Hu Jian, Jiang Xinhua, Gao Julin, Yu Xiaofang
College of Computer and Information Engineering, Inner Mongolia Agricultural University, Hohhot, China.
Key Laboratory of Agricultural and Animal Husbandry Big Data Research and Application, Inner Mongolia Autonomous Region, Hohhot, China.
Front Plant Sci. 2024 Apr 23;15:1368697. doi: 10.3389/fpls.2024.1368697. eCollection 2024.
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.
玉米叶部病害对产量和品质有显著影响。然而，由于自然环境下拍摄图像的背景复杂且病害斑在类别间相似度高，从这些图像中识别病害具有挑战性。本研究提出了一种轻量级多级注意力融合网络（LFMNet），它能够在自然环境中识别具有高相似度的玉米叶部病害。LFMNet的主要组件是PMFFM和MAttion模块，相对于现有的基本模块有三个关键改进。首先，通过不同扩张率的局部卷积的密集连接提高对玉米叶部病害尺度变化的适应性，同时减少参数。第二个改进是根据原始PPA上输入特征图的大小替换自适应池化核，并通过卷积层进行重塑以增强复杂背景下玉米叶片的特征提取。第三个改进是基于GMDC替换不同池化核以获得不同尺度的特征并生成特征加权矩阵以增强重要区域特征。实验结果表明，LFMNet模型在测试数据集上的准确率达到94.12%，优于现有重量级网络如ResNet50和Inception v3，以及轻量级网络如DenseNet 121、MobileNet(V3-large)和ShuffleNet V2。参数数量仅为0.88m，优于当前主流轻量级网络。它在识别叶片中具有相似病害斑的病害类型方面也很有效。
