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:
May. 27, 2025

Filed:

Jan. 23, 2023
Applicant:

Zhejiang University, Zhejiang, CN;

Inventors:

Xuping Feng, Hangzhou, CN;

Xiulin Bai, Hangzhou, CN;

Yong He, Hangzhou, CN;

Jinnuo Zhang, Hangzhou, CN;

Mingzhu Tao, Hangzhou, CN;

Qingguan Wu, Hangzhou, CN;

Assignee:

Zhejiang University, Hangzhou, CN;

Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06T 7/00 (2017.01); A01G 22/22 (2018.01); B64U 20/87 (2023.01); G06T 3/4038 (2024.01); B64U 101/30 (2023.01); B64U 101/40 (2023.01);
U.S. Cl.
CPC ...
G06T 7/0004 (2013.01); A01G 22/22 (2018.02); B64U 20/87 (2023.01); G06T 3/4038 (2013.01); B64U 2101/30 (2023.01); B64U 2101/40 (2023.01); G06T 2207/10036 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30128 (2013.01);
Abstract

A method for monitoring rice bacterial blight includes: obtaining a multi-spectral image, severities of the rice bacterial blight, and accumulated temperature data of a rice field at different growth stages; obtaining resistance of rice varieties to the bacterial blight; extracting a mean canopy spectral reflectance of each plot in the rice field; conducting regression of the severity of the rice bacterial blight using a convolutional neural network based on the mean canopy spectral reflectance and the severity of the rice bacterial blight, and outputting a depth spectrum feature; training a disease severity regression model with the accumulated temperature data, the depth spectrum feature, and the resistance to the bacterial blight for each plot as an input and the corresponding severity as an output; and monitoring a severity of the rice bacterial blight in a to-be-monitored rice field using the disease severity regression model.


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