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

Image data quality control method and system in crop live-action observation

A quality control method and image data technology, applied in image data processing, image enhancement, image analysis, etc., can solve the problems that quality control methods cannot be used directly and are rarely studied.

Active Publication Date: 2019-04-19
CMA METEOROLOGICAL OBSERVATION CENT
View PDF8 Cites 2 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

These elements are different from conventional meteorological elements, and traditional quality control methods cannot be used directly
At present, scientists at home and abroad mainly focus on crop segmentation algorithms and image feature extraction algorithms. There are few studies on crop images and crop growth characteristic parameters based on image recognition.

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
  • Image data quality control method and system in crop live-action observation
  • Image data quality control method and system in crop live-action observation
  • Image data quality control method and system in crop live-action observation

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0120] The classification model is a segmentation hyperplane.

[0121] In this example, given a set of point sets (features) in a separable space, there must exist a hyperplane π: ω·x+b=0 that can transform these features x i , i=1,...,n are divided into two different categories.

Embodiment 2

[0123] The classification model is a kernel function.

[0124] In this embodiment, for nonlinear classification problems, the processing method of SVM is to select a kernel function, and solve the problem of linear inseparability in the original space by mapping the data to a high-dimensional space.

[0125] In any of the above embodiments, preferably, the formulas of the first dark channel image and the second dark channel image are:

[0126]

[0127] Among them, J c Represents the R, G, B color channels of the image, Ω(x, y) represents the image block centered on the pixel (x, y), and (x, y) represents the coordinate value of the image pixel.

[0128] Such as Figure 11 As shown, the left column is the original image, and the middle column is the dark channel image corresponding to the original image. It can be seen from the figure that the gray value of the dark channel image of the polluted image is higher than that of the uncontaminated image. To describe the charac...

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 relates to an image data quality control method and system in crop live-action observation, and the method comprises the following steps: generating a gray value of an image missing graytone and an image incomplete rate of a corresponding image missing gray tone according to a historical image; Identifying an image missing amount of the to-be-detected image and an image missing ratecorresponding to the image missing amount according to the gray value; And comparing the image missing rate with the image incomplete rate. According to the technical scheme, the incomplete image inthe to-be-detected image is identified and eliminated by utilizing the color characteristic parameters of the crop image, namely the grey tone when the historical image is lost, so that the complete image is provided for subsequent calculation of the crop coverage and the leaf area index, and the calculation accuracy is improved.

Description

technical field [0001] The invention relates to the field of agricultural meteorological observation, in particular to an image data quality control method and an image data quality control system in field crop field survey. Background technique [0002] The real scene automatic monitoring system of crops is based on machine learning, image processing and wireless multimedia network technologies and methods, using CCD sensors, image collectors and communication devices to collect images of crops under natural light conditions and transmit them to computer terminals, through the built-in image recognition algorithm Extract image feature parameters, and then invert to obtain crop growth feature information. It has the advantages of 24-hour continuous work, high time resolution, non-contact, non-destructive, etc. It is a useful supplement to traditional agricultural meteorological observation, and has important application value in the field of agricultural disaster monitoring....

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): G06T7/00G06T7/90
CPCG06T7/0002G06T7/90G06T2207/30168G06T2207/30188
Inventor 李翠娜白晓东余正泓许立兵
Owner CMA METEOROLOGICAL OBSERVATION CENT
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