Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8
Apple leaf diseases have a major influence on apple productivity and quality, demanding a precise and efficient recognition system. Using the YOLOv8 family of object detection models, we created a disease recognition model for the apple leaf dataset in this study. The developed model was fine-tuned...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-06-01
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| Series: | Journal of Agriculture and Food Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S266615432500211X |
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| author | Yong-Suk Lee Maheshkumar Prakash Patil Jeong Gyu Kim Yong Bae Seo Dong-Hyun Ahn Gun-Do Kim |
| author_facet | Yong-Suk Lee Maheshkumar Prakash Patil Jeong Gyu Kim Yong Bae Seo Dong-Hyun Ahn Gun-Do Kim |
| author_sort | Yong-Suk Lee |
| collection | DOAJ |
| description | Apple leaf diseases have a major influence on apple productivity and quality, demanding a precise and efficient recognition system. Using the YOLOv8 family of object detection models, we created a disease recognition model for the apple leaf dataset in this study. The developed model was fine-tuned extensively by hyperparameter optimization to identify the best variant for practical deployment. Firstly, fine-tuning with different YOLOv8 series was conducted on an apple leaf dataset including various types of images. Among them, the YOLOv8s demonstrated the best balance with a fitness of 0.97171, a precision of 0.97082, a recall of 0.96837, a mAP@0.5 of 0.98016, and an image processing speed of 1.58 ms. Further hyperparameter optimization was conducted using the One-Factor-At-a-Time (OFAT) and Random Search (RS) methods. In this case, the optimal settings determined as per the OFAT method were a batch size of 48, a learning rate of 0.01, a weight decay of 0.0005, a momentum of 0.963, and 200 epochs. These settings were adopted as the baseline for RS. RS then searched for 50 additional configurations; the best configuration, C34 (batch size of 48, learning rate of 0.0137, momentum of 0.9433, and weight decay of 0.0009), achieved a fitness score of 0.97688, a precision of 0.97797, a recall of 0.97295, and a mAP@0.5 of 0.98257. The correlation analysis showed that learning rate and momentum significantly impacted the performance of the models. Overall, the C34 model demonstrates high accuracy, rapid processing speed, and robustness suitable for training real-time, large-scale apple leaf disease recognition. |
| format | Article |
| id | doaj-art-2b2c0deb8b0c4762b0c72700d417d71c |
| institution | Kabale University |
| issn | 2666-1543 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Agriculture and Food Research |
| spelling | doaj-art-2b2c0deb8b0c4762b0c72700d417d71c2025-08-20T03:25:08ZengElsevierJournal of Agriculture and Food Research2666-15432025-06-012110184010.1016/j.jafr.2025.101840Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8Yong-Suk Lee0Maheshkumar Prakash Patil1Jeong Gyu Kim2Yong Bae Seo3Dong-Hyun Ahn4Gun-Do Kim5Department of Food Science and Technology, Institute of Food Science, Pukyong National University, Busan, 48513, Republic of Korea; Industry University Cooperation Foundation, Pukyong National University, Busan, 48513, Republic of KoreaIndustry University Cooperation Foundation, Pukyong National University, Busan, 48513, Republic of KoreaDepartment of Microbiology, Pukyong National University, Busan, 48513, Republic of KoreaDepartment of Microbiology, Pukyong National University, Busan, 48513, Republic of KoreaDepartment of Food Science and Technology, Institute of Food Science, Pukyong National University, Busan, 48513, Republic of Korea; Corresponding author. Department of Food Science and Technology, Institute of Food Science, Pukyong National University, Busan, 48513, Republic of Korea.Department of Microbiology, Pukyong National University, Busan, 48513, Republic of Korea; Corresponding author. Department of Microbiology, Pukyong National University, Busan, 48513, Republic of Korea.Apple leaf diseases have a major influence on apple productivity and quality, demanding a precise and efficient recognition system. Using the YOLOv8 family of object detection models, we created a disease recognition model for the apple leaf dataset in this study. The developed model was fine-tuned extensively by hyperparameter optimization to identify the best variant for practical deployment. Firstly, fine-tuning with different YOLOv8 series was conducted on an apple leaf dataset including various types of images. Among them, the YOLOv8s demonstrated the best balance with a fitness of 0.97171, a precision of 0.97082, a recall of 0.96837, a mAP@0.5 of 0.98016, and an image processing speed of 1.58 ms. Further hyperparameter optimization was conducted using the One-Factor-At-a-Time (OFAT) and Random Search (RS) methods. In this case, the optimal settings determined as per the OFAT method were a batch size of 48, a learning rate of 0.01, a weight decay of 0.0005, a momentum of 0.963, and 200 epochs. These settings were adopted as the baseline for RS. RS then searched for 50 additional configurations; the best configuration, C34 (batch size of 48, learning rate of 0.0137, momentum of 0.9433, and weight decay of 0.0009), achieved a fitness score of 0.97688, a precision of 0.97797, a recall of 0.97295, and a mAP@0.5 of 0.98257. The correlation analysis showed that learning rate and momentum significantly impacted the performance of the models. Overall, the C34 model demonstrates high accuracy, rapid processing speed, and robustness suitable for training real-time, large-scale apple leaf disease recognition.http://www.sciencedirect.com/science/article/pii/S266615432500211XApple leaf diseaseYOLOv8Hyperparameter optimizationOne-factor-at-a-timeRandom search |
| spellingShingle | Yong-Suk Lee Maheshkumar Prakash Patil Jeong Gyu Kim Yong Bae Seo Dong-Hyun Ahn Gun-Do Kim Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8 Journal of Agriculture and Food Research Apple leaf disease YOLOv8 Hyperparameter optimization One-factor-at-a-time Random search |
| title | Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8 |
| title_full | Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8 |
| title_fullStr | Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8 |
| title_full_unstemmed | Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8 |
| title_short | Hyperparameter optimization of apple leaf dataset for the disease recognition based on the YOLOv8 |
| title_sort | hyperparameter optimization of apple leaf dataset for the disease recognition based on the yolov8 |
| topic | Apple leaf disease YOLOv8 Hyperparameter optimization One-factor-at-a-time Random search |
| url | http://www.sciencedirect.com/science/article/pii/S266615432500211X |
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