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A Rice Pest Identification Method Based on Improved Residual Network

A recognition method and technology of pests, applied in the field of deep learning, can solve problems such as information loss, achieve high recognition accuracy, improve recognition accuracy, and enrich image feature information

Active Publication Date: 2022-05-20
SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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AI Technical Summary

Problems solved by technology

Embed the capsule network into the residual network to build an improved residual network model, which can solve the problem of information loss in the process of building the network

Method used

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  • A Rice Pest Identification Method Based on Improved Residual Network
  • A Rice Pest Identification Method Based on Improved Residual Network
  • A Rice Pest Identification Method Based on Improved Residual Network

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Embodiment Construction

[0059] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0060] The rice pest identification method based on the improved residual network of the embodiment of the present invention comprises the following steps:

[0061] (1) Input training data set

[0062] In 2019, Wu Xiaoping and others published a large-scale pest recognition dataset IP102, and carried out professional image annotation work. The data set category is still hierarchical, divided into 8 major crop categories and 102 pest subcategories. IP102 is the largest pest identification data set so far, containing 75,000 pest samples, and its categories almost include the most common pest specie...

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Abstract

The invention discloses a rice pest identification method based on an improved residual network. The method includes the following steps: a training stage: step 1: acquiring a large-scale pest identification data set; step 2: preprocessing the pest identification data set, Including: rotation, flip, illumination processing, contrast processing, color balance processing and sharpness processing; Step 3: Build an image classification network model, that is, an improved residual network model; Step 4: Divide the pest identification data set into a certain proportion Training set and test set, train the constructed image classification network model through the training set, and save the trained image classification network model; test stage: Step 5: Input the test set image into the trained and improved residual network model for Rice pest identification, output the accuracy of identification results. The invention can make up for the defect that the residual network loses a lot of information when outputting, and improve the recognition accuracy of the model.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a rice pest identification method based on an improved residual network. Background technique [0002] In recent years, with the rise of artificial intelligence, deep learning has been widely concerned and applied in the fields of computer vision, natural language processing, and emotional computing. Many researchers have applied deep learning to the field of agriculture, and in the identification of crop pests Preliminary explorations have been made. [0003] At present, the convolutional neural network has been widely used in the field of image recognition, and the representative networks mainly include AlexNet, VGG, GoogleNet, ResNet and DenseNet. Based on the above network models, many improved network models have been proposed, which are compared with conventional However, in the process of constructing a deep convolutional neural network, when the gradient signal is ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V10/764G06V10/84G06V10/82G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/045G06F18/24133G06F18/29
Inventor 郑禄陈楚雷建云帖军田莎莎张慧丽单一鸣牛悦
Owner SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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