CNN and transfer learning-based disease intelligent identification method and system

Abstract

The invention provides a CNN and transfer learning-based disease intelligent identification method and system, which can reduce interference of a picture background, can achieve high recognition accuracy under the condition of a limited sample number, and supports higher training sample multi-classification operation efficiency. The disease image identification method comprises the following steps: image preprocessing: normalizing the size of the image, quickly positioning a disease region by using Fast-RCNN, and eliminating background interference; image feature extraction: using a triplet similarity measurement model to extract image features, and then using SIFT features as compensation features to perform weighted fusion; and disease classification and recognition: learning a first image feature of a normal plant image by adopting a deep convolutional neural network, then learning a second image feature of the disease plant image by using transfer learning, and finally performing classification and recognition by combining the first image feature and the second image feature.

Description

technical field [0001] The invention relates to the field of intelligent identification, in particular to a method and system for intelligent identification of diseases based on CNN and transfer learning. Background technique [0002] At present, there are many technologies that can be used for recognition in the field of computer vision, and the two most commonly used in the field of agriculture are the OpenCV vision algorithm library based on image morphology and the SVM support vector machine. [0003] The OpenCV vision algorithm starts with the color and shape features of the disease graph, extracts the disease features in the visible light range, and then classifies and processes the images according to the features to realize the diagnosis. This type of method has extremely high requirements on the quality of disease images, and different lighting and backgrounds will affect the recognition results. This method has poor recognition accuracy, weak anti-noise ability, an...

AI Technical Summary

Problems solved by technology

There are two disadvantages in this method: (1) This method uses quadratic programming to solve the support vector. When the number of samples is large, the solution process of the matrix will take up a lot of running memory and computing time, and the computing performance is poor.

(2) The classic support vector machine algorithm only gives the algorithm o

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