ReviewFrontiers in plant science2025
A review of plant leaf disease identification by deep learning algorithms.
Review in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- HGSM-YOLO: A Small-Lesion-Oriented Lightweight YOLO11n Framework for Citrus Leaf Disease Detection.Sensors (Basel, Switzerland) · 2026Article
- A lightweight graph-enhanced deep learning framework for explainable cucumber leaf disease diagnosis.Scientific reports · 2026Article
- GBR-DETR: A Real-Time Tomato Leaf Disease Detection Model for Edge Device Deployment.Sensors (Basel, Switzerland) · 2026Article
- YOLO-SDA: an innovative YOLOv12-derived model with superior performance in recognizing peanut foliar diseases.Frontiers in plant science · 2026Article
- LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments.Frontiers in plant science · 2026Article
- Challenges and strategies for harnessing large language models in plant protection.Frontiers in plant science · 2026Article
- AgriFusionNet: a context-aware multimodal leaf disease diagnosis and classification system for sustainable plant health monitoring.Frontiers in fungal biology · 2026Article
- From detection accuracy to safety assurance in intelligent plant health early warning systems.Frontiers in plant science · 2026Review
- ViTKAB: an efficient deep learning network for cotton leaf disease identification.Frontiers in plant science · 2025Article
- Detection of Taiqiu sweet persimmons during the color-transition period with an improved YOLO11-FC2T model and causal analysis.Frontiers in plant science · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant leaf disease control is crucial given the prevalence of plant leaf diseases around the world. The most crucial aspect of controlling plant leaf diseases is appropriately identifying them. Deep learning-based plant leaf disease recognition is a viable alternative to artificial methods that are useless and inaccurate. The proposed work aims to combine plant leaf disease datasets from various countries, review current research and progress in deep learning algorithms for plant disease recognition, and explain how different types of data are developed and used in this area using different deep learning networks. The feasibility of several network models for deep learning-based plant leaf disease detection is discussed. Solving shortcomings such as sunlight irradiation in plant planting conditions, similar disease incidence of different plant leaf diseases, and varied symptoms of the same disease in different damage periods or infection degrees are all essential study topics in the growth of this discipline. To address the concerns raised above and establish the field's future development potential, we must research high-performance neural networks based on the benefits and downsides of diverse networks. The proposed work can serve as a foundation for future research and breakthroughs in the identification of plant leaf diseases.
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Registered trials
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