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Recognition Method of No-seedling Grids of Trays based on Deep Convolutional Neural Network

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成果类型:
期刊论文、会议论文
作者:
Xiao, Zhang;Liu, Xingxing;Tan, Yu;Tian, Fang;Yang, Shenghui;...
通讯作者:
Liu, XX
作者机构:
[Yang, Shenghui; Liu, Xingxing; Tan, Yu; Xiao, Zhang; Tian, Fang; Li, Baosheng] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China.
[Xiao, Zhang] Univ South China, Coll Mech Engn, Hengyang 421001, Peoples R China.
通讯机构:
[Liu, XX ] C
China Agr Univ, Coll Engn, Beijing 100083, Peoples R China.
语种:
英文
关键词:
Image processing;Deep convolutional neural network;GoogLeNet;Tray No-seedling Grids Recognition
期刊:
PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC)
ISSN:
2161-2927
年:
2019
卷:
2019-July
页码:
8695-8700
会议名称:
2019 Chinese Control Conference (CCC)
会议论文集名称:
2019 Chinese Control Conference (CCC)
会议时间:
July 2019
会议地点:
Guangzhou, China
会议主办单位:
[Xiao, Zhang;Liu, Xingxing;Tan, Yu;Tian, Fang;Yang, Shenghui;Li, Baosheng] China Agr Univ, Coll Engn, Beijing 100083, Peoples R China.^[Xiao, Zhang] Univ South China, Coll Mech Engn, Hengyang 421001, Peoples R China.
会议赞助商:
Chinese Association of Automation (CAA); Guangdong University of Technology; Systems Engineering Society of China (SESC); Technical Committee on Control Theory (TCCT), Chinese Association of Automation (CAA)
主编:
Fu, M Sun, J
出版地:
345 E 47TH ST, NEW YORK, NY 10017 USA
出版者:
IEEE
ISBN:
978-1-7281-2329-5
基金类别:
National Key Research and Development Program of China [2016YFD0700302]; China Ministry of Science and Technology
机构署名:
本校为其他机构
院系归属:
机械工程学院
摘要:
In the process of replanting, it is necessary to recognize that whether the tray grids are short of seedlings. In the seedling cultivating process, the phenomenon of seedling leaves intruding the adjacent grids due to various reasons make it difficult to identifying the tray grid that if it is lacking seedlings. The traditional image processing methods face the problems of poor human selection features and complex feature extraction process. In this paper, the recognition method of no-seedlings grids of tray based on deep convolutional neural network (GoogLeNet model) is proposed, which uses 9...

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