LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity

Jian Hu; Xinhua Jiang; Julin Gao; Xiaofang Yu

doi:10.3389/fpls.2024.1368697

LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity

Front Plant Sci. 2024 Apr 23:15:1368697. doi: 10.3389/fpls.2024.1368697. eCollection 2024.

Authors

Jian Hu^{1

2

3}, Xinhua Jiang^{1

2}, Julin Gao⁴, Xiaofang Yu⁴

Affiliations

¹ College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.
² Key Laboratory of Agricultural and Animal Husbandry Big Data Research and Application, Inner Mongolia Autonomous Region, Hohhot, China.
³ Department of Information Engineering, Inner Mongolia Technical College of Mechanics and Electrics, Hohhot, China.
⁴ College of Agricultural, Inner Mongolia Agricultural University, Hohhot, China.

Abstract

Maize leaf diseases significantly impact yield and quality. However, recognizing these diseases from images taken in natural environments is challenging due to complex backgrounds and high similarity of disease spots between classes.This study proposes a lightweight multi-level attention fusion network (LFMNet) which can identify maize leaf diseases with high similarity in natural environment. The main components of LFMNet are PMFFM and MAttion blocks, with three key improvements relative to existing essential blocks. First, it improves the adaptability to the change of maize leaf disease scale through the dense connection of partial convolution with different expansion rates and reduces the parameters at the same time. The second improvement is that it replaces a adaptable pooling kernel according to the size of the input feature map on the original PPA, and the convolution layer to reshape to enhance the feature extraction of maize leaves under complex background. The third improvement is that it replaces different pooling kernels to obtain features of different scales based on GMDC and generate feature weighting matrix to enhance important regional features. Experimental results show that the accuracy of the LFMNet model on the test dataset reaches 94.12%, which is better than the existing heavyweight networks, such as ResNet50 and Inception v3, and lightweight networks such as DenseNet 121,MobileNet(V3-large) and ShuffleNet V2. The number of parameters is only 0.88m, which is better than the current mainstream lightweight network. It is also effective to identify the disease types with similar disease spots in leaves.

Keywords: attention mechanism; complex background; identification of maize leaf diseases; lightweight model; multi-level.

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by Inner Mongolia Natural Science Foundation under Grant 2023LHMS06017, the National Natural Science Foundation of China under Grant 62061037, the National Natural Science Foundation of China under Grant 31960494, Inner Mongolia Autonomous Region Science and Technology Major under Grant 2021ZD003.