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A method and system for intelligent disease identification based on CNN and transfer learning

A technology of intelligent recognition and transfer learning, applied in the field of intelligent recognition, can solve problems such as occupying large operating memory and computing time, increasing model complexity and development costs, and poor computing performance, and achieves low image quality requirements, fast computing speed, Good universal effect

Active Publication Date: 2020-08-04
AGRI INFORMATION & RURAL ECONOMIC INST SICHUAN ACAD OF AGRI SCI
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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 of two-class classification, but in the application of disease identification in the agricultural field, it is generally necessary to solve multi-class classification problems. The classic SVM algorithm needs to be combined with other algorithms to achieve multi-class classification. Classification problems, increasing model complexity and development costs

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  • A method and system for intelligent disease identification based on CNN and transfer learning
  • A method and system for intelligent disease identification based on CNN and transfer learning
  • A method and system for intelligent disease identification based on CNN and transfer learning

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no. 1 example

[0054] In this embodiment, an intelligent disease identification system based on CNN and transfer learning is an algorithm model combining convolutional neural network and transfer learning to realize image intelligent diagnosis of common plant diseases. The plant in the present embodiment is example with corn, and this system is with corn common 10 classes (big spot, small spot, round spot, gray spot, stem rot, common rust, silk smut, ear rot disease, Curvularia leaf spot, sheath blight) diseases as the research object, according to the identification results, this system can accurately identify 10 common corn diseases, the correct rate can reach more than 90%, which provides an effective way for the prevention and control of corn diseases technical support.

[0055] See figure 1 , figure 1 It is a flowchart of the architecture of the identification system in this embodiment, which is mainly divided into three layers:

[0056] Service layer: It is used to display the resul...

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Abstract

The present invention provides a disease intelligent identification method and system based on CNN and transfer learning, which can reduce the interference of picture background, can achieve higher identification accuracy with a limited number of samples, and support multi-classification of training samples with higher operating efficiency , the disease image recognition method includes the following steps: image preprocessing, normalizing the size of the image, and then using Faster-RCNN to quickly locate the disease area to remove background interference; image feature extraction, using the triplet similarity measurement model to extract image features, and then using SIFT features are used as compensation features for weighted fusion; for disease classification and recognition, the deep convolutional neural network is used to learn the first image features of normal plant images, and then transfer learning is used to learn the second image features of diseased plant images, and finally combined with the first image features Classify and identify with 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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/70G06T7/12G06N3/04G06N3/08
CPCG06T7/0002G06T7/70G06T7/12G06N3/08G06T2207/30188G06N3/045
Inventor 曹艳刘永波胡亮唐江云雷波
Owner AGRI INFORMATION & RURAL ECONOMIC INST SICHUAN ACAD OF AGRI SCI
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