Computed tomography enables non-destructive phenotyping of fruit internal structure but traditionally requires hundreds of projections, limiting throughput. Under sparse-view conditions, conventional and learning-based methods both suffer from streaking artifacts and regional distortions that degrade trait quantification. This study present CitrusGS, an integrated framework that achieves high-fidelity 3D reconstruction and precise morphological phenotyping of citrus fruit from only 15 projections using radiative 3D Gaussian splatting. Our method employs sparse-point initialization, optimized loss composite, and dual-stage pruning to suppress artifacts while preserving anatomically critical details with significantly higer convergence efficiency. In the citrus fruit datasets, CitrusGS achieves 29.78 dB PSNR and 0.870 SSIM, outperforming corresponding baseline method by 1.58 dB and 0.067 in SSIM, and enables automated extraction of ten external and internal phenotypic traits with R 2 larger than 0.944. Moreover, the framework shows initial zero-shot transferability across pathological citrus samples and additional horticultural specimens without retraining. By reconciling acquisition efficiency with anatomical fidelity using low-cost X-ray hardware, CitrusGS provides a promising framework for high-throughput, non-destructive phenotyping in breeding and grading applications.
Why it matches plant phenotyping methods柑橘果実の疎視野CT再構成法を開発・検証し、内部・外部形質を自動抽出するフェノタイピングが中心である。
abstractThis study present CitrusGS, an integrated framework that achieves high-fidelity 3D reconstruction and precise morphological phenotyping of citrus fruit from only 15 projections using radiative 3D Gaussian splatting.
Reproduction assets found保存済みの本文根拠を更新済みルールで再検証し、公開資産1件を確認しました。Code · publicOur codes are available at https://github.com/Petrichoror/CitrusGS .Open asset ↗Petrichoror/CitrusGSlines:325-387Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
This article presents a multispectral imaging dataset dedicated to training a machine learning algorithm for the in situ detection of Huanglongbing (HLB). HLB, also known as citrus greening disease, is a major pathology caused by the bacterial pathogen Candidatus Liberibacter asiaticus , particularly in species of the citrus genus. The dataset is constituted of terrestrial images acquired in a commercial sweet orange orchard of the variety Pera Rio ( Citrus sinensis (L.) Osbeck). The images describe large portions of canopy, with healthy leaves and sections infected by HLB as well as some confounding factors naturally present in orchards. Multispectral images were acquired with a multi-lens camera within the visible-near-infrared domain, resulting in 14 narrow spectral bands. The image acquisition was conducted during two field campaigns in 2023 and 2024. In total, the dataset contains 2,978 images divided into two classes HLB (1,681) and non-HLB (1,297). Originally, data are stored in TIFF format as 14 monochromatic images, organised by spectra band. Additionally, an HDF5-format version is provided, where images are stored as 3D arrays with spectral bands in ascending order. This format is compatible with various programming languages, enables efficient data handling, and is optimised for machine learning and image processing applications, supporting reproducible and portable analysis. This dataset is a valuable resource for the development and benchmarking of classification models, including deep learning approaches, aimed at the detection of HLB. Phytopathology imaging datasets are scarce yet essential for advancing digital agriculture and the development of robust tools for crop disease detection worldwide.
Why it matches plant phenotyping methods柑橘葉・樹冠のマルチスペクトル画像からHLB感染状態を推定するデータセットであり、植物病害状態の表現型取得と機械学習ベンチマークを中心とする。
abstractThis article presents a multispectral imaging dataset dedicated to training a machine learning algorithm for the in situ detection of Huanglongbing (HLB).
Reproduction assets foundThe article is a Data in Brief describing a public multispectral HLB citrus image dataset deposited on Data INRAE (Recherche Data Gouv, DOI 10.57745/054NAB), plus an authors' GitHub repository with preprocessing, registration, and model training scripts. Both are paper-specific, public, and directly actionable.Dataset · publicData accessibility
Repository name: Data INRAE
Data access link: https://doi.org/10.57745/054NABOpen asset ↗Data INRAE · 10.57745/054NABhtml-lines:89-123Code / dataset availability confirmedEurope PMC · checked 5 Sept 2026
The citrus diseases are affecting the fruit production worldwide thereby posing an economical burden. Major research is moving towards finding solutions using Artificial Intelligence (AI) and Image processing methods. Due to factors like illumination variations, leaf form, and disease symptoms, image data has intrinsic uncertainties that are typically difficult for traditional machine learning techniques to handle. In this paper, the interpretability of fuzzy logic is combined with the resilience of deep learning to propose a novel Fuzzy Convolutional Neural Network (Fuzzy-CNN) architecture for the automated diagnosis of citrus leaf diseases. The hybrid method uses a Convolutional Neural Network (CNN) to obtain complex features of citrus images, and a Fuzzy Inference System (FIS) to improve the classification results. The proposed approach encodes accurate data into fuzzy sets and applies linguistic concepts to determine the severity of a disease, which will contribute to the further development of the decision. In order to test and verify the proposed approach, several experiments were carried out, which proved that Fuzzy-CNN is more effective than regular CNN models with the approximate accuracy difference approximately 1.8, and especially in cases when the symptoms of disease are not clear. To strengthen experimental validation, the proposed method is evaluated on two independent datasets, including an external benchmark dataset, imbalance-aware evaluation metrics are employed to ensure robustness and generalizability. Experimental results demonstrate consistent and statistically significant improvements over existing neuro-fuzzy and machine learning approaches. This research contributes to early detection by collaborating the potential of fuzzy neural networks and offering a flexible solution for real-time disease detection in citrus crops.
Why it matches plant phenotyping methods柑橘葉画像から病害および重症度を推定するFuzzy-CNN手法を開発し、独立データセットとベンチマークで検証しており、植物フェノタイピング手法が中心である。
abstractpropose a novel Fuzzy Convolutional Neural Network (Fuzzy-CNN) architecture for the automated diagnosis of citrus leaf diseases.
Reproduction assets foundThe paper's Data Availability statement links two public image datasets used for the citrus disease phenotyping/classification experiments (a Mendeley citrus leaves dataset and a Kaggle orange fruit dataset), and a third public Kaggle citrus disease dataset is cited as the external benchmark dataset used for validationDataset · publicThe data used in the current study is publicly available from the following links. [https://data.mendeley.com/datasets/3f83gxmv57/2]Open asset ↗data.mendeley.com · 3f83gxmv57/2html-lines:525-539Dataset · publicThe data used in the current study is publicly available from the following links. [https://data.mendeley.com/datasets/3f83gxmv57/2] [https://www.kaggle.com/datasets/sgandhi2003/orange-fruit-dataset]Open asset ↗www.kaggle.com · sgandhi2003/orange-fruit-datasethtml-lines:525-539Code / 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 · checked 15 Sept 2026
Oranges, mandarins, bitter oranges, and lemons are examples of citrus fruits that make delicious meals and are highly nutritious. Citrus fruits suffer from a variety of infections that affect their yield. The Department of Agriculture wants to increase the production of oranges and lemons. On the other hand, several plant diseases and their advanced stages have impacted production. The quality of fruit influences market value and its financial effect. Therefore, accurate detection of ailments and their severity is crucial for improving the output and market value of oranges and lemons. To automatically evaluate and predict diseases in citrus leaves and fruits, this paper has proposed a modified convolutional neural network (ICNN) model. Python is used to create the ICNN model, and testing is performed using benchmark datasets from various repositories. The research presented here shows that ICNN performs better than traditional deep learning and machine learning models, such as the Convolutional Neural Network (CNN) and K-Nearest Neighbours (KNN). This illustrates how machine learning models require supplementary approaches to extract parameters from data that arrives in non-automated ways. Additionally, to improve the accuracy of their classification or prediction, deep learning models require pre-trained models. As a result, ICNN, an enhanced deep learning model that can automatically predict disease with higher accuracy than other models, represents an advancement over standard CNNs. Compared with KNN and CNN, ICNN achieves 99.69% accuracy.
Why it matches plant phenotyping methods柑橘の葉・果実の病害と重症度を画像から自動推定するCNN手法を開発し、ベンチマークデータセットで比較評価しており、植物フェノタイピング手法が中心である。
abstractTo automatically evaluate and predict diseases in citrus leaves and fruits, this paper has proposed a modified convolutional neural network (ICNN) model.
Reproduction assets foundThe paper's Data Availability statement explicitly lists three public Kaggle URLs as the datasets used and analysed in the study (citrus/plant leaf disease image datasets). These are paper-specific, publicly accessible image assets directly supporting the phenotyping/disease-classification analysis. The Mendeley URL (3Dataset · publicg agricultural specialists to properly understand and accept the model’s predictions.
Author contributions
Arunapriya.R – Problem Statements, Implementation and Testing Dr.S.P.Valli – Results, Conclusion, and Summary.
Data availability
The datasets used and/or analysed during the current study available and mentioned in below [ https://www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ]. (https:/ www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ). [ https://www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ]. (https:/ www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ). [ https://wwOpen asset ↗kagglelines:372-388Dataset · publicts, Conclusion, and Summary.
Data availability
The datasets used and/or analysed during the current study available and mentioned in below [ https://www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ]. (https:/ www.kaggle.com/code/ghazanfarali96/leaf-disease-classification-using-cnn-lstm-rnn ). [ https://www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ]. (https:/ www.kaggle.com/code/moazeldsokyx/plant-leaf-diseases-detection-using-cnn ). [ https://www.kaggle.com/code/ritzing/plant-disease-detection-using-keras-cnn-model ]. (https:/ www.kaggle.com/code/ritzing/plant-disease-detection-using-keras-cnn-model ).
