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The Plant Pathology Challenge 2020 data set to classify foliar disease of apples

https://doi.org/10.1002/aps3.11390
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01328-8_16openalex-snapshot
Disease Detection on the Plant Leaves by Deep Learning
inferredcites →https://doi.org/10.1007/s10658-018-1590-9openalex-snapshot
Apple blotch disease (Marssonina coronaria (Ellis & Davis) Davis) – review and research prospects
inferredcites →https://doi.org/10.1007/s11263-015-0816-yopenalex-snapshot
ImageNet Large Scale Visual Recognition Challenge
inferredcites →https://doi.org/10.1007/s11760-015-0821-1openalex-snapshot
Apple disease classification using color, texture and shape features from images
inferredcites →https://doi.org/10.1016/0378-1097(93)90497-popenalex-snapshot
A PCR primer-specific to Cylindrocarpon heteronema for detection of the pathogen in apple wood
inferredcites →https://doi.org/10.1016/j.aspen.2016.11.015openalex-snapshot
Mass trapping of apple leafminer, Phyllonorycter ringoniella with sex pheromone traps in apple orchards
inferredcites →https://doi.org/10.1016/j.biosystemseng.2016.01.017openalex-snapshot
A review on the main challenges in automatic plant disease identification based on visible range images
inferredcites →https://doi.org/10.1016/j.biosystemseng.2018.05.013openalex-snapshot
Factors influencing the use of deep learning for plant disease recognition
inferredcites →https://doi.org/10.1016/j.compag.2010.02.007openalex-snapshot
A review of advanced techniques for detecting plant diseases
inferredcites →https://doi.org/10.1016/j.compag.2018.02.016openalex-snapshot
Deep learning in agriculture: A survey
inferredcites →https://doi.org/10.1016/j.eja.2007.02.005openalex-snapshot
Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: Non-parametric statistical approaches and physiological implications
inferredcites →https://doi.org/10.1016/j.patcog.2015.11.021openalex-snapshot
On developing and enhancing plant-level disease rating systems in real fields
inferredcites →https://doi.org/10.1016/s0261-2194(98)80014-8openalex-snapshot
SPRAYCHECK — a model for evaluating grower timing of black spot (Venturia inaequalis) fungicides in apple orchards
inferredcites →https://doi.org/10.1094/pd-77-0372openalex-snapshot
Delaying the Onset of Fungicide Programs for Control of Apple Scab in Orchards with Low Potential Ascospore Dose ofVenturia inaequalis
inferredcites →https://doi.org/10.1094/pdis-03-14-0290-reopenalex-snapshot
An Automatic Method to Detect and Measure Leaf Disease Symptoms Using Digital Image Processing
inferredcites →https://doi.org/10.1094/pdis-03-15-0319-reopenalex-snapshot
Leaf Doctor: A New Portable Application for Quantifying Plant Disease Severity
inferredcites →https://doi.org/10.1094/pdis-03-15-0340-feopenalex-snapshot
Plant Disease Detection by Imaging Sensors – Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping
inferredcites →https://doi.org/10.1094/pdis-05-18-0734-reopenalex-snapshot
High Humidity and Age-Dependent Fruit Susceptibility Promote Development of Trichothecium Black Spot on Apple
inferredcites →https://doi.org/10.1094/pdis.2001.85.10.1036openalex-snapshot
Parasitic and Biological Fitness of Venturia inaequalis: Relationship to Disease Management Strategies
inferredcites →https://doi.org/10.1094/phyto-76-112openalex-snapshot
Forecasting Ascospore Dose of Venturia inaequalis in Commercial Apple Orchards
inferredcites →https://doi.org/10.1094/phyto.2000.90.9.973openalex-snapshot
A Polymerase Chain Reaction-Based Method to Specifically DetectAlternaria alternataApple Pathotype (A. mali), the Causal Agent of Alternaria Blotch of Apple
inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot
Deep Residual Learning for Image Recognition
inferredcites →https://doi.org/10.1109/iccv.2015.314openalex-snapshot
HD-CNN: Hierarchical Deep Convolutional Neural Networks for Large Scale Visual Recognition
inferredcites →https://doi.org/10.1128/aem.60.12.4324-4331.1994openalex-snapshot
PCR primers that allow intergeneric differentiation of ascomycetes and their application to Verticillium spp
inferredcites →https://doi.org/10.1155/2016/3289801openalex-snapshot
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
inferredcites →https://doi.org/10.3389/fpls.2016.01419openalex-snapshot
