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Pest image classification method based on grading-prediction convolutional neural network

A technology of convolutional neural network and classification method, which is applied in the field of pest image classification based on hierarchical prediction convolutional neural network, can solve the problem of low correct rate of pest image classification, achieve rich feature expression and denoising ability, and reduce noise interference Effect

Active Publication Date: 2017-08-04
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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Problems solved by technology

[0004] The purpose of the present invention is to solve the defect of low correct rate of pest image classification in the prior art, and to provide a pest image classification method based on hierarchical prediction convolutional neural network to solve the above problems

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  • Pest image classification method based on grading-prediction convolutional neural network
  • Pest image classification method based on grading-prediction convolutional neural network
  • Pest image classification method based on grading-prediction convolutional neural network

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Embodiment Construction

[0040] In order to have a further understanding and understanding of the structural features of the present invention and the effects achieved, the preferred embodiments and drawings are used in conjunction with detailed descriptions, which are described as follows:

[0041] Such as figure 1 As shown, a pest image classification method based on hierarchical prediction convolutional neural network according to the present invention includes the following steps:

[0042] The first step is to collect and preprocess the training images. Several images are collected as training images, and all training images are processed for size normalization and processed into 256×256 pixels to obtain several training samples.

[0043] The second step is to label the image sample data. Manually annotate the content of the sample image, mark the image segmentation boundary, category and pest type, divide the image into three categories: pest, crop, and background, and combine the training samples as ...

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Abstract

The invention relates to a pest image classification method based on a grading-prediction convolutional neural network. Compared to the prior art, by using the method of the invention, a defect that a pest image classification correct rate is low is overcome. The method comprises the following steps of collecting and preprocessing training images; marking image sample data; training a classification model based on the grading-prediction convolutional neural network; preprocessing images to be tested; and based on a grading model, automatically carrying out pest image classification. In the invention, a grading prediction framework is adopted; a segmentation result of the images is predicted firstly and then an integral image is combined; and final classification prediction is performed.

Description

Technical field [0001] The invention relates to the technical field of predictive classification, in particular to a pest image classification method based on a hierarchical predictive convolutional neural network. Background technique [0002] Pests are the main enemy in the growth of crops. They occur throughout the growth period of crops and can cause a large reduction in crop production. The current pest identification and classification work is mainly done by a few plant protection experts and agricultural technicians. However, there are so many kinds of pests that every plant protection expert can only identify some of them. There are more and more signs that the contradiction between the increasing demand for pest classification and the relatively small number of pest classification experts has intensified. Nowadays, in the field of pattern recognition, deep learning-based learning algorithms have become a hot topic for many scholars, and they are widely used in the fiel...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/241G06F18/214
Inventor 余健谢成军张洁李瑞陈天娇陈红波王儒敬宋良图
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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