Declarations
Competing interOpen asset ↗kagglelines:372-388Code / dataset availability confirmedOpenAlex · Europe PMC · checked 5 Sept 2026
Efficient large-scale 3D reconstruction of orchard environments is essential for robotic inspection and precision agriculture, yet existing methods struggle with unstructured scenes, variable illumination, and computational bottlenecks. We propose InspectGaussian, a coarse-to-fine Gaussian reconstruction framework tailored for orchard inspection robots. The pipeline integrates an RGB-D-based data acquisition strategy using ORB-SLAM3, which is enhanced by a dense mapping module for robust large-scale pose estimation and point cloud generation. A divide-and-conquer strategy is then employed: individual plant views are extracted via a YOLO-World-based detection and 3D matching algorithm, followed by plant-specific reconstruction using an improved 3D Gaussian Splatting (3DGS) method incorporating depth regularization and region-aware refinement. Experimental results in citrus orchards demonstrate that InspectGaussian achieves 96% average precision and 93% recall in plant view extraction, while surpassing state-of-the-art methods in reconstruction fidelity (31.226 PSNR, 0.915 SSIM, 0.067 LPIPS) and point cloud accuracy (7 mm error). These results confirm its effectiveness in capturing fine structural and textural details while maintaining scalability and efficiency. This framework provides a practical solution for high-throughput, in-field plant phenotyping and lays the foundation for intelligent orchard monitoring and management.
Why it matches plant phenotyping methods植物個体の3D再構成とRGB-D・検出・Gaussian Splattingを統合した手法開発であり、植物の構造的形質取得を目的とするため、フェノタイピング手法が中心である。
abstractWe propose InspectGaussian, a coarse-to-fine Gaussian reconstruction framework tailored for orchard inspection robots.
Reproduction assets foundThe paper's authors explicitly state their analysis code is publicly available on GitHub. Phenotype datasets (RGB-D orchard image sequences, LiDAR point clouds, manual trait measurements) are only available upon request, so they do not qualify as public assets.Code · publicOur code are available at https://github.com/zlhzau/InspectGaussian.git .Open asset ↗zlhzau/InspectGaussianlines:489-515Code / dataset availability confirmedEurope PMC · bioRxiv · checked 6 Sept 2026
Citrus farming plays an essential role in agriculture; however, diseases like canker, greening, black spot, and melanose significantly reduce yield and fruit quality. Efficient classification of citrus leaf diseases is important for crop health maintenance and optimal crop yield. Traditional methods for leaf disease detection are slow, labor-intensive, and often inaccurate, which highlights the need for automated solutions. This research presents a novel hybrid approach for identifying citrus diseases by combining a vision transformer with deep learning architectures. Using Bidirectional Encoder Representation from Image Transformers (BEIT) and MobileNetV2 as feature extractors, the proposed model captures distinctive features from images, which are then classified using Support Vector Machine (SVM). The dataset includes four different disease categories and a healthy class. Data augmentation techniques are applied to improve model robustness. The experimental findings demonstrate that CitriBEiTNet achieves a remarkable training accuracy of 99.82% and a testing accuracy of 99.57%, outperforming current leading techniques. This model provides an efficient, scalable, and economical approach for early disease identification, enabling farmers to take preventive measures and improve agricultural yields.
Why it matches plant phenotyping methods柑橘葉画像から病害状態を自動分類する深層学習手法の開発が研究の中心であり、植物の病害表現型を直接推定している。
abstractThis research presents a novel hybrid approach for identifying citrus diseases by combining a vision transformer with deep learning architectures.
Reproduction assets foundThe paper uses a public Kaggle citrus leaf image dataset (1,023 images across black spot, canker, greening, healthy) as its phenotyping input, with an explicit public URL. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicThe Kaggle dataset is publicly available at:
https://www.kaggle.com/datasets/sourabh2001/citrus-leaves-dataset/data.Open asset ↗Kaggle · sourabh2001/citrus-leaves-datasetpdf-page:5 lines:1-61Code / dataset availability confirmedCrossref · checked 15 Sept 2026
The farming of citrus is a crucial component of Pakistan’s fruit-based agricultural economy. But, the foliar diseases citrus canker, black spot, and greening have been posing a constant threat on citrus’s productivity. An optimal solution is an early and accurate detection of these diseases to improve the productivity. Therefore, this paper proposes an automated citrus leaf disease detection and classification framework based on deep convolutional neural networks (DCNNs). The proposed solution has five stages: image acquisition (dataset), preprocessing, data augmentation, deep feature extraction and optimization, and disease classification. Firstly, the images are obtained from a public dataset downloaded from Kaggle. Secondly, preprocessing techniques are used to improve the image quality and shape, thirdly the data augmentation techniques are used to enhance the model generalization, fourthly pre-trained models DenseNet-121, MobileNet, and InceptionV3 with transfer learning technique to extract deep features, and finally Adam optimizer and categorical cross-entropy loss function are used to fine tune the pre-trained models for classifications. The proposed model is evaluated on accuracy, precision, recall, and F1-score metrics. All the models demonstrated robust performance while DenseNet-121 achieved the best performance. The evaluation results assured the robustness of the use of transfer learning-based DCNN in citrus leaf disease detection.
Why it matches plant phenotyping methods柑橘葉の病害状態を画像から直接検出・分類する深層学習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。
abstractTherefore, this paper proposes an automated citrus leaf disease detection and classification framework based on deep convolutional neural networks (DCNNs).
Reproduction assets foundThe paper's phenotyping input is a public Kaggle citrus leaf image dataset (654 RGB images of healthy, blackspot, canker, and greening leaves) explicitly cited with a URL matching an allowed URL. No author code or trained models are reported as publicly available.Dataset · publictaset is essential. Additionally, the dataset must be prepared
so that our model can fully comprehend the data. The model will then be able to effectively
use that dataset for learning. A random sample of infected and healthy leaves images from the
datasets shown in Figure 1. The details of the images are provided in Table 1.
1
https://www.kaggle.com/datasets/sourabh2001/citrus-leaves-dataset?resource=downloadOpen asset ↗Kaggle · sourabh2001/citrus-leaves-datasetpdf-raw-page:3 lines:1-48Code / dataset availability confirmedOpenAlex · checked 14 Sept 2026
Leaf area is a critical trait in plant physiology and agronomy, yet conventional measurement approaches such as those using ImageJ remain labor-intensive, user-dependent, and difficult to scale for high-throughput phenotyping. To address these limitations, we developed a fully automated, open-source Python tool for quantifying citrus leaf area from scanned images using multi-mask HSV segmentation, contour-hierarchy filtering, and batch calibration. The tool was validated against ImageJ across 11 citrus cultivars (n = 412 leaves), representing a broad range of leaf sizes and morphologies. Agreement between methods was near perfect, with correlation coefficients exceeding 0.997, mean bias within ±0.14 cm2, and error rates below 2.5%. Bland–Altman analysis confirmed narrow limits of agreement (±0.3 cm2) while scatter plots showed robust performance across both small and large leaves. Importantly, the Python tool successfully handled challenging imaging conditions, including low-contrast leaves and edge-aligned specimens, where ImageJ required manual intervention. Processing efficiency was markedly improved, with the full dataset analyzed in 7 s compared with over 3 h using ImageJ, representing a >1600-fold speed increase. By eliminating manual thresholding and reducing user variability, this tool provides a reliable, efficient, and accessible framework for high-throughput leaf area quantification, advancing reproducibility and scalability in digital phenotyping.
Why it matches plant phenotyping methods柑橘葉面積の画像ベース測定ツールを開発し、ImageJとの比較検証と高スループット性能評価を行っており、植物フェノタイピング手法が研究の中心である。
abstractwe developed a fully automated, open-source Python tool for quantifying citrus leaf area from scanned images using multi-mask HSV segmentation, contour-hierarchy filtering, and batch calibration.
Reproduction assets foundThe paper's authors publicly released the Python leaf-area analysis tool (source code and documentation) on GitHub with an archived citable version on Zenodo, as stated in the Data Availability Statement.Code · publich received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The Python-based tool created in this study for automated leaf area
analysis, along with its source code and documentation, is publicly available on GitHub and Zenodo
at: https://github.com/esuarez-12/Leaf-Area-Analyzer, accessed on 26 August 2025, and a perma-
nent, citable version of the tool, corresponding to version v1.0.0, has been archived on Zenodo with
the following DOI: https://doi.org/10.5281/zenodo.16951132. These materials are openly accessible
and provided under an open-source license to support reproducibility and furtherOpen asset ↗esuarez-12/Leaf-Area-Analyzer · Leaf-Area-Analyzerpdf-raw-page:16 lines:1-45Code · publicmated leaf area
analysis, along with its source code and documentation, is publicly available on GitHub and Zenodo
at: https://github.com/esuarez-12/Leaf-Area-Analyzer, accessed on 26 August 2025, and a perma-
nent, citable version of the tool, corresponding to version v1.0.0, has been archived on Zenodo with
the following DOI: https://doi.org/10.5281/zenodo.16951132. These materials are openly accessible
and provided under an open-source license to support reproducibility and further research.
