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A review on the main challenges in automatic plant disease identification based on visible range images
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Plant leaf disease classification using deep attention residual network optimized by opposition-based symbiotic organisms search algorithm
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Comprehensive Image Analysis of Seed and Plant for Classification of Cotton Genotypes Using Deep Learning Methodologies
inferred← citeshttps://doi.org/10.1007/s10343-026-01388-wopenalex-snapshot
Grad-CAM++ Guided Deep Learning-Based Genus-Level Identification of Plant-Parasitic Nematodes in Microscopic Images
inferred← citeshttps://doi.org/10.1007/s10343-026-01409-8openalex-snapshot
Deep Learning Techniques for Microscopic Identification and Detection of Plant-Parasitic Nematodes
inferred← citeshttps://doi.org/10.1007/s10462-020-09813-wopenalex-snapshot
Deep neural network based Rider-Cuckoo Search Algorithm for plant disease detection
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Leveraging explainable AI for sustainable agriculture: a comprehensive review of recent advances
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The recognition of rice images by UAV based on capsule network
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A new automatic method for disease symptom segmentation in digital photographs of plant leaves
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Deep learning system for paddy plant disease detection and classification
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Designing an optimal multi-objective model for a sustainable closed-loop supply chain: a case study of pomegranate in Iran
inferred← citeshttps://doi.org/10.1007/s11042-020-09244-3openalex-snapshot
Fruits diseases classification: exploiting a hierarchical framework for deep features fusion and selection
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A comprehensive study of feature extraction techniques for plant leaf disease detection
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Deep transfer modeling for classification of Maize Plant Leaf Disease
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Optimized deep learning system for smart maize leaf disease detection in IoT platform via routing algorithm
inferred← citeshttps://doi.org/10.1007/s11042-022-13925-6openalex-snapshot
A sugar beet leaf disease classification method based on image processing and deep learning
inferred← citeshttps://doi.org/10.1007/s11042-022-14004-6openalex-snapshot
Intelligent plant disease diagnosis using convolutional neural network: a review
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Smart farming for detection and identification of tomato plant diseases using light weight deep neural network
inferred← citeshttps://doi.org/10.1007/s11042-023-15940-7openalex-snapshot
Hybrid deep learning model for multi biotic lesions detection in solanum lycopersicum leaves
inferred← citeshttps://doi.org/10.1007/s11042-023-16238-4openalex-snapshot
Performance analysis of segmentation models to detect leaf diseases in tomato plant
inferred← citeshttps://doi.org/10.1007/s11042-023-17449-5openalex-snapshot
Enhancing rice crop health: a light weighted CNN-based disease detection system with mobile application integration
inferred← citeshttps://doi.org/10.1007/s11042-024-19413-3openalex-snapshot
Classification of Camellia oleifera using a dual recognition strategy based on deep learning
inferred← citeshttps://doi.org/10.1007/s11042-026-21408-1openalex-snapshot
Deep transfer learning-based ensemble framework for identifying plant-parasitic nematodes from microscopic images
inferred← citeshttps://doi.org/10.1007/s11103-025-01582-wopenalex-snapshot
Cutting-edge computational approaches to plant phenotyping
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An Ensemble of Deep Transfer Learning Models for Plant Disease Prediction Using Thermal Images
inferred← citeshttps://doi.org/10.1007/s11277-020-07279-1openalex-snapshot
Web Facilitated Anthracnose Disease Segmentation from the Leaf of Mango Tree Using Radial Basis Function (RBF) Neural Network
inferred← citeshttps://doi.org/10.1007/s11540-024-09787-0openalex-snapshot
Improved Potato Crop Disease Classification Using Ensembled Convolutional Neural Network
inferred← citeshttps://doi.org/10.1007/s11540-025-09956-9openalex-snapshot
The Application of YOLOv8n, YOLOv8s, and YOLOv8m for Potato Leaf Diseases Classification
inferred← citeshttps://doi.org/10.1007/s11694-026-04797-7openalex-snapshot
Lightweight vision transformer architectures and comparative analysis of multi-head classifiers with integrated gradients for red chilli powder adulteration detection
inferred← citeshttps://doi.org/10.1007/s11760-024-03188-zopenalex-snapshot
Recognize and classify illnesses on tomato leaves using EfficientNet's transfer learning approach with different size dataset
inferred← citeshttps://doi.org/10.1007/s11760-026-05152-5openalex-snapshot
Cross-domain thermal image augmentation for agricultural disease detection: a multi-crop validated framework with adaptive connectivity and optimized loss function
inferred← citeshttps://doi.org/10.1007/s11831-018-9255-6openalex-snapshot
Plants Disease Identification and Classification Through Leaf Images: A Survey
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Applications of Computer Vision in Plant Pathology: A Survey
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Machine Learning and Deep Learning Based Computational Techniques in Automatic Agricultural Diseases Detection: Methodologies, Applications, and Challenges
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Empowering Agriculture from Pixels to Diagnosis: A Review of Computer Vision Techniques for Plant Disease Detection
inferred← citeshttps://doi.org/10.1007/s11831-026-10582-yopenalex-snapshot
A Comprehensive Review of Image Segmentation Techniques for Plant Disease Detection in Precision Agriculture: Models, Trends, and Challenges
