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Greenhouse Cucumber Detection and Ch...

Ghorbanirezaei, Shahryar,

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Greenhouse Cucumber Detection and Characterization for Harvesting Framework Implementation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Greenhouse Cucumber Detection and Characterization for Harvesting Framework Implementation // Shahryar Ghorbanirezaei.
Author:	Ghorbanirezaei, Shahryar,
Description:	1 electronic resource (114 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-08.
Contained By:	Masters Abstracts International85-08.
Subject:	Materials science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30993380
ISBN:	9798381729078

Greenhouse Cucumber Detection and Characterization for Harvesting Framework Implementation /
Ghorbanirezaei, Shahryar,

Greenhouse Cucumber Detection and Characterization for Harvesting Framework Implementation /Shahryar Ghorbanirezaei. - 1 electronic resource (114 pages)

Source: Masters Abstracts International, Volume: 85-08.

Cucumbers constitute a significant portion of greenhouse vegetables cultivated in southern Ontario. The human labour shortage and potential injuries associated with manual labour in cucumber harvesting highlight the need to explore automation as a viable solution. The proposed automated system in this study comprises two key components: an image processing unit and a cutting robotic arm. The image processing phase involves the identification of cucumbers using six models using shape and colour features. Four models are successfully employed in YOLOv8, yielding results in the form of bounding boxes and keypoints, with no false positives. Near-real-time cucumber detection is also achieved using YOLO. One model has been successfully tested in RoboFlow. Finally, one new model is being initially investigated for the direct management of raw RGB-XYZ data in Python. About harvesting unit, a robotic arm equipped with a cutting tool is programmed to move to specific positions and perform cutting tasks. To enable this functionality, a 3D camera and cutter are integrated into the TCP of a UR3e robot. The 3D camera installed on TCP of the robot can move at angles and positions to capture cucumbers hidden from view. Additionally, analyses of cucumber geometry and force are carried out to improve the understanding of cucumber properties. Finally, initial cost and OEE analyses are conducted to assess the potential improvement resulting from transitioning from manual harvesting to automation.

English

ISBN: 9798381729078Subjects--Topical Terms:

543314
Materials science.
Subjects--Index Terms:

3D camera

Greenhouse Cucumber Detection and Characterization for Harvesting Framework Implementation /
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245 1 0 $a Greenhouse Cucumber Detection and Characterization for Harvesting Framework Implementation / $c Shahryar Ghorbanirezaei.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (114 pages)
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500 $a Source: Masters Abstracts International, Volume: 85-08.
500 $a Advisors: Urbanic, J. Committee members: Hassanzadeh, M.; Johrendt, J.
502 $b M.A.Sc. $c University of Windsor (Canada) $d 2024.
520 $a Cucumbers constitute a significant portion of greenhouse vegetables cultivated in southern Ontario. The human labour shortage and potential injuries associated with manual labour in cucumber harvesting highlight the need to explore automation as a viable solution. The proposed automated system in this study comprises two key components: an image processing unit and a cutting robotic arm. The image processing phase involves the identification of cucumbers using six models using shape and colour features. Four models are successfully employed in YOLOv8, yielding results in the form of bounding boxes and keypoints, with no false positives. Near-real-time cucumber detection is also achieved using YOLO. One model has been successfully tested in RoboFlow. Finally, one new model is being initially investigated for the direct management of raw RGB-XYZ data in Python. About harvesting unit, a robotic arm equipped with a cutting tool is programmed to move to specific positions and perform cutting tasks. To enable this functionality, a 3D camera and cutter are integrated into the TCP of a UR3e robot. The 3D camera installed on TCP of the robot can move at angles and positions to capture cucumbers hidden from view. Additionally, analyses of cucumber geometry and force are carried out to improve the understanding of cucumber properties. Finally, initial cost and OEE analyses are conducted to assess the potential improvement resulting from transitioning from manual harvesting to automation.
546 $a English
590 $a School code: 0115
650 4 $a Materials science. $3 543314
650 4 $a Automotive engineering. $3 2181195
650 4 $a Mechanical engineering. $3 649730
653 $a 3D camera
653 $a Cucumber characteristics
653 $a Image detection
653 $a Robotic arm
690 $a 0794
690 $a 0548
690 $a 0540
710 2 $a University of Windsor (Canada). $b MECHANICAL ENGINEERING. $e degree granting institution. $3 3819042
720 1 $a Urbanic, J. $e degree supervisor.
773 0 $t Masters Abstracts International $g 85-08.
790 $a 0115
791 $a M.A.Sc.
792 $a 2024
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