Acknowledgments: The authors would like to thank Jake Price and the UGA Cooperative Extension
Lowndes County Office for the use of their citrus trees. The UGA Citrus Lab is committed to
advancing citOpen asset ↗10.5281/zenodo.16951132pdf-raw-page:16 lines:1-45Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Citrus fruits, especially lemons, play a vital economic and nutritional role worldwide but are increasingly threatened by a wide range of diseases that diminish yield quality and quantity. Traditional manual and automated methods for disease detection requires domain expert, ample observation time, and is often ineffective during early infection stages. This paper presents a novel automated approach for the symptom based detection and classification of citrus leaf diseases using a nonlinear Fuzzy Rank-Based Ensemble (NL-FuRBE) methodology, enhanced by image quality improvement techniques. The study emphasizes the significance of timely disease diagnosis in citrus crops, which are vital for global food security and economic stability. The methodology begins with image quality enhancement through Vector-Valued Anisotropic Diffusion (VAD) and morphological filtering, evaluated using PSNR, SSIM, and NIQE metrics to ensure optimal visual clarity for classifier input. The core ensemble integrates three deep learning (DL) architectures-VGG19, AlexNet, and Xception-using a fuzzy rank-based scoring mechanism built on nonlinear transformations (exponential, tanh, and sigmoid functions) to address prediction uncertainty and model bias. A comprehensive dataset of lemon leaf diseases, consisting of 1354 images across nine classes, was utilized for training and evaluation. Experimental results using five-fold cross-validation demonstrate that the proposed model achieves superior performance with an average accuracy of 96.51%, outperforming conventional ensemble and state-of-the-art approaches. The results validate the proposed NL-FuRBE as an effective, automated, and cost-efficient tool for precision agriculture and early disease diagnosis in citrus farming.
Why it matches plant phenotyping methods柑橘葉の病徴を画像から検出・分類する手法を新規開発し、画像処理、アンサンブル分類、交差検証で技術性能を評価しているため、植物病害状態の表現型取得が中心である。
abstractThis paper presents a novel automated approach for the symptom based detection and classification of citrus leaf diseases using a nonlinear Fuzzy Rank-Based Ensemble (NL-FuRBE) methodology, enhanced by image quality improvement techniques.
Reproduction assets foundThe paper's lemon leaf disease image dataset (1354 images, nine classes) is publicly deposited on Mendeley Data with explicit availability statement and DOI; no author code or models are shared.Dataset · publicThe dataset used during the current study are available in the Mendeley Data repository under the title “Comprehensive Lemon Leaf Disease Dataset for Advanced Detection and Sustainable Agriculture” (DOI: 10.17632/44nrn4593f.1), https://data.mendeley.com/datasets/44nrn4593f/1.Open asset ↗Mendeley Data · 10.17632/44nrn4593f.1html-lines:805-842Code / 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 15 Sept 2026
Background Aiming at the problems of complex and diverse field symptoms of citrus Huanglong disease (HLB), low efficiency and insufficient recognition accuracy of traditional detection methods, this study proposes an efficient detection algorithm based on improved You Only Look Once (YOLO)v8. Methods Firstly, a new character to float (C2f) Attention inverse residual moving block (IRMB) module is designed, which significantly enhances the model's sensitivity to tiny disease features while reducing the number of parameters by fusing the lightweight IRMB with the adaptive attention gating mechanism, and solves the problem of losing key texture information due to downsampling in the traditional C2f module. Secondly, the three-channel aggregated attention module Powerneck is proposed in the Neck section, which realizes efficient cross-scale feature interactions, effectively suppresses background noise interference, and improves robustness in complex field scenes through SimFusion_4in feature alignment, information fusion module (IFM) global context fusion, and Power channel dynamic weighting strategy. In addition, the detection head design is optimized by structural reparameterization technique to further accelerate the inference process. Results The experimental results show that on the citrus dataset containing 12 diseases and two health states, the mAP50 of this model reaches 97% and the accuracy is 91.5%, which is 1.1% and 1.2% higher than that of the original YOLOv8, respectively, and the inference speed is improved by 14.6% to 370 frames per second (FPS). Comparison of the different models shows that the C2f Attention IRMB, through the mechanism of dual attention The comparison of different models shows that C2f Attention IRMB strengthens the feature expression ability through the dual-attention mechanism, and the Powerneck module reduces redundant computation through dynamic channel pruning, and the two synergistically optimize the model performance significantly. Compared with mainstream models such as YOLOv5m and YOLOv7x, this method is more advantageous in the balance of accuracy and speed, and can meet the demand of real-time detection in the field. Discussion The algorithm provides an efficient tool for early and accurate identification of citrus Huanglong disease, which is of great practical significance for reducing pesticide misuse and improving the efficiency of orchard management, and also provides new ideas for the design of lightweight target detection models in agricultural scenarios.
Why it matches plant phenotyping methods柑橘HLBの植物症状を画像から検出するYOLOv8改良アルゴリズムを開発・評価しており、病害状態の推定手法が研究の中心である。
abstractthis study proposes an efficient detection algorithm based on improved You Only Look Once (YOLO)v8.
Reproduction assets foundThe paper's citrus HLB detection experiments are built on a public citrus disease image dataset (5,080 training / 630 test images, 12 disease symptoms plus two healthy states) deposited in the Science Data Bank by Chi et al., explicitly cited in the Data Availability Statement with DOI and URL. No author analysis code,Dataset · publicThe Image datasets of Citrus Huanglongbing field symptom recognition are available at Chi Meixiang, Chen Shaoping, Huang Ting, Chen Shixiong, Liang Yong, and Qiu Rongzhou. 2024. “Image Datasets of Citrus Huanglongbing Field Symptom Recognition.” Science Data Bank. doi: 10.57760/sciencedb.j00001.00947 .Open asset ↗Science Data Bank · 10.57760/sciencedb.j00001.00947lines:411-413Code / dataset availability confirmedCrossref · checked 13 Sept 2026
Citrus Huanglongbing (HLB) is one of the most devastating diseases in the global citrus industry, but its early detection under complex field conditions remains a major challenge. Existing methods often suffer from insufficient dataset diversity and poor generalization, and struggle to accurately detect subtle early-stage lesions and multiple HLB symptoms in natural backgrounds. To address these issues, we propose an enhanced YOLO11-based framework, DCH-YOLO11. We constructed a multi-symptom HLB leaf dataset (MS-HLBD) containing 9219 annotated images across five classes: Healthy (1862), HLB blotchy mottling (2040), HLB Zinc deficiency (1988), HLB yellowing (1768), and Canker (1561), collected under diverse field conditions. To improve detection performance, the DCH-YOLO11 framework incorporates three novel modules: the C3k2 Dynamic Feature Fusion (C3k2_DFF) module, which enhances early and subtle lesion detection through dynamic feature fusion; the C2PSA Context Anchor Attention (C2PSA_CAA) module, which leverages context anchor attention to strengthen feature extraction in complex vein regions; and the High-efficiency Dynamic Feature Pyramid Network (HDFPN) module, which optimizes multi-scale feature interaction to boost detection accuracy across different object sizes. On the MS-HLBD dataset, DCH-YOLO11 achieved a precision of 91.6%, recall of 87.1%, F1-score of 89.3, and mAP50 of 93.1%, surpassing Faster R-CNN, SSD, RT-DETR, YOLOv7-tiny, YOLOv8n, YOLOv9-tiny, YOLOv10n, YOLO11n, and YOLOv12n by 13.6%, 8.8%, 5.3%, 3.2%, 2.0%, 1.6%, 2.6%, 1.8%, and 1.6% in mAP50, respectively. On a publicly available citrus HLB dataset, DCH-YOLO11 achieved a precision of 82.7%, recall of 81.8%, F1-score of 82.2, and mAP50 of 89.4%, with mAP50 improvements of 8.9%, 4.0%, 3.8%, 3.2%, 4.7%, 3.2%, and 3.4% over RT-DETR, YOLOv7-tiny, YOLOv8n, YOLOv9-tiny, YOLOv10n, YOLO11n, and YOLOv12n, respectively. These results demonstrate that DCH-YOLO11 achieves both state-of-the-art accuracy and excellent generalization, highlighting its strong potential for robust and practical citrus HLB detection in real-world applications.
Why it matches plant phenotyping methods柑橘葉のHLB症状を画像から検出するYOLOベースの表現型取得手法を開発し、専用データセットと公開データセットで性能検証しているため、植物病害表現型の方法研究として中心的である。
abstractwe propose an enhanced YOLO11-based framework, DCH-YOLO11.
Reproduction assets foundThe paper's authors publicly release their DCH-YOLO11 model implementation and analysis code on GitHub, as stated in the Data Availability Statement. The MS-HLBD image dataset itself is not stated as publicly deposited (further materials only by request), so only the code asset qualifies.Code · publicThe project’s code and model implementation are publicly
available at https://github.com/CdW8/DCH-YOLO11 (accessed on 6 July 2025).Open asset ↗CdW8/DCH-YOLO11pdf-page:23 lines:1-59Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Abstract Citrus fruits, especially lemons, play a vital economic and nutritional role worldwide but are increasingly threatened by a wide range of diseases that diminish yield quality and quantity. Traditional manual and automated methods for disease detection requires domain expert, ample observation time, and is often ineffective during early infection stages. This paper presents a novel automated approach for the symptom based detection and classification of citrus leaf diseases using a Non-Linear Fuzzy Rank-Based Ensemble (NL-FuRBE) methodology, enhanced by image quality improvement techniques. The study emphasizes the significance of timely disease diagnosis in citrus crops, which are vital for global food security and economic stability. The methodology begins with image quality enhancement through Vector-Valued Anisotropic Diffusion (VAD) and morphological f iltering, evaluated using PSNR, SSIM, and NIQE metrics to ensure optimal visual clarity for classifier input. The core ensemble integrates three deep learning architectures—VGG19, AlexNet, and Xception—using a fuzzy rank-based scoring mechanism built on non-linear transformations (exponential, tanh, and sigmoid functions) to address prediction uncertainty and model bias. A comprehensive dataset of lemon leaf diseases, consisting of 1354 images across nine classes, was utilized for training and evaluation. Experimental results using five-fold cross-validation demonstrate that the proposed model achieves superior performance with an avearge accuracy of 96.51%, outperforming conventional ensemble and state-of-the-art approaches. The results validate the proposed NL-FuRBE as an effective, automated, and cost-efficient tool for precision agriculture and early disease diagnosis in citrus farming.
