Plant Diseases Identification Based on Binarized Neural Network

Abstract

Although the use of the convolutional neural network (CNN) improved the accuracy of object recognition, it still had a long-running time. In order to solve these problems, the training and testing datasets were split at four different proportions to reduce the impact of inherent error. Using model fine-tuning, the model converged in a small number of iterations, and the average recognition accuracy of BWN test can reach 96.8%. In the segmented dataset, the recognition accuracy of the former was 4.7 percentage points higher than the latter by comparing color dataset and grayscale dataset, which proved that a certain amount of color features will have a positive impact on the model. The segmented dataset was 0.9 percentage points higher than the color dataset; it shows that the model focused more on features of contour and texture by eliminating the background of images. The experiments showed that the binarized convolutional neural network can effectively improve recognition efficiency and accuracy compared with traditional methods.