Crop disease identification method based on deep fusion convolutional network model

Abstract

The invention discloses a crop disease identification method based on a deep fusion convolutional neural network. An IR _ CNN model provided by the method is formed by cascading effective modules in Inception v1 and ResNet50, and can be used for respectively extracting crop disease image diversity and deep features and fusing the crop disease image diversity and the deep features. The IR _ CNN model module is composed of neural networks with different branches, so that the width of the overall network is increased; full connection or even general convolution is converted into sparse connection, so that the calculated amount of the network is reduced. According to the method of the invention, the feature extraction capabilities of different network models are combined, so that x diversity features and deep features in crop disease images can be better obtained, the features are fused subsequently; various disease categories of different crops, especially complex crop diseases, can be better identified through training learning. The method has high identification precision.

Description

technical field [0001] The invention belongs to the technical field of image recognition, and in particular relates to a crop disease recognition method based on a deep fusion convolution network model. Background technique [0002] Crops are various plants cultivated by humans in agricultural production activities. They are divided into two categories: food crops and economic crops. No matter what kind of crops they are, they are of vital value to the country and the people. However, due to the effects of different environments, crops will be subjected to a series of violations such as bad weather, external fungi, and human factors during their growth, which has a very bad impact on the production of crops. Therefore, crop diseases not only restrict the stable and high yield of agricultural products in our country, but also affect the quality of agricultural products. In recent years, crop diseases have brought huge losses to my country's agricultural production. According...

AI Technical Summary

Problems solved by technology

However, in the convolutional neural network model structure, as the number of network layers deepens, deeper features can be extracted, but some shallow features may be lost during training; and as the network widens, more features can be extracted. A shallow feature and reduce the amount of parameters to a certain extent, but it is difficult to extract deeper features

In addition, because the disease area information in crop images is too complex, the existing CNN (convolutional neural network) model has certain defects in the identification of various crop diseases

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