{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/113142"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/113142","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Using artificial intelligence to predict NDVI/NDRE from standard RGB aerial imagery","abstract":"The student, Corey Davidson, submitted this Thesis for approval on 2021-07-05 at 17:13.","abstract_html":"The student, Corey Davidson, submitted this Thesis for approval on 2021-07-05 at 17:13.","abstract_has_math":false,"creators":["Davidson, Corey Landon"],"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","Czarnecki, Joby"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-12T22:34:55Z","date_published":"2022-01-12T22:34:55Z","updated_at":"2026-07-22T22:24:53Z","subjects":["Machine Learning","Pix2Pix","AI","NDVI","Deep Learning","Imagery"],"languages":["en"],"rights":["Copyright 2021 Corey Davidson"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/113142","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Chowdhary, Girish","Allen, Cody","Czarnecki, Joby"]},{"key":"dc:creator","label":"Author","values":["Davidson, Corey Landon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-01-12T22:34:55Z","2024-01-12T22:35:30Z","2021-07-08","2021-08"]},{"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":["Machine Learning","Pix2Pix","AI","NDVI","Deep Learning","Imagery"]}]},{"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 Corey Davidson"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/113142"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The student, Corey Davidson, submitted this Thesis for approval on 2021-07-05 at 17:13.","This Thesis was approved for publication on 2021-07-08 at 13:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16758 on 2022-01-12 at 12:52:55","Made available in DSpace on 2022-01-12T22:34:55Z (GMT). No. of bitstreams: 2 DAVIDSON-THESIS-2021.pdf: 244905283 bytes, checksum: 50951e09e91effce0c3ce12cebabe5fa (MD5) LICENSE.txt: 4211 bytes, checksum: 9adf3f30ae913fa1d355e627e4c5b17d (MD5) Previous issue date: 2021-07-08","Embargo set by: Seth Robbins for item 121068 Lift date: 2024-01-12T22:35:30Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only","The growth of precision agriculture has allowed farmers access to more data and greater efficiency for their farms. With consistently tight profit margins, farmers need ways to take advantage of the advancement of technology to lower their costs or increase their revenue. One area where these advancements can prove beneficial are in the measurement of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE). Currently, an expensive multispectral camera, typically attached to an Unmanned Aerial Vehicle (UAV) during flight, is required for measuring these indices. This makes obtaining NDVI and NDRE somewhat cost prohibitive for most farmers. Color maps representing these vegetation indices can be used to identify problem areas, plant health, or even places where spot applications are needed. This work demonstrates a solution to this cost issue. The solution involves the use of artificial intelligence, or more specifically, the use of a conditional Generative Adversarial Network known as Pix2Pix. By using Pix2Pix along with training data from UAV flights, this research shows the capabilities of predicting accurate NDVI and NDRE with a low-cost Red-Green-Blue (RGB) camera. This thesis explores and assesses a cost-efficient method that can accurately predict these vegetation indices, resulting in cost-savings in the range of $5000.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Corey Davidson, accepted the attached license on 2021-07-05 at 17:02."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Using artificial intelligence to predict NDVI/NDRE from standard RGB aerial imagery"]}]}],"canonical_facts":{"dc:contributor":["Chowdhary, Girish","Allen, Cody","Czarnecki, Joby"],"dc:creator":["Davidson, Corey Landon"],"dc:date":["2022-01-12T22:34:55Z","2024-01-12T22:35:30Z","2021-07-08","2021-08"],"dc:description":["The student, Corey Davidson, submitted this Thesis for approval on 2021-07-05 at 17:13.","This Thesis was approved for publication on 2021-07-08 at 13:24.","DSpace SAF Submission Ingestion Package generated from Vireo submission #16758 on 2022-01-12 at 12:52:55","Made available in DSpace on 2022-01-12T22:34:55Z (GMT). No. of bitstreams: 2 DAVIDSON-THESIS-2021.pdf: 244905283 bytes, checksum: 50951e09e91effce0c3ce12cebabe5fa (MD5) LICENSE.txt: 4211 bytes, checksum: 9adf3f30ae913fa1d355e627e4c5b17d (MD5) Previous issue date: 2021-07-08","Embargo set by: Seth Robbins for item 121068 Lift date: 2024-01-12T22:35:30Z Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD system","U of I Only","The growth of precision agriculture has allowed farmers access to more data and greater efficiency for their farms. With consistently tight profit margins, farmers need ways to take advantage of the advancement of technology to lower their costs or increase their revenue. One area where these advancements can prove beneficial are in the measurement of vegetation indices such as the Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red Edge Index (NDRE). Currently, an expensive multispectral camera, typically attached to an Unmanned Aerial Vehicle (UAV) during flight, is required for measuring these indices. This makes obtaining NDVI and NDRE somewhat cost prohibitive for most farmers. Color maps representing these vegetation indices can be used to identify problem areas, plant health, or even places where spot applications are needed. This work demonstrates a solution to this cost issue. The solution involves the use of artificial intelligence, or more specifically, the use of a conditional Generative Adversarial Network known as Pix2Pix. By using Pix2Pix along with training data from UAV flights, this research shows the capabilities of predicting accurate NDVI and NDRE with a low-cost Red-Green-Blue (RGB) camera. This thesis explores and assesses a cost-efficient method that can accurately predict these vegetation indices, resulting in cost-savings in the range of $5000.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2023-08-01","The student, Corey Davidson, accepted the attached license on 2021-07-05 at 17:02."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/2142/113142"],"dc:language":["en"],"dc:rights":["Copyright 2021 Corey Davidson"],"dc:subject":["Machine Learning","Pix2Pix","AI","NDVI","Deep Learning","Imagery"],"dc:title":["Using artificial intelligence to predict NDVI/NDRE from standard RGB aerial imagery"],"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":"2026-07-22T22:24:53Z"}