- 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:1-61Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
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:1-56Code / dataset availability confirmedEurope PMC · Crossref · checked 14 Sept 2026
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:1-91Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
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:473-503Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
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:497-525Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
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:110-130Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Chlorophyll levels are a key indicator of plant nitrogen status, which plays a critical role in optimizing agricultural yields. This study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement. Measurements were taken from a diverse set of five leaf types, including smooth, uniform leaves (banana and mango), textured leaves (jasmine and sugarcane), and narrow leaves (rice). Partial least squares regression models were used to fit sensor spectra to chlorophyll levels, using nested cross-validation to ensure robust model evaluation. Sensor performance was assessed using R2 and mean absolute error (MAE) scores. The AS7265x demonstrated the best performance on smooth, uniform leaves with validation R2 scores of 0.96-0.95. Its performance decreased for the other leaves, with R2 scores of 0.75-0.85. The AS7262 and AS7263 sensors, while slightly less accurate, achieved reasonable R2 scores ranging from 0.93 to 0.86 for smooth leaves, and from 0.85 to 0.73 for the other leaves. All sensors, particularly the AS7265x, show potential for non-destructive chlorophyll measurement in agricultural applications. Their low cost and reasonable accuracy make them suitable for agricultural applications such as monitoring plant nitrogen levels.
Why it matches plant phenotyping methods低コストマルチスペクトルセンサーによる葉のクロロフィル測定法を評価・比較し、交差検証で性能を検証しているため、植物フェノタイピング手法が中心です。
abstractThis study evaluated the performance of three low-cost multi-spectral sensors, AS7262, AS7263, and AS7265x, for non-destructive chlorophyll measurement.
Reproduction assets foundThe authors publicly release raw sensor data, analysis scripts, firmware, and GUI in the GitHub repository KyleLopin/asm_chloro_test, plus supplementary information including extracted chlorophyll reference measurements (S2) at the MDPI supplement URL.Code · publicRaw data, scripts to generate the data and figures used in the manuscript, programs to run the sensors, and GUI used to collect the data are available at https://github.com/KyleLopin/asm_chloro_test (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:187-200Code · publicThe microcontroller code to operate the sensor and a GUI for data collection are available at https://github.com/KyleLopin/asm_chloro_test/tree/master/source (accessed on 25 March 2025).Open asset ↗KyleLopin/asm_chloro_test · KyleLopin/asm_chloro_testlines:155-167Supplement · publicThe following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/s25072198/s1 . Supplementary Information S1: Device Electrical Characterization. Supplementary Information S2: Extracted Chlorophyll Reference Measurements.Open asset ↗lines:176-186Code / dataset availability confirmedCrossref · checked 14 Sept 2026
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:74-108Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
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:1-26Code / dataset availability confirmedOpenAlex · checked 13 Sept 2026
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:1-58Code / dataset availability confirmedCrossref · checked 14 Sept 2026
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:1-112Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
: 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:34-41Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
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:1-56