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Open AccessArticle
YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease Detection
by
Jingyu Wang
Jingyu Wang
Jingyu Wang was born in Shanxi province, China and is now a third-year undergraduate student in and [...]
Jingyu Wang was born in Shanxi province, China and is now a third-year undergraduate student in computer science and technology at College of Information Science and Engineering of Shanxi Agricultural University in China. Her research topics mainly include computer vision, deep learning, programming, and target recognition.
1,
Miaomiao Li
Miaomiao Li
Miaomiao Li was born in Shanxi province, China and is now a third-year undergraduate student in and [...]
Miaomiao Li was born in Shanxi province, China and is now a third-year undergraduate student in computer science and technology at College of Information Science and Engineering of Shanxi Agricultural University in China. Her research topics mainly include computer vision, deep learning, and algorithm.
1,
Chen Han
Chen Han
Chen Han was born in Shanxi province, China and is now a third-year undergraduate student in science [...]
Chen Han was born in Shanxi province, China and is now a third-year undergraduate student in computer science and technology at College of Information Science and Engineering of Shanxi Agricultural University in China. His research topics mainly include computer vision, deep learning, and programming.
1 and
Xindong Guo
Xindong Guo
Xindong Guo received his Master's Degree in Computer Science from the Northwest Polytechnical 2015 [...]
Xindong Guo received his Master's Degree in Computer Science from the Northwest Polytechnical University in 2015 and is now pursuing PhD from the North University of China. He has been working as lecturer at Shanxi Agricultural University since 2015. His research topics mainly include 3D computer vision, deep learning, and agricultural informatization.
1,2,*
1
College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China
2
College of Computer Science and Technology, North University of China, Taiyuan 030051, China
*
Author to whom correspondence should be addressed.
Agriculture 2024, 14(8), 1240; https://doi.org/10.3390/agriculture14081240 (registering DOI)
Submission received: 29 June 2024
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Revised: 20 July 2024
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Accepted: 25 July 2024
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Published: 27 July 2024
Abstract
Deploying deep convolutional neural networks on agricultural devices with limited resources is challenging due to their large number of parameters. Existing lightweight networks can alleviate this problem but suffer from low performance. To this end, we propose a novel lightweight network named YOLOv8-RCAA (YOLOv8-RepVGG-CBAM-Anchorfree-ATSS), aiming to locate and detect tea leaf diseases with high accuracy and performance. Specifically, we employ RepVGG to replace CSPDarkNet63 to enhance feature extraction capability and inference efficiency. Then, we introduce CBAM attention to FPN and PAN in the neck layer to enhance the model perception of channel and spatial features. Additionally, an anchor-based detection head is replaced by an anchor-free head to further accelerate inference. Finally, we adopt the ATSS algorithm to adapt the allocating strategy of positive and negative samples during training to further enhance performance. Extensive experiments show that our model achieves precision, recall, F1 score, and mAP of 98.23%, 85.34%, 91.33%, and 98.14%, outperforming the traditional models by 4.22~6.61%, 2.89~4.65%, 3.48~5.52%, and 4.64~8.04%, respectively. Moreover, this model has a near-real-time inference speed, which provides technical support for deploying on agriculture devices. This study can reduce labor costs associated with the detection and prevention of tea leaf diseases. Additionally, it is expected to promote the integration of rapid disease detection into agricultural machinery in the future, thereby advancing the implementation of AI in agriculture.
Share and Cite
MDPI and ACS Style
Wang, J.; Li, M.; Han, C.; Guo, X.
YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease Detection. Agriculture 2024, 14, 1240.
https://doi.org/10.3390/agriculture14081240
AMA Style
Wang J, Li M, Han C, Guo X.
YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease Detection. Agriculture. 2024; 14(8):1240.
https://doi.org/10.3390/agriculture14081240
Chicago/Turabian Style
Wang, Jingyu, Miaomiao Li, Chen Han, and Xindong Guo.
2024. "YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease Detection" Agriculture 14, no. 8: 1240.
https://doi.org/10.3390/agriculture14081240
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