Automatic Identification of Soybean Leaf Diseases Based on UAV Image and Deep Convolution Neural Network

In order to realize fast and efficient classification and recognition of soybean leaf disease images under complex field background, convolution neural network models with different depth were compared and evaluated with accuracy, training time and learning error as performance indexes of deep learning model.Firstly, the soybean leaf disease image data set collected by UAV was divided into training set and test set according to the ratio of 7∶3.In order to expand the data image, the original image of training set was enhanced.Four kinds of deep neural network models, including perception-v3,vgg-19,resnet-50 and xception, were used to test the models based on different weights of fine-tuning and transfer learning training strategies, and field validation experiments were carried out.The results showed that the accuracy of perception-v3 model was the highest（99.04%） under the FT 75% training strategy; Compared with other models, FT 100% and FT 75% deep learning models showed higher accuracy and lower learning error, but the training time was longer.The trained perception-v3 ft 75% model was applied to the computer vision system, and the results showed that the system could effectively realize the intelligent recognition of soybean leaf diseases under the complex field background.