Why it matches plant phenotyping methods柑橘葉の症状を画像から検出・分類する手法が研究の中心であり、植物の病害状態を直接推定するため、植物フェノタイピング手法として採用する。
abstractThis paper presents a novel automated approach for the symptom based detection and classification of citrus leaf diseases using a Non-Linear Fuzzy Rank-Based Ensemble (NL-FuRBE) methodology, enhanced by image quality improvement techniques.
Reproduction assets foundThe paper's core phenotyping input is a public lemon leaf disease image dataset (1354 images, 9 classes) deposited on Mendeley Data, explicitly cited as the training/evaluation dataset and named in the Data Availability Statement with DOI and URL. No author analysis code or trained model checkpoints are disclosed.Dataset · publicThe dataset used during the current study are available in the Mendeley Data repository under the title “Comprehen-
sive Lemon Leaf Disease Dataset for Advanced Detection and Sustainable Agriculture” (DOI: 10.17632/44nrn4593f.1),
https://data.mendeley.com/datasets/44nrn4593f/1Open asset ↗Mendeley Data · 10.17632/44nrn4593f.1pdf-page:24 lines:1-54Code / dataset availability confirmedOpenAlex · Europe PMC · checked 6 Sept 2026
3D phenotyping of the external and internal structures is important to breed new fruit species. As manual phenotyping is error-prone and time-consuming, developing high-throughput solutions with enhanced precision and low costs is necessary. This study presents CitrusGAN, a generative adversarial network-based method to reconstruct 3D citrus CT models from sparse-view X-ray images. The input X-rays are arranged in orthogonal pairs to provide additional information, and customized loss functions enable more effective learning of the mapping from 2D X-ray features to 3D CT volumes. Experimental results show that 6 views can generate high-quality citrus CT volumes, with a structural similarity index of 92.1 % and a peak signal-to-noise ratio of 26.374 dB compared with the real CT models. Moreover, the morphology of the generated model can be conveniently measured in the 3D space, facilitating the extraction of phenotypic traits including fruit length, width, height, volume, surface area, peel thickness, number of segments, and edible rate with high precision. As X-rays can be obtained using low-cost X-ray machines with high efficiency, the proposed method can be potentially developed into high-throughput equipment for fruit production lines or portable devices to realize in-field phenotyping.
Why it matches plant phenotyping methods柑橘の疎視野X線から3D CTモデルを再構成し、形態形質を抽出する手法を開発・検証しており、植物フェノタイピング手法が研究の中心である。
abstractThis study presents CitrusGAN, a generative adversarial network-based method to reconstruct 3D citrus CT models from sparse-view X-ray images.
Reproduction assets foundThe paper's Data availability statement explicitly states that the code and datasets (the citrus X-ray/CT phenotyping dataset and CitrusGAN analysis code) are publicly available at the authors' GitHub repository.Code · publicData availability
The code and datasets are available at https://github.com/Petrichoror/CitrusGAN . Other data will be made available on request.Open asset ↗Petrichoror/CitrusGANlines:244-347Code / dataset availability confirmedCrossref · Europe PMC · checked 6 Sept 2026
Identification of the phenotypes of fruits is critical for understanding complex genetic traits. Computed tomography (CT) imaging technology enables the noninvasive acquisition of three-dimensional images of fruit interiors, thus providing a robust data foundation for phenotypic analysis. Accurate segmentation of internal fruit tissues is essential, as it directly influences the accuracy and reliability of the results. Current methods are not optimized for the unique features of plant fruit images. This study introduces XFruitSeg, which is a general deep learning model for segmenting plant fruit CT images. The model uses a U-shaped encoder-decoder architecture and integrates multitask learning. A large convolutional kernel network, RepLKNet, expands the receptive field for feature extraction. Multiscale skip connections and a deep supervision mechanism improve the model's capacity to learn features of various sizes, and a contour feature learning branch specifically targets the interorganizational boundaries. An optimized composite loss function enhances the model's robustness when applied to imbalanced categories. Additionally, a dataset named XrayFruitData was established, which contains high-resolution images of twelve plant fruit varieties, with accurate annotations for orange, mangosteen, and durian fruits for model evaluation. Compared with four mainstream advanced models, XFruitSeg achieved superior segmentation performance on the orange, mangosteen, and durian datasets, with mean Dice coefficients of 95.21 %, 93.24 %, and 94.70 % and mean intersection over union (mIoU) scores of 91.09 %, 87.91 %, and 90.35 %, respectively. The results of extensive ablation experiments demonstrate the effectiveness of each component. Therefore, the proposed XFruitSeg model has been proven to be beneficial for high-precision analysis of internal fruit phenotyping traits.
Why it matches plant phenotyping methods果実CT画像から内部組織を分割し、表現型解析を可能にする深層学習モデルと評価用データセットを開発・検証しており、植物フェノタイピング手法が中心である。
abstractThis study introduces XFruitSeg, which is a general deep learning model for segmenting plant fruit CT images.
Reproduction assets foundThe paper's CT fruit segmentation dataset (XrayFruitData), model weights, and source code are publicly available on the authors' GitHub repository, explicitly stated in the Data availability section and dataset description.Code · publicSome of the raw data, model weights and source codes are accessible at https://github.com/BME-PhenoTeam/Xray4Plant-FruitOpen asset ↗BME-PhenoTeam/Xray4Plant-Fruitlines:530-585Code / dataset availability confirmedCrossref · checked 14 Sept 2026
Published31 May 2025International Journal for Research in Applied Science and Engineering TechnologyCited by 0 · OpenAlex ↗
Plant diseases threaten global food security and cause significant financial losses in agriculture. Early detection and precise diagnosis are critical for effective disease management. This study explores a novel approach to plant disease identification using the You Only Look Once (YOLOv12) algorithm combined with few-shot learning techniques. By leveraging a limited dataset, the model is trained to classify citrus plant leaf images into four categories: healthy, greening, black spot, and canker. The proposed system enhances disease detection efficiency, enabling farmers to take timely preventive measures. Our approach demonstrates the potential of few-shot learning in agricultural disease diagnosis, reducing the need for extensive labeled datasets while maintaining high accuracy.
Why it matches plant phenotyping methods柑橘葉画像から健全・greening・黒点病・かんきつかいよう病を分類するYOLOv12とfew-shot learning手法が研究の中心であり、植物病害状態の画像ベース推定に該当する。
abstractThis study explores a novel approach to plant disease identification using the You Only Look Once (YOLOv12) algorithm combined with few-shot learning techniques.
Reproduction assets foundThe paper's phenotyping inputs consist of the public PlantVillage leaf-image dataset (54,000+ images, 38 classes), explicitly named as the data used for the authors' few-shot disease-classification experiments. No author code, models, or supplementary deposits are mentioned, and no dataset URL is provided in the text,故Dataset · publicThe
study utilized the PlantVillage dataset, a publicly available collection of over 54,000 images of both healthy and diseased plant
leaves across 38 different classes, representing 14 species of crops.Open asset ↗PlantVillagepdf-page:11 lines:1-57Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Fruits, which are packed with nutrients, vitamins, and antioxidants, have been known for their numerous health benefits and curative powers, and are utilized in conventional medicine. Aegle Marmelos, Lemon, and Hog Plum are tangy fruits widely recognized in Asian countries for containing a plentiful supply of bioactive substances. They are also highly valuable in boosting metabolism, possessing tremendous therapeutic properties, and holding financial significance. The leaves of these fruit trees are as essential as their fruits, as they contain versatile medicinal and dietary benefits of immense value. However, these leaves are often affected by various fungal and other diseases, which reduce the ability for healthy growth and productivity of both fruits and leaves. Plants infected with various leaf diseases can produce fewer fruits, which are also of lower quality due to failure to reach maturity and lack of sufficient nutritional value. For these reasons, there is a risk of an outbreak in orchards, which can lead to significant financial losses for both producers and the agricultural sector. This signifies that the early identification of leaf diseases and the management of orchards are essential to minimize the impact of leaf diseases and mitigate these issues, ensuring the healthy production of valuable medicinal fruits. In this paper, various infected leaf images are collected from different regions of Rangpur, providing a comprehensive dataset comprising 3941 images. The dataset includes images of three different plant leaves, where 1513 images of Aegle Marmelos, 1232 images of Lemon, and 1196 of Hog plum, where each of the categories encompasses several classes of common leaf diseases. Through this dataset, an early and accurate digital detection system can be employed, allowing producers to clearly identify diseases instead of relying on traditional methods. The precise and timely identification of leaf diseases enables the control of these diseases by taking necessary actions, ensuring the sustainability of plants, and promoting the healthy growth of these invaluable medicinal fruits.
