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

Medicinal plant leaf disease image recognition method based on deep learning

A technology of medicinal plants and deep learning, applied in neural learning methods, image enhancement, image data processing, etc., can solve the problems of large number of convolution kernel parameters, low model generalization ability, low recognition accuracy, etc., to achieve auxiliary Diagnose diseases, enhance anti-interference ability, and better over-fitting effect

Active Publication Date: 2020-01-21
UNIV OF ELECTRONICS SCI & TECH OF CHINA
View PDF12 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The background technology is shooting and sampling under laboratory conditions, and due to the small amount of sample data, it is easy to lead to low model generalization ability
[0009] The second method adopts the basic structure of CNN, which is easy to lead to overfitting, and the number of convolution kernel parameters is too large, so the training efficiency is low; in addition, the training samples are all taken from the laboratory environment, and for pictures with complex backgrounds taken in the field, the recognition low accuracy

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
  • Medicinal plant leaf disease image recognition method based on deep learning
  • Medicinal plant leaf disease image recognition method based on deep learning
  • Medicinal plant leaf disease image recognition method based on deep learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] Please refer to figure 1 , figure 2 , image 3 with Figure 4 , the embodiment of the present invention provides a method for image recognition of medicinal plant leaf diseases based on deep learning, comprising the following steps:

[0045] S1. Collect a number of leaf disease images of medicinal plants, and rename each image in the form of plant name + disease name;

[0046] S2, performing enhanced processing on the renamed medicinal plant leaf disease image;

[0047] S3. Image data preprocessing, uniformly adjusting the size of the images of leaf diseases of medicinal plants after each enhancement process to 299x299;

[0048] S4, training depth CNN model, depth CNN model comprises convolutional pooling network, Inception-I network, average pooling network, Dropout layer and Softmax layer in series, and the last two convolutional layers of the convolutional pooling network in series are Depth separable convolutional layers, including random pooling layers in the I...

Embodiment 2

[0064] For step S1 in Example 1, the images of leaf diseases of medicinal plants are collected by a digital camera, and there are 500 images of leaf diseases of medicinal plants in total.

Embodiment 3

[0066] For step S2 in Embodiment 1, the enhancement processing on the leaf disease image includes image rotation, mirror symmetry, brightness adjustment and PCA dithering.

[0067] In this embodiment, image rotation refers to rotating all pixels of the image around the center of the image at an angle of 0-360 degrees; mirror symmetry refers to using the vertical line in the image as the axis, exchanging all pixels in the image, that is, horizontal symmetry . Set the coordinates of any point P in the image as (x0, y0), and the coordinates after rotating θ degrees counterclockwise are (x, y). The formula for calculating polar coordinates before and after rotation is as follows:

[0068] x0=γcosα, y0=γsinα

[0069] x=γcos(α+θ), y=γsin(α+θ)

[0070] Among them, γ represents the polar radius of P point; α represents the polar angle of P point.

[0071] In this embodiment, brightness adjustment refers to adjusting image sharpness value, brightness value and contrast.

[0072] In...

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 medicinal plant leaf disease image recognition method based on deep learning, and relates to the technical field of medicinal plant leaf disease prevention, and the method comprises the steps: collecting a plurality of medicinal plant leaf disease images; carrying out enhancement processing on the leaf disease image of the medicinal plant; uniformly adjusting the size ofeach enhanced medical plant leaf disease image to be 299 * 299; training a deep CNN model, wherein the deep CNN model comprises a convolution pooling network, an Inception-I network, an average pooling network, a Dropout layer and a Softmax layer which are connected in series, the last two convolution layers of the convolution pooling network connected in series are depth separable convolution layers, and the Inception-I network comprises a random pooling layer; and identifying the size-adjusted leaf disease images of the medicinal plants through a deep CNN model, the recognition result beingthe type of the disease of the leaf of each medicinal plant, and classifying the disease of the leaf of each medicinal plant based on the recognition result. The recognition method can effectively assist planters to diagnose diseases and improve the diagnosis efficiency.

Description

technical field [0001] The invention relates to the technical field of leaf disease protection of medicinal plants, in particular to an image recognition method for leaf diseases of medicinal plants based on deep learning. Background technique [0002] During the growth of medicinal plants, leaves have many opportunities to contact pathogens, and are also greatly affected by external environmental conditions. Diseases are prone to occur, which affects the yield of medicinal plants and the efficacy of final drugs. According to the records and statistics of popular science literature, there are 394 kinds of diseases in 61 kinds of medicinal plants, among which there are 220 kinds of leaf diseases, accounting for 58.1%. The common disease types of leaves are downy mildew, white rust, powdery mildew, rust, leaf spot, leaf blight and so on. [0003] In order to minimize the impact of pests and diseases on the growth process of medicinal plants, pests and diseases should be detec...

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): G06K9/00G06K9/42G06K9/62G06N3/04G06N3/08G06T3/60G06T5/00
CPCG06N3/084G06T3/60G06V20/00G06V10/32G06N3/045G06F18/2431G06F18/2415G06F18/241G06T5/90
Inventor 刘勇国李巧勤杨尚明蔡茁李杨何家欢
Owner UNIV OF ELECTRONICS SCI & TECH 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

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