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:
Dec. 17, 2024

Filed:

Jul. 04, 2024
Applicant:

Institute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou, CN;

Inventors:

Sai Xu, Guangzhou, CN;

Huazhong Lu, Guangzhou, CN;

Xin Liang, Guangzhou, CN;

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G06K 9/00 (2022.01); A01G 25/16 (2006.01); G01N 33/02 (2006.01); G06V 10/75 (2022.01); G06V 10/82 (2022.01); G06V 20/68 (2022.01);
U.S. Cl.
CPC ...
G01N 33/025 (2013.01); A01G 25/167 (2013.01); G06V 10/751 (2022.01); G06V 20/68 (2022.01);
Abstract

A non-destructive fruit defect detection method and system based on neural networks are used to solve problem of inaccurate selection of high-quality fruits by current consumers. The system includes a standard formulation module configured to formulate monitoring standards for different batches and varieties of the fruits to obtain standard detection parameters for the different batches and varieties of the fruits, a preliminary identification module configured to preliminarily identify external conditions of the different batches and varieties of the fruits, a non-destructive detection module configured to non-destructively detect the different batches and varieties of the fruits, generate a fruit abnormal signal or obtain growth deviation values of the different batches and varieties of the fruits, and a quality judgment module configured to judge quality of the different batches and varieties of the fruits. Accurate non-destructive detection for the different batches and varieties of the fruits are realized.


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