Using Deep Learning for Image-Based Plant Disease Detection
inferredcites →https://doi.org/10.3390/sym10010011openalex-snapshot
Identification of Apple Leaf Diseases Based on Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.4039/tce.2017.37openalex-snapshot
Development of kairomone-based lures and traps targeting Spilonota ocellana (Lepidoptera: Tortricidae) in apple orchards treated with sex pheromones
inferredcites →https://doi.org/10.48550/arxiv.1310.1531openalex-snapshot
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
inferredcites →https://doi.org/10.48550/arxiv.1908.02900openalex-snapshot
iCassava 2019 Fine-Grained Visual Categorization Challenge

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Classifying Early Apple Scab Infections in Multispectral Imagery with Convolutional Neural Networks
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Recent Advancements and Challenges in Deep Learning and Internet of Things for Precision Agriculture
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Crop disease identification segmentation algorithm based on Mask‐RCNN
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COLEN-An Improvised Deep Learning Model for Plant Disease Detection Using Varying Color Space
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The PV-ALE Dataset: Enhancing Apple Leaf Disease Classification Through Transfer Learning with Convolutional Neural Networks
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Apple Leaf Disease Classification Using Deep Learning Approach on Lab-Built and Realistic Datasets
inferred← citeshttps://doi.org/10.1007/978-3-032-08504-7_22openalex-snapshot
Classification of Plant Pathology Using EfficientNet
inferred← citeshttps://doi.org/10.1007/978-3-032-15812-3_8openalex-snapshot
Synthetic Data Augmented Leaflet-Level Ash Dieback Detection
inferred← citeshttps://doi.org/10.1007/978-3-032-27117-4_21openalex-snapshot
LeafGuard: A Deep Learning Framework for Disease Detection in Apple Orchards
inferred← citeshttps://doi.org/10.1007/978-3-032-31236-5_33openalex-snapshot
YOLOv11-Based Real-Time Plant Disease Detection System for Nigerian Agriculture
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FGrade: A Large Volume Dataset for Grading Tomato Freshness Quality
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Apple Leaves Diseases Detection Using Deep Convolutional Neural Networks and Transfer Learning
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Towards Resilient Apple Cultivation: Machine Learning, Artificial Intelligence, and Disease Control
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CBAM-LDNet: Convolutional Block Attention Module Based Lite DenseNet for Plant Disease Identification
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A Comparative Study on Two Apple Leaves Datasets Captured Under Diverse Conditions
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Apple Leaf Disease Segmentation in the Wild: A Multi-task Collaborative Learning Approach
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Attention Based Twin Convolutional Neural Network with Inception Blocks for Plant Disease Detection Using Wavelet Transform
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Early Phase Identification and Detection for Plant Poor Growth in Rural Areas: A Survey of the State of the Art
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Exploring the Deep Learning Techniques in Plant Disease Detection: A Review of Recent Advances
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Recognition of Apple Leaves Infection Using DenseNet121 with Additional Layers
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NeuraLeaf: Disentangled Neural Parametric Modeling of Leaf Shape, Deformation, and Appearance
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Plant Disease Detection and Classification by Deep Learning—A Review
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An Improved CNN-Based Apple Appearance Quality Classification Method With Small Samples
inferred← citeshttps://doi.org/10.1109/access.2021.3094802openalex-snapshot
Apple Leaf Disease Recognition and Sub-Class Categorization Based on Improved Multi-Scale Feature Fusion Network
inferred← citeshttps://doi.org/10.1109/access.2022.3204051openalex-snapshot