Why it matches plant phenotyping methods植物葉の病害状態を画像で評価する大規模データセットの構築が中心であり、病害表現型の画像ベース解析に該当する。
abstractIn this paper, various infected leaf images are collected from different regions of Rangpur, providing a comprehensive dataset comprising 3941 images.
Reproduction assets foundThe paper's own smartphone leaf-image dataset (3,941 raw + 12,295 augmented images of Aegle Marmelos, Hog plum, and lemon leaves) is publicly deposited on Mendeley Data with an explicit direct URL and DOI, matching the allowed URL list. No separate analysis code or trained model checkpoint is stated as available.Dataset · publicden in Rangpur district (latitude: 25° 34′ 30.6942″, longitude: 89° 16′ 22.2672″),
2. Chotali Kosba Para village fruits garden in Rangpur district (latitude: 25° 34′ 30.6942″, longitude: 89° 16′ 22.2672″)
Data accessibility
Repository name: Mendeley Data
Data identification number: DOI: 10.17632/54r883j5zr.1
Direct URL to data: https://data.mendeley.com/datasets/54r883j5zr/1
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Value of the Data
•Open asset ↗Mendeley Data · 10.17632/54r883j5zr.1lines:1-45Code / dataset availability confirmedEurope PMC · checked 6 Sept 2026
Citrus farming is one of the major agricultural sectors of Pakistan and currently represents almost 30% of total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, and mandarins face various diseases like canker, scab, and black spot, which lower fruit quality and yield. Traditional manual disease diagnosis is not only slow, less accurate, and expensive but also relies heavily on expert intervention. To address these issues, this research examines the implementation of an automated disease classification system using deep learning and optimal feature selection. The system incorporates data augmentation and transfer learning with pre-trained models such as DenseNet-201 and AlexNet to improve diagnostic accuracy, efficiency, and cost-effectiveness. Experimental results on a citrus leaves dataset show an impressive 99.6% classification accuracy. The proposed framework outperforms existing methods, offering a robust and scalable solution for disease detection in citrus farming, contributing to more sustainable agricultural practices.
Why it matches plant phenotyping methods柑橘葉の病徴を対象に、深層学習による自動病害分類システムを開発・評価しており、植物状態の取得・推定方法が中心である。
abstractthis research examines the implementation of an automated disease classification system using deep learning and optimal feature selection.
Reproduction assets foundThe paper's Data Availability Statement points to a public Kaggle dataset of citrus leaf disease images used for the study's phenotyping/disease-classification experiments. No author analysis code or trained model checkpoints are disclosed.Dataset · publicavailable at https://www.kaggle.com/datasets/ greatly affecting fruit yield and quality. Early detection is vital to prevent crop losses and theOpen asset ↗Kagglepdf-page:1 lines:1-63Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
A comprehensive dataset on lemon leaf disease can surely bring a lot of potentials into the development of agricultural research and the improvement of disease management strategies. This dataset was developed from 1354 raw images taken with professional agricultural specialist guidance from July to September 2024 in Charpolisha, Jamalpur, and further enhanced with augmented techniques, adding 9000 images. The augmentation process involves a set of techniques-flipping, rotation, zooming, shifting, adding noise, shearing, and brightening-to increase variety for different lemon leaf condition representations. Each of these images was standardized to 800 × 800 pixels resolution, so that consistency may be maintained among the dataset. All images were labelled in the nine prefixed categories: anthracnose, bacterial blight, citrus canker, curl virus, deficiency leaf, dry leaf, healthy leaf, sooty mould, and spider mites. In the present study, a DenseNet-121 architecture was used, where 20 % of the dataset was kept for validation and the remaining 80 % for training. A trained model with a batch size of 32 was trained for 30 epochs, achieving an accuracy of 98.56 % with augmentation, and 96.19 % without it. The dataset will not only act as a benchmark in developing accurate machine learning models for early disease detection, but it will also contribute to the cause of sustainable lemon cultivation practices by facilitating timely and effective disease management interventions .
Why it matches plant phenotyping methodsレモン葉の病害・健全状態を画像で表現するデータセットを構築し、分類性能を検証しており、植物病害表現型の取得・ベンチマークが中心です。
abstractA comprehensive dataset on lemon leaf disease can surely bring a lot of potentials into the development of agricultural research and the improvement of disease management strategies.
Reproduction assets foundThe paper's own lemon leaf disease image dataset (1354 original + 9000 augmented images) is publicly deposited on Mendeley Data with DOI 10.17632/44nrn4593f.1 and a direct URL, making it a paper-specific, publicly actionable asset.Dataset · publicder mites. Since then, the collection of images has been highly varied, which is good enough for deep learning applications.
Data source location
Town/City/Region: Charpolisha, Jamalpur.
Country: Bangladesh .
Data accessibility
Repository name: Mendeley Data.
Data identification number: 10.17632/44nrn4593f.1
Direct URL to data: https://data.mendeley.com/datasets/44nrn4593f/1
Related research article
None .
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Value of the Data
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The dataset contains various images of lemon leaves infected with different diseases, right from the most common to the rare ones. Thus, it will be very helpful in agriculture and scientific aspects for extending research in plant pathology. This dataset thus finds itOpen asset ↗Mendeley Data · 10.17632/44nrn4593f.1lines:1-49Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
The data are aerial images and ground tree measurement data of 3 citrus rootstock trials. Developing new citrus rootstock varieties requires field trials to test to identify selections with improved horticultural performance. A bud from a scion variety is grafted onto the rootstock and grown in a nursery until the grafted plant is ready to be planted in the field, which is in about one year. Trees in the field are assessed each year by measuring height, canopy diameter in 2 dimensions, overall health, and fruit number and quality factors when the trees begin to have a significant crop (∼3 years). Data collection of each tree is done manually. The image and ground data sets are of 3 rootstock trials that includes a 3-year-old Bingo mandarin hybrid trial of 206 trees, a 6-year-old Valencia orange trial of 643 trees, and a 7-year-old Valencia orange trials of 648 trees. Data for each trial includes aerial images and ground data of height, canopy diameters, and an overall health rating. The combination of ground validated measures and aerial images make this data set useful for building AI-based aerial image data collection applications. The data will be useful for 1) visualizing the effects of different rootstock selections and varieties on scion growth, effects that may not be fully captured with single measure metrics; and 2) development of image analysis applications and segmentation algorithms that can extract data from the images that are suitable for replacing some or all the ground measures.
Why it matches plant phenotyping methods柑橘樹の高さ、樹冠径、健康状態を対象とする航空画像・地上測定データセットで、画像解析やセグメンテーションによる形質抽出の開発用途が明示されており、表現型取得法が中心である。
abstractThe combination of ground validated measures and aerial images make this data set useful for building AI-based aerial image data collection applications.
Reproduction assets foundThis Data in Brief article describes its own paper-specific phenotyping assets: UAV RGB images and ground-measured canopy height/width/health data for three citrus rootstock trials, publicly deposited in USDA Ag Data Commons under DOIs 10.15482/USDA.ADC/26946823 (Bingo trial) and 10.15482/USDA.ADC/26946841 (Valencia 5–Dataset · publicRepository name: USDA Ag Data Commons
[ 1 ] Direct URL to Rows 1–4 Bingo rootstock data: 10.15482/USDA.ADC/26946823USDA Ag Data Commons · 10.15482/USDA.ADC/26946823lines:1-53Code / dataset availability confirmedEurope PMC · checked 7 Sept 2026
Huanglongbing (HLB), associated with the psyllid-vectored phloem-limited bacterium, Candidatus Liberibacter asiaticus (CLas), is a disease threat to all citrus production worldwide. Currently, there are no sustainable curative or prophylactic treatments available. In this study, we utilized mass spectrometry (MS)-based metabolomics in combination with 3D molecular mapping to visualize complex chemistries within plant tissues to explore how these chemistries change in vivo in HLB-infected trees. We demonstrate how spatial information from molecular maps of branches and single leaves yields insight into the biology not accessible otherwise. In particular, we found evidence that flavonoid biosynthesis is disrupted in HLB-infected trees, and an increase in the polyamine, feruloylputrescine, is highly correlated with an increase in disease severity. Based on mechanistic details revealed by these molecular maps, followed by metabolic modeling, we formulated and tested the hypothesis that CLas infection either directly or indirectly converts the precursor compound, ferulic acid, to feruloylputrescine to suppress the antimicrobial effects of ferulic acid and biosynthetically downstream flavonoids. Using in vitro bioassays, we demonstrated that ferulic acid and bioflavonoids are indeed highly bactericidal to CLas, with the activity on par with a reference antibiotic, oxytetracycline, recently approved for HLB management. We propose these compounds should be evaluated as therapeutics alternatives to the antibiotics for HLB treatment. Overall, the utilized 3D metabolic mapping approach provides a promising methodological framework to identify pathogen-specific inhibitory compounds in planta for potential prophylactic or therapeutic applications.
