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:
Aug. 29, 2023

Filed:

Oct. 29, 2020
Applicant:

Zhejiang University, Zhejiang, CN;

Inventors:

Tao Lin, Zhejiang, CN;

Renhai Zhong, Zhejiang, CN;

Jinfan Xu, Zhejiang, CN;

Hao Jiang, Zhejiang, CN;

Yibin Ying, Zhejiang, CN;

Kuan-Chong Ting, Zhejiang, CN;

Assignee:

ZHEJIANG UNIVERSITY, Zhejiang, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06Q 50/02 (2012.01); G01W 1/02 (2006.01); G06N 3/049 (2023.01); G06N 3/084 (2023.01); G06Q 10/04 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G06Q 50/02 (2013.01); G01W 1/02 (2013.01); G06N 3/045 (2023.01); G06N 3/049 (2013.01); G06N 3/084 (2013.01); G06Q 10/04 (2013.01);
Abstract

A crop yield estimation method based on spatio-temporal deep learning including: obtaining regional historical crop yield data and meteorological data, preprocessing the meteorological data and the yield data to respectively obtain meteorological parameters and a detrended yield as input and output of the crop yield spatio-temporal deep learning model; constructing the spatio-temporal deep learning model for crop yield estimation, and optimizing hyperparameters; and building a training set by taking the meteorological parameters as an input and the detrended yield as output to train the model and obtain parameters of the model; for the crop yield to be estimated, feeding meteorological parameters into the trained model, and obtaining the crop yield estimation result. The model combined temporal and spatial learning to achieve better crop yield estimation accuracy and stability at large spatial scales.


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