated agricultural applications.
Why it matches plant phenotyping methodsマンゴー果実の生育段階という植物状態を画像から推定するDense-YOLOv4手法を開発し、精度と速度を評価しており、表現型取得手法が研究の中心である。
abstractThe current work proposes a real-time object detection framework Dense-YOLOv4 based on an improved version of the YOLOv4 algorithm by including DenseNet in the backbone to optimize feature transfer and reuse.
Imaging systems mounted to ground vehicles are used to image fruit tree canopies for estimation of fruit load, but frequently need correction for fruit occluded by branches, foliage or other fruits. This can be achieved using an orchard ‘occlusion factor’, estimated from a manual count of fruit load on a sample of trees (referred to as the reference method). It was hypothesised that canopy images could hold information related to the number of occluded fruit. Five approaches to correct for occluded fruit based on canopy images were compared using data of three mango orchards in two seasons. However, no attribute correlates to the number of hidden fruit were identified. Several image features obtained through segmentation of fruit and canopy areas, such as the proportion of fruit that were partly occluded, were used in training Random forest and multi-layered perceptron (MLP) models for estimation of a correction factor per tree. In another approach, deep learning convolutional neural networks (CNNs) were directly trained against harvest fruit count on trees. The supervised machine learning methods for direct estimation of fruit load per tree delivered an improved prediction outcome over the reference method for data of the season/orchard from which training data was acquired. For a set of 2017 season tree images (n=98 trees), a R2 of 0.98 was achieved for the correlation of the number of fruits predicted by a Random forest model and the ground truth fruit count on the trees, compared to a R2 of 0.68 for the reference method. The best prediction of whole orchard (n = 880 trees) fruit load, in the season of the training data, was achieved by the MLP model, with an error to packhouse count of 1.6% compared to the reference method error of 13.6%. However, the performance of these models on new season data (test set images) was at best equivalent and generally poorer than the reference method. This result indicates that training on one season of data was insufficient for the development of a robust model. This outcome was attributed to variability in tree architecture and foliage density between seasons and between orchards, such that the characters of the canopy visible from the interrow that relate to the proportion of hidden fruit are not consistent. Training of these models across several seasons and orchards is recommended.
Why it matches plant phenotyping methods果樹画像から樹体ごとの果実負荷量を推定し、遮蔽補正と複数の機械学習手法を比較・検証することが研究の中心であるため、植物フェノタイピング手法研究に該当する。
titleCorrection for Occluded Fruit in Tree Fruit Load Estimation by Machine Vision With Deep Learning
Color is one of the key sensory characteristics in the evaluation of the quality of mangos ( Mangifera indica ) especially with regard to determining the optimal level of ripeness. However, an objective color determination of entire fruits can be a challenging task. Conventional evaluation methods such as colorimetric or spectrophotometric procedures are primarily limited to a homogenous distribution of the color. Accordingly, a direct assessment of the mango quality with regard to color requires more pronounced color determination procedures. In this study, the color of the peel and the pulp of the mango cultivars "Nam Dokmai", "Mahachanok", and "Kent" was evaluated and categorized into various levels of ripeness using a charge-coupled device (CCD) camera in combination with a computer vision system and color standards. The color evaluation process is based on a transformation of the RGB (red, green, and blue) color space values into the HSI (hue, saturation, and intensity) color system and the Natural Color Standard (NCS). The results showed that for pulp color codes, 0560-Y20R and 0560-Y40R can be used as appropriate indicators for the ripeness of the cultivars "Nam Dokmai" and "Mahachanok". The peels of these two mango cultivars present two distinct colors (1050-Y40R and 1060-Y40R), which can be used to determine the fruit maturity during the post-ripening process. However, in the case of the cultivar "Kent", peel color detection was not an applicable approach for determining ripeness; thus, the determination of the pulp color with the color code 0550-Y20R gave promising results.
