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

Sunflower disease recognition method based on random forest method

A disease identification and sunflower technology, applied in character and pattern recognition, computer parts, image data processing, etc., can solve problems such as difficult to distinguish new diseases, ambiguity, etc., to improve classification performance, accuracy, and recognition effect. Effect

Inactive Publication Date: 2017-11-07
INNER MONGOLIA UNIV OF TECH
View PDF6 Cites 21 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is to overcome the defects of the prior art, provide a sunflower disease identification method based on the random forest method, and solve the problems of subjectivity, limitations, ambiguity and indiscernibility of naked eye judgment in the disease identification process. The problem of new diseases

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
  • Sunflower disease recognition method based on random forest method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0030] Such as figure 1 As shown, the present invention provides a kind of sunflower disease identification method based on random forest method, including four common diseases of sunflower leaves: powdery mildew, bacterial leaf spot, black spot, downy mildew, and as research object:

[0031] A: Disease image collection. The color of the collected leaf image should be as close as possible to the color of the leaf itself.

[0032] B: Disease image preprocessing. Combined with the actual situation, the preprocessing method suitable for sunflower disease image recognition is adopted, the image effect is enhanced by the histogram equalization dehazing algorithm in the airspace, and the weight adaptive image denoising method based on morphology is used to analyze the sunflower leaf disease image. Perform denoising processing.

[0033] C: Lesion image segmentation. After analyzing and comparing various methods of image segmentation, the optimal color image segmentation method is...

Embodiment 2

[0059]In terms of disease spot segmentation: the various models of the color image color space were compared and analyzed. Through comparison, and according to the actual situation of the images collected in Daejeon, the HSV color space, which is sensitive to human vision, was finally selected as the color space for sunflower disease segmentation. By comparing the five image segmentation processing methods, and considering their respective advantages and disadvantages, the K-means clustering algorithm and the watershed algorithm are finally selected to realize the segmentation of color disease images of sunflower leaves and obtain better disease spots. Segment images to provide accurate and good basic images for image feature analysis of sunflower leaf diseases.

[0060] In terms of identification and diagnosis: on the basis of in-depth analysis of disease characteristics, the random forest method is selected to diagnose and identify sunflower diseases. Simulation experiments h...

Embodiment 3

[0069] Disease image segmentation: The purpose of image segmentation is very clear, which is to extract the interesting part of the image for research and processing. The basis is image recognition, and the premise is to identify the disease of the input disease image, which is specifically applied to examples middle. Image segmentation is a key step in the subsequent processing of sunflower disease images, and its accuracy directly has a great impact on disease identification. The key to the image segmentation algorithm is the region in the image, which is divided according to the differences between the regions. Every image segmentation algorithm is not comprehensive, and the segmentation methods may be different for different targets. Therefore, for multiple images with outstanding characteristics, the same method cannot be used only for segmentation. Therefore, the following conditions must be met for each segmentation algorithm: a pixel must only belong to one sub-regio...

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 sunflower disease recognition method based on a random forest method. The sunflower disease recognition method can recognize four common diseases of sunflower leaves including powdery mildew, bacterial leaf spot, black spot, and downy mildew and comprises steps of: A: disease image acquisition in which an acquired leaf image color is required to be as close as possible to the color of a leaf itself; B: disease image processing in which a processing method suitable for sunflower disease image recognition is used; C: disease image segmentation in which an optimal color image segmentation method is selected by analyzing and comparing various image segmentation methods; D: disease image feature extraction in which parameters such as the color feature and the texture feature of the disease image are extracted for research; and E: disease recognition and diagnosis in which the sunflower disease is diagnosed and recognized by using the random forest method. The sunflower disease recognition method mainly solves the subjectivity, the limitation, and the fuzziness of eye determination and difficulty in new disease determination in a process of disease recognition, improves the accuracy of diseases recognition, and provides agricultural workers with good help for recognizing and preventing sunflower diseases.

Description

technical field [0001] The invention relates to the field of identification of agricultural diseases and insect pests, in particular to a sunflower disease identification method based on a random forest method. Background technique [0002] As we all know, the traditional method of disease diagnosis is mainly for plant protection personnel to identify with naked eyes, combined with the form of pathogenic bacteria of plant diseases to judge. This method has low diagnostic efficiency, and it is difficult to judge the type of disease in a timely and accurate manner. "Precision agriculture" provides farmers with new ideas. By using information technology to quickly and effectively identify plant diseases, compared with traditional identification methods, the identification speed is fast, the accuracy is high, and it is time-sensitive. Taking crop diseases such as apples, cucumbers and peppers as examples, the images of plant leaf diseases were analyzed, and the color information...

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): G06T7/00G06T5/30G06T5/40G06T7/11G06T7/12G06T7/45G06T7/90G06K9/62
CPCG06T5/30G06T5/40G06T7/0002G06T7/11G06T7/12G06T7/45G06T7/90G06T2207/30188G06T2207/20152G06T2207/10024G06T2207/20004G06F18/23213G06F18/24323G06T5/73G06T5/70
Inventor 吕芳狄鹏慧刘波波
Owner INNER MONGOLIA UNIV OF TECH
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