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

Crop pest monitoring method based on multispectral remote sensing image of deep learning

A deep learning and remote sensing image technology, applied in the field of satellite remote sensing image processing and application, can solve problems such as poor timeliness of crop pest monitoring, difficulty in obtaining hyperspectral UAV data, and unstable single index spectral information.

Active Publication Date: 2019-09-27
JILIN UNIV
View PDF6 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problems of poor timeliness of existing methods for monitoring crop pests, instability of single index spectral information, and difficulty in obtaining hyperspectral UAV data, etc.

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
  • Crop pest monitoring method based on multispectral remote sensing image of deep learning
  • Crop pest monitoring method based on multispectral remote sensing image of deep learning
  • Crop pest monitoring method based on multispectral remote sensing image of deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0060] The LSTM network is used to classify the combination of 10 characteristic bands, as shown in Table 1, the Landsat8 multispectral image with a spatial resolution of 30m is used as the experimental data, and the Landsat8 original data has 6 different bands, namely blue band, green band band, red band, near-infrared band, short-wave infrared 1 with a band range of 1.560-1.651um, and short-wave infrared 2 with a band range of 2.1-2.3um (Table 1). The experimental area is located in Nong'an City, Jilin Province ( figure 1 ), and the surrounding crops are densely planted, mostly rice, corn, soybeans and other common crops in Northeast China. After investigation and verification, the experimental area was attacked by insects in 2012. Therefore use the Landsat8 data on August 9th, 2012 to verify the effectiveness of the present invention's method for monitoring crop pests, with reference to the overall flow chart of this example ( figure 2 ).

[0061] Table 1

[0062] ...

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 crop pest monitoring method based on a multispectral remote sensing image of deep learning, and belongs to the technical field of satellite remote sensing image processing and application. The objective of the invention is to solve the problems of poor timeliness of crop pest monitoring, unstable single-index spectral information, difficulty in acquiring hyperspectral unmanned aerial vehicle data and the like in the existing method. According to the invention, combination of ten groups of characteristic band spectrums is adopted. An LSTM long short-term memory network is constructed. Crop insect pest disasters can be automatically and efficiently identified from the multispectral satellite remote sensing image, and certain technical support is provided for the fields of agricultural production disaster prediction and prevention, agricultural insurance claim settlement and the like. The method has the advantages that the crop insect pest disasters can be automatically and efficiently identified from the multispectral satellite remote sensing image;

Description

technical field [0001] The invention belongs to the technical field of satellite remote sensing image processing and application. Background technique [0002] Crop pests are the number one natural biological disaster that threatens the yield and quality of crops, and are easily affected by many factors such as crop varieties, crop planting methods, and growth environments. In recent years, severe global climate changes have provided a good growth environment for the hatching of pests. Large-scale outbreaks of pests have greatly threatened the normal growth of crops, leading to increasingly prominent global food production security issues. For a long time, in order to monitor the scope and degree of occurrence of crop diseases and insect pests, the method of field investigation by plant protection personnel has been mainly used. This method has a certain degree of authenticity, but it has the disadvantages of time-consuming, laborious and poor timeliness. With the rapid dev...

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): G06K9/00G06K9/32G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06V20/188G06V10/255G06V10/56G06F18/24G06F18/214
Inventor 顾玲嘉王钰涵任瑞治
Owner JILIN 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