ReviewPlant methods2021
Plant diseases and pests detection based on deep learning: a review.
Review in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 170 papers.
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Who cites it
170 citing papers in PubMed.
- Machine learning-enabled chemical ecology for integrated pest management: from volatiles to field applications.Journal of insect science (Online) · 2026Review
- YOLOv11-MPD: A Multi-Part Maize Disease Detection Algorithm for Complex Field Environments.Sensors (Basel, Switzerland) · 2026Article
- A YOLO11-MSC-Based Method for Small Pest Detection in Insect Pest Monitoring Lamp Images.Sensors (Basel, Switzerland) · 2026Article
- A low-cost "plant-scanner" platform for automated detection of Ustilago maydis infection in maize using deep learning.Scientific reports · 2026Article
- SLR-YOLO: An Improved YOLO-Based Method for Accurate Detection of Potato Leaf Diseases in Complex Field Images.Plants (Basel, Switzerland) · 2026Article
- Hybrid deep learning-based multimodal framework for plant leaf disease classification using RGB, Excess Green (ExG), and pseudo-thermal representations with MobileNetV2.Scientific reports · 2026Article
- FCDNet: An Efficient and Cost-Effective Strawberry Disease Detection Model for Smart Farming Management.Plants (Basel, Switzerland) · 2026Article
- A hybrid deep learning model for robust and efficient plant leaf disease detection using ResNet50, PCA, and SVM.Scientific reports · 2026Article
- Classification of rice plant diseases using efficient DenseNet121.Scientific reports · 2026Article
- Lightweight Vision-Transformer Network for Early Insect Pest Identification in Greenhouse Agricultural Environments.Insects · 2026Article
- PlantPathNet: a novel network based on cross-layer feature integration for plant disease classification.Frontiers in plant science · 2026Article
- From architectural complexity to minimalist optimization: refining microscopic tea disease detection via Opti-YOLOv11n.Frontiers in plant science · 2026Article
- A lightweight YOLOv11-based model for rice false smut detection under complex field conditions.Frontiers in plant science · 2026Article
- Harnessing Artificial Intelligence for Sustainable Agriculture: A Comprehensive Scientometric Review.Recent advances in food, nutrition & agriculture · 2026Review
- A resource-efficient framework for plant disease classification: integrating reduced-order modeling with treatment-based label engineering.Frontiers in plant science · 2026Article
- ZamYOLO-maize: a YOLOv8n-based deep learning framework for automated detection and classification of maize leaf diseases in field conditions in Zambia.Frontiers in artificial intelligence · 2026Article
- High-throughput phenotyping for climate-resilient forests: integrating multi-sensor fusion and root-shoot dynamics.Frontiers in plant science · 2026Review
- Bridging CNNs and vision transformers for efficient tea leaf phytopathogen diagnosis: GL-MobFormer.Frontiers in plant science · 2026Article
- YOLOv11n-DualPC-Lite: a lightweight, high-precision real-time detection model for maize leaf diseases.Frontiers in plant science · 2026Article
- TDAVM-UNet: task-driven attention VM-UNet for crop disease detection from UAV imagery.Frontiers in plant science · 2026Article
110 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.