High-precision crop pest and disease damage identification method

A recognition method and crop technology, applied in neural learning methods, character and pattern recognition, biological neural network models, etc., can solve the problems of lack of advanced semantic information, low efficiency of extraction algorithms, failure to achieve high precision, etc., and achieve good discrimination ability , Occupy small memory, fast effect

Inactive Publication Date: 2020-11-27
SICHUAN UNIV
View PDF14 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the extraction algorithm of these features is inefficient and lacks advanced semantic information, and cannot accurately extract distinguishing features for similar pests and diseases, so it cannot meet the high-precision requirements

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • High-precision crop pest and disease damage identification method
  • High-precision crop pest and disease damage identification method
  • High-precision crop pest and disease damage identification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0025] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0026] A high-precision identification method for crop diseases and insect pests, comprising the following steps:

[0027] S1. The user inputs a crop leaf of any size and scales it to a uniform size;

[0028] S2, the picture obtained in step S1 is converted to YCrCb color space by RGB passway;

[0029] S3, the 3-channel picture of YCrCb color space obtained in step S2 is merged into the original RGB space to form the input of 6 channels, and then through corresponding normalization processing, it is sent into the network;

[0030] S4. Send the data obtained in step S3 into the network structure proposed in this design, and after training, the predicted classification category and saliency map can be obtained.

[0031] In this embodiment, in orde...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention provides a high-precision crop pest and disease damage identification method, which comprises the following steps of: S1, enabling a user to input a crop leaf with any size, and scalingthe crop leaf to a uniform size; S2, converting the picture obtained in the step S1 from an RGB channel to a YCrCb color space; S3, merging the YCrCb color space three-channel picture obtained in thestep S2 into an original RGB space to form six-channel input, and then sending the six-channel input into a network through corresponding normalization processing; S4, sending the data obtained in thestep S3 into a network structure proposed by the design, and training to obtain a prediction classification category and a saliency map. The invention belongs to the field of computer vision application, considers the workload and specialty of crop disease and insect pest identification, uses a deep learning technology to replace traditional manual work to greatly reduce the cost, has the advantages of high precision, high speed and the like, and can deploy a model at mobile terminals such as a mobile phone, a tablet personal computer and the like offline to facilitate the use of a user.

Description

technical field [0001] The invention relates to the field of computer vision applications, in particular to a high-precision identification method for crop diseases and insect pests. Background technique [0002] my country has a vast area of ​​crop cultivation, among which pests and diseases have the greatest impact on crop yield. When crops suffer from pests and diseases, their normal physiological functions will be destroyed and they will not be able to grow normally, thus affecting the final yield and economic benefits. At present, the actual methods used to identify crop diseases and insect pests at home and abroad mainly include acoustic detection, trapping, near-infrared, etc. Due to the low efficiency of manual detection and noise interference, it is difficult to accurately identify the type and density of pests and diseases. [0003] The use of image recognition, image processing and other technologies to identify pests and diseases has made progress. For example, ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/20G06V10/56G06N3/045G06F18/24
Inventor 雷印杰陈浩楠王浩
Owner SICHUAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap