{"id":{"repo_id":"tamu","oai_identifier":"oai:oaktrust.library.tamu.edu:1969.1/1598761"},"canonical_url":"https://search.dev.ndltd.org/etd/tamu/oai:oaktrust.library.tamu.edu:1969.1/1598761","repository":{"repo_id":"tamu","name":"Texas A&M University","base_url":"https://oaktrust.library.tamu.edu/server/oai/request"},"display":{"title":"Analyzing Unoccupied Aerial Systems Benefits to Enhance Plant Breeding Pipeline Efficiency","abstract":"Sorghum ((Sorghum bicolor (L.) Moench ssp. bicolor) is commonly grown as a grain or forage crop. Grain sorghum hybrids are short in stature and the grain is used for animal feed, food products, and alcohol. Alternatively, forage sorghum hybrids are taller, leafier, and used for grazing, silage, and industrial purposes. Breeding of sorghum can be time consuming, expensive, and subject to error. Unoccupied aerial systems (UAS, also known as UAVs or drones) have gained popularity because of their ability to collect data for decision making using fewer resources, while improving the overall quality of data. The goal of this dissertation was to determine how UAS can be integrated into a sorghum breeding program to efficiently collect data on important agronomic traits. Within the first chapter, a high-throughput phenotyping pipeline was built to evaluate plant height, days to mid-anthesis and biomass yield. Using a set of twelve trials and various machine learning models, several forage sorghum agronomic traits were collected via a UAS with increased repeatability in comparison to traditional manual measurements. The objective of the second chapter was to evaluate stay-green within grain sorghum hybrids across multi-environment trials and to build a machine learning model to quantify stay-green using UAS derived indices. Two stay-green models were trained, tested, and validated using nine trials portraying that the UAS could collect stay-green data with high accuracy. The objective of the third chapter was to assess the phenomic relationship between selected and non-selected F₃ sorghum B-and R-lines within a family and across families while exploiting these relationships to determine if plots could be selected via UAS data. Selections were performed by multiple individuals who portrayed that selections are greatly variable and influenced by the environment. The models were not able to replicate individuals, due to the heavily biased methodology. Overall, this dissertation depicts the benefits, limitations and applications of a UAS into various sectors of a sorghum breeding program.","abstract_html":"Sorghum ((Sorghum bicolor (L.) Moench ssp. bicolor) is commonly grown as a grain or forage crop. Grain sorghum hybrids are short in stature and the grain is used for animal feed, food products, and alcohol. Alternatively, forage sorghum hybrids are taller, leafier, and used for grazing, silage, and industrial purposes. Breeding of sorghum can be time consuming, expensive, and subject to error. Unoccupied aerial systems (UAS, also known as UAVs or drones) have gained popularity because of their ability to collect data for decision making using fewer resources, while improving the overall quality of data. The goal of this dissertation was to determine how UAS can be integrated into a sorghum breeding program to efficiently collect data on important agronomic traits. Within the first chapter, a high-throughput phenotyping pipeline was built to evaluate plant height, days to mid-anthesis and biomass yield. Using a set of twelve trials and various machine learning models, several forage sorghum agronomic traits were collected via a UAS with increased repeatability in comparison to traditional manual measurements. The objective of the second chapter was to evaluate stay-green within grain sorghum hybrids across multi-environment trials and to build a machine learning model to quantify stay-green using UAS derived indices. Two stay-green models were trained, tested, and validated using nine trials portraying that the UAS could collect stay-green data with high accuracy. The objective of the third chapter was to assess the phenomic relationship between selected and non-selected F₃ sorghum B-and R-lines within a family and across families while exploiting these relationships to determine if plots could be selected via UAS data. Selections were performed by multiple individuals who portrayed that selections are greatly variable and influenced by the environment. The models were not able to replicate individuals, due to the heavily biased methodology. Overall, this dissertation depicts the benefits, limitations and applications of a UAS into various sectors of a sorghum breeding program.","abstract_has_math":false,"creators":["Beechinor, Kayla Ann 1998-"],"institution":"Texas A&M University","degree_name":"Doctor of Philosophy","degree_level":null,"degree_discipline":"Plant Breeding","degree_department":null,"school":null,"contributors":[],"advisors":["Rooney, William"],"committee_chairs":[],"committee_members":["Sorin Popescu","Nithya Rajan","Seth C. 