Recognition method of soybean leaf diseases using residual neural network based on transfer learning

Abstract

Leaf disease is an important factor restricting the high quality and high yield of the soybean plant. Insufficient control of soybean diseases will destroy the local ecological environment and break the stability of the food chain. To overcome the low accuracy in recognizing soybean leaf diseases using traditional deep learning models and complexity in chemical analysis operations, in this study, a recognition model of soybean leaf diseases was proposed based on an improved deep learning model. First, four types of soybean diseases (Septoria Glycines Hemmi, Soybean Brown Leaf Spot, Soybean Frogeye Leaf Spot, and Soybean Phyllosticta Leaf Spot) were taken as research objects. Second, image preprocessing and data expansion of original images were carried out using image registration, image segmentation, region calibration and data enhancement. The data set containing 53, 250 samples was randomly divided into the training set, verification set, and test set according to the ratio of 7:2:1. Third, the convolution layer weight of the pre-training model based on the ImageNet open data set was transferred to the convolution layer of the ResNet18 model to reconstruct the global average pooling layer and the fully connected layer for constructing recognition model of TRNet18 model. Finally, the recognition accuracy of the four leaf diseases reached 99.53%, the Macro-F1 was 99.54%, and the average recognition time was 0.047184 s. Compared with AlexNet , ResNet18, ResNet50, and TRNet50 models, the recognition accuracy and Macro-F1 of the TRNet18 model were improved by 6.03% and 5.99% respectively, and the model recognition time was saved by 16.67%, The results showed that the proposed TRNet18 model had higher classification accuracy and stronger robustness, which can not only provide a reference for accurate recognition of other crop diseases, but also be transplanted to the mobile terminal for recognition of crop leaf diseases. • This method used transfer learning to accelerate the learning speed of network model. • The method used residual neural network to achieve high disease recognition accuracy. • This study built a lightweight TRNet18 that can be transplanted to mobile terminals. • This method can be used as technical support for crop disease detection and prediction. • This method can be extended to the disease diagnosis of a variety of field crops.