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
inferred← citeshttps://doi.org/10.1109/access.2023.3284760openalex-snapshot
A Systematic Literature Review on Plant Disease Detection: Motivations, Classification Techniques, Datasets, Challenges, and Future Trends
inferred← citeshttps://doi.org/10.1109/access.2023.3324722openalex-snapshot
Machine Learning and Deep Learning for Plant Disease Classification and Detection
inferred← citeshttps://doi.org/10.1109/access.2025.3541980openalex-snapshot
Leaf Level Ash Dieback Disease Detection and Online Severity Estimation With UAVs
inferred← citeshttps://doi.org/10.1109/access.2025.3584404openalex-snapshot
A Systematic Review of the Current State of Transfer Learning Accelerated CNN-Based Plant Leaf Disease Classification
inferred← citeshttps://doi.org/10.1109/aece62803.2024.10911782openalex-snapshot
Comparative Analysis of Deep Learning Algorithms for Disease Detection in Agriculture
inferred← citeshttps://doi.org/10.1109/aikiie60097.2023.10390058openalex-snapshot
An Inception Architecture with Transfer Learning for Apple Plant Leaf Disease Detection
inferred← citeshttps://doi.org/10.1109/ccet56606.2022.10080181openalex-snapshot
A Systematic Study: Implication of Deep Learning in Plant Disease Detection
inferred← citeshttps://doi.org/10.1109/conecct52877.2021.9622569openalex-snapshot
Pest and Disease Detection from Plant Leaves using Enhanced AlexNet Model
inferred← citeshttps://doi.org/10.1109/conecct55679.2022.9865733openalex-snapshot
Comparative Evaluation of the Convolutional Neural Network based Transfer Learning Models for Classification of Plant Disease
inferred← citeshttps://doi.org/10.1109/dese58274.2023.10099689openalex-snapshot
Automated Plant Disease Diagnosis in Apple Trees Based on Supervised Machine Learning Model
inferred← citeshttps://doi.org/10.1109/honet53078.2021.9615488openalex-snapshot
Cassava Leaf Disease Classification using Deep Neural Networks
inferred← citeshttps://doi.org/10.1109/i2ct61223.2024.10543627openalex-snapshot
Machine and Deep Learning Approaches for Crop Disease Detection: An In-Depth Analysis
inferred← citeshttps://doi.org/10.1109/iatmsi56455.2022.10119391openalex-snapshot
Progressive Web Application for Plant Disease Detection using CNN
inferred← citeshttps://doi.org/10.1109/ic2e61754.2024.00008openalex-snapshot
Domain-Aware Model Training as a Service for Use-Inspired Models
inferred← citeshttps://doi.org/10.1109/ic3it66137.2025.11341023openalex-snapshot
Ensemble CNNs for on-Field Detection of Plant Pathologies and Nutritional Disorders
inferred← citeshttps://doi.org/10.1109/icaaic56838.2023.10141161openalex-snapshot
Identification of Disease in Cassava Leaf using Deep Learning
inferred← citeshttps://doi.org/10.1109/icaisc56366.2023.10085039openalex-snapshot
CNN-LSTM Learning Approach for Classification of Foliar Disease of Apple
inferred← citeshttps://doi.org/10.1109/icaml64299.2024.00019openalex-snapshot
Research on the Classification of Apple Leaf Pathology Images Based on Deep Learning
inferred← citeshttps://doi.org/10.1109/icccnt56998.2023.10307097openalex-snapshot
Tree Disease Detection Using Image Classification
inferred← citeshttps://doi.org/10.1109/icccnt56998.2023.10307170openalex-snapshot
Plant Pathology Detection for Massive Heterogeneous Data Using Deep Learning
inferred← citeshttps://doi.org/10.1109/iccit64611.2024.11022424openalex-snapshot
Apple Leaf Disease Analysis Using Deep Hybrid Attention Network
inferred← citeshttps://doi.org/10.1109/iccmc53470.2022.9753985openalex-snapshot
Apple Leaf Disease Detection using Deep Learning
inferred← citeshttps://doi.org/10.1109/iccpct58313.2023.10244809openalex-snapshot
Utilization of Deep Learning for the Diagnosis of Diseases in Apple Leaves
inferred← citeshttps://doi.org/10.1109/iceca63461.2024.10800909openalex-snapshot
A Deep-Siamese CNN-based Approach for Identifying Mycosphaerella Coffeeicola Disease in Coffee Foliage
inferred← citeshttps://doi.org/10.1109/iceet65156.2024.10913946openalex-snapshot
Semi-supervised Segmentation-driven Classification Pipeline for Grading Cassava Leaf Diseases
inferred← citeshttps://doi.org/10.1109/icicos62600.2024.10636886openalex-snapshot
Comparative Analysis of the Architecture of Convolutional Neural Network Algorithms for Disease Prediction in Paddy Plants
