{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140555"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140555","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Predicting Corn Response to Variable Synthetic Fertilizer Treatments Using UAV-Derived Imagery","abstract":"Efficient nutrient management is essential for optimizing corn (Zea mays L.) productivity while minimizing environmental and economic costs. Traditional methods for assessing crop responses to nutrients are often damaging and labor-intensive, limiting accurate assessment of spatial and temporal variations. Accurate in-season yield potential estimation plays a vital role in guiding nutrient management decisions and supporting grain marketing strategies. This study evaluated the potential of Unmanned Aerial Vehicle (UAV) derived imagery to estimate chlorophyll (Chl) status and predict corn grain yield potential in-season under variable nitrogen (N), phosphorus (P), and potassium (K) fertilizer treatments across different growth stages. Two field trials (NP and K) were conducted at two locations in Virginia, Kentland Farm in Blacksburg (Kentland), Valley and Ridge province, and the Northern Piedmont Center in Orange (Orange), Piedmont province. These sites vary in altitude, soil type, and climatic conditions, providing contrasting environments for evaluating crop responses to fertilizers. In both trials, factorial arrangement of treatments (varied N, P, and K fertilizer rates) with four replications was implemented with a randomized complete block design (RCBD). Chlorophyll readings (ChlR) were collected using the Soil Plant Analysis Development (SPAD)-502 and atLEAF Chl meters at three growth stages: early vegetative (EV), late vegetative (LV), and reproductive (Repr). These measurements were synchronized with UAV flights performed on the same day. UAV flights were conducted using DJI Mavic equipped with an RGB sensor for visible light and four monochrome sensors for multispectral imaging (red: 650 nm ± 16 nm, green: 560 nm ± 16 nm, near-infrared (NIR): 840 nm ± 26 nm, red-edge: 730 nm ± 16 nm). UAV-derived vegetation indices (VIs) responsive to Chl and indicative of crop yield potential were computed to model ChlR and yield through single and multi-index regression analyses. Multi-index model performance was evaluated through repeated k-fold cross-validation (CV) (k = 5; 30 repetitions). Indices included the Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red-Edge (CIRE), Normalized Difference Red-Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), MERIS Terrestrial Chlorophyll Index (MTCI), Normalized Difference Chlorophyll Index (NDCI), Canopy Chlorophyll Content Index (CCCI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). Weather variation, early-season drought, and late-season rainfall strongly influenced yield formation and grain moisture, overshadowing fertilizer treatment effects. No significant yield differences were detected among N, P, or K levels (p > 0.05). UAV-derived VIs demonstrated significant correlations with both ChlR and yield, with stronger relationships observed during the LV stage when canopy closure and Chl concentration were most stable. In the K trial at Kentland, the relationships between VIs and both ChlR and yield were generally moderate, while in the K trial at Orange, correlations were consistently strong and significant. For ChlR prediction, green and red-edge based indices (GNDVI, NDRE, CIRE, and MTCI) were the most reliable indices, explaining 40 to 55 percent of the variation across both sites and trials. For yield prediction, GNDVI, NDVI, and NDCI consistently exhibited strong relationships at Orange during the LV stage, with R² values ranging from 0.50 to 0.72 across both trials. In contrast, Kentland showed comparatively lower predictive performance, with only moderate relationships observed in the K trial during the LV stage. The use of a polynomial regression (quadratic) model further improved prediction accuracy compared to the linear model in all trials. Multi-index regression further improved predictive accuracy. The best-performing yield models were observed in the K trial at Orange during the LV stage, achieving CV R² values up to 0.71 (CV RMSE of 13.9), while the best ChlR models were found in the NP trial at Orange with CV R² of 0.46 (CV RMSE of 2.45). Model performance was lower for EV and Repr stages. Overall, these findings demonstrate that UAV-based multispectral imaging is an effective tool for monitoring corn canopy Chl status and assessing yield potential, with prediction accuracy varying across growth stages.","abstract_html":"Efficient nutrient management is essential for optimizing corn (Zea mays L.) productivity while minimizing environmental and economic costs. Traditional methods for assessing crop responses to nutrients are often damaging and labor-intensive, limiting accurate assessment of spatial and temporal variations. Accurate in-season yield potential estimation plays a vital role in guiding nutrient management decisions and supporting grain marketing strategies. This study evaluated the potential of Unmanned Aerial Vehicle (UAV) derived imagery to estimate chlorophyll (Chl) status and predict corn grain yield potential in-season