Why it matches plant phenotyping methodsCCDカメラとコンピュータビジョンを用いてマンゴー果実の色から成熟度を客観的に判定する手法を開発しており、植物器官の形質取得が研究の中心である。
titleDeveloping an Automatic Color Determination Procedure for the Quality Assessment of Mangos ( Mangifera indica ) Using a CCD Camera and Color Standards.
Airborne Laser Scanning (ALS) and Terrestrial Laser Scanning (TLS) systems are useful tools for deriving horticultural tree structure estimates. However, there are limited studies to guide growers and agronomists on different applications of the two technologies for horticultural tree crops, despite the importance of measuring tree structure for pruning practices, yield forecasting, tree condition assessment, irrigation and fertilization optimization. Here, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume. ALS and TLS measurements were similar for crown area, fractional cover and maximum crown height (coefficient of determination (R2) ≥ 0.94, relative root mean square error (rRMSE) ≤ 4.47%). Due to the limited ability of ALS data to measure lower branches and within crown structure, crown volume estimates from ALS and TLS data were less correlated (R2 = 0.81, rRMSE = 42.66%) with the ALS data found to consistently underestimate crown volume. To illustrate the effects of different spatial resolution, capacity and coverage of ALS and TLS data, we also calculated leaf area, leaf area density and vertical leaf area profile from the TLS data, while canopy height, tree row dimensions and tree counts) at the orchard level were calculated from ALS data. Our results showed that ALS data have the ability to accurately measure horticultural crown structural parameters, which mainly rely on top of crown information, and measurements of hedgerow width, length and tree counts at the orchard scale is also achievable. While the use of TLS data to map crown structure can only cover a limited number of trees, the assessment of all crown strata is achievable, allowing measurements of crown volume, leaf area density and vertical leaf area profile to be derived for individual trees. This study provides information for growers and horticultural industries on the capacities and achievable mapping accuracies of standard ALS data for calculating crown structural attributes of horticultural tree crops.
Why it matches plant phenotyping methodsALS/TLSによる果樹の樹冠構造形質の取得・比較検証が研究の中心であり、精度評価と適用可能性を明示的に扱っている。
abstractHere, we evaluated ALS data against near coincident TLS data in avocado, macadamia and mango orchards to demonstrate and assess their accuracies and potential application for mapping crown area, fractional cover, maximum crown height, and crown volume.
A pixel-based segmentation method was demonstrated to be confounded by developmental stage in estimation of flowering of mango. Categorization of panicles into three developmental stages was undertaken with a single and a two-stage deep learning framework (YOLO and R2CNN), using either upright or rotated bounding boxes. For a validation image set and for total panicle count, the models MangoYOLO(-upright), MangoYOLO-rotated, YOLOv3-rotated, R2CNN(-rotated) and R2CNN-upright achieved: (i) RMSEs of 25.6, 16.0, 15.4, 25.8 and 32.3 panicles per tree image, (ii) Mean average precision (mAP) scores of 72.2, 69.1, 65.0, 62.5 and 70.9% and (iii) weighted F1-scores of 76.5, 76.1, 74.9, 74.0 and 82.0, respectively. For a test set of images involving a different orchard and cultivar and use of a different camera, the R2 for machine vision to human count of panicles per tree was 0.86, 0.80, 0.83, 0.81 and 0.76 for the same models, respectively. Thus, models generalised well, but with no consistent benefit from use of rotated over upright bounding boxes. While the YOLOv3-rotated model was superior in terms of total panicle count, the R2CNN-upright model was more accurate for panicle stage classification. To demonstrate practical application, panicle counts were made weekly for an orchard of 994 trees, with a peak detection routine applied to document multiple flowering events.
Why it matches plant phenotyping methodsマンゴー花序の発達段階分類と個体当たり花序数推定のための画像解析・深層学習手法を開発し、複数モデル、異なる園地・品種・カメラで性能検証しているため、植物表現型取得法が中心である。
abstractCategorization of panicles into three developmental stages was undertaken with a single and a two-stage deep learning framework (YOLO and R2CNN), using either upright or rotated bounding boxes.
