Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m
The automated recognition of disease in tomato leaves can greatly enhance yield and allow farmers to manage challenges more efficiently. This study investigates the performance of YOLOv11 for tomato leaf disease recognition. All accessible versions of YOLOv11 were first fine-tuned on an improved tom...
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MDPI AG
2025-02-01
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| Series: | Plants |
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| Online Access: | https://www.mdpi.com/2223-7747/14/5/653 |
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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 | The automated recognition of disease in tomato leaves can greatly enhance yield and allow farmers to manage challenges more efficiently. This study investigates the performance of YOLOv11 for tomato leaf disease recognition. All accessible versions of YOLOv11 were first fine-tuned on an improved tomato leaf disease dataset consisting of a healthy class and 10 disease classes. YOLOv11m was selected for further hyperparameter optimization based on its evaluation metrics. It achieved a fitness score of 0.98885, with a precision of 0.99104, a recall of 0.98597, and a mAP@.5 of 0.99197. This model underwent rigorous hyperparameter optimization using the one-factor-at-a-time (OFAT) algorithm, with a focus on essential parameters such as batch size, learning rate, optimizer, weight decay, momentum, dropout, and epochs. Subsequently, random search (RS) with 100 configurations was performed based on the results of OFAT. Among them, the C47 model demonstrated a fitness score of 0.99268 (a 0.39% improvement), with a precision of 0.99190 (0.09%), a recall of 0.99348 (0.76%), and a mAP@.5 of 0.99262 (0.07%). The results suggest that the final model works efficiently and is capable of accurately detecting and identifying tomato leaf diseases, making it suitable for practical farming applications. |
| format | Article |
| id | doaj-art-da95b8a16e2643c8a5bcea22ba641793 |
| institution | DOAJ |
| issn | 2223-7747 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Plants |
| spelling | doaj-art-da95b8a16e2643c8a5bcea22ba6417932025-08-20T02:59:15ZengMDPI AGPlants2223-77472025-02-0114565310.3390/plants14050653Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11mYong-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 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 KoreaDepartment of Microbiology, Pukyong National University, Busan 48513, Republic of KoreaThe automated recognition of disease in tomato leaves can greatly enhance yield and allow farmers to manage challenges more efficiently. This study investigates the performance of YOLOv11 for tomato leaf disease recognition. All accessible versions of YOLOv11 were first fine-tuned on an improved tomato leaf disease dataset consisting of a healthy class and 10 disease classes. YOLOv11m was selected for further hyperparameter optimization based on its evaluation metrics. It achieved a fitness score of 0.98885, with a precision of 0.99104, a recall of 0.98597, and a mAP@.5 of 0.99197. This model underwent rigorous hyperparameter optimization using the one-factor-at-a-time (OFAT) algorithm, with a focus on essential parameters such as batch size, learning rate, optimizer, weight decay, momentum, dropout, and epochs. Subsequently, random search (RS) with 100 configurations was performed based on the results of OFAT. Among them, the C47 model demonstrated a fitness score of 0.99268 (a 0.39% improvement), with a precision of 0.99190 (0.09%), a recall of 0.99348 (0.76%), and a mAP@.5 of 0.99262 (0.07%). The results suggest that the final model works efficiently and is capable of accurately detecting and identifying tomato leaf diseases, making it suitable for practical farming applications.https://www.mdpi.com/2223-7747/14/5/653hyperparameter optimizationtomato leaf diseaseYOLOv11one-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 for Tomato Leaf Disease Recognition Based on YOLOv11m Plants hyperparameter optimization tomato leaf disease YOLOv11 one-factor-at-a-time random search |
| title | Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m |
| title_full | Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m |
| title_fullStr | Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m |
| title_full_unstemmed | Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m |
| title_short | Hyperparameter Optimization for Tomato Leaf Disease Recognition Based on YOLOv11m |
| title_sort | hyperparameter optimization for tomato leaf disease recognition based on yolov11m |
| topic | hyperparameter optimization tomato leaf disease YOLOv11 one-factor-at-a-time random search |
| url | https://www.mdpi.com/2223-7747/14/5/653 |
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