Fast and accurate wheat grain quality detection based on improved YOLOv5-Reference-Cited by-同舟云学术

Fast and accurate wheat grain quality detection based on improved YOLOv5

Published:2022-11 Issue: Volume:202 Page:107426
ISSN:0168-1699
Container-title:Computers and Electronics in Agriculture
language:en
Short-container-title:Computers and Electronics in Agriculture

Author:

Zhao Wenyi^ORCID,Liu Shiyuan^ORCID,Li Xinyi,Han Xi,Yang Huihua

Publisher

Elsevier BV

Subject

Horticulture,Computer Science Applications,Agronomy and Crop Science,Forestry

Reference32 articles.

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4. Multiple view image analysis of freefalling U.S. wheat grains for damage assessment;Delwiche;Comput. Electron. Agric.,2013

5. SIMCA identification model establishment of gibberellic disease wheat grain based on near infrared spectrum characteristics;Erqi;J. Chin. Cereals Oils Assoc.,2016

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