(Retracted) Image recognition technology of crop diseases based on neural network model fusion

Abstract

The neural network is an important model of machine learning that utilizes a hierarchical structure composed of multiple neurons to fit complex nonlinear classification surfaces. Deep learning is a new direction in the field of machine learning. Learning the inherent laws and representation levels of sample data, the information obtained during the learning process plays a great role in interpreting data such as images and speech. The crop disease image recognition technology based on the neural network model is studied to develop a system with an improved accuracy. First, we analyze the background and significance of image recognition technology applied to crops; then, on the basis of understanding the general background, the pattern recognition method based on the image is deeply studied; and, finally, the experimental design of crop disease image recognition based on convolutional neural network (CNN) is tested. The experimental results show that, in the problem of disease identification, compared with a single CNN model, the proposed model further improves the highest verification accuracy. The integration of DenseNet and the visual geometry group model has the best effect on cucumber and rice disease identification, and the verification accuracy can reach 96.24%.