Recognition of field maize leaf diseases based on improved regional convolutional neural network

Objective  To realize intelligent diagnosis of maize leaf diseases with similar spots and complicated background in real field conditions by introducing and improving a regional convolutional neural network algorithm, Faster R-CNN.

Method  We obtained 1 150 maize leaf images with complicated background for nine kinds of common diseases from maize field and public dataset websites. After manual annotation of the original images, offline data augmentation was used to enlarge the image data. The Faster R-CNN algorithm was introduced and improved for adaptive application by adding batch normalization processing layer and introducing center cost function to improve the identification accuracy of similar disease spots. We used the stochastic gradient descent algorithm to train and optimize this model. Four pre-trained convolution structures for feature extraction were selected and compared in Faster R-CNN training and testing to get the most optimal model. During the test, the trained model was used to select test sets under different weather conditions for comparison, and improved Faster R-CNN was also compared with unimproved Faster R-CNN and SSD algorithm.

Result  In the framework of improved Faster R-CNN, VGG16 convolutional feature extraction network had better performance than others. The testing image data set was used to verify the model performance, and the average precision of final recognition result was 0.971 8, the average recall rate was 0.971 9, F1 was 0.971 8, and the overall average accuracy reached 97.23%. The recognition effect under sunny conditions was better than that of cloudy conditions. The average precision of improved Faster R-CNN increased by 0.088 6 and the detection time per image decreased by 0.139 s compared with unimproved Faster R-CNN algorithm. The average precision of proposed method was 0.0425 higher than that of SSD algorithm, and the detection time per image decreased by 0.018 s. The results indicated that the improved Faster R-CNN algorithm was superior to unimproved Faster R-CNN and SSD algorithm in the field of intelligent detection of maize diseases under complex field conditions.

Conclusion  It is feasible to introduce improved Faster R-CNN algorithm into the intelligent diagnosis of maize diseases under complex field conditions, and it has higher accuracy and faster detection speed, which can avoid the subjectivity of traditional artificial identification. The proposed method lays a foundation for precise prevention and control of maize disease in field environment.