under variable nitrogen (N), phosphorus (P), and potassium (K) fertilizer treatments across different growth stages. Two field trials (NP and K) were conducted at two locations in Virginia, Kentland Farm in Blacksburg (Kentland), Valley and Ridge province, and the Northern Piedmont Center in Orange (Orange), Piedmont province. These sites vary in altitude, soil type, and climatic conditions, providing contrasting environments for evaluating crop responses to fertilizers. In both trials, factorial arrangement of treatments (varied N, P, and K fertilizer rates) with four replications was implemented with a randomized complete block design (RCBD). Chlorophyll readings (ChlR) were collected using the Soil Plant Analysis Development (SPAD)-502 and atLEAF Chl meters at three growth stages: early vegetative (EV), late vegetative (LV), and reproductive (Repr). These measurements were synchronized with UAV flights performed on the same day. UAV flights were conducted using DJI Mavic equipped with an RGB sensor for visible light and four monochrome sensors for multispectral imaging (red: 650 nm ± 16 nm, green: 560 nm ± 16 nm, near-infrared (NIR): 840 nm ± 26 nm, red-edge: 730 nm ± 16 nm). UAV-derived vegetation indices (VIs) responsive to Chl and indicative of crop yield potential were computed to model ChlR and yield through single and multi-index regression analyses. Multi-index model performance was evaluated through repeated k-fold cross-validation (CV) (k = 5; 30 repetitions). Indices included the Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red-Edge (CIRE), Normalized Difference Red-Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), MERIS Terrestrial Chlorophyll Index (MTCI), Normalized Difference Chlorophyll Index (NDCI), Canopy Chlorophyll Content Index (CCCI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). Weather variation, early-season drought, and late-season rainfall strongly influenced yield formation and grain moisture, overshadowing fertilizer treatment effects. No significant yield differences were detected among N, P, or K levels (p > 0.05). UAV-derived VIs demonstrated significant correlations with both ChlR and yield, with stronger relationships observed during the LV stage when canopy closure and Chl concentration were most stable. In the K trial at Kentland, the relationships between VIs and both ChlR and yield were generally moderate, while in the K trial at Orange, correlations were consistently strong and significant. For ChlR prediction, green and red-edge based indices (GNDVI, NDRE, CIRE, and MTCI) were the most reliable indices, explaining 40 to 55 percent of the variation across both sites and trials. For yield prediction, GNDVI, NDVI, and NDCI consistently exhibited strong relationships at Orange during the LV stage, with R² values ranging from 0.50 to 0.72 across both trials. In contrast, Kentland showed comparatively lower predictive performance, with only moderate relationships observed in the K trial during the LV stage. The use of a polynomial regression (quadratic) model further improved prediction accuracy compared to the linear model in all trials. Multi-index regression further improved predictive accuracy. The best-performing yield models were observed in the K trial at Orange during the LV stage, achieving CV R² values up to 0.71 (CV RMSE of 13.9), while the best ChlR models were found in the NP trial at Orange with CV R² of 0.46 (CV RMSE of 2.45). Model performance was lower for EV and Repr stages. Overall, these findings demonstrate that UAV-based multispectral imaging is an effective tool for monitoring corn canopy Chl status and assessing yield potential, with prediction accuracy varying across growth stages.","abstract_has_math":false,"creators":["Khulal, Aarati"],"institution":"Virginia Tech","degree_name":"Master of Science","degree_level":"masters","degree_discipline":"Crop and Soil Environmental Sciences","degree_department":"Crop and Soil Environmental Sciences","school":null,"contributors":[],"advisors":[],"committee_chairs":["Walsh, Olga S."],"committee_members":["Gan, Huijie","Rijal, Santosh"],"year":2025,"date_issued":"2025-12-23","date_published":"2025-12-23","updated_at":"2026-07-22T22:20:02Z","subjects":["Unmanned Aerial Vehicle (UAV)","Chlorophyll","SPAD","Growth stage","Precision Agriculture","Regression models","Vegetation Index (VI)","Nutrient Management","Soil Fertility"],"languages":["en"],"rights":["In Copyright"],"rights_urls":["http://rightsstatements.org/vocab/InC/1.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45157"],"render_values":[{"text":"vt_gsexam:45157","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140555","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Walsh, Olga S."]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Gan, Huijie","Rijal, Santosh"]},{"key":"dc:contributor.department","label":"Department","values":["Crop and Soil Environmental Sciences"]},{"key":"dc:creator","label":"Author","values":["Khulal, Aarati"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-12-24T09:00:16Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-12-24T09:00:16Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-12-23"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Crop and Soil Environmental Sciences"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Unmanned Aerial Vehicle (UAV)","Chlorophyll","SPAD","Growth stage","Precision Agriculture","Regression models","Vegetation Index (VI)","Nutrient Management","Soil Fertility"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["In Copyright"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://rightsstatements.org/vocab/InC/1.