{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/86075"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/86075","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Plant Specific Direct Chemical Application Field Robot","abstract":"This study addressed the issues in the conventional chemical application such as chemical drift and application inaccuracy via developing a plant specific direct application field robot. The robot was designed to autonomously detect plants, to identify weeds and to control weeds in the field. The developed robot was equipped with a stereovision with processing algorithms to detect and identify individual weeds in the field image and the custom designed end effector with a robotic arm to carry out plant specific direct application. The developed vision system with the algorithms was dealt with unpredictable outdoor illuminations to sense plants in the field via a series of image processing. The developed algorithm processed field images with the processing error less than 3 % in terms of identifying individual plants, and the algorithm identified 92.5 % and 95.1 % of weeds in the image via trained artificial neural network. The direct application end effector with the arm executed the direct application via cutting weed stem and wiping chemical on the weed surface. The indoor experiment resulted that 90.9 % of the weed treated by the end effector had herbicide symptoms after 6 day from the application. The plant specific direct chemical application field robot was developed by utilizing the vision system and the end effector with the arm to a field robot platform. The potential of developed robot was examined by testing the robot in the field. While testing in the field, the robot autonomously controlled 63.6 % of weeds, and required 12.923 seconds to control a weed from detection to application inspection for reapplication via machine vision.","abstract_html":"This study addressed the issues in the conventional chemical application such as chemical drift and application inaccuracy via developing a plant specific direct application field robot. The robot was designed to autonomously detect plants, to identify weeds and to control weeds in the field. The developed robot was equipped with a stereovision with processing algorithms to detect and identify individual weeds in the field image and the custom designed end effector with a robotic arm to carry out plant specific direct application. The developed vision system with the algorithms was dealt with unpredictable outdoor illuminations to sense plants in the field via a series of image processing. The developed algorithm processed field images with the processing error less than 3 % in terms of identifying individual plants, and the algorithm identified 92.5 % and 95.1 % of weeds in the image via trained artificial neural network. The direct application end effector with the arm executed the direct application via cutting weed stem and wiping chemical on the weed surface. The indoor experiment resulted that 90.9 % of the weed treated by the end effector had herbicide symptoms after 6 day from the application. The plant specific direct chemical application field robot was developed by utilizing the vision system and the end effector with the arm to a field robot platform. The potential of developed robot was examined by testing the robot in the field. While testing in the field, the robot autonomously controlled 63.6 % of weeds, and required 12.923 seconds to control a weed from detection to application inspection for reapplication via machine vision.","abstract_has_math":false,"creators":["Jeon, Hong Young"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Agricultural Engineering","degree_department":null,"school":null,"contributors":["Lei F. Tian"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-28T14:53:52Z","date_published":"2015-09-28T14:53:52Z","updated_at":"2026-07-22T22:26:26Z","subjects":["Engineering, Robotics"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["(MiAaPQ)AAI3337809"],"render_values":[{"text":"(MiAaPQ)AAI3337809","href":null,"code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/2142/86075","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Lei F. 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The robot was designed to autonomously detect plants, to identify weeds and to control weeds in the field. The developed robot was equipped with a stereovision with processing algorithms to detect and identify individual weeds in the field image and the custom designed end effector with a robotic arm to carry out plant specific direct application. The developed vision system with the algorithms was dealt with unpredictable outdoor illuminations to sense plants in the field via a series of image processing. The developed algorithm processed field images with the processing error less than 3 % in terms of identifying individual plants, and the algorithm identified 92.5 % and 95.1 % of weeds in the image via trained artificial neural network. The direct application end effector with the arm executed the direct application via cutting weed stem and wiping chemical on the weed surface. The indoor experiment resulted that 90.9 % of the weed treated by the end effector had herbicide symptoms after 6 day from the application. The plant specific direct chemical application field robot was developed by utilizing the vision system and the end effector with the arm to a field robot platform. The potential of developed robot was examined by testing the robot in the field. While testing in the field, the robot autonomously controlled 63.6 % of weeds, and required 12.923 seconds to control a weed from detection to application inspection for reapplication via machine vision.","Made available in DSpace on 2015-09-28T14:53:52Z (GMT). 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The robot was designed to autonomously detect plants, to identify weeds and to control weeds in the field. The developed robot was equipped with a stereovision with processing algorithms to detect and identify individual weeds in the field image and the custom designed end effector with a robotic arm to carry out plant specific direct application. The developed vision system with the algorithms was dealt with unpredictable outdoor illuminations to sense plants in the field via a series of image processing. The developed algorithm processed field images with the processing error less than 3 % in terms of identifying individual plants, and the algorithm identified 92.5 % and 95.1 % of weeds in the image via trained artificial neural network. The direct application end effector with the arm executed the direct application via cutting weed stem and wiping chemical on the weed surface. The indoor experiment resulted that 90.9 % of the weed treated by the end effector had herbicide symptoms after 6 day from the application. The plant specific direct chemical application field robot was developed by utilizing the vision system and the end effector with the arm to a field robot platform. The potential of developed robot was examined by testing the robot in the field. While testing in the field, the robot autonomously controlled 63.6 % of weeds, and required 12.923 seconds to control a weed from detection to application inspection for reapplication via machine vision.","Made available in DSpace on 2015-09-28T14:53:52Z (GMT). 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