A method for identifying fruit tree diseases and pests under complex background based on cavity convolution

Abstract

A method for identifying diseases and pests of fruit trees under complex background based on cavity convolution is disclosed, At first, that original train set with sample labels is obtain by collecting images of diseases and insect pests of fruit tree leaves, and then the expanded train sample set is obtained by data augmentation operation, and then the average value is obtained, all the originaltraining set images are averaged, and then randomly scrambled to form a training data set; Then, the cavity convolution neural network model is established, and the disease classification training iscarried out by using the training data set. The characteristic image of the input image is extracted by the cavity convolution layer, and the nonlinear characteristic image of the input image is obtained by using the nonlinear activation function. Pooling layer is used to reduce the convolution layer weight parameters. Using multi-scale convolution kernel to obtain different features of the inputimage, and finally using Soft Max classifier to classify the disease types of the input image; The invention shortens the training time of the model, accelerates the recognition speed and improves the recognition accuracy.

Description

technical field [0001] The invention relates to the technical field of agricultural disease and insect pest identification, in particular to a method for identifying fruit tree disease and insect pests in complex backgrounds based on atrous convolution. Background technique [0002] Fruit tree pests and diseases are key factors affecting crop yield and quality. Since there are tens of thousands of types of fruit tree pests and diseases, accurate identification of different pests and diseases is a prerequisite for prevention and control. The traditional way of identifying pests and diseases mainly relies on personal experience for identification, which is time-consuming and laborious with low accuracy. In recent years, with the development of technologies such as computer vision and pattern recognition, researchers have achieved some success in identifying pests and diseases by using deep learning and machine learning methods. Although the current pest identification method ...

AI Technical Summary

Problems solved by technology

At present, dilated convolution has not been applied to the identification of fruit tree diseases and insect pests in complex backgrounds

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