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

A multi-type crop leaf disease identification method based on a dynamic neural network

A dynamic neural network and identification method technology, applied in the field of plant disease detection, can solve problems such as the inability to better identify various crop leaf diseases, and achieve the effects of meeting real-time requirements, improving accuracy, and reducing consumption of computing resources.

Pending Publication Date: 2022-02-08
OCEAN UNIV OF CHINA
View PDF0 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to solve the problem that the traditional fixed network structure cannot better identify various crop leaf diseases, the present invention proposes a method for identifying various crop leaf diseases based on a dynamic neural network, which can dynamically adjust the network structure of the model, and has strong robustness. Sticky, it has a good performance effect on the identification of various crop leaf 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
  • A multi-type crop leaf disease identification method based on a dynamic neural network
  • A multi-type crop leaf disease identification method based on a dynamic neural network
  • A multi-type crop leaf disease identification method based on a dynamic neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0064] In order to understand the above-mentioned purpose, features and advantages of the present invention more clearly, the present invention will be further described below in conjunction with the accompanying drawings and embodiments. Many specific details are set forth in the following description to facilitate a full understanding of the present invention. However, the present invention can also be implemented in other ways than those described here. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0065] The invention uses a segmented convolutional network to eliminate the background of leaf disease images and extract effective information, and uses a dynamic convolution module to adaptively adjust the convolution kernel according to the severity of plant leaf diseases to extract disease features, and then introduces The shallow classifier and the early exit mechanism dynamically adjust the network structure, realizing the au...

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 provides a multi-type crop leaf disease identification method based on a dynamic neural network, and the method comprises the steps of randomly selecting part of crop leaf disease image data for pixel-level marking, and training a convolutional neural network for image segmentation; then, automatically segmenting the crop leaf disease image by using the trained convolutional neural network, and extracting effective information in the image; and finally, designing a dynamic neural network which comprises three parts of a dynamic convolution module, a shallow classifier and an early-going mechanism, and realizing leaf disease identification based on a dynamic network structure. According to the scheme, the method can realize automatic segmentation and extraction of effective information of the crop leaf disease image and recognition of various types of crop leaf diseases, the adopted dynamic neural network can dynamically adjust the network structure according to the complexity of the crop leaf diseases, and the use of computing resources is reduced under the condition that the requirement for high recognition accuracy rate is met.

Description

technical field [0001] The invention belongs to the field of plant disease detection, and in particular relates to a dynamic neural network-based identification method for various types of crop leaf diseases. Background technique [0002] Crop diseases have always been a problem for agricultural growers. The traditional method of judging the type of crop leaf disease is through naked eye observation. This method requires experts to go to the scene to observe and judge, which has certain limitations. Timely and effective analysis of the types of crop diseases will help to quickly carry out corresponding disease control measures, thereby reducing economic losses. [0003] At present, there are many methods for identifying crop leaf diseases based on deep learning. Most of them design feature extraction methods for different diseases of a specific crop, and then identify the specific crop disease. However, there are relatively few technologies for identifying diseases of vario...

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): G06V20/60G06V20/70G06V10/26G06V10/34G06V10/764G06V10/774G06V10/82G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/2414G06F18/214
Inventor 张江南董军宇高峰王海李文博刘永朔
Owner OCEAN UNIV OF CHINA
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