Construction and verification of machine vision algorithm model based on apple leaf disease images

Frontiers in Plant Science

Plant Science

Zhang Yue¹  Gao Ang¹  Han Xiang¹  Ren Han¹  Song Yuepeng²  Ren Longlong² 

[1] College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an, Shandong, China;College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an, Shandong, China;Key Laboratory of Horticultural Machinery and Equipment of Shandong Province, Shandong Agricultural University, Tai’an, Shandong, China;Intelligent Engineering Laboratory of Agricultural Equipment of Shandong Province, Shandong Agricultural University, Tai’an, Shandong, China;

关键词: apple leaf disease;    deep learning;    deep separable convolution;    re-parameterization;    leaf detection network;   

DOI  :  10.3389/fpls.2023.1246065

 received in 2023-06-24, accepted in 2023-08-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

Apple leaf diseases without timely control will affect fruit quality and yield, intelligent detection of apple leaf diseases was especially important. So this paper mainly focuses on apple leaf disease detection problem, proposes a machine vision algorithm model for fast apple leaf disease detection called LALNet (High-speed apple leaf network). First, an efficient sacked module for apple leaf detection, known as EALD (efficient apple leaf detection stacking module), was designed by utilizing the multi-branch structure and depth-separable modules. In the backbone network of LALNet, (High-speed apple leaf network) four layers of EALD modules were superimposed and an SE(Squeeze-and-Excitation) module was added in the last layer of the model to improve the attention of the model to important features. A structural reparameterization technique was used to combine the outputs of two layers of deeply separable convolutions in branch during the inference phase to improve the model’s operational speed. The results show that in the test set, the detection accuracy of the model was 96.07%. The total precision was 95.79%, the total recall was 96.05%, the total F1 was 96.06%, the model size was 6.61 MB, and the detection speed of a single image was 6.68 ms. Therefore, the model ensures both high detection accuracy and fast execution speed, making it suitable for deployment on embedded devices. It supports precision spraying for the prevention and control of apple leaf disease.

【 授权许可】

Unknown   
Copyright © 2023 Ang, Han, Yuepeng, Longlong, Yue and Xiang