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:
Jun. 18, 2024

Filed:

Nov. 15, 2021
Applicant:

FJ Dynamics Technology Co., Ltd, Xiangyang, CN;

Inventors:

Di Wu, Nanjing, CN;

Zhao-Di Chen, Xiangyang, CN;

Min-Xin Chen, Xiangyang, CN;

Chen-Jian Wu, Xiangyang, CN;

Hong Chen, Xiangyang, CN;

Assignee:
Attorney:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G06F 18/214 (2023.01); G06F 18/21 (2023.01); G06F 18/25 (2023.01); G06N 3/08 (2023.01); G06T 3/40 (2024.01); G06V 20/40 (2022.01);
U.S. Cl.
CPC ...
G06F 18/214 (2023.01); G06F 18/2163 (2023.01); G06F 18/217 (2023.01); G06F 18/25 (2023.01); G06N 3/08 (2013.01); G06T 3/40 (2013.01); G06V 20/46 (2022.01);
Abstract

A method for constructing a convolutional neural network model based on farmland images is applied in an electronic device. The method includes following steps: obtaining a number of farmland images of at least one farmland; obtaining a plurality of standard segmentation farmland images corresponding to each of the farmland images; dividing the farmland images and the standard segmentation farmland images into a training image set and a test image set; taking the farmland images and the standard segmentation farmland images in the training image set as input of a convolutional neural network, and constructing a convolutional neural network model based on the farmland images; and verifying the convolutional neural network model by using the farmland images in the test image set and the standard segmentation farmland images, and optimizing a plurality of parameters of the convolutional neural network model based on the farmland images.


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