{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99224"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99224","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Development of UAV-based lidar crop height mapping system","abstract":"Crop height monitoring is important to appropriate field management. Previous studies have indicated that a Light Detection and Ranging (LiDAR) sensor was capable of accurate and fast data collection. As technology of Unmanned Aerial Vehicles (UAV) advanced, the airborne LiDAR system became a promising remote sensing based method for non-destructive crop height measurement. The objective of this study was to develop a UAV-based LiDAR system for crop height measurement. The system consisted of a 360-degree 2D laser scanner and an onboard computer mounted on an open source UAV platform. A data processing and visualization algorithm was developed to process dense spatial point cloud data and generate a crop height map of the target field. Outdoor experiments were conducted in a real corn field to evaluate the height measurement performance of the UAV-based LiDAR system. The results show that the average R2 value of 0.87, average mean error (absolute error) of 4.47% and average RMSE of 0.143 m, were achieved in comparison with manual measurements. The UAV-based LiDAR system could cover a 2 acre corn field within eight and half minutes, saving about 90% operation time compared with a tractor-based LiDAR system. The UAV-based LiDAR system developed in this study demonstrated its accurate and efficient crop height measurement in a real crop field, and is expected to be applied in practical agricultural production.","abstract_html":"Crop height monitoring is important to appropriate field management. Previous studies have indicated that a Light Detection and Ranging (LiDAR) sensor was capable of accurate and fast data collection. As technology of Unmanned Aerial Vehicles (UAV) advanced, the airborne LiDAR system became a promising remote sensing based method for non-destructive crop height measurement. The objective of this study was to develop a UAV-based LiDAR system for crop height measurement. The system consisted of a 360-degree 2D laser scanner and an onboard computer mounted on an open source UAV platform. A data processing and visualization algorithm was developed to process dense spatial point cloud data and generate a crop height map of the target field. Outdoor experiments were conducted in a real corn field to evaluate the height measurement performance of the UAV-based LiDAR system. The results show that the average R2 value of 0.87, average mean error (absolute error) of 4.47% and average RMSE of 0.143 m, were achieved in comparison with manual measurements. The UAV-based LiDAR system could cover a 2 acre corn field within eight and half minutes, saving about 90% operation time compared with a tractor-based LiDAR system. The UAV-based LiDAR system developed in this study demonstrated its accurate and efficient crop height measurement in a real crop field, and is expected to be applied in practical agricultural production.","abstract_has_math":false,"creators":["Zhu, Huangjian"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Agricultural & Biological Engr","degree_department":null,"school":null,"contributors":["Tian, Lei"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-13T15:25:22Z","date_published":"2018-03-13T15:25:22Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Unmanned aerial vehicle","Light Detection and Ranging (LiDAR)","Remote sensing","Point cloud","Crop height"],"languages":["en"],"rights":["Copyright 2017 Huangjian Zhu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99224","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tian, Lei"]},{"key":"dc:creator","label":"Author","values":["Zhu, Huangjian"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-13T15:25:22Z","2020-03-14T09:15:08Z","2017-12-04","2017-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Agricultural & Biological Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Unmanned aerial vehicle","Light Detection and Ranging (LiDAR)","Remote sensing","Point cloud","Crop height"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Huangjian Zhu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99224"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Crop height monitoring is important to appropriate field management. Previous studies have indicated that a Light Detection and Ranging (LiDAR) sensor was capable of accurate and fast data collection. As technology of Unmanned Aerial Vehicles (UAV) advanced, the airborne LiDAR system became a promising remote sensing based method for non-destructive crop height measurement. The objective of this study was to develop a UAV-based LiDAR system for crop height measurement. The system consisted of a 360-degree 2D laser scanner and an onboard computer mounted on an open source UAV platform. A data processing and visualization algorithm was developed to process dense spatial point cloud data and generate a crop height map of the target field. Outdoor experiments were conducted in a real corn field to evaluate the height measurement performance of the UAV-based LiDAR system. The results show that the average R2 value of 0.87, average mean error (absolute error) of 4.47% and average RMSE of 0.143 m, were achieved in comparison with manual measurements. The UAV-based LiDAR system could cover a 2 acre corn field within eight and half minutes, saving about 90% operation time compared with a tractor-based LiDAR system. The UAV-based LiDAR system developed in this study demonstrated its accurate and efficient crop height measurement in a real crop field, and is expected to be applied in practical agricultural production.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01","The student, Huangjian Zhu, accepted the attached license on 2017-12-02 at 16:28.","The student, Huangjian Zhu, submitted this Thesis for approval on 2017-12-02 at 16:56.","This Thesis was approved for publication on 2017-12-04 at 12:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11813 on 2018-03-13 at 09:56:44","Made available in DSpace on 2018-03-13T15:25:22Z (GMT). No. of bitstreams: 2 ZHU-THESIS-2017.pdf: 3174604 bytes, checksum: c14b158378743238b61142e7052b78d5 (MD5) LICENSE.txt: 4210 bytes, checksum: c7b95ae00c039efc5bfa04f3fedc5cc9 (MD5) Previous issue date: 2017-12-04","Embargo set by: Seth Robbins for item 105187 Lift date: 2020-03-13T15:25:40Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Embargo set by: Seth Robbins for item 105187 Lift date: 2020-03-13T15:28:52Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only Restriction Lifted for Item 105187 on 2020-03-14T09:15:08Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Development of UAV-based lidar crop height mapping system"]}]}],"canonical_facts":{"dc:contributor":["Tian, Lei"],"dc:creator":["Zhu, Huangjian"],"dc:date":["2018-03-13T15:25:22Z","2020-03-14T09:15:08Z","2017-12-04","2017-12"],"dc:description":["Crop height monitoring is important to appropriate field management. Previous studies have indicated that a Light Detection and Ranging (LiDAR) sensor was capable of accurate and fast data collection. As technology of Unmanned Aerial Vehicles (UAV) advanced, the airborne LiDAR system became a promising remote sensing based method for non-destructive crop height measurement. The objective of this study was to develop a UAV-based LiDAR system for crop height measurement. The system consisted of a 360-degree 2D laser scanner and an onboard computer mounted on an open source UAV platform. A data processing and visualization algorithm was developed to process dense spatial point cloud data and generate a crop height map of the target field. Outdoor experiments were conducted in a real corn field to evaluate the height measurement performance of the UAV-based LiDAR system. The results show that the average R2 value of 0.87, average mean error (absolute error) of 4.47% and average RMSE of 0.143 m, were achieved in comparison with manual measurements. The UAV-based LiDAR system could cover a 2 acre corn field within eight and half minutes, saving about 90% operation time compared with a tractor-based LiDAR system. The UAV-based LiDAR system developed in this study demonstrated its accurate and efficient crop height measurement in a real crop field, and is expected to be applied in practical agricultural production.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2019-12-01","The student, Huangjian Zhu, accepted the attached license on 2017-12-02 at 16:28.","The student, Huangjian Zhu, submitted this Thesis for approval on 2017-12-02 at 16:56.","This Thesis was approved for publication on 2017-12-04 at 12:41.","DSpace SAF Submission Ingestion Package generated from Vireo submission #11813 on 2018-03-13 at 09:56:44","Made available in DSpace on 2018-03-13T15:25:22Z (GMT). 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