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45157"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140555"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Efficient nutrient management is essential for optimizing corn (Zea mays L.) productivity while minimizing environmental and economic costs. Traditional methods for assessing crop responses to nutrients are often damaging and labor-intensive, limiting accurate assessment of spatial and temporal variations. Accurate in-season yield potential estimation plays a vital role in guiding nutrient management decisions and supporting grain marketing strategies. This study evaluated the potential of Unmanned Aerial Vehicle (UAV) derived imagery to estimate chlorophyll (Chl) status and predict corn grain yield potential in-season under variable nitrogen (N), phosphorus (P), and potassium (K) fertilizer treatments across different growth stages. Two field trials (NP and K) were conducted at two locations in Virginia, Kentland Farm in Blacksburg (Kentland), Valley and Ridge province, and the Northern Piedmont Center in Orange (Orange), Piedmont province. These sites vary in altitude, soil type, and climatic conditions, providing contrasting environments for evaluating crop responses to fertilizers. In both trials, factorial arrangement of treatments (varied N, P, and K fertilizer rates) with four replications was implemented with a randomized complete block design (RCBD). Chlorophyll readings (ChlR) were collected using the Soil Plant Analysis Development (SPAD)-502 and atLEAF Chl meters at three growth stages: early vegetative (EV), late vegetative (LV), and reproductive (Repr). These measurements were synchronized with UAV flights performed on the same day. UAV flights were conducted using DJI Mavic equipped with an RGB sensor for visible light and four monochrome sensors for multispectral imaging (red: 650 nm ± 16 nm, green: 560 nm ± 16 nm, near-infrared (NIR): 840 nm ± 26 nm, red-edge: 730 nm ± 16 nm). UAV-derived vegetation indices (VIs) responsive to Chl and indicative of crop yield potential were computed to model ChlR and yield through single and multi-index regression analyses. Multi-index model performance was evaluated through repeated k-fold cross-validation (CV) (k = 5; 30 repetitions). Indices included the Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red-Edge (CIRE), Normalized Difference Red-Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), MERIS Terrestrial Chlorophyll Index (MTCI), Normalized Difference Chlorophyll Index (NDCI), Canopy Chlorophyll Content Index (CCCI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). Weather variation, early-season drought, and late-season rainfall strongly influenced yield formation and grain moisture, overshadowing fertilizer treatment effects. No significant yield differences were detected among N, P, or K levels (p > 0.05). UAV-derived VIs demonstrated significant correlations with both ChlR and yield, with stronger relationships observed during the LV stage when canopy closure and Chl concentration were most stable. In the K trial at Kentland, the relationships between VIs and both ChlR and yield were generally moderate, while in the K trial at Orange, correlations were consistently strong and significant. For ChlR prediction, green and red-edge based indices (GNDVI, NDRE, CIRE, and MTCI) were the most reliable indices, explaining 40 to 55 percent of the variation across both sites and trials. For yield prediction, GNDVI, NDVI, and NDCI consistently exhibited strong relationships at Orange during the LV stage, with R² values ranging from 0.50 to 0.72 across both trials. In contrast, Kentland showed comparatively lower predictive performance, with only moderate relationships observed in the K trial during the LV stage. The use of a polynomial regression (quadratic) model further improved prediction accuracy compared to the linear model in all trials. Multi-index regression further improved predictive accuracy. The best-performing yield models were observed in the K trial at Orange during the LV stage, achieving CV R² values up to 0.71 (CV RMSE of 13.9), while the best ChlR models were found in the NP trial at Orange with CV R² of 0.46 (CV RMSE of 2.45). Model performance was lower for EV and Repr stages. Overall, these findings demonstrate that UAV-based multispectral imaging is an effective tool for monitoring corn canopy Chl status and assessing yield potential, with prediction accuracy varying across growth stages."