Simulation analysis of rice disease identification method based on improved ShuffleNetV2

Huihuang Xiong; Yongzhong Hu

doi:10.1117/12.3009214

11 October 2023 Simulation analysis of rice disease identification method based on improved ShuffleNetV2

Huihuang Xiong, Yongzhong Hu

Proceedings Volume 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023); 129182C (2023) https://doi.org/10.1117/12.3009214
Event: International Conference on Computer Science and Communication Technology (ICCSCT 2023), 2023, Wuhan, China

Abstract

In the current deep learning rice disease recognition method, there are problems such as more complex models, poor adaptability to be deployed on edge devices and mobile terminals with limited computational resources, and poor real-time and accurate recognition of crop diseases, so we propose a lightweight rice disease recognition method that improves ShuffleNetV2. Based on the improved Shuffle Block unit called AugShuffle Block, the ECA (efficient channel attention) module is added, and average pooling is used instead of maximum pooling to obtain more feature information, at the same time, the number of Shuffle Block units used in each Stage module in the network structure is reduced to reduce the number of parameters in the model and the amount of computation. Experiments are conducted on the Kaggle rice disease dataset. The results show that the number of parameters of the model is 0.944 × 10⁶, the computation amount is 105.47 × 10⁶ (FLOPs), and the average accuracy of disease recognition reaches 96.02%, provides a reference for the deployment of the rice disease recognition method for applications on resource-constrained devices such as mobile terminals.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Huihuang Xiong and Yongzhong Hu "Simulation analysis of rice disease identification method based on improved ShuffleNetV2", Proc. SPIE 12918, Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023), 129182C (11 October 2023); https://doi.org/10.1117/12.3009214

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KEYWORDS

Diseases and disorders

Education and training

Neurological disorders

Convolution

Performance modeling

Cardiovascular disorders

Data modeling

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