{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/105844"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/105844","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Autonomous robotic system for high throughput plant phenotyping (width estimation) in agricultural fields","abstract":"U of I Only Restriction Lifted for Item 112989 on 2021-11-27T10:15:27Z.","abstract_html":"U of I Only Restriction Lifted for Item 112989 on 2021-11-27T10:15:27Z.","abstract_has_math":false,"creators":["Choudhuri, Anwesa"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Aerospace Engineering","degree_department":null,"school":null,"contributors":["Chowdhary, Girish"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-11-26T20:49:37Z","date_published":"2019-11-26T20:49:37Z","updated_at":"2026-07-22T22:24:45Z","subjects":["Robotics, Deep Learning, Computer Vision, Agriculture"],"languages":["en"],"rights":["Copyright 2019 Anwesa Choudhuri"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/105844","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish"]},{"key":"dc:creator","label":"Author","values":["Choudhuri, Anwesa"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-11-26T20:49:37Z","2021-11-27T10:15:27Z","2019-07-19","2019-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"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":["Robotics, Deep Learning, Computer Vision, Agriculture"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2019 Anwesa Choudhuri"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/105844"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["U of I Only Restriction Lifted for Item 112989 on 2021-11-27T10:15:27Z.","We present an autonomous robotic system for the estimation of crop stem width in highly cluttered and variable agricultural fields. Stem width is an important phenotype (observable trait) needed by breeders and plant-biologists to measure plant growth. However, its manual measurement is cumbersome, inaccurate, and inefficient. There is an immense need to automate such a task in order to increase the productivity of plants in future. The presented system aims to achieve this goal. It navigates autonomously through every row of an agricultural field under the plant canopy. This navigation is based on deep optical flow on videos collected by the robot and lane estimates from a low cost LiDAR sensor. The phenotyping is performed using deep learning or a sequence of image processing steps to eliminate background. Width is estimated based on the robot velocity from wheel encoders, and validated by the lane estimates from the LiDAR. This system has been tested and exhaustively validated against available hand-measurements on biomass sorghum (Sorghum bicolor) in real experimental fields. Experiments indicate that this system is also effective for other kinds of crops, like corn. The width estimation match on sorghum is 93.5% (using optical flow) and 92.38% (using LiDAR lane estimates) when compared against manual measurements by trained agronomists. Thus, our results clearly establish the feasibility of using small robots for stem-width estimation under the canopy in realistic field settings. Furthermore, the techniques presented here can be utilized for automating other important phenotypic measurements.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01","The student, Anwesa Choudhuri, accepted the attached license on 2019-07-19 at 11:31.","The student, Anwesa Choudhuri, submitted this Thesis for approval on 2019-07-19 at 11:47.","This Thesis was approved for publication on 2019-07-19 at 13:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14390 on 2019-11-26 at 13:06:25","Made available in DSpace on 2019-11-26T20:49:37Z (GMT). No. of bitstreams: 2 CHOUDHURI-THESIS-2019.pdf: 12934001 bytes, checksum: 586e18b229766162aba86a436b85020b (MD5) LICENSE.txt: 4213 bytes, checksum: 53c213e45dbdf96ffbec5d12b538cf14 (MD5) Previous issue date: 2019-07-19","Embargo set by: Seth Robbins for item 112989 Lift date: 2021-11-26T20:49:41Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Autonomous robotic system for high throughput plant phenotyping (width estimation) in agricultural fields"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish"],"dc:creator":["Choudhuri, Anwesa"],"dc:date":["2019-11-26T20:49:37Z","2021-11-27T10:15:27Z","2019-07-19","2019-08"],"dc:description":["U of I Only Restriction Lifted for Item 112989 on 2021-11-27T10:15:27Z.","We present an autonomous robotic system for the estimation of crop stem width in highly cluttered and variable agricultural fields. Stem width is an important phenotype (observable trait) needed by breeders and plant-biologists to measure plant growth. However, its manual measurement is cumbersome, inaccurate, and inefficient. There is an immense need to automate such a task in order to increase the productivity of plants in future. The presented system aims to achieve this goal. It navigates autonomously through every row of an agricultural field under the plant canopy. This navigation is based on deep optical flow on videos collected by the robot and lane estimates from a low cost LiDAR sensor. The phenotyping is performed using deep learning or a sequence of image processing steps to eliminate background. Width is estimated based on the robot velocity from wheel encoders, and validated by the lane estimates from the LiDAR. This system has been tested and exhaustively validated against available hand-measurements on biomass sorghum (Sorghum bicolor) in real experimental fields. Experiments indicate that this system is also effective for other kinds of crops, like corn. The width estimation match on sorghum is 93.5% (using optical flow) and 92.38% (using LiDAR lane estimates) when compared against manual measurements by trained agronomists. Thus, our results clearly establish the feasibility of using small robots for stem-width estimation under the canopy in realistic field settings. Furthermore, the techniques presented here can be utilized for automating other important phenotypic measurements.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2021-08-01","The student, Anwesa Choudhuri, accepted the attached license on 2019-07-19 at 11:31.","The student, Anwesa Choudhuri, submitted this Thesis for approval on 2019-07-19 at 11:47.","This Thesis was approved for publication on 2019-07-19 at 13:39.","DSpace SAF Submission Ingestion Package generated from Vireo submission #14390 on 2019-11-26 at 13:06:25","Made available in DSpace on 2019-11-26T20:49:37Z (GMT). No. of bitstreams: 2 CHOUDHURI-THESIS-2019.pdf: 12934001 bytes, checksum: 586e18b229766162aba86a436b85020b (MD5) LICENSE.txt: 4213 bytes, checksum: 53c213e45dbdf96ffbec5d12b538cf14 (MD5) Previous issue date: 2019-07-19","Embargo set by: Seth Robbins for item 112989 Lift date: 2021-11-26T20:49:41Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/105844"],"dc:language":["en"],"dc:rights":["Copyright 2019 Anwesa Choudhuri"],"dc:subject":["Robotics, Deep Learning, Computer Vision, Agriculture"],"dc:title":["Autonomous robotic system for high throughput plant phenotyping (width estimation) in agricultural fields"],"dc:type":["text"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:45Z"}