A comprehensive analysis of YOLO architectures for tomato leaf disease identification
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 a...
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-11064-0 |
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| author | Leo Thomas Ramos Angel D. Sappa |
| author_facet | Leo Thomas Ramos Angel D. Sappa |
| author_sort | Leo Thomas Ramos |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2c19378735154c65bc17e8b7bf67337c |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-2c19378735154c65bc17e8b7bf67337c2025-08-20T03:43:26ZengNature PortfolioScientific Reports2045-23222025-07-0115112610.1038/s41598-025-11064-0A comprehensive analysis of YOLO architectures for tomato leaf disease identificationLeo Thomas Ramos0Angel D. Sappa1Computer Vision Center, Universitat Autònoma de BarcelonaComputer Vision Center, Universitat Autònoma de BarcelonaAbstract 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.https://doi.org/10.1038/s41598-025-11064-0Leaf disease detectionObject detectionPlant disease detectionDeep learningPrecision agricultureYOLO |
| spellingShingle | Leo Thomas Ramos Angel D. Sappa A comprehensive analysis of YOLO architectures for tomato leaf disease identification Scientific Reports Leaf disease detection Object detection Plant disease detection Deep learning Precision agriculture YOLO |
| title | A comprehensive analysis of YOLO architectures for tomato leaf disease identification |
| title_full | A comprehensive analysis of YOLO architectures for tomato leaf disease identification |
| title_fullStr | A comprehensive analysis of YOLO architectures for tomato leaf disease identification |
| title_full_unstemmed | A comprehensive analysis of YOLO architectures for tomato leaf disease identification |
| title_short | A comprehensive analysis of YOLO architectures for tomato leaf disease identification |
| title_sort | comprehensive analysis of yolo architectures for tomato leaf disease identification |
| topic | Leaf disease detection Object detection Plant disease detection Deep learning Precision agriculture YOLO |
| url | https://doi.org/10.1038/s41598-025-11064-0 |
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