inferred← citeshttps://doi.org/10.1109/icics52457.2021.9464561openalex-snapshot
A Mobile-Based Novice Agriculturalist Plant Care Support System: Classifying Plant Diseases using Deep Learning
inferred← citeshttps://doi.org/10.1109/icicyta64807.2024.10913279openalex-snapshot
Classification of Apple Leaf Diseases Using a Modified EfficientNet Model
inferred← citeshttps://doi.org/10.1109/iciip53038.2021.9702553openalex-snapshot
A Novel Model for Automatic Crop Disease Detection
inferred← citeshttps://doi.org/10.1109/iciptm54933.2022.9754197openalex-snapshot
Detection of Fungal Disease in Plant Leaf using Support Vector Machine Method Compared with K-means Clustering Algorithm with increase in Accuracy
inferred← citeshttps://doi.org/10.1109/icm56065.2022.10005433openalex-snapshot
CTPlantNet: A Hybrid CNN-Transformer Architecture for Plant Disease Classification
inferred← citeshttps://doi.org/10.1109/icme57554.2024.10687699openalex-snapshot
Coherentice: Invertible Concept-Based Explainability Framework for CNNs beyond Fidelity
inferred← citeshttps://doi.org/10.1109/icosec54921.2022.9951943openalex-snapshot
A Technical Survey on Deep Learning and AI Solutions for Plant Quality and Health Indicators Monitoring in Agriculture
inferred← citeshttps://doi.org/10.1109/icuis67429.2025.11380297openalex-snapshot
A Comprehensive Study on Plant Disease Detection using Deep Learning Models
inferred← citeshttps://doi.org/10.1109/inmic50486.2020.9318158openalex-snapshot
Date Palm Disease Identification Using Features Extraction and Deep Learning Approach
inferred← citeshttps://doi.org/10.1109/inocon57975.2023.10101362openalex-snapshot
An Optimized Ant Gardient Convolutional Neural Network for Disease Detection in Apple Leaves
inferred← citeshttps://doi.org/10.1109/isas66241.2025.11101895openalex-snapshot
Detection of Diseases on Apple Leaves with Deep Learning
inferred← citeshttps://doi.org/10.1109/iscas48785.2022.9937822openalex-snapshot
Combining CNN and MLP for Plant Pathology Recognition in Natural Scenes
inferred← citeshttps://doi.org/10.1109/ivcnz61134.2023.10344195openalex-snapshot
NCAF: NTD-based Concept Activation Factorisation Framework for CNN Explainability
inferred← citeshttps://doi.org/10.1109/ivcnz67716.2025.11281868openalex-snapshot
SAM-Based Leaf Segmentation with Morphological Quality Assessment for Enhanced Plant Disease Detection
inferred← citeshttps://doi.org/10.1109/jbhi.2021.3129245openalex-snapshot
AGMB-Transformer: Anatomy-Guided Multi-Branch Transformer Network for Automated Evaluation of Root Canal Therapy
inferred← citeshttps://doi.org/10.1109/jrfid.2022.3206841openalex-snapshot
Cotton Disease Detection Based on ConvNeXt and Attention Mechanisms
inferred← citeshttps://doi.org/10.1109/ncc60321.2024.10485918openalex-snapshot
An Analytical CNN: Use of Wavelets for Learning Image Structures in Cross-Domain Generalization
inferred← citeshttps://doi.org/10.1109/qpain69676.2026.11545532openalex-snapshot
Crop Disease Detection in Bangladesh Using ConvNeXt-Tiny and Conventional CNN with Grad-CAM Interpretability
inferred← citeshttps://doi.org/10.1109/sii58957.2024.10417302openalex-snapshot
Tomato Leaf Disease Diagnosis Using Bayesian Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1109/smartgencon60755.2023.10441917openalex-snapshot
Severity Levels of Apple Disease Recognition Using CNN and Random Forest: An Integrated Approach
inferred← citeshttps://doi.org/10.1109/tafe.2024.3447792openalex-snapshot
Real-Time Plant Disease Identification: Fusion of Vision Transformer and Conditional Convolutional Network With C3GAN-Based Data Augmentation
inferred← citeshttps://doi.org/10.1109/tai.2022.3222644openalex-snapshot
Learning Deep Asymmetric Tolerant Part Representation
inferred← citeshttps://doi.org/10.1109/tase.2024.3382731openalex-snapshot
Advances and Challenges in Computer Vision for Image-Based Plant Disease Detection: A Comprehensive Survey of Machine and Deep Learning Approaches
inferred← citeshttps://doi.org/10.1109/tcbbio.2024.3515149openalex-snapshot
An Ultra Lightweight Interpretable Convolution-Vision Transformer Fusion Model for Plant Disease Identification: ConViTX
inferred← citeshttps://doi.org/10.1109/tcsvt.2024.3370731openalex-snapshot
Learning Contrastive Self-Distillation for Ultra-Fine-Grained Visual Categorization Targeting Limited Samples