For the most part, mango leaves principally get influenced by three basic sicknesses they are Anthracnose, Bacterial canker, Powdery mildew.The previously mentioned infections influence the development of mango tree, decline the life expectancy and decrease the natural product creation.Thinking about this we were doing the task under the territory of leaf malady characterization.The fundamental point of our venture is to arrange the state of the leaf, needs to recognize the malady from which it is experiencing.Thus, it will be for the most part helpful for ranchers keeping from crop misfortune financially.They can kill the illness in the underlying state itself.We were utilizing the profound learning system to recognize the tainted leaves.We propose a CNN single-stream model to order the picture.Our informational collection comprises a sum of 800 pictures arranged into two kinds, the main sort comprises a preparation set and the subsequent kind comprises of the testing set.Preparing comprises of 150 pictures and testing comprises of 50 pictures in every envelope.
Why it matches plant phenotyping methodsマンゴー葉の画像から病気状態を推定するCNN分類法が研究の中心であり、植物病害の表現型推定に該当する。
abstractThe fundamental point of our venture is to arrange the state of the leaf, needs to recognize the malady from which it is experiencing.
Hyperspectral imaging (HSI) is a powerful technology already used for many objectives in agriculture. Applications include disease monitoring, plant phenotyping, yield estimation or fruit composition and ripeness. However, the cost of hyperspectral sensors is typically an order of magnitude higher than simpler RGB cameras, which can be prohibitive. Given that in HSI processing the spectral data often contains redundancies, the full spectra are not always required for a specific application and there is an opportunity to design a lower cost multi-spectral sensing system by dimensionality reduction. In past work, HSI dimensionality reduction has been applied in the form of band selection to achieve faster computation times. If, however, the objective is to design a lower cost multi-spectral camera system, band selection is poorly suited because real-world sensor and optical filter responses do not typically replicate the individual bands of a hyperspectral sensor. The objective of this paper is to develop a new methodology for filter selection by simulating several imaging devices with different real-world optical filters, to use a high cost HSI device to design a lower cost multi-spectral solution for a specific application. In this paper, we apply the technique to the specific task of mango fruit maturity estimation (dry matter), which was recently shown to be possible using HSI. Mango HSI acquired under field conditions from an UGV was used as input for the experiments. These involved the simulation of imaging devices, using support vector machines for modelling, and testing several filter combinations by brute force or optimisation with genetic algorithms. The mango prediction performance of the simulations was compared to the best performance obtained with full HSI data, which had an R2 of 0.74. The best values came from the simulation of a four-sensor device with four distinct filters, achieving R2 up to 0.69 for mango dry matter estimation. The results showed that genetic algorithms, when compared to brute force approaches, were able to obtain the best solution in an efficient way, and that a good performance for mango ripeness estimation can be achieved from the combination of four spectral filters that would allow to implement them into a low-cost, custom-made multi-spectral sensor. The methods exposed in this paper are more broadly applicable to applications beyond mango maturity estimation.
Why it matches plant phenotyping methodsマンゴー果実の成熟度（乾物含量）推定を対象に、ハイパースペクトル画像から低コストマルチスペクトルセンサー用フィルターを設計する手法を開発・比較検証しており、植物形質取得法が中心である。
abstractThe objective of this paper is to develop a new methodology for filter selection by simulating several imaging devices with different real-world optical filters, to use a high cost HSI device to design a lower cost multi-spectral solution for a specific application.
