https://doi.org/10.4081/jae.2024.1594

Grape detection in natural environment based on improved YOLOv8 network

Early Access
    Published:20 August 2024
    Automatic grape picking, disadvantages-enhance, EMA, grape detection, smart agriculture, YOLOv8
    Abstract Views: 134
    PDF: 73
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    Authors

    Meng Junjie
    Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.
    Cao Ziang
    Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.
    Guo Dandan
    Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.
    Wang Yuwei
    Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.
    Zhang Dashan
    Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.
    Liu Bingyou
    Key Laboratory of Electric Drive and Control of Anhui Province, Anhui Polytechnic University, Wuhu, Ahhui, China.
    Hou Wenhui
    hwh303@ahau.edu.cn
    Anhui Provincial Engineering Laboratory of Intelligent Agricultural Machinery, School of Engineering, Anhui Agriculture University, Hefei, Anhui, China.

    In the pursuit of intelligent and efficient grape picking, rapid and precise detection of grape locations serves as the fundamental cornerstone. However, amidst the natural environment, grape detection encounters various interference factors, such as fluctuating light intensity, grape leaf obstructions, and grape overlap, all of which can undermine detection accuracy. To address these challenges, this study proposes a grape detection method leveraging an enhanced YOLOv8 network, wherein the conventional CIoU is replaced with Wise-IoU to augment network precision. Additionally, an efficient multi-scale attention module is introduced to heighten the network's focus on grapes. To expedite detection, the original network backbone is substituted with the CloFormer_xxs network. The collected grape images undergo preprocessing to ensure image quality, forming the basis of the dataset. Furthermore, the dataset is augmented using disadvantages-enhance, a novel data enhancement mode, thereby enhancing the robustness and generalization of network. The comprehensive comparison and ablation experiments are conducted to demonstrate the advantageous effects of the proposed modules on the network. Subsequently, the improved network's superiority in grape detection is validated through comparative analyses with other networks, showcasing superior accuracy and faster detection speeds. The network achieves a remarkable accuracy of 92.1%, average accuracy of 94.7%, with preprocessing and post-processing times of 15ms and 0.8ms, respectively. Consequently, the enhanced network presented in this study offers a viable solution for facilitating intelligent and efficient grape picking operations.

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    How to Cite

    Junjie, M. (2024) “Grape detection in natural environment based on improved YOLOv8 network”, Journal of Agricultural Engineering. doi: 10.4081/jae.2024.1594.
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