inferred← citeshttps://doi.org/10.1109/tnnls.2024.3355492openalex-snapshot
Self-Supervised Lie Algebra Representation Learning via Optimal Canonical Metric
inferred← citeshttps://doi.org/10.1109/wincom62286.2024.10655890openalex-snapshot
Plant Disease Recognition: A Comprehensive Mini Review
inferred← citeshttps://doi.org/10.1111/jph.13374openalex-snapshot
Smart agriculture: An intelligent approach for apple leaf disease identification based on convolutional neural network
inferred← citeshttps://doi.org/10.1111/nph.17697openalex-snapshot
Deep learning‐based quantification of arbuscular mycorrhizal fungi in plant roots
inferred← citeshttps://doi.org/10.1145/3426020.3426042openalex-snapshot
A study on applying homomorphic filter and Deep Neural Network for apple trees diseases classification
inferred← citeshttps://doi.org/10.1145/3460112.3471966openalex-snapshot
Species Distribution Modeling for Machine Learning Practitioners: A Review
inferred← citeshttps://doi.org/10.1145/3529836.3529850openalex-snapshot
Apple Leaf Disease Recognition Based on Attention Mechanics and Multi-Scale Feature Fusion
inferred← citeshttps://doi.org/10.1155/2024/5561625openalex-snapshot
BLSENet: A Novel Lightweight Bilinear Convolutional Neural Network Based on Attention Mechanism and Feature Fusion Strategy for Apple Leaf Disease Classification
inferred← citeshttps://doi.org/10.1155/int/5563178openalex-snapshot
Adaptive Attention‐Enhanced Multiscale Vision Transformers for Real‐Time Leaf Disease Detection in Smart Agriculture
inferred← citeshttps://doi.org/10.11606/d.55.2021.tde-02022022-121135openalex-snapshot
Deep learning em dois estágios para detecção e classificaçãode doenças em folhas de plantas com aplicação em dispositivos móveis
inferred← citeshttps://doi.org/10.1186/s13007-021-00722-9openalex-snapshot
Plant diseases and pests detection based on deep learning: a review
inferred← citeshttps://doi.org/10.1186/s13007-025-01453-xopenalex-snapshot
SCA-MobiPlant: smartphone-deployed multistage attention fusion model for accurate field detection of chili leaf curl complex
inferred← citeshttps://doi.org/10.1186/s13040-025-00497-yopenalex-snapshot
Deep vision in agriculture: assessing the function of YOLO in the classification of plant leaf diseases (PLDs)
inferred← citeshttps://doi.org/10.12720/jait.14.6.1301-1311openalex-snapshot
Identification and Classification of Diseases Based on Object Detection and Majority Voting of Bounding Boxes
inferred← citeshttps://doi.org/10.12720/jait.15.7.862-872openalex-snapshot
Deep Image Processing Based Periodically Leaves Diseases Detection and Classification through Wireless Visual Sensors Network (WVSN)
inferred← citeshttps://doi.org/10.12731/2658-6649-2021-13-3-103-118openalex-snapshot
КЛАССИФИКАЦИЯ ВНЕКОРНЕВЫХ ЗАБОЛЕВАНИЙ ЯБЛОНЕВЫХ КУЛЬТУР МЕТОДАМИ КОМПЬЮТЕРНОГО ЗРЕНИЯ
inferred← citeshttps://doi.org/10.1371/journal.pone.0343715openalex-snapshot
Interpretable crop pest and disease identification based on comparative concept tree
inferred← citeshttps://doi.org/10.1371/journal.pone.0349501openalex-snapshot
LeafDet: A lightweight and interpretable deep learning framework for tomato leaf disease detection
inferred← citeshttps://doi.org/10.1371/journal.pone.0352500openalex-snapshot
Lightweight real-time detectors of apple-leaf diseases operating on embedded devices
inferred← citeshttps://doi.org/10.1541/ieejeiss.144.824openalex-snapshot
A Simple Diagnostic Method for Citrus Greening Disease with Deep Learning
inferred← citeshttps://doi.org/10.17485/ijst/v14i1.1977openalex-snapshot
An artificial intelligence based approach for increasing agricultural yield
inferred← citeshttps://doi.org/10.17714/gumusfenbil.1549410openalex-snapshot
AppleCNN: A new CNN-based deep learning model for classification of apple leaf diseases
inferred← citeshttps://doi.org/10.17771/pucrio.acad.61365openalex-snapshot
MACHINE LEARNING FOR DIAGNOSIS OF FOLIAR DISEASES IN APPLE TREES
inferred← citeshttps://doi.org/10.18280/ria.360512openalex-snapshot
Automatic Detection and Classification of Apple Leaves Diseases Using MobileNet V2
inferred← citeshttps://doi.org/10.18280/ria.370221openalex-snapshot
Evaluation of CNN Models in Identifying Plant Diseases on a Mobile Device
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