YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease Detection

Jingyu Wang, Miaomiao Li, Chen Han and Xindong Guo ()
Additional contact information
Jingyu Wang: College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Miaomiao Li: College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Chen Han: College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China
Xindong Guo: College of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China

Agriculture, 2024, vol. 14, issue 8, 1-20

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.

Keywords: tea leaf diseases; disease detection; YOLOv8-RCAA; CBAM attention mechanism; RepVGG (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/14/8/1240/pdf (application/pdf)
https://www.mdpi.com/2077-0472/14/8/1240/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:14:y:2024:i:8:p:1240-:d:1444163

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().