: Pre-harvest fruit yield estimation is useful to guide harvesting and marketing resourcing, but machine vision estimates based on a single view from each side of the tree ("dual-view") underestimates the fruit yield as fruit can be hidden from view. A method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting from 10 frame-per-second videos captured of trees from a platform moving along the inter row at 5 km/h. The deep learning based mango fruit detection algorithm, MangoYOLO, was used to detect fruit in each frame. The Hungarian algorithm was used to correlate fruit between neighbouring frames, with the improvement of enabling multiple-to-one assignment. The Kalman filter was used to predict the position of fruit in following frames, to avoid multiple counts of a single fruit that is obscured or otherwise not detected with a frame series. A "borrow" concept was added to the Kalman filter to predict fruit position when its precise prediction model was absent, by borrowing the horizontal and vertical speed from neighbouring fruit. By comparison with human count for a video with 110 frames and 192 (human count) fruit, the method produced 9.9% double counts and 7.3% missing count errors, resulting in around 2.6% over count. In another test, a video (of 1162 frames, with 42 images centred on the tree trunk) was acquired of both sides of a row of 21 trees, for which the harvest fruit count was 3286 (i.e., average of 156 fruit/tree). The trees had thick canopies, such that the proportion of fruit hidden from view from any given perspective was high. The proposed method recorded 2050 fruit (62% of harvest) with a bias corrected Root Mean Square Error (RMSE) = 18.0 fruit/tree while the dual-view image method (also using MangoYOLO) recorded 1322 fruit (40%) with a bias corrected RMSE = 21.7 fruit/tree. The video tracking system is recommended over the dual-view imaging system for mango orchard fruit count.
Why it matches plant phenotyping methods動画画像と深層学習・追跡アルゴリズムを組み合わせ、樹上マンゴー果実数（収量関連形質）を推定する手法の開発・比較検証が中心である。
abstractA method is proposed involving deep learning, Kalman filter, and Hungarian algorithm for on-tree mango fruit detection, tracking, and counting
Reproduction assets foundThe paper states that the tree images and video used for the MangoYOLO–Kalman–Hungarian fruit tracking/counting analysis are available as a supplementary data file, and the Supplementary Materials section lists Video S1 (Fruit Tracking Count) at the MDPI supplementary URL. This is a paper-specific, publicly accessible,Supplement · publicThe images and video are available as a supplementary data file to this manuscript.Open asset ↗lines:34-41Plant phenotyping relevance match · UnverifiedCrossref · checked 15 Sept 2026
Published11 Apr 2019Third International Seminar on Photonics, Optics, and Its Applications (ISPhOA 2018)Cited by 2 · OpenAlex ↗
Fruit consumption rate of Indonesian population is still far below the level of sufficiency of fruits consumption recommended by the Food Agriculture Organization / World Health Organization (FAO / WHO). Maintaining the quality of fruit is expected to maintain stability in the fulfillment of national fruit needs. Early detection to determine the growth rate can be seen from the level of greenery or chlorophyll content during the growth period. So we need a method to measure the concentration of chlorophyll in the leaves by using Diffuse Reflectance Spectroscopy which is non destructive. This technique does not require complicated sample preparation as in the determination of chlorophyll content through Absorption Spectroscopy. Tissue model of leaf (phantom) is made of gelatin with known chlorophyll content variation, used as a preliminary stage for testing Diffuse Reflectance Spectroscopy technique. Determination of chlorophyll content by Absorption Spectroscopy technique will be used as a comparison. To determine the value of optical parameters (absorption coefficient μa and reduced scattering coefficient μs'), obtained from fitting between the measured reflectance spectra of phantom and the reflectance model of leaves. Chlorophyll content determined from correlation equation y = 0,944 x + 5,0069 with a coefficient of determination (R2) of 0,9422 for mango leaves, y = 0,5759 x + 5,6772 with coefficient of determination (R2) of 0.9945 for starfruit leaves, y = 0,1168 x + 3,7704 with a coefficient of determination (R2) of 0.9789 for guava leaves.
