Mr. Xinrui Wu Profile

Xinrui Wu

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Proceedings Article | 27 March 2024 Paper

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, 131052A (2024) https://doi.org/10.1117/12.3026599

KEYWORDS: Feature extraction, Education and training, Data modeling, Performance modeling, Deep learning, Agriculture, Neural networks, Image classification, Image processing, Convolution

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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.

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