Patent
Crop disease identification method based on deep fusion convolutional network model
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TLDR
In this article, a deep fusion convolutional neural network (IR-CNN) was proposed for crop disease identification using feature extraction capabilities of different network models, so that x diversity features and deep features in crop disease images could 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.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.read more
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