Why it matches plant phenotyping methods植物組織内の化学状態を3D分子マッピングで可視化し、HLB感染と病徴重症度に関連する状態を抽出する方法論的枠組みが研究の中心であり、単なる代謝測定ではない。
abstractwe utilized mass spectrometry (MS)-based metabolomics in combination with 3D molecular mapping to visualize complex chemistries within plant tissues
Reproduction assets foundThe paper deposits its citrus LC-MS/MS metabolomics raw data in MassIVE and provides GNPS molecular networking job links for its 2D/3D molecular mapping analyses. These are paper-specific, publicly accessible assets directly reproducing the study's measurements and computational analysis.Dataset · publicThe data were deposited in the MassIVE online repository and are available below links: https://massive.ucsd.edu/ProteoSAFe/dataset.jsp?task=bc1261c22e6c49d1b4f6490c7414845bOpen asset ↗MassIVE · bc1261c22e6c49d1b4f6490c7414845blines:146-218Code / 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 14 Sept 2026
In Bangladesh, sweet orange cultivation has been popular among fruit growers as the fruit is in demand. However, the disease of sweet oranges decreases fruit production. Research suggests that computer-aided disease diagnosis and machine learning (IML) models can improve fruit production by detecting and classifying diseases. In this line, a dataset of sweet oranges is required to diagnose the disease. Moreover, like many other fruits, sweet orange disease may vary from country to country. Therefore, in Bangladesh, a sweet orange dataset is required. Lastly, since different ML algorithms require datasets in various formats, only a few existing datasets fulfil the necessity. To fulfil the limitations, a sweet orange dataset in Bangladesh is collected. The dataset was collected in August and comprises high-quality images documenting multiple disease conditions, including Citrus Canker, Citrus Greening, Citrus Mealybugs, Die Back, Foliage Damage, Spiny Whitefly, Powdery Mildew, Shot Hole, Yellow Dragon, Yellow Leaves, and Healthy Leaf . These images provide an opportunity to apply machine learning and computer vision techniques to detect and classify diseases. This dataset aims to help researchers advance agri engineering through ML. Other sweet orange growing countries with having similar environments may find helpful information. Lastly, such experiments using our dataset will assist farmers in taking preventive measures and minimising economic losses.
Why it matches plant phenotyping methodsスイートオレンジ葉の病害状態を画像で記録した再利用可能なデータセットの構築が中心であり、植物病害表現型の画像ベース推定に該当する。
abstracta dataset of sweet oranges is required to diagnose the disease
Reproduction assets foundThe paper is a Data in Brief article describing a sweet orange leaf disease image dataset (5,813 images, 11 classes, plus TXT annotations), publicly deposited on Mendeley Data with an explicit direct URL and DOI (10.17632/f7cr74mwpj.1). This is a paper-specific public plant image/annotation dataset directly reproducingDataset · publicl .
Data source location
City: Khemerdia, Bheramara, Kushtia
Country: Bangladesh
Latitude and longitude (and GPS coordinates, if possible) for collected samples/data: 35.3602°N and 113.9505°E, Altitude: 75 msl
Data accessibility
Repository name: Mendeley Data
Data identification number: 10.17632/f7cr74mwpj.1
Direct URL to data: https://data.mendeley.com/datasets/f7cr74mwpj/1
1.
Value of the Data
•Open asset ↗Mendeley Data · 10.17632/f7cr74mwpj.1lines:1-46Code / dataset availability confirmedOpenAlex · checked 15 Sept 2026
Published23 May 2024International Journal of Applied Earth Observation and GeoinformationCited by 9 · OpenAlex ↗
In smallholder areas, the abandonment of orchards is a recent phenomenon with socioeconomic and environmental consequences. Biomass estimation and monitoring of these areas is essential to analyze their influence on the CO2 balance and to quantify carbon pools. In the current context of energy supply uncertainties and considering the demanding use of alternative energy sources, the quantification of fruit tree biomass in abandoned areas is a question of great interest. In this study, the above biomass of abandoned orange trees was estimated using tree parameters calculated from 3D points derived from images captured by a UAV and applying the Structure from Motion (SfM) technique. From these data, a canopy height model was calculated and used to apply a developed crown contour detection algorithm. Using this information and 3D points, the tree parameters crown area, crown diameter, crown length, maximum tree height, minimum tree height, mean and standard deviation of crown point heights were calculated for a set of 36 felled and weighted orange trees. Stepwise regression was used to estimate the above biomass values. All previously reported variables were included. The crown area parameter produced the most accurate model with R2, RMSE and RMSE % values of 0.85, 10.165 kg and 19.56 %, respectively. These results demonstrate the potential of UAV-SfM-derived 3D point clouds to estimate the above-ground biomass of abandoned fruit trees, relevant information for environmental analysis and biofuel energy production.
Why it matches plant phenotyping methodsUAV-SfMによる3D画像から樹冠形状・樹高などの植物形質を抽出し、樹冠検出アルゴリズムと回帰モデルで個体バイオマスを推定する方法が中心である。
abstractthe above biomass of abandoned orange trees was estimated using tree parameters calculated from 3D points derived from images captured by a UAV and applying the Structure from Motion (SfM) technique
Reproduction assets foundThe article's 'Research data' section states that the point cloud, crown delineation source code, reference data, and tree parameters are publicly available in a Mendeley Data repository (doi:10.17632/j3k2mctbnv.1). This is a paper-specific, public, actionable asset. Note: the Mendeley DOI itself is not in the allowed-Dataset · publicThe dataset containing point cloud, source code for crown delinea
tion, reference data, and parameters of abandoned orange groves, is
available for download from Mendeley data repository doi: 10
.17632/j3k2mctbnv.1.Open asset ↗Mendeley data repositorypdf-raw-page:9 lines:1-76Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Introduction: Fruit diseases have a serious impact on fruit production, causing a significant drop in economic returns from agricultural products. Due to its excellent performance, deep learning is widely used for disease identification and severity diagnosis of crops. This paper focuses on leveraging the high-latitude feature extraction capability of deep convolutional neural networks to improve classification performance. Methods: The proposed neural network is formed by combining the Inception module with the current state-of-the-art EfficientNetV2 for better multi-scale feature extraction and disease identification of citrus fruits. The VGG is used to replace the U-Net backbone to enhance the segmentation performance of the network. Results: Compared to existing networks, the proposed method achieved recognition accuracy of over 95%. In addition, the accuracies of the segmentation models were compared. VGG-U-Net, a network generated by replacing the backbone of U-Net with VGG, is found to have the best segmentation performance with an accuracy of 87.66%. This method is most suitable for diagnosing the severity level of citrus fruit diseases. In the meantime, transfer learning is applied to improve the training cycle of the network model, both in the detection and severity diagnosis phases of the disease. Discussion: The results of the comparison experiments reveal that the proposed method is effective in identifying and diagnosing the severity of citrus fruit diseases identification.
Why it matches plant phenotyping methods柑橘果実の病害識別と重症度診断を対象に、CNN、セグメンテーション、転移学習を組み合わせた画像解析手法を開発・比較しており、感染植物の状態を推定する方法が中心である。
abstractThe proposed neural network is formed by combining the Inception module with the current state-of-the-art EfficientNetV2 for better multi-scale feature extraction and disease identification of citrus fruits.
Reproduction assets foundThe paper's citrus fruit disease image dataset is publicly available on Kaggle, as stated in the data availability statement. No author analysis code or trained model checkpoints are reported as publicly available.Dataset · publicg U-Net with VGG to encode the backbone feature extraction network. Future work will enhance the performance of the segmentation network for the detection of small spot targets and extend this system to other crops.
Data availability statement
Publicly available datasets were analyzed in this study. This data can be found here: https://www.kaggle.com/datasets/jonathansilva2020/orange-diseases-dataset .
Author contributions
ZH: Formal Analysis, Investigation, Methodology, Writing–original draft, Writing–review and editing. XJ: Formal Analysis, Investigation, Validation, Writing–review and editing. SH: Methodology, Validation, Writing–review and editing. SQ: Formal Analysis, Investigation, MetOpen asset ↗Kaggle · orange-diseases-datasetlines:369-447Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Citrus leaves, which are a rich source of plant volatiles, have the beneficial attributes of rapid growth, large biomass, and availability throughout the year. Establishing the leaf volatile profiles of different citrus genotypes would make a valuable contribution to citrus species identification and chemotaxonomic studies. In this study, we developed an efficient and convenient static headspace (HS) sampling technique combined with gas chromatography-mass spectrometry (GC-MS) analysis and optimized the extraction conditions (a 15-min incubation at 100 ˚C without the addition of salt). Using a large set of 42 citrus cultivars, we validated the applicability of the optimized HS-GC-MS system in determining leaf volatile profiles. A total of 83 volatile metabolites, including monoterpene hydrocarbons, alcohols, sesquiterpene hydrocarbons, aldehydes, monoterpenoids, esters, and ketones were identified and quantified. Multivariate statistical analysis and hierarchical clustering revealed that mandarin ( Citrus reticulata Blanco) and orange ( Citrus sinensis L. Osbeck) groups exhibited notably differential volatile profiles, and that the mandarin group cultivars were characterized by the complex volatile profiles, thereby indicating the complex nature and diversity of these mandarin cultivars. We also identified those volatile compounds deemed to be the most useful in discriminating amongst citrus cultivars. This method developed in this study provides a rapid, simple, and reliable approach for the extraction and identification of citrus leaf volatile organic compound, and based on this methodology, we propose a leaf volatile profile-based classification model for citrus.
