{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117694"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117694","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Agriculture-vision: A large aerial image database for agricultural pattern analysis","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","abstract_has_math":false,"creators":["Chiu, Mang Tik"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Shi, Humphrey"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["vision","agriculture","segmentation","aerial image"],"languages":["en","eng"],"rights":["Copyright 2022 Mang Tik Chiu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117694","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Shi, Humphrey"]},{"key":"dc:creator","label":"Author","values":["Chiu, Mang Tik"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-08-30"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer 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":["vision","agriculture","segmentation","aerial image"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Mang Tik Chiu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117694"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Mang Tik Chiu, accepted the attached license on 2022-08-25 at 23:17.","The student, Mang Tik Chiu, submitted this Thesis for approval on 2022-08-25 at 23:23.","This Thesis was approved for publication on 2022-08-30 at 10:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17225 on 2023-04-12 at 07:22:44","The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its application in agriculture. Nevertheless, while visual pattern recognition on farmlands carries enormous economic values, little progress has been made to merge computer vision and crop sciences due to the lack of suitable agricultural image datasets. Meanwhile, problems in agriculture also pose new challenges in computer vision. For example, semantic segmentation of aerial farmland images requires inference over extremely large-size images with extreme annotation sparsity. These challenges are not present in most of the common object datasets, and we show that they are more challenging than many other aerial image datasets. In this thesis, we present a pilot study in computer vision for agriculture, specifically semantic segmentation of agricultural patterns. We collected $94,986$ high-quality aerial images from $3,432$ farmlands across the US, where each image consists of Red-Green-Blue (RGB) and Near-infrared (NIR) channels with resolutions as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers. As a pilot study of aerial agricultural semantic segmentation, we perform comprehensive experiments using popular semantic segmentation models; we also propose an effective model designed for aerial agricultural pattern recognition. Our experiments demonstrate several challenges of vision and agriculture."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Agriculture-vision: A large aerial image database for agricultural pattern analysis"]}]}],"canonical_facts":{"dc:contributor":["Shi, Humphrey"],"dc:creator":["Chiu, Mang Tik"],"dc:date":["2022-12","2022-08-30"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2023-04-12 without embargo terms","The student, Mang Tik Chiu, accepted the attached license on 2022-08-25 at 23:17.","The student, Mang Tik Chiu, submitted this Thesis for approval on 2022-08-25 at 23:23.","This Thesis was approved for publication on 2022-08-30 at 10:53.","DSpace SAF Submission Ingestion Package generated from Vireo submission #17225 on 2023-04-12 at 07:22:44","The success of deep learning in visual recognition tasks has driven advancements in multiple fields of research. Particularly, increasing attention has been drawn towards its application in agriculture. Nevertheless, while visual pattern recognition on farmlands carries enormous economic values, little progress has been made to merge computer vision and crop sciences due to the lack of suitable agricultural image datasets. Meanwhile, problems in agriculture also pose new challenges in computer vision. For example, semantic segmentation of aerial farmland images requires inference over extremely large-size images with extreme annotation sparsity. These challenges are not present in most of the common object datasets, and we show that they are more challenging than many other aerial image datasets. In this thesis, we present a pilot study in computer vision for agriculture, specifically semantic segmentation of agricultural patterns. We collected $94,986$ high-quality aerial images from $3,432$ farmlands across the US, where each image consists of Red-Green-Blue (RGB) and Near-infrared (NIR) channels with resolutions as high as 10 cm per pixel. We annotate nine types of field anomaly patterns that are most important to farmers. As a pilot study of aerial agricultural semantic segmentation, we perform comprehensive experiments using popular semantic segmentation models; we also propose an effective model designed for aerial agricultural pattern recognition. Our experiments demonstrate several challenges of vision and agriculture."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117694"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Mang Tik Chiu"],"dc:subject":["vision","agriculture","segmentation","aerial image"],"dc:title":["Agriculture-vision: A large aerial image database for agricultural pattern analysis"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}