Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Grape disease and pest identification method and device based on deep learning

A technology of deep learning and recognition methods, applied in character and pattern recognition, image data processing, instruments, etc., can solve problems such as difficulty in identifying pests and diseases, low efficiency of manual detection, and not widely used, so as to save manpower and broad market application Foreground, the effect of improving detection accuracy and speed

Active Publication Date: 2020-05-05
CHANGAN UNIV
View PDF9 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Since the last century, research at home and abroad has mainly used physical mechanisms to identify grape diseases and insect pests, mainly including acoustic detection, trapping, near-infrared, etc. However, due to low efficiency of manual detection and noise interference, these methods are difficult to meet the requirements of identification
[0004] With the rapid development of computer vision technology, many scholars use machine learning methods to identify grape diseases and insect pests, but the model is complex and not widely used
Deep learning methods have been widely used in the field of identification of grape diseases and insect pests, but the recognition rate of simple deep learning methods in complex backgrounds is low

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
  • Grape disease and pest identification method and device based on deep learning
  • Grape disease and pest identification method and device based on deep learning
  • Grape disease and pest identification method and device based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0049] like figure 1 Shown is a flowchart of a method for identifying grape diseases and insect pests based on deep learning proposed by the present invention.

[0050] refer to figure 1 , a method for identifying grape diseases and insect pests based on deep learning, comprising the following steps:

[0051] Step 101, processing the acquired grape strain image to obtain image feature information;

[0052] Step 102, analyzing the feature information of the image to extract the feature information of diseases and insect pests;

[0053] Step 103, comparing the extracted information on pests and diseases with the preset data feature database to obtain the types of pests and diseases of grapes.

[0054] The present invention uses the method of deep learning for the detection of diseases and insect pests, replaces the manual detection of grape diseases and insect pests, effectively reduces the diagnostic errors caused by manual subjectivity, saves a lot of labor costs, and impro...

Embodiment 2

[0091] The present embodiment provides a grape disease and insect pest identification device based on deep learning, including:

[0092] The image feature processing module 201 is used to process the acquired grape strain image to obtain image feature information;

[0093] The pest analysis module 202 is used to analyze the feature information of the image and extract the feature information of the pest;

[0094] The pest type judging module 203 is used to compare the extracted pest information with the preset data feature database to obtain the type of grape pests.

[0095] This embodiment provides a device for identifying grape diseases and insect pests based on deep learning. Through the foregoing detailed description of a method for identifying grape diseases and insect pests based on deep learning, those skilled in the art can clearly know about a method for identifying grape diseases and insect pests based on deep learning in this embodiment. The specific structure and ...

Embodiment 3

[0097] see Figure 5 , is a flow chart of a specific manner of an embodiment proposed by the present invention.

[0098] Such as Figure 5 As shown, a kind of grape disease and insect pest recognition method based on deep learning that the present invention adopts comprises the following steps:

[0099] Step 1: Process the image of the grape plant, and obtain the image feature as Char=[YP, GS, GG, YB, XS, JX, TT], where YP is the blade, GS is the fruit, YB is the petiole, XS is the shoot, JX is tendril and TT is rattan. The specific operation is:

[0100] Step 11: Segment the grape plant image based on the Graph-Based Segmentation image segmentation algorithm. The specific operation is as follows:

[0101] Step 111: Calculate the degree of dissimilarity between each pixel on the grape plant image and its 8 or 4 neighbors;

[0102] see Figure 4 , the solid line is to calculate only 4 domains, and the dotted line is to calculate 8 neighborhoods. Since it is an undirected...

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 discloses a grape disease and insect pest identification method based on deep learning. The method comprises the following steps: processing an acquired grape plant image to obtain imagefeature information; analyzing the image feature information to extract disease and pest feature information; and comparing the extracted disease and pest information with a preset data feature library to obtain a grape disease and pest type. The invention further provides a grape disease and insect pest recognition device based on deep learning. According to the invention, a deep learning methodis used for pest detection so that manual detection of grape diseases and insect pests is replaced, diagnosis errors caused by manual subjectivity are effectively reduced, a large amount of labor cost is saved, the accuracy and detection speed of grape disease and insect pest detection are improved, the working efficiency of grape planters is effectively improved, a large amount of manpower and material resources are saved, and the method has very wide market application prospects.

Description

technical field [0001] The invention relates to the field of identification of grape diseases and insect pests, in particular to a method and device for identification of grape diseases and insect pests based on deep learning. Background technique [0002] Grape diseases and insect pests are one of the main natural disasters that affect grape production. It is a major natural disaster encountered during grape growth, which seriously affects grape production, quality and benefits. [0003] Since the last century, research at home and abroad has mainly used physical mechanisms to identify grape diseases and insect pests, mainly including acoustic detection, trapping, and near-infrared. [0004] With the rapid development of computer vision technology, many scholars use machine learning methods to identify grape diseases and insect pests, but the models are complex and not widely used. Deep learning methods have been widely used in the field of identification of grape diseases...

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
IPC IPC(8): G06T7/00G06T7/162G06K9/62
CPCG06T7/0012G06T7/162G06T2207/20081G06T2207/20084G06T2207/20072G06F18/2411
Inventor 李颖杨晓萌金彦林李海峰杨润佳康佳园杨向东
Owner CHANGAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

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

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com