Why it matches plant phenotyping methods葉の揮発性化合物プロファイルを取得・識別する分析法の最適化と、42品種での適用性検証が研究の中心であり、植物器官の化学的表現型を測定する再利用可能な方法を提示している。
abstractwe developed an efficient and convenient static headspace (HS) sampling technique combined with gas chromatography-mass spectrometry (GC-MS) analysis and optimized the extraction conditions
Reproduction assets foundThe paper's leaf volatile phenotype measurements (83 VOCs across 42 citrus cultivars, Table S1) are included in the article's Supplementary Material, which is publicly available at the Frontiers supplementary-material URL. No author analysis code, scripts, or trained models are deposited; the databases cited (FlavornetSupplement · publicof their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2022.1050289/full#supplementary-material
Click here for additional data file.
Click here for additional data file.
References
Azam M. Jiang Q. Zhang B. Xu C. Chen K.
( 2013 ).
Citrus leaf volatiles as affected by develapmental stage and genetic type
. Int. J. Mol. Sci.
14 , 17744 – 17766 . doi:
10.3390/ijms140917744
23Open asset ↗lines:346-482Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plant diseases are a major cause of reduction in agricultural output, which leads to severe economic losses and unstable food supply. The citrus plant is an economically important fruit crop grown and produced worldwide. However, citrus plants are easily affected by various factors, such as climate change, pests, and diseases, resulting in reduced yield and quality. Advances in computer vision in recent years have been widely used for plant disease detection and classification, providing opportunities for early disease detection, and resulting in improvements in agriculture. Particularly, the early and accurate detection of citrus diseases, which are vulnerable to pests, is very important to prevent the spread of pests and reduce crop damage. Research on citrus pest disease is ongoing, but it is difficult to apply research results to cultivation owing to a lack of datasets for research and limited types of pests. In this study, we built a dataset by self-collecting a total of 20,000 citrus pest images, including fruits and leaves, from actual cultivation sites. The constructed dataset was trained, verified, and tested using a model that had undergone five transfer learning steps. All models used in the experiment had an average accuracy of 97% or more and an average f1 score of 96% or more. We built a web application server using the EfficientNet-b0 model, which exhibited the best performance among the five learning models. The built web application tested citrus pest disease using image samples collected from websites other than the self-collected image samples and prepared data, and both samples correctly classified the disease. The citrus pest automatic diagnosis web system using the model proposed in this study plays a useful auxiliary role in recognizing and classifying citrus diseases. This can, in turn, help improve the overall quality of citrus fruits.
Why it matches plant phenotyping methods柑橘の葉・果実画像から病害状態を分類するデータセット、深層学習モデル、診断Webシステムを構築・検証しており、植物状態の取得・推定手法が中心である。
abstractIn this study, we built a dataset by self-collecting a total of 20,000 citrus pest images, including fruits and leaves, from actual cultivation sites.
Reproduction assets foundThe authors explicitly state they published their self-collected citrus pest image dataset (20,000 images, six classes) free of charge at their public GitHub repository, which is a paper-specific, publicly actionable asset. No code availability statement was found for the analysis scripts or trained models.Dataset · publicrus images that are either infected or non-infected by pests in Jeju Island, South Korea, in 2021. The constructed dataset provides a total of 20,000 high-quality images with a resolution of 1920 × 1090. Currently, Citrus Open Datasets are either low resolution or paid. We published the datasets used in the study free of charge https://github.com/LeeSaeBom/citrus (accessed on 19 August 2022). A detailed description of the dataset is provided in Section 5 .
We use EfficientNet and ViT models, which are the latest algorithms in this area, including VGGNet, ResNet, and DenseNet models, which are commonly used for the classification and detection of plant pests and diseases [ 21 , 22 , 23 ]Open asset ↗LeeSaeBom/citruslines:28-38Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Intelligent detection and localization of mature citrus fruits is a critical challenge in developing an automatic harvesting robot. Variable illumination conditions and different occlusion states are some of the essential issues that must be addressed for the accurate detection and localization of citrus in the orchard environment. In this paper, a novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed. First, a new loss function (polarity binary cross-entropy with logit loss) for YOLO v5s is designed to calculate the loss value of class probability and objectness score, so that a large penalty for false and missing detection is applied during the training process. Second, to recover the missing depth information caused by randomly overlapping background participants, Cr-Cb chromatic mapping, the Otsu thresholding algorithm, and morphological processing are successively used to extract the complete shape of the citrus, and the kriging method is applied to obtain the best linear unbiased estimator for the missing depth value. Finally, the citrus spatial position and posture information are obtained according to the camera imaging model and the geometric features of the citrus. The experimental results show that the recall rates of citrus detection under non-uniform illumination conditions, weak illumination, and well illumination are 99.55%, 98.47%, and 98.48%, respectively, approximately 2-9% higher than those of the original YOLO v5s network. The average error of the distance between the citrus fruit and the camera is 3.98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization.
Why it matches plant phenotyping methods収穫ロボット向けの位置検出が主目的だが、果実形状・姿勢・3D直径を画像から抽出し、精度を検証する技術開発が中心であり、再利用可能な植物器官形質の推定に該当する。
abstracta novel method for the detection and localization of mature citrus using improved You Only Look Once (YOLO) v5s with binocular vision is proposed.
Reproduction assets foundThe authors explicitly state that their citrus image dataset (4855 binocular image groups with depth maps) and analysis code are publicly available on GitHub, matching the allowed URL.Dataset · publicnt occlusion conditions in natural orchards. Future work will focus on few-shot learning and reduce the number of citrus fruits in the training dataset to improve citrus detection and localization.
Data availability statement
The original contributions presented in this study are publicly available. This data can be found here: https://github.com/AshesBen/citrus-detection-localization .
Author contributions
All authors contributed to the method and result of the study, dataset generation, model training and testing, analysis of results, and the drafting, revising, and approving of the contents of the manuscript.
Funding
We acknowledged support from the Natural Science Foundation of GuangdongOpen asset ↗AshesBen/citrus-detection-localizationlines:335-356Code · public98 mm, and the average errors of the citrus diameters in the 3D direction are less than 2.75 mm. The average detection time per frame is 78.96 ms. The results indicate that our method can detect and localize citrus fruits in the complex environment of orchards with high accuracy and speed. Our dataset and codes are available at https://github.com/AshesBen/citrus-detection-localization .
Keywords: citrus detection, citrus localization, binocular vision, YOLO v5s, loss function
status released display-pdf yes is-olf no is-manuscript no is-preprint no is-journal-matter no is-scanned no is-retracted no
Received 2022 Jun 18; Accepted 2022 Jul 12; Collection date 2022.
IntroductionOpen asset ↗AshesBen/citrus-detection-localizationlines:1-28Code / dataset availability confirmedbioRxiv · checked 13 Sept 2026
O_LICitrus come in diverse sizes and shapes, and play a key role in world culture and economy. Citrus oil glands in particular contain essential oils which include plant secondary metabolites associated with flavor and aroma. Capturing and analyzing nuanced information behind the citrus fruit shape and its oil gland distribution provides a morphology-driven path to further our insight into phenotype-genotype interactions. C_LIO_LIWe investigated the shape of citrus fruit of 51 accessions based on 3D X-ray CT scan reconstructions. Accessions include all three ancestral citrus species, accessions from related genera, and several interspecific hybrids. We digitally separate and compare the size of fruit endocarp, mesocarp, exocarp, and oil gland tissue. Based on the centers of the oil glands, overall fruit shape is approximated with an ellipsoid. Possible oil gland distributions on this ellipsoid surface are explored using directional statistics. C_LIO_LIThere is a strong allometry along fruit tissues; that is, we observe a strong linear relationship between the volume of any pair of major tissues. This suggests that the relative growth of fruit tissues with respect to each other follows a power law. We also observe that on average, glands distance themselves from their nearest neighbor following a square root relationship, which suggests normal diffusion dynamics at play. C_LIO_LIThe observed allometry and square root models point to the existence of biophysical developmental constraints that govern novel relationships between fruit dimensions from both evolutionary and breeding perspectives. Understanding these biophysical interactions prompt an exciting research path on fruit development and breeding. C_LI Societal Impact StatementCitrus are intrinsically connected to human health and culture, including preventing human diseases like scurvy, and inspiring sacred rituals. Citrus fruits come in a stunning number of different sizes and shapes, ranging from small clementines to oversized pummelos, and fruits display a vast diversity of flavors and aromas. These qualities are key in both traditional and modern medicine and the production of cleaning and perfume products. By quantifying and modeling overall fruit shape and oil gland distribution, we can gain further insight into citrus development and the impacts of domestication and improvement on multiple characteristics of the fruit.
Why it matches plant phenotyping methods3D X線CT再構成とデジタル分離により、柑橘果実の形状・組織体積・油腺分布を定量化しモデル化しており、植物表現型の取得・解析が研究の中心です。
abstractCapturing and analyzing nuanced information behind the citrus fruit shape and its oil gland distribution provides a morphology-driven path to further our insight into phenotype-genotype interactions.
Reproduction assets foundThe paper explicitly deposits its processed citrus X-ray CT 3D reconstructions, segmented tissues, oil gland point clouds, and ellipsoidal approximations in Dryad, and its full image-processing and analysis code on GitHub. Both are paper-specific, public, and actionable.Code · public357 All our code is available at the https://github.com/amezqui3/vitaminC_morphology repos-Open asset ↗GitHub · amezqui3/vitaminC_morphologypdf-page:19 lines:1-48Code / dataset availability confirmedEurope PMC · checked 14 Sept 2026
Fruit yield estimation is crucial for establishing fruit harvest and marketing strategies. Recently, computer vision and deep learning techniques have been used to estimate citrus fruit yield and have exhibited notable fruit detection ability. However, computer-vision-based citrus fruit counting has two key limitations: inconsistent fruit detection accuracy and double-counting of the same fruit. Using oranges as the experimental material, this paper proposes a deep-learning-based orange counting algorithm using video sequences to help overcome these problems. The algorithm consists of two sub-algorithms, OrangeYolo for fruit detection and OrangeSort for fruit tracking. The OrangeYolo backbone network is partially based on the YOLOv3 algorithm, which has been improved upon to detect small objects (fruits) at multiple scales. The network structure was adjusted to detect small-scale targets while enabling multiscale target detection. A channel attention and spatial attention multiscale fusion module was introduced to fuse the semantic features of the deep network with the shallow textural detail features. OrangeYolo can achieve mean Average Precision (mAP) values of 0.957 in the citrus dataset, higher than the 0.905, 0.911, and 0.917 achieved with the YOLOv3, YOLOv4, and YOLOv5 algorithms. OrangeSort was designed to alleviate the double-counting problem associated with occluded fruits. A specific tracking region counting strategy and tracking algorithm based on motion displacement estimation were established. Six video sequences taken from two fields containing 22 trees were used as the validation dataset. The proposed method showed better performance (Mean Absolute Error (MAE) = 0.081, Standard Deviation (SD) = 0.08) than video-based manual counting and produced more accurate results than the existing standards Sort and DeepSort (MAE = 0.45 and 1.212; SD = 0.4741 and 1.3975).
Why it matches plant phenotyping methods柑橘果実の検出・追跡により樹上果実数（収量推定に使う形質）を定量する画像解析手法を開発し、既存手法および手動計数と検証・比較しており、植物フェノタイピング手法が中心です。
abstractthis paper proposes a deep-learning-based orange counting algorithm using video sequences to help overcome these problems.
Reproduction assets foundThe authors publicly released the annotated orange fruit image dataset used to train and test OrangeYolo on GitHub, with explicit data availability statement. A supplementary tracking example video is also on YouTube. No analysis code release is stated.Dataset · publicW.W., and Y. S. collected field data with self-designed field rover. All authors discussed, wrote the manuscript, and gave final approval for publication.
Data availability
The dataset used during this study is available in a repository in accordance with funder data retention policies. We have published the dataset at GitHub ( https://github.com/I3-Laboratory/orange-dataset ).
Conflict of interest statement
The authors declare that they have no conflicts of interest.
Supplementary data
Supplementary data is available at Horticulture Research online.
Supplementary Material
Web_Material_uhac003
Click here for additional data file.
Reference
1.
Anderson
NT
, Walsh KB , Wulfsohn D
.
TechnologieOpen asset ↗GitHub · I3-Laboratory/orange-datasetlines:986-1102Code / dataset availability confirmedEurope PMC · OpenAlex · Crossref · checked 9 Sept 2026
Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.
Why it matches plant phenotyping methods低コストUGV・LiDARによる3D植物計測システムを開発し、樹冠形態指標を抽出・検証しており、植物フェノタイピング手法が研究の中心である。
abstractAn unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth.
Reproduction assets foundThe paper's Data Availability Statement provides an authors' public GitHub repository containing their code implementation for the UGV-LiDAR citrus crop mapping/phenotyping system. No separate phenotype dataset or point cloud deposit is stated.Code · publicOur code implementation is available online at https://github.com/HaroldMurcia/miniRover_LiDAR_citrush_crop.git , accessed on 25 November 2021.Open asset ↗HaroldMurcia/miniRover_LiDAR_citrush_croplines:356-358Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Due to the rich vitamin content in citrus fruit, citrus is an important crop around the world. However, the yield of these citrus crops is often reduced due to the damage of various pests and diseases. In order to mitigate these problems, several convolutional neural networks were applied to detect them. It is of note that the performance of these selected models degraded as the size of the target object in the image decreased. To adapt to scale changes, a new feature reuse method named bridge connection was developed. With the help of bridge connections, the accuracy of baseline networks was improved at little additional computation cost. The proposed BridgeNet-19 achieved the highest classification accuracy (95.47%), followed by the pre-trained VGG-19 (95.01%) and VGG-19 with bridge connections (94.73%). The use of bridge connections also strengthens the flexibility of sensors for image acquisition. It is unnecessary to pay more attention to adjusting the distance between a camera and pests and diseases.
Why it matches plant phenotyping methods柑橘の病害を画像から分類するCNN手法の改良と精度比較が中心であり、植物の病害状態を直接推定するフェノタイピング手法に該当する。
abstracta new feature reuse method named bridge connection was developed
Reproduction assets foundThe paper explicitly states that the implementation of the phenotyping/classification models (BridgeNet-19 and benchmark CNNs for citrus pest and disease image classification) is publicly available on the authors' GitHub repository. The image dataset itself is described but no separate public deposit URL is given in a Code · publicImplementation of models is available at https://github.com/xingshulicc/xingshulicc/tree/master/citrus_pest_Open asset ↗github.com/xingshulicc/xingshuliccpdf-page:11 lines:1-65Code / dataset availability confirmedEurope PMC · checked 15 Sept 2026
Plants are as vulnerable by diseases as animals. Citrus is a major plant grown mainly in the tropical areas of the world due to its richness in vitamin C and other important nutrients. The production of the citrus fruit has been widely affected by citrus diseases which ultimately degrades the fruit quality and causes financial loss to the growers. During the past decade, image processing and computer vision methods have been broadly adopted for the detection and classification of plant diseases. Early detection of diseases in citrus plants helps in preventing them to spread in the orchards which minimize the financial loss to the farmers. In this article, an image dataset citrus fruits, leaves, and stem is presented. The dataset holds citrus fruits and leaves images of healthy and infected plants with diseases such as Black spot, Canker, Scab, Greening, and Melanose. Most of the images were captured in December from the Orchards in Sargodha region of Pakistan when the fruit was about to ripen and maximum diseases were found on citrus plants. The dataset is hosted by the Department of Computer Science, University of Gujrat and acquired under the mutual cooperation of the University of Gujrat and the Citrus Research Center, Government of Punjab, Pakistan. The dataset would potentially be helpful to researchers who use machine learning and computer vision algorithms to develop computer applications to help farmers in early detection of plant diseases. The dataset is freely available at https://data.mendeley.com/datasets/3f83gxmv57/2.
Why it matches plant phenotyping methods柑橘の健全・感染状態を画像で収集したデータセット自体が中心で、植物病害状態の画像ベース表現型判定に利用できるため。
titleA citrus fruits and leaves dataset for detection and classification of citrus diseases through machine learning.
Reproduction assets foundThis Data in Brief article presents a paper-specific public dataset of 759 citrus fruit and leaf images (healthy and diseased) used for plant disease phenotyping, hosted on Mendeley Data with an explicit public URL.Dataset · publicion of the University of Gujrat and the Citrus Research Center, Government of Punjab, Pakistan. The dataset would potentially be helpful to researchers who use machine learning and computer vision algorithms to develop computer applications to help farmers in early detection of plant diseases. The dataset is freely available at https://data.mendeley.com/datasets/3f83gxmv57/2.
Keywords
Image classification Feature extraction Feature selection pmc-status-qastatus 0 pmc-status-live yes pmc-status-embargo no pmc-status-released yes pmc-prop-open-access yes pmc-prop-olf no pmc-prop-manuscript no pmc-prop-legally-suppressed no pmc-prop-has-pdf yes pmc-prop-has-supplement no pmc-prop-pdf-only no pmOpen asset ↗3f83gxmv57/2lines:1-59Code / dataset availability confirmedEurope PMC · checked 9 Sept 2026
Pests and diseases can cause severe damage to citrus fruits. Farmers used to rely on experienced experts to recognize them, which is a time consuming and costly process. With the popularity of image sensors and the development of computer vision technology, using convolutional neural network (CNN) models to identify pests and diseases has become a recent trend in the field of agriculture. However, many researchers refer to pre-trained models of ImageNet to execute different recognition tasks without considering their own dataset scale, resulting in a waste of computational resources. In this paper, a simple but effective CNN model was developed based on our image dataset. The proposed network was designed from the aspect of parameter efficiency. To achieve this goal, the complexity of cross-channel operation was increased and the frequency of feature reuse was adapted to network depth. Experiment results showed that Weakly DenseNet-16 got the highest classification accuracy with fewer parameters. Because this network is lightweight, it can be used in mobile devices.
Why it matches plant phenotyping methods柑橘の病害・害虫を画像から認識するCNNモデルを開発し、分類精度とパラメータ効率を評価しているため、植物の病害状態を推定する方法が中心である。
titleCitrus Pests and Diseases Recognition Model Using Weakly Dense Connected Convolution Network.
Reproduction assets foundThe paper's citrus pest/disease image dataset is publicly hosted via the authors' mycloud link (Appendix B), and the models/code are publicly available on the authors' GitHub (Appendix C). Both are paper-specific, public, and actionable.Dataset · publicImage dataset is available at: https://files.mycloud.com/home.php?brand=webfiles#23a3c71/Open asset ↗pdf-page:16 lines:1-41Code · publicModels and code are available at: https://github.com/xingshulicc/xingshulicc/tree/master/citrus_Open asset ↗xingshulicc/xingshuliccpdf-page:16 lines:1-41