Why it matches plant phenotyping methods葉のクロロフィル濃度という植物形質を、非破壊拡散反射分光法で測定する手法の開発・検証が中心であり、吸収分光法との比較や相関評価も行っている。
abstractwe need a method to measure the concentration of chlorophyll in the leaves by using Diffuse Reflectance Spectroscopy which is non destructive.
In this letter, we present a cheap, lightweight, and fast fruit counting pipeline. Our pipeline relies only on a monocular camera, and achieves counting performance comparable to a state-of-the-art fruit counting system that utilizes an expensive sensor suite including a monocular camera, LiDAR and GPS/INS on a mango dataset. Our pipeline begins with a fruit and tree trunk detection component that uses state-of-the-art convolutional neural networks (CNNs). It then tracks fruits and tree trunks across images, with a Kalman Filter fusing measurements from the CNN detectors and an optical flow estimator. Finally, fruit count and map are estimated by an efficient fruit-as-feature semantic structure from motion algorithm that converts two-dimensional (2-D) tracks of fruits and trunks into 3-D landmarks, and uses these landmarks to identify double counting scenarios. There are many benefits of developing such a low cost and lightweight fruit counting system, including applicability to agriculture in developing countries, where monetary constraints or unstructured environments necessitate cheaper hardware solutions.
Why it matches plant phenotyping methods果実数という植物器官の明示的形質を、単眼カメラ画像から検出・追跡・三次元推定する手法開発が研究の中心であるため、植物フェノタイピング手法として含める。
abstractwe present a cheap, lightweight, and fast fruit counting pipeline
Fruit yield estimation in orchard blocks is an important objective in the context of precision agriculture, as it makes it easier for the farmer to plan ahead and efficiently use resources. Nevertheless, its implementation is labour-intensive and involves the manual counting of the fruit present in the trees. While colour (RGB) has been widely shown to be successful and arguably sufficient for yield estimation in orchards, hyperspectral imaging (HSI) shows promise for more nuanced tasks such as disease detection, cultivar classification and fruit maturity estimation. Therefore, it is important to ask how appropriate is HSI for the task of yield estimation, with a view to performing all of these tasks with just one sensor. This paper presents a novel mango yield estimation pipeline using ground based line-scan HSI acquired from an unmanned ground vehicle. Hyperspectral images were collected on a commercial mango orchard block in December 2017 and pre-processed for illumination compensation. After tree delimitation and mango pixel identification, an optimisation process was carried out to obtain the best models for fruit counting, using mango counts obtained by manually counting the fruit on-tree, and using state-of-the-art RGB techniques for yield estimation. Models were validated and tested on hundreds of trees, and subsequently mapped. In testing, determination coefficients reached values of up to 0.75 against field counts (predicting 18 trees) and 0.83 against RGB mango counts (predicting 216 trees). These results suggest that line-scan HSI can be used to accurately estimate yield in orchards, especially in scenarios in which this technology is already chosen for the determination of other traits.
Why it matches plant phenotyping methods果実収量という植物形質を、地上走査型ハイパースペクトル画像と解析パイプラインで推定し、樹木数百本で検証しているため、フェノタイピング手法の応用・技術検証が中心です。
abstractThis paper presents a novel mango yield estimation pipeline using ground based line-scan HSI acquired from an unmanned ground vehicle.