inferred← citeshttps://doi.org/10.1007/s12355-025-01714-yopenalex-snapshot
A Reproducible Deep Learning Framework for Field-Deployable Classification of Sugarcane Pest Damage Using Ensemble CNNs
inferred← citeshttps://doi.org/10.1007/s12393-022-09307-1openalex-snapshot
Defect Detection in Fruit and Vegetables by Using Machine Vision Systems and Image Processing
inferred← citeshttps://doi.org/10.1007/s12652-021-03289-4openalex-snapshot
Cost-optimized hybrid convolutional neural networks for detection of plant leaf diseases
inferred← citeshttps://doi.org/10.1007/s12652-021-03440-1openalex-snapshot
DPD-DS for plant disease detection based on instance segmentation
inferred← citeshttps://doi.org/10.1007/s13198-020-00972-1openalex-snapshot
Systematic review of deep learning techniques in plant disease detection
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Conventional and cutting-edge advances in plant virus detection: emerging trends and techniques
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Determination of the severity of Septoria leaf spot in tomato by using digital images
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An Improved Deep Convolutional Neural Network for Image-Based Apple Plant Leaf Disease Detection and Identification
inferred← citeshttps://doi.org/10.1007/s40333-022-0086-9openalex-snapshot
Image recognition and empirical application of desert plant species based on convolutional neural network
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Deep Neural Network (DNN) Mechanism for Identification of Diseased and Healthy Plant Leaf Images Using Computer Vision
inferred← citeshttps://doi.org/10.1007/s40858-021-00448-yopenalex-snapshot
RGB-based phenotyping of foliar disease severity under controlled conditions
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Measuring plant disease severity in R: introducing and evaluating the pliman package
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A special issue on phytopathometry — visual assessment, remote sensing, and artificial intelligence in the twenty-first century
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A systematic review of plant leaf disease detection and classification using machine learning and deep learning techniques
inferred← citeshttps://doi.org/10.1007/s41348-020-00368-0openalex-snapshot
Plant disease detection using computational intelligence and image processing
inferred← citeshttps://doi.org/10.1007/s41348-021-00500-8openalex-snapshot
Artificial intelligence in tomato leaf disease detection: a comprehensive review and discussion
inferred← citeshttps://doi.org/10.1007/s41348-022-00583-xopenalex-snapshot
Automatic identification of cassava leaf diseases utilizing morphological hidden patterns and multi-feature textures with a distributed structure-based classification approach
inferred← citeshttps://doi.org/10.1007/s41870-020-00437-5openalex-snapshot
Identification of plant diseases using convolutional neural networks
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Multilevel detection and classification of diseased plant leaf images using high-resolution superlet transform and E-ResNet
inferred← citeshttps://doi.org/10.1007/s42161-020-00683-3openalex-snapshot
Rice plant disease classification using color features: a machine learning paradigm
inferred← citeshttps://doi.org/10.1007/s42161-022-01178-zopenalex-snapshot
A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with image processing
inferred← citeshttps://doi.org/10.1007/s42452-024-05959-2openalex-snapshot
Investigating the accuracy of classification in unbalanced data in order to diagnose two common potato leaf diseases (early blight and late blight) using image processing and machine learning
inferred← citeshttps://doi.org/10.1007/s42979-024-02620-yopenalex-snapshot
Lightweight Convolutional Neural Network Model for Cassava Leaf Diseases Classification
inferred← citeshttps://doi.org/10.1007/s42979-025-04027-9openalex-snapshot
Detection of Tomato Disease Using Texture Features and Genetic Bee Colony Algorithm
inferred← citeshttps://doi.org/10.1007/s42979-025-04239-zopenalex-snapshot
Deep Learning Based Automated Classification of Plant-Parasitic Nematodes Using a Proposed CNN Model and Pre-Trained Networks: MobileNetV3, VGG16, and InceptionV3
inferred← citeshttps://doi.org/10.1007/s43621-026-03623-wopenalex-snapshot
A comprehensive review of artificial intelligence and Internet of Things integration based plant disease detection for sustainable agriculture
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Enhanced fungal disease detection in brinjal leaves using deep semantic segmentation with U2-Net
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Insights into Artificial Intelligence Bias: Implications for Agriculture
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From detection to action: artificial intelligence in integrated pest and invasive plant management
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Crop disease classification using deep learning approach: an overview and a case study
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Artificial intelligence for plant disease recognition
inferred← citeshttps://doi.org/10.1016/b978-0-443-27324-7.00003-3openalex-snapshot
Conventional breeding for biotic stress management in horticultural crops
inferred← citeshttps://doi.org/10.1016/b978-0-443-27465-7.00006-6openalex-snapshot
Plant disease diagnosis and forecasting in the era of artificial intelligence, machine learning, and deep learning
inferred← citeshttps://doi.org/10.1016/b978-0-443-33633-1.00005-8openalex-snapshot
Machine learning for uncovering plant–pathogen interactions
inferred← citeshttps://doi.org/10.1016/bs.adcom.2023.08.004openalex-snapshot
ClubNet: Deep learning model for computation, calibration and estimation of biotic stress in crops
inferred← citeshttps://doi.org/10.1016/j.advengsoft.2023.103472openalex-snapshot
Rider Water Wave-enabled deep learning for disease detection in rice plant
