LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity - Hu, Jian; Jiang, Xinhua; Gao, Julin; Yu, Xiaofang (Frontiers in Plant Science, 2024)
The identification of maize leaf diseases in natural environment is faced with many important challenges, such as complex background interference, high similarity of disease spots between classes, real-time and so on. In order to overcome these challenges, we propose 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.