{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/110652"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/110652","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"The mechanical and algorithmic design of in-field robotic leaf sampling device","abstract":"Leaf samples analysis is a significant tool to acquire the actual nutrition information of crops. After that, farmers can adjust fertilization programs to prevent nutritional problems and improve the yield of crops. The traditional way for leaf sampling is manual, and researchers need to go to the field and use paper hole punchers with a catch-tube to collect leaf samples. The temperature in summer is hot, and some crop like corn is difficult for researchers to walk through, therefore the manual way of leaf sampling is not a good option. In this thesis, an automatic method of leaf sampling is presented to solve the difficulty of leaf sampling. The contributions of this thesis are the following: (1) Build the end effector of leaf sampling device to punch and store leaf samples separately, (2) Train a neural network to detect the leaves with high horizontal level, (3) Combine point cloud data from the depth camera and vison data from the camera via the sensor fusion to get the leaf rolling angle and grasp point. The method in this thesis can produce a consistent leaf rolling angle estimate quantitatively and qualitatively on multiple corn leaves, especially on leaves with multiple different angles.","abstract_html":"Leaf samples analysis is a significant tool to acquire the actual nutrition information of crops. After that, farmers can adjust fertilization programs to prevent nutritional problems and improve the yield of crops. The traditional way for leaf sampling is manual, and researchers need to go to the field and use paper hole punchers with a catch-tube to collect leaf samples. The temperature in summer is hot, and some crop like corn is difficult for researchers to walk through, therefore the manual way of leaf sampling is not a good option. In this thesis, an automatic method of leaf sampling is presented to solve the difficulty of leaf sampling. The contributions of this thesis are the following: (1) Build the end effector of leaf sampling device to punch and store leaf samples separately, (2) Train a neural network to detect the leaves with high horizontal level, (3) Combine point cloud data from the depth camera and vison data from the camera via the sensor fusion to get the leaf rolling angle and grasp point. The method in this thesis can produce a consistent leaf rolling angle estimate quantitatively and qualitatively on multiple corn leaves, especially on leaves with multiple different angles.","abstract_has_math":false,"creators":["Wu, Junzhe"],"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":["Chowdhary, Girish","Allen, Cody Michael","Stasiewicz, Matthew Jon"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-09-17T02:34:23Z","date_published":"2021-09-17T02:34:23Z","updated_at":"2026-07-22T22:24:52Z","subjects":["Leaf sampling","End effector","Neural network","Sensor fusion."],"languages":["en"],"rights":["Copyright 2021 Junzhe Wu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/110652","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish","Allen, Cody Michael","Stasiewicz, Matthew Jon"]},{"key":"dc:creator","label":"Author","values":["Wu, Junzhe"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-09-17T02:34:23Z","2021-04-28","2021-05"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"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":["Leaf sampling","End effector","Neural network","Sensor fusion."]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2021 Junzhe Wu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/110652"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Leaf samples analysis is a significant tool to acquire the actual nutrition information of crops. After that, farmers can adjust fertilization programs to prevent nutritional problems and improve the yield of crops. The traditional way for leaf sampling is manual, and researchers need to go to the field and use paper hole punchers with a catch-tube to collect leaf samples. The temperature in summer is hot, and some crop like corn is difficult for researchers to walk through, therefore the manual way of leaf sampling is not a good option. In this thesis, an automatic method of leaf sampling is presented to solve the difficulty of leaf sampling. The contributions of this thesis are the following: (1) Build the end effector of leaf sampling device to punch and store leaf samples separately, (2) Train a neural network to detect the leaves with high horizontal level, (3) Combine point cloud data from the depth camera and vison data from the camera via the sensor fusion to get the leaf rolling angle and grasp point. The method in this thesis can produce a consistent leaf rolling angle estimate quantitatively and qualitatively on multiple corn leaves, especially on leaves with multiple different angles.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Junzhe Wu, accepted the attached license on 2021-04-20 at 22:36.","The student, Junzhe Wu, submitted this Thesis for approval on 2021-04-20 at 22:57.","This Thesis was approved for publication on 2021-04-28 at 09:08.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16280 on 2021-09-16 at 17:02:48","Made available in DSpace on 2021-09-17T02:34:23Z (GMT). No. of bitstreams: 3 WU-THESIS-2021.pdf: 1403479 bytes, checksum: 2bd66b35c5b213509c856a9db77a144e (MD5) JUNZHE-THESIS-2021.docx: 12228713 bytes, checksum: 018c0b8acbeb359ec9c53a31d8976adb (MD5) LICENSE.txt: 4206 bytes, checksum: 0085cb21b779017879cd821c3a65ae70 (MD5) Previous issue date: 2021-04-28","Embargo set by: Seth Robbins for item 118495 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Open Restriction set for Item 118495 on 2021-10-05T15:20:27Z with date null by eliasbh2@illinois.edu.","Open"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["The mechanical and algorithmic design of in-field robotic leaf sampling device"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish","Allen, Cody Michael","Stasiewicz, Matthew Jon"],"dc:creator":["Wu, Junzhe"],"dc:date":["2021-09-17T02:34:23Z","2021-04-28","2021-05"],"dc:description":["Leaf samples analysis is a significant tool to acquire the actual nutrition information of crops. After that, farmers can adjust fertilization programs to prevent nutritional problems and improve the yield of crops. The traditional way for leaf sampling is manual, and researchers need to go to the field and use paper hole punchers with a catch-tube to collect leaf samples. The temperature in summer is hot, and some crop like corn is difficult for researchers to walk through, therefore the manual way of leaf sampling is not a good option. In this thesis, an automatic method of leaf sampling is presented to solve the difficulty of leaf sampling. The contributions of this thesis are the following: (1) Build the end effector of leaf sampling device to punch and store leaf samples separately, (2) Train a neural network to detect the leaves with high horizontal level, (3) Combine point cloud data from the depth camera and vison data from the camera via the sensor fusion to get the leaf rolling angle and grasp point. The method in this thesis can produce a consistent leaf rolling angle estimate quantitatively and qualitatively on multiple corn leaves, especially on leaves with multiple different angles.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-05-01","The student, Junzhe Wu, accepted the attached license on 2021-04-20 at 22:36.","The student, Junzhe Wu, submitted this Thesis for approval on 2021-04-20 at 22:57.","This Thesis was approved for publication on 2021-04-28 at 09:08.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16280 on 2021-09-16 at 17:02:48","Made available in DSpace on 2021-09-17T02:34:23Z (GMT). No. of bitstreams: 3 WU-THESIS-2021.pdf: 1403479 bytes, checksum: 2bd66b35c5b213509c856a9db77a144e (MD5) JUNZHE-THESIS-2021.docx: 12228713 bytes, checksum: 018c0b8acbeb359ec9c53a31d8976adb (MD5) LICENSE.txt: 4206 bytes, checksum: 0085cb21b779017879cd821c3a65ae70 (MD5) Previous issue date: 2021-04-28","Embargo set by: Seth Robbins for item 118495 Lift date: 2023-09-17T02:34:57Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Open Restriction set for Item 118495 on 2021-10-05T15:20:27Z with date null by eliasbh2@illinois.edu.","Open"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/110652"],"dc:language":["en"],"dc:rights":["Copyright 2021 Junzhe Wu"],"dc:subject":["Leaf sampling","End effector","Neural network","Sensor fusion."],"dc:title":["The mechanical and algorithmic design of in-field robotic leaf sampling device"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Agricultural & Biological Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:52Z"}