In the value chain, yields are key information for both growers and other stakeholders in market supply and exports. However, orchard yields are often still based on an extrapolation of tree production which is visually assessed on a limited number of trees; a tedious and inaccurate task that gives no yield information at a finer scale than the orchard plot. In this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems. A methodological toolbox was developed and tested to estimate and map tree species, structure, and yields in mango orchards of various cropping systems (from monocultivar to plurispecific orchards) in the Niayes region, West Senegal. Tree structure parameters (height, crown area and volume), species, and mango cultivars were measured using unmanned aerial vehicle (UAV) photogrammetry and geographic, object-based image analysis. This procedure reached an average overall accuracy of 0.89 for classifying tree species and mango cultivars. Tree structure parameters combined with a fruit load index, which takes into account year and management effects, were implemented in predictive production models of three mango cultivars. Models reached satisfying accuracies with R2 greater than 0.77 and RMSE% ranging from 20% to 29% when evaluated with the measured production of 60 validation trees. In 2017, this methodology was applied to 15 orchards overflown by UAV, and estimated yields were compared to those measured by the growers for six of them, showing the proper efficiency of our technology. The proposed method achieved the breakthrough of rapidly and precisely mapping mango yields without detecting fruits from ground imagery, but rather, by linking yields with tree structural parameters. Such a tool will provide growers with accurate yield estimations at the orchard scale, and will permit them to study the parameters that drive yield heterogeneity within and between orchards.
Why it matches plant phenotyping methodsUAVフォトグラメトリと画像解析により樹体構造・品種・個体収量を推定・地図化する手法を開発し、検証・実 orchard 適用しており、植物フェノタイピングが中心である。
abstractIn this work, we propose a method to accurately map individual tree production at the orchard scale by developing a trade-off methodology between mechanistic yield modelling and extensive fruit counting using machine vision systems.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Supplement · publicSupplementary Materials: The following are available online at http://www.mdpi.com/2072-4292/10/12/1900/
s1, Figure S1: Image abacus used by expert in the field to estimate load index for (a) ‘Kent’, (b) ‘Keitt’, (c) and
‘BDH’ cultivar. Load index categories (low, medium and high) are displayed in column and different tree heights
(small, medium and tall) are represented in line. Table S1: Mean fruit weight and standard deviation (SD) for the
three variety in Niayes region. Table S2: Description of the 150 calibration trees: cultivar; number of fruit detected
by the KNN-based machine vision and yield measured; load index; and tree structure parameters (tree height,
crown area and volume).Open asset ↗pdf-page:18 lines:1-56Plant phenotyping relevance match · UnverifiedarXiv · checked 15 Sept 2026
We present a cheap, lightweight, and fast fruit counting pipeline that uses a single monocular camera. Our pipeline that relies only on a monocular camera, achieves counting performance comparable to state-of-the-art fruit counting system that utilizes an expensive sensor suite including LiDAR and GPS/INS on a mango dataset. Our monocular camera pipeline begins with a fruit detection component that uses a deep neural network. It then uses semantic structure from motion (SFM) to convert these detections into fruit counts by estimating landmark locations of the fruit in 3D, and using these landmarks to identify double counting scenarios. There are many benefits of developing a low cost and lightweight fruit counting system, including applicability to agriculture in developing countries, where monetary constraints or unstructured environments necessitate cheaper hardware solutions.
Why it matches plant phenotyping methods単眼カメラによる果実検出・3D位置推定・重複除去を統合し、果実数という植物器官の定量形質を抽出する手法が研究の中心であるため、植物フェノタイピング手法として採用する。
abstractWe present a cheap, lightweight, and fast fruit counting pipeline that uses a single monocular camera.
In field (on tree) fruit sizing has value in assessing crop health and for yield estimation. As the mobile phone is a sensor and communication rich device carried by almost all farm staff, an Android application ("FruitSize") was developed for measurement of fruit size in field using the phone camera, with a typical assessment rate of 240 fruit per hour achieved. The application was based on imaging of fruit against a backboard with a scale using a mobile phone, with operational limits set on camera to object plane angle and camera to object distance. Image processing and object segmentation techniques available in the OpenCV library were used to segment the fruit from background in images to obtain fruit sizes. Phone camera parameters were accessed to allow calculation of fruit size, with camera to fruit perimeter distance obtained from fruit allometric relationships between fruit thickness and width. Phone geolocation data was also accessed, allowing for mapping fruits of data. Under controlled lighting, RMSEs of 3.4, 3.8, 2.4, and 2.0 mm were achieved in estimation of avocado, mandarin, navel orange, and apple fruit diameter, respectively. For mango fruit, RMSEs of 5.3 and 3.7 mm were achieved on length and width, benchmarked to manual caliper measurements, under controlled lighting, and RMSEs of 5.5 and 4.6 mm were obtained in-field under ambient lighting.
