Lightweight identification model for apple leaf diseases and pests based on mobile terminals
Bin Liu¹, Runchang Jia¹, Xianyu Zhu¹, Cong Yu¹, Zhuohan Yao¹, Haixi Zhang¹, Dongjian He²
¹College of Information Engineering, Northwest A&F University, Yangling, China
²College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, China
Published in Transactions of the Chinese Society of Agricultural Engineering (TCSAE), 2022
Keywords: mobile terminals, ALS-Net, apple leaf diseases, lightweight model
Abstract
This paper presents a lightweight identification model for apple leaf diseases and pests based on mobile terminals. The proposed ALS-Net architecture combines depthwise separable convolution and channel shuffle to achieve high accuracy while maintaining low computational complexity. The model is specifically designed for deployment on mobile devices with limited computational resources. Experimental results demonstrate the effectiveness of the proposed approach in real-world agricultural applications.
Recommended citation: Bin Liu, Runchang Jia, Xianyu Zhu, Cong Yu, Zhuohan Yao, Haixi Zhang, Dongjian He, "Lightweight identification model for apple leaf diseases and pests based on mobile terminals." Transactions of the Chinese Society of Agricultural Engineering, 2022.
@article{liu2022lightweight,
title={Lightweight identification model for apple leaf diseases and pests based on mobile terminals},
author={Liu, Bin and Jia, Runchang and Zhu, Xianyu and Yu, Cong and Yao, Zhuohan and Zhang, Haixi and He, Dongjian},
journal={Transactions of the Chinese Society of Agricultural Engineering},
year={2022},
publisher={Chinese Society of Agricultural Engineering}
}