TY  - EJOU
AU  - Lu, Jianbo 
AU  - Shi, Ruxin 
AU  - Tong, Jin 
AU  - Cheng, Wenqi 
AU  - Ma, Xiaoya 
AU  - Liu, Xiaobin 

TI  - Lightweight Method for Plant Disease Identification Using Deep Learning
T2  - Intelligent Automation \& Soft Computing

PY  - 2023
VL  - 37
IS  - 1
SN  - 2326-005X

AB  - In the deep learning approach for identifying plant diseases, the high complexity of the network model, the large number of parameters, and great computational effort make it challenging to deploy the model on terminal devices with limited computational resources. In this study, a lightweight method for plant diseases identification that is an improved version of the ShuffleNetV2 model is proposed. In the proposed model, the depthwise convolution in the basic module of ShuffleNetV2 is replaced with mixed depthwise convolution to capture crop pest images with different resolutions; the efficient channel attention module is added into the ShuffleNetV2 model network structure to enhance the channel features; and the ReLU activation function is replaced with the ReLU6 activation function to prevent the generation of large gradients. Experiments are conducted on the public dataset PlantVillage. The results show that the proposed model achieves an accuracy of 99.43%, which is an improvement of 0.6 percentage points compared to the ShuffleNetV2 model. Compared to lightweight network models, such as MobileNetV2, MobileNetV3, EfficientNet, and EfficientNetV2, and classical convolutional neural network models, such as ResNet34, ResNet50, and ResNet101, the proposed model has fewer parameters and higher recognition accuracy, which provides guidance for deploying crop pest identification methods on resource-constrained devices, including mobile terminals.
KW  - Plant disease identification; mixed depthwise convolution; lightweight; ShuffleNetV2; attention mechanism

DO  - 10.32604/iasc.2023.038287