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Moench ssp. bicolor) is commonly grown as a grain or forage crop. Grain sorghum hybrids are short in stature and the grain is used for animal feed, food products, and alcohol. Alternatively, forage sorghum hybrids are taller, leafier, and used for grazing, silage, and industrial purposes. Breeding of sorghum can be time consuming, expensive, and subject to error. Unoccupied aerial systems (UAS, also known as UAVs or drones) have gained popularity because of their ability to collect data for decision making using fewer resources, while improving the overall quality of data. The goal of this dissertation was to determine how UAS can be integrated into a sorghum breeding program to efficiently collect data on important agronomic traits. Within the first chapter, a high-throughput phenotyping pipeline was built to evaluate plant height, days to mid-anthesis and biomass yield. Using a set of twelve trials and various machine learning models, several forage sorghum agronomic traits were collected via a UAS with increased repeatability in comparison to traditional manual measurements. The objective of the second chapter was to evaluate stay-green within grain sorghum hybrids across multi-environment trials and to build a machine learning model to quantify stay-green using UAS derived indices. Two stay-green models were trained, tested, and validated using nine trials portraying that the UAS could collect stay-green data with high accuracy. The objective of the third chapter was to assess the phenomic relationship between selected and non-selected F₃ sorghum B-and R-lines within a family and across families while exploiting these relationships to determine if plots could be selected via UAS data. Selections were performed by multiple individuals who portrayed that selections are greatly variable and influenced by the environment. The models were not able to replicate individuals, due to the heavily biased methodology. Overall, this dissertation depicts the benefits, limitations and applications of a UAS into various sectors of a sorghum breeding program."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Analyzing Unoccupied Aerial Systems Benefits to Enhance Plant Breeding Pipeline Efficiency"]}]}],"canonical_facts":{"dc:contributor.advisor":["Rooney, William"],"dc:contributor.committeemember":["Sorin Popescu","Nithya Rajan","Seth C. Murray"],"dc:creator":["Beechinor, Kayla Ann 1998-"],"dc:date.accessioned":["2026-02-04T22:29:36Z"],"dc:date.issued":["2025-08"],"dc:description.abstract":["Sorghum ((Sorghum bicolor (L.) Moench ssp. bicolor) is commonly grown as a grain or forage crop. Grain sorghum hybrids are short in stature and the grain is used for animal feed, food products, and alcohol. Alternatively, forage sorghum hybrids are taller, leafier, and used for grazing, silage, and industrial purposes. Breeding of sorghum can be time consuming, expensive, and subject to error. Unoccupied aerial systems (UAS, also known as UAVs or drones) have gained popularity because of their ability to collect data for decision making using fewer resources, while improving the overall quality of data. The goal of this dissertation was to determine how UAS can be integrated into a sorghum breeding program to efficiently collect data on important agronomic traits. Within the first chapter, a high-throughput phenotyping pipeline was built to evaluate plant height, days to mid-anthesis and biomass yield. Using a set of twelve trials and various machine learning models, several forage sorghum agronomic traits were collected via a UAS with increased repeatability in comparison to traditional manual measurements. The objective of the second chapter was to evaluate stay-green within grain sorghum hybrids across multi-environment trials and to build a machine learning model to quantify stay-green using UAS derived indices. Two stay-green models were trained, tested, and validated using nine trials portraying that the UAS could collect stay-green data with high accuracy. The objective of the third chapter was to assess the phenomic relationship between selected and non-selected F₃ sorghum B-and R-lines within a family and across families while exploiting these relationships to determine if plots could be selected via UAS data. Selections were performed by multiple individuals who portrayed that selections are greatly variable and influenced by the environment. The models were not able to replicate individuals, due to the heavily biased methodology. Overall, this dissertation depicts the benefits, limitations and applications of a UAS into various sectors of a sorghum breeding program."],"dc:format.mimetype":["application/pdf"],"dc:identifier.uri":["https://hdl.handle.net/1969.1/1598761"],"dc:language.iso":["English"],"dc:subject":["Agriculture, General"],"dc:title":["Analyzing Unoccupied Aerial Systems Benefits to Enhance Plant Breeding Pipeline Efficiency"],"dc:type":["Thesis"],"thesis:degree_discipline":["Plant Breeding"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Texas A&M University"]},"updated_at":"2026-08-21T16:48:44Z"}