inferred← citeshttps://doi.org/10.1016/j.agsy.2024.103886openalex-snapshot
D2CNN: Double-staged deep CNN for stress identification and classification in cropping system
inferred← citeshttps://doi.org/10.1016/j.aiia.2022.04.001openalex-snapshot
Few-shot learning for biotic stress classification of coffee leaves
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AgriMAPO: A multimodal automatic prompt optimization approach for crop disease classification using large language models
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An effective automatic system deployed in agricultural Internet of Things using Multi-Context Fusion Network towards crop disease recognition in the wild
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A lightweight CNN supported by partially skip-connected CAE for plant disease identification across multiple agricultural environments
inferred← citeshttps://doi.org/10.1016/j.atech.2021.100010openalex-snapshot
UAV-based coffee yield prediction utilizing feature selection and deep learning
inferred← citeshttps://doi.org/10.1016/j.atech.2022.100081openalex-snapshot
Diagnosis of grape leaf diseases using automatic K-means clustering and machine learning
inferred← citeshttps://doi.org/10.1016/j.atech.2022.100083openalex-snapshot
A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools
inferred← citeshttps://doi.org/10.1016/j.atech.2023.100183openalex-snapshot
Coffee disease classification at the edge using deep learning
inferred← citeshttps://doi.org/10.1016/j.atech.2023.100185openalex-snapshot
Recognition of sugarcane orange and brown rust through leaf image processing
inferred← citeshttps://doi.org/10.1016/j.atech.2024.100480openalex-snapshot
Feature engineering to identify plant diseases using image processing and artificial intelligence: A comprehensive review
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Early detection of the Taro Leaf Blight disease in the West African sub-region using deep image classification models
inferred← citeshttps://doi.org/10.1016/j.atech.2025.101023openalex-snapshot
Classifying early-stage soybean fungal diseases on hyperspectral images using convolutional neural networks
inferred← citeshttps://doi.org/10.1016/j.atech.2025.101249openalex-snapshot
ToT-Net: A generalized and real-time crop disease detection framework via task-level meta-learning and lightweight multi-scale transformer
inferred← citeshttps://doi.org/10.1016/j.atech.2026.101838openalex-snapshot
Improved strawberry disease classification under class imbalance through in-backbone latent diffusion
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LMDD-YOLO: A lightweight multi-scale detection model for citrus leaf disease in complex scenarios
inferred← citeshttps://doi.org/10.1016/j.atech.2026.102570openalex-snapshot
Wheat stripe rust monitoring based on weighted multi-temporal features from UAV multispectral imagery under field conditions
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Evaluation of the effectiveness of antagonistic bacteria against Phytophthora blight disease in pepper with artificial intelligence
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Recent advances in E-monitoring of plant diseases
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Identifying multiple plant diseases using digital image processing
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Deoxynivalenol screening in wheat kernels using hyperspectral imaging
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Factors influencing the use of deep learning for plant disease recognition
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2018.09.012openalex-snapshot
Detection of sprout damage in wheat kernels using NIR hyperspectral imaging
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Plant disease identification from individual lesions and spots using deep learning
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2019.06.008openalex-snapshot
Development of Fusarium head blight classification index using hyperspectral microscopy images of winter wheat spikelets
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2019.11.011openalex-snapshot
Machine learning applications to non-destructive defect detection in horticultural products
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2020.02.016openalex-snapshot
The use of UAVs in monitoring yellow sigatoka in banana
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2020.07.001openalex-snapshot
Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2021.06.014openalex-snapshot
Disease classification in aubergine with local symptomatic region using deep learning models
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2021.08.011openalex-snapshot
Deep learning architectures for semantic segmentation and automatic estimation of severity of foliar symptoms caused by diseases or pests
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2021.11.016openalex-snapshot
NemaNet: A convolutional neural network model for identification of soybean nematodes
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2022.09.005openalex-snapshot
Early detection of Sclerotinia sclerotiorum on oilseed rape leaves based on optical properties
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A suction cup-based soft robotic gripper for cucumber harvesting: Design and validation
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Machine/deep learning techniques for disease and nutrient deficiency disorder diagnosis in rice crops: A systematic review
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Directed acyclic graphs-based diagnosis approach using small data sets for sustainability
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Three-channel convolutional neural networks for vegetable leaf disease recognition
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Fractional PID controller in an active image stabilization system for mitigating vibration effects in agricultural tractors
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