Why it matches plant phenotyping methodsスマートフォン画像から果実サイズを抽出するアプリケーションを開発し、手動ノギス測定との誤差検証も行っており、植物表現型取得法が研究の中心である。
abstractan Android application ("FruitSize") was developed for measurement of fruit size in field using the phone camera
Plant phenotyping relevance match · UnverifiedCrossref · Europe PMC · checked 10 Sept 2026
Photosynthesis is an important physiological response for determination of CO2 fertilization in greenhouses and estimation of crop growth. In order to estimate the whole plant photosynthetic rate, it is necessary to investigate how light interception by crops changes with environmental and morphological factors. The objectives of this study were to analyze plant light interception using a three-dimensional (3D) plant model and ray-tracing, determine the spatial distribution of the photosynthetic rate, and estimate the whole plant photosynthetic rate of Irwin mango (Mangifera indica L. cv. Irwin) grown in greenhouses. In the case of mangoes, it is difficult to measure actual light interception at the canopy level due to their vase shape. A two-year-old Irwin mango tree was used to measure the whole plant photosynthetic rate. Light interception and whole plant photosynthetic rate were measured under artificial and natural light conditions using a closed chamber (1 × 1 × 2 m). A 3D plant model was constructed and ray-tracing simulation was conducted for calculating the photosynthetic rate with a two-variable leaf photosynthetic rate model of the plant. Under artificial light, the estimated photosynthetic rate increased from 2.0 to 2.9 μmolCO2·m−2·s−1 with increasing CO2 concentration. On the other hand, under natural light, the photosynthetic rate increased from 0.2 μmolCO2·m−2·s−1 at 06:00 to a maximum of 7.3 μmolCO2·m−2·s−1 at 09:00, then gradually decreased to −1.0 μmolCO2·m−2·s−1 at 18:00. In validation, simulation results showed good agreement with measured results with R2 = 0.79 and RMSE = 0.263. The results suggest that this method could accurately estimate the whole plant photosynthetic rate and be useful for pruning and adequate CO2 fertilization.
Why it matches plant phenotyping methods3D植物モデルとレイトレーシングにより個体全体の光合成速度を推定する手法を開発・検証しており、植物生理形質の取得が研究の中心です。
abstractIn order to estimate the whole plant photosynthetic rate, it is necessary to investigate how light interception by crops changes with environmental and morphological factors.
In-field mango fruit sizing is useful for estimation of fruit maturation and size distribution, informing the decision to harvest, harvest resourcing (e.g., tray insert sizes), and marketing. In-field machine vision imaging has been used for fruit count, but assessment of fruit size from images also requires estimation of camera-to-fruit distance. Low cost examples of three technologies for assessment of camera to fruit distance were assessed: a RGB-D (depth) camera, a stereo vision camera and a Time of Flight (ToF) laser rangefinder. The RGB-D camera was recommended on cost and performance, although it functioned poorly in direct sunlight. The RGB-D camera was calibrated, and depth information matched to the RGB image. To detect fruit, a cascade detection with histogram of oriented gradients (HOG) feature was used, then Otsu's method, followed by color thresholding was applied in the CIE L*a*b* color space to remove background objects (leaves, branches etc.). A one-dimensional (1D) filter was developed to remove the fruit pedicles, and an ellipse fitting method employed to identify well-separated fruit. Finally, fruit lineal dimensions were calculated using the RGB-D depth information, fruit image size and the thin lens formula. A Root Mean Square Error (RMSE) = 4.9 and 4.3 mm was achieved for estimated fruit length and width, respectively, relative to manual measurement, for which repeated human measures were characterized by a standard deviation of 1.2 mm. In conclusion, the RGB-D method for rapid in-field mango fruit size estimation is practical in terms of cost and ease of use, but cannot be used in direct intense sunshine. We believe this work represents the first practical implementation of machine vision fruit sizing in field, with practicality gauged in terms of cost and simplicity of operation.
