An Improved Crop Disease Identification Method Based on Lightweight Convolutional Neural Network

Abstract

Identifying crop disease fast, intelligently and accurately, plays a vital role in agricultural informatization development, while existing methods are almost performed manually, which depends on expert experience, and thus the identifying result is inevitably influenced by personal preferences. To address these issues, an improved crop disease identification method based on convolutional neural network is proposed to process images of crops for identifying diseases. Firstly, the original crop images were cut and normalized, and the irrelevant noises were removed by image data enhancement to improve the generalization ability and recognition accuracy of the training network. Then a neural network with nine convolutional layers is built to work on crop images, the first stage of training loads data samples, and divide the training set and verification set, and then set the learning rate, image intensifier, and optimizer and compile the training convolution model. Finally, it saves the loss and accuracy data during the training process and evaluates the accuracy of the model. In order to improve the training learning rate, Adam optimizer combining momentum algorithm and RMSprop algorithm is used to dynamically adjust the learning rate; the combination of the two algorithms makes the loss function converge to the lowest point faster. Then the feature map after each convolution is obtained by using transferred revolution, and the model is adjusted according to the feature map to further improve the effect of model recognition. Finally, validations were carried out by PlantVillage dataset, which consists of images of about 38 kinds of crops. The experiment result shows that the validation accuracy and the test accuracy are 95.7% and 94.3%, respectively; in addition, the recognition accuracy of apple, corn, grape, and other single classes is about 97%, which proves that the convolutional neural network in this paper has faster training speed and higher accuracy. In addition, the proposed method is less time consuming, which is of great significance to promote the development of smart agriculture and precision agriculture.