Recognition Method of Tomato Leaf Diseases Based on Deep Residual Network

Intelligent recognition of greenhouse vegetable diseases plays an important role in the efficient production and management. The color, texture and shape of some diseases in greenhouse vegetables are often very similar, it is necessary to find a method to judge vegetable diseases. Based on the massive image data of greenhouse vegetable diseases, the depth learning model can automatically extract image details, which has better disease recognition effect than the artificial design features. For the traditional deep learning model of vegetable disease image recognition, the model recognition accuracy can be improved by increasing the network level. However, as the network level increases to a certain depth, it will lead to the degradation/disappearance of the network gradient, which degrades the recognition performance of the learning model. Therefore, a method of vegetable disease identification based on deep residual network model was studied in this paper. Firstly, considering that the super parameter value in the deep network model had a great influence on the accuracy of network identification, Bayesian optimization algorithm was used to autonomously learn the hyper-parameters such as regularization parameters, network width, stochastic momentum, et al, which were difficult to determine in the network, eliminate the complexity of manual parameter adjustment, reduce the difficulty of network training and save the time of network construction. On this basis, the gradient could flow directly from the latter layer to the former layer through the identical activation function by adding residual elements to the traditional deep neural network. The deep residual recognition model took the whole image as the input, and obtained the optimal feature through multi-layer convolution screening in the network, which not only avoided the interference of human factors, but also solved the problem of the performance degradation of the disease recognition model caused by the deep network, and realizes the high-dimensional feature extraction and effective disease recognition of the vegetable image. Relevant simulation results showed that compared with other traditional models for vegetable disease identification, the deep residual neural network showed better stability, accuracy and robustness. The deep residual network model based on hyperparametric self-learning achieved good recognition performance on the open data set of tomato diseases, and the recognition accuracy of 4 common diseases of tomato leaves reached more than 95%. The researth could provide a basic method for fast and accurate recognition of tomato leaf diseases