Mobile-CANet: a new high-precision and lightweight method for rice pest classification

Junyu Wei; Yuxin Wang; Xinrui Wu; Liang Shi; Hanyin Lai; Ju Li

doi:10.1117/12.3026599

27 March 2024 Mobile-CANet: a new high-precision and lightweight method for rice pest classification

Junyu Wei, Yuxin Wang, Xinrui Wu, Liang Shi, Hanyin Lai, Ju Li

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131052A (2024) https://doi.org/10.1117/12.3026599
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

Rice pests can adversely impact the quality and productivity of rice, and proper identification of pests is beneficial for growers to taking prompt steps to manage pests. Although the current crop pest identification model has achieved a high recognition accuracy in recent years with the application of deep learning, it is challenging to deploy on the edge devices with limited computing resources and low performance, and it cannot meet the needs of practical agricultural production. Therefore, we propose an effective real-time lightweight pest detection method, Mobile-CANet, which is based on the convolutional neural networks. This study develops a new network structure to enhance the feature extraction capabilities in order to address the issue of location information being lost during feature extraction. The capacity to extract features is enhanced by this network structure, which also incorporates transfer learning to enhance generalization ability. The results of the experiments show that this method's accuracy and number of parameters are superior to those of the existing picture classification models. On the publicly available dataset of rice pests, it can reach 85.3% accuracy with only 3.312M parameters, which can offer technical assistance for the implementation of mobile algorithms in the future.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Junyu Wei, Yuxin Wang, Xinrui Wu, Liang Shi, Hanyin Lai, and Ju Li "Mobile-CANet: a new high-precision and lightweight method for rice pest classification", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131052A (27 March 2024); https://doi.org/10.1117/12.3026599

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KEYWORDS

Feature extraction

Data modeling

Education and training

Deep learning

Performance modeling

Agriculture

Image classification

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