ArticleScientific reports2025
A comprehensive analysis of YOLO architectures for tomato leaf disease identification.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Attention-driven YOLOv11n-DeiT model for enhanced detection of tomato leaf diseases.Scientific reports · 2026Article
- A learning-dynamics-driven framework for robust small-object detection in complex field environments: YOLOv11l-AGRI-EMA framework.Scientific reports · 2026Article
- Wheat spike and spikelet detection and counting from high-resolution digital imagery using YOLO with Oriented Bounding Boxes.Scientific reports · 2026Article
- GBR-DETR: A Real-Time Tomato Leaf Disease Detection Model for Edge Device Deployment.Sensors (Basel, Switzerland) · 2026Article
- Enhancing Pig Behavior Recognition in Complex Environments: A Transfer Learning-Assisted YOLO11 Network with Wavelet Convolution and Synergistic Attention.Animals : an open access journal from MDPI · 2026Article
- Lightweight scalable deep learning framework for real time detection of potato leaf diseases.Scientific reports · 2026Article
- Real-time detection of plant leaf diseases based on improved YOLOv13-LM in complex field environments.Frontiers in plant science · 2026Article
- Deep learning techniques for early detection and classification of leaf diseases in crops.Frontiers in plant science · 2026Article
- YOLOv12 Algorithm-Aided Detection and Classification of Lateral Malleolar Avulsion Fracture and Subfibular Ossicle Based on CT Images: Multicenter Study.JMIR medical informatics · 2025Article
- AI-Assisted surgical vision: evaluating YOLOv8 and YOLOv12 for real-time detection in colon cancer surgery.Frontiers in surgery · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
Tomato leaf disease detection is critical in precision agriculture for safeguarding crop health and optimizing yields. This study compares the latest YOLO architectures, including YOLOv8, YOLOv9, YOLOv10, YOLOv11, and YOLOv12, using the Tomato-Village dataset, which contains 14,368 images across six disease classes. All models are trained under identical settings to ensure a fair evaluation based on precision, recall, mean Average Precision, training time, and inference speed. Results show that YOLOv11 consistently outperforms the other architectures, achieving the highest accuracy with competitive training times and acceptable latency. YOLOv10, YOLOv8, and YOLOv12 also deliver strong results, with YOLOv12n emerging as the most effective lightweight model for resource-constrained environments. In contrast, YOLOv9 demonstrates the weakest performance, requiring more training time and exhibiting higher latency. Overall, YOLOv11 is positioned as the most effective solution for tomato leaf disease detection, providing a strong benchmark for future advancements in agricultural technology.
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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 Socratic graph.