Why it matches plant phenotyping methodsRGB-D画像と画像解析を用いて、樹上マンゴー果実の長さ・幅を推定する手法を開発・較正・検証しており、植物形質取得が研究の中心である。
abstractThe RGB-D camera was calibrated, and depth information matched to the RGB image.
Machine vision technologies hold the promise of enabling rapid and accurate fruit crop yield predictions in the field. The key to fulfilling this promise is accurate segmentation and detection of fruit in images of tree canopies. This paper proposes two new methods for automated counting of fruit in images of mango tree canopies, one using texture-based dense segmentation and one using shape-based fruit detection, and compares the use of these methods relative to existing techniques:—(i) a method based on K-nearest neighbour pixel classification and contour segmentation, and (ii) a method based on super-pixel over-segmentation and classification using support vector machines. The robustness of each algorithm was tested on multiple sets of images of mango trees acquired over a period of 3 years. These image sets were acquired under varying conditions (light and exposure), distance to the tree, average number of fruit on the tree, orchard and season. For images collected under the same conditions as the calibration images, estimated fruit numbers were within 16 % of actual fruit numbers, and the F1 measure of detection performance was above 0.68 for these methods. Results were poorer when models were used for estimating fruit numbers in trees of different canopy shape and when different imaging conditions were used. For fruit-background segmentation, K-nearest neighbour pixel classification based on colour and smoothness or pixel classification based on super-pixel over-segmentation, clustering of dense scale invariant feature transform features into visual words and bag-of-visual-word super-pixel classification using support vector machines was more effective than simple contrast and colour based segmentation. Pixel classification was best followed by fruit detection using an elliptical shape model or blob detection using colour filtering and morphological image processing techniques. Method results were also compared using precision–recall plots. Imaging at night under artificial illumination with careful attention to maintaining constant illumination conditions is highly recommended.
Why it matches plant phenotyping methodsマンゴー樹冠画像から果実数を自動推定する画像解析手法を開発し、複数条件・年次データで性能比較と検証を行っており、植物形質（収量関連の果実数）取得が中心である。
abstractThis paper proposes two new methods for automated counting of fruit in images of mango tree canopies
ISHS XXIX International Horticultural Congress on Horticulture: Sustaining Lives, Livelihoods and Landscapes (IHC2014): International Symposia on the Physiology of Perennial Fruit Crops and Production Systems and Mechanisation, Precision Horticulture and Robotics Automating mango crop yield estimation
Why it matches plant phenotyping methodsマンゴーの収量という植物生産形質の推定を自動化する手法が題名上の中心であり、単なる生物学的実験の routine 測定ではない。
The use of technology in terms of improving the production processes of the agro-industry, is increasing significantly. This paper presents the results of the design of an algorithm based on image processing and artificial intelligence for segmenting fruits of Mangifera Indica, with the purpose to establish the shape, the degree of maturity based on color and to estimate the fruit size. This work can be used as a support tool for the agriculturists, for the management of harvest in the crops. Algorithm results allow to determine the condition of the fruit in the harvesting process, helping agriculturist to improve crop productivity.
Why it matches plant phenotyping methodsマンゴー果実の画像処理・AIによる形状、成熟度、サイズ推定アルゴリズムの開発が中心であり、植物器官の観測可能な形質を抽出するため、植物フェノタイピング手法として含める。
abstractThis paper presents the results of the design of an algorithm based on image processing and artificial intelligence for segmenting fruits of Mangifera Indica, with the purpose to establish the shape, the degree of maturity based on color and to estimate the fruit size.