The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Oct. 04, 2022

Filed:

Apr. 14, 2022
Applicant:

Guangdong Polytechnic Normal University, Guangzhou, CN;

Inventors:

Yu Tang, Guangzhou, CN;

Shaoming Luo, Guangzhou, CN;

Jiepeng Yang, Guangzhou, CN;

Yiqing Fu, Guangzhou, CN;

Jinfei Zhao, Guangzhou, CN;

Jiahao Li, Guangzhou, CN;

Zhiping Tan, Guangzhou, CN;

Huasheng Huang, Guangzhou, CN;

Qiwei Guo, Guangzhou, CN;

Weizhao Chen, Guangzhou, CN;

Assignee:
Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); G06T 7/00 (2017.01); G06N 3/08 (2006.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01); G06V 10/776 (2022.01); G01N 33/00 (2006.01); G01N 21/65 (2006.01);
U.S. Cl.
CPC ...
G06T 7/0014 (2013.01); G01N 21/65 (2013.01); G01N 33/0098 (2013.01); G06N 3/08 (2013.01); G06V 10/774 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20212 (2013.01); G06T 2207/30188 (2013.01);
Abstract

Disclosed is a method for detecting an infection stage of anthracnose pathogen with pre-analysis capacity, comprising: obtaining a plurality of sample sensing data sequences; obtaining a sample citrus leaf image; obtaining a first prediction result; if the first prediction result is that the sample citrus crop is not infected by anthracnose, obtaining sample Raman spectral data and sample hyperspectral data; obtaining a first judgment result, and obtaining a second judgment result; performing labeling to obtain second training data; training a neural network model to obtain a second anthracnose prediction model; obtaining a plurality of to-be-analyzed sensing data sequences; obtaining a to-be-analyzed citrus leaf image; obtaining a second prediction result; if the second prediction result is that the to-be-analyzed citrus crop is not infected by anthracnose, obtaining a third prediction result.


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