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["Corn is a globally important crop and is one of the most widely planted crops in the United States. To manage such large production areas, farmers need reliable methods to understand how their crops are responding to fertilizers and other inputs. Traditional techniques for assessing plant health are often time consuming and cannot quantify the wide variability that occurs across large fields. These limitations highlight the need for modern, sensor-based approaches that can monitor crop performance more quickly and accurately. Such tools also support yield prediction early in the growing season, which helps farmers plan grain harvest, storage, and marketing logistics. Chlorophyll (Chl) is the green pigment in leaves closely linked to plant health and nitrogen (N) status. Chlorophyll content can be measured with handheld meters and can be used as an indicator of crop condition. These handheld sensors provide quick measurements but capture only point-based data that cannot be generalized for large-scale field assessment. This study examined the use of Unmanned Aerial Vehicles (UAV), commonly known as drones, as a modern approach for assessing crop responses at the field scale. The first objective was to determine how nutrient treatments affected ChlR and grain yield. The second objective was to use UAV based vegetation indices (VIs) to predict yield before harvest. The third objective was to evaluate how well UAV derived VIs are compared with ChlR from handheld Chl sensors. The final objective was to identify the crop growth stage that provided the most accurate predictions of Chl status and yield potential. Two field experiments were conducted in Virginia at Kentland Farm in Blacksburg and at the Northern Piedmont Center in Orange, two locations that differ in climate, soil type, and elevation. Corn was grown under different rates of N, phosphorus (P), and potassium (K) fertilizers. ChlR measurements were collected at the early vegetative (EV), late vegetative (LV), and reproductive (Repr) corn growth stages. The UAV flights were carried out on the same days at each of those stages to capture visible and multispectral images. Weather conditions had a stronger influence on final grain yield than fertilizer treatments. Early season drought and late season rainfall played major roles in shaping plant growth and yield response. VIs derived from UAV images showed moderate to strong relationships with ChlR and with grain yield, especially during the LV stage. Green and red-edge based indices provided the most accurate ChlR estimates, and indices such as NDVI and GNDVI produced the strongest yield predictions. Higher predictability was observed at the Orange location compared to Kentland. Based on the findings, the LV stage provided the highest accuracy for data collection and prediction. Overall, this study demonstrates that UAV-based multispectral imaging is a valuable tool for monitoring Chl status and estimating yield potential during the growing season."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Master of Science"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["Predicting Corn Response to Variable Synthetic Fertilizer Treatments Using UAV-Derived Imagery"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Walsh, Olga S."],"dc:contributor.committeemember":["Gan, Huijie","Rijal, Santosh"],"dc:contributor.department":["Crop and Soil Environmental Sciences"],"dc:creator":["Khulal, Aarati"],"dc:date.accessioned":["2025-12-24T09:00:16Z"],"dc:date.available":["2025-12-24T09:00:16Z"],"dc:date.issued":["2025-12-23"],"dc:description.abstract":["Efficient nutrient management is essential for optimizing corn (Zea mays L.) productivity while minimizing environmental and economic costs. Traditional methods for assessing crop responses to nutrients are often damaging and labor-intensive, limiting accurate assessment of spatial and temporal variations. Accurate in-season yield potential estimation plays a vital role in guiding nutrient management decisions and supporting grain marketing strategies. This study evaluated the potential of Unmanned Aerial Vehicle (UAV) derived imagery to estimate chlorophyll (Chl) status and predict corn grain yield potential in-season under variable nitrogen (N), phosphorus (P), and potassium (K) fertilizer treatments across different growth stages. Two field trials (NP and K) were conducted at two locations in Virginia, Kentland Farm in Blacksburg (Kentland), Valley and Ridge province, and the Northern Piedmont Center in Orange (Orange), Piedmont province. These sites vary in altitude, soil type, and climatic conditions, providing contrasting environments for evaluating crop responses to fertilizers. In both trials, factorial arrangement of treatments (varied N, P, and K fertilizer rates) with four replications was implemented with a randomized complete block design (RCBD). Chlorophyll readings (ChlR) were collected using the Soil Plant Analysis Development (SPAD)-502 and atLEAF Chl meters at three growth stages: early vegetative (EV), late vegetative (LV), and reproductive (Repr). These measurements were synchronized with UAV flights performed on the same day. UAV flights were conducted using DJI Mavic equipped with an RGB sensor for visible light and four monochrome sensors for multispectral imaging (red: 650 nm ± 16 nm, green: 560 nm ± 16 nm, near-infrared (NIR): 840 nm ± 26 nm, red-edge: 730 nm ± 16 nm). UAV-derived vegetation indices (VIs) responsive to Chl and indicative of crop yield potential were computed to model ChlR and yield through single and multi-index regression analyses. Multi-index model performance was evaluated through repeated k-fold cross-validation (CV) (k = 5; 30 repetitions). Indices included the Normalized Difference Vegetation Index (NDVI), Chlorophyll Index Red-Edge (CIRE), Normalized Difference Red-Edge Index (NDRE), Green Normalized Difference Vegetation Index (GNDVI), MERIS Terrestrial Chlorophyll Index (MTCI), Normalized Difference Chlorophyll Index (NDCI), Canopy Chlorophyll Content Index (CCCI), and Optimized Soil-Adjusted Vegetation Index (OSAVI). Weather variation, early-season drought, and late-season rainfall strongly influenced yield formation and grain moisture, overshadowing fertilizer treatment effects. No significant yield differences were detected among N, P, or K levels (p > 0.05). UAV-derived VIs demonstrated significant correlations with both ChlR and yield, with stronger relationships observed during the LV stage when canopy closure and Chl concentration were most stable. In the K trial at Kentland, the relationships between VIs and both ChlR and yield were generally moderate, while in the K trial at Orange, correlations were consistently strong and significant. For ChlR prediction, green and red-edge based indices (GNDVI, NDRE, CIRE, and MTCI) were the most reliable indices, explaining 40 to 55 percent of the variation across both sites and trials. For yield prediction, GNDVI, NDVI, and NDCI consistently exhibited strong relationships at Orange during the LV stage, with R² values ranging from 0.50 to 0.72 across both trials. In contrast, Kentland showed comparatively lower predictive performance, with only moderate relationships observed in the K trial during the LV stage. The use of a polynomial regression (quadratic) model further improved prediction accuracy compared to the linear model in all trials. Multi-index regression further improved predictive accuracy. The best-performing yield models were observed in the K trial at Orange during the LV stage, achieving CV R² values up to 0.71 (CV RMSE of 13.9), while the best ChlR models were found in the NP trial at Orange with CV R² of 0.46 (CV RMSE of 2.45). Model performance was lower for EV and Repr stages. Overall, these findings demonstrate that UAV-based multispectral imaging is an effective tool for monitoring corn canopy Chl status and assessing yield potential, with prediction accuracy varying across growth stages."],"dc:description.abstractgeneral":["Corn is a globally important crop and is one of the most widely planted crops in the United States. To manage such large production areas, farmers need reliable methods to understand how their crops are responding to fertilizers and other inputs. Traditional techniques for assessing plant health are often time consuming and cannot quantify the wide variability that occurs across large fields. These limitations highlight the need for modern, sensor-based approaches that can monitor crop performance more quickly and accurately. Such tools also support yield prediction early in the growing season, which helps farmers plan grain harvest, storage, and marketing logistics. Chlorophyll (Chl) is the green pigment in leaves closely linked to plant health and nitrogen (N) status. Chlorophyll content can be measured with handheld meters and can be used as an indicator of crop condition. These handheld sensors provide quick measurements but capture only point-based data that cannot be generalized for large-scale field assessment. This study examined the use of Unmanned Aerial Vehicles (UAV), commonly known as drones, as a modern approach for assessing crop responses at the field scale. The first objective was to determine how nutrient treatments affected ChlR and grain yield. The second objective was to use UAV based vegetation indices (VIs) to predict yield before harvest. The third objective was to evaluate how well UAV derived VIs are compared with ChlR from handheld Chl sensors. The final objective was to identify the crop growth stage that provided the most accurate predictions of Chl status and yield potential. Two field experiments were conducted in Virginia at Kentland Farm in Blacksburg and at the Northern Piedmont Center in Orange, two locations that differ in climate, soil type, and elevation. Corn was grown under different rates of N, phosphorus (P), and potassium (K) fertilizers. ChlR measurements were collected at the early vegetative (EV), late vegetative (LV), and reproductive (Repr) corn growth stages. The UAV flights were carried out on the same days at each of those stages to capture visible and multispectral images. Weather conditions had a stronger influence on final grain yield than fertilizer treatments. Early season drought and late season rainfall played major roles in shaping plant growth and yield response. VIs derived from UAV images showed moderate to strong relationships with ChlR and with grain yield, especially during the LV stage. Green and red-edge based indices provided the most accurate ChlR estimates, and indices such as NDVI and GNDVI produced the strongest yield predictions. Higher predictability was observed at the Orange location compared to Kentland. Based on the findings, the LV stage provided the highest accuracy for data collection and