Plant disease and pest detection method based on SVM (support vector machine) learning

A machine learning, pest and disease technology, applied in instruments, computer parts, character and pattern recognition, etc., can solve problems such as varying effects, achieve high practicability, easy operation, and avoid complex operation steps.

Inactive Publication Date: 2013-02-06
FUDAN UNIV
View PDF3 Cites 41 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

For different environments, light, and differen...

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
  • Plant disease and pest detection method based on SVM (support vector machine) learning
  • Plant disease and pest detection method based on SVM (support vector machine) learning
  • Plant disease and pest detection method based on SVM (support vector machine) learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0048] Embodiment 1: the specific operation steps of the inventive method are as appended figure 1 As shown, firstly, in a large number of monitoring videos of agricultural scenes, a large number of normally growing plant leaves and plant leaves with diseases and insect pests are obtained, and a random sampling strategy is used to extract part of the pictures from the normal growing plant leaves and plant leaves with diseases and insect pests as Sample, extract features (including color features, HSV features, edge features and HOG features) for each leaf image, and combine these features into feature vectors; then use the SVM machine learning method to train the feature vectors of each leaf image, After training, a classifier is formed, and then a large number of plant leaf images are detected by this classifier to detect whether plant leaves are damaged by diseases and insect pests.

[0049] (1) Obtain images of a large number of plant leaves in agricultural scenes

[0050]...

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 belongs to the technical field of digital image processing and pattern recognition and particularly relates to a plant disease and pest detection method based on SVM (support vector machine) learning. The plant disease and pest detection method comprises the following steps: acquiring a large number of regularly grown plant leaves and the plant leaves with diseases and pests from a large number of monitoring videos of agricultural scenes; extracting a part of pictures of the regularly grown plant leaves and the plant leaves with diseases and pests as samples; extracting characteristics [including color characteristic, HSV (herpes simplex virus) characteristic, edge characteristic and HOG (histogram of oriented gradient) characteristic] of each leaf picture; combining the characteristics into characteristic vectors; training the characteristic vectors of each leaf picture through an SVM learning method; forming a classifier after training; and detecting the large number of plant leaf pictures by the classifier to detect whether diseases or pests occur to the plant leaves. Compared with a biologic plant disease and pest detection method, the plant disease and pest detection method based on the SVM learning is higher in real time and easier to implement; and the shortcoming of detecting the plant diseases and pests by working in the fields is overcome.

Description

technical field [0001] The invention belongs to the technical field of digital image processing and pattern recognition, and in particular relates to a method for detecting plant diseases and insect pests in agricultural video monitoring. Background technique [0002] As an interdisciplinary cutting-edge technology, plant disease and pest detection integrates theoretical knowledge in various fields such as plant growth in agriculture, image processing, pattern recognition, and artificial intelligence in the computer industry. It has broad application prospects in the field of detection of plant diseases and insect pests in greenhouses and fields in agricultural scenes, and the research on detection methods of plant diseases and insect pests has important practical significance and theoretical value. [0003] The detection of plant diseases and insect pests is to detect whether plant diseases and insect pests occur on the leaves of plants during the growth of plants in agricu...

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): G06K9/62
Inventor 蒋龙泉鲁帅董文彧郭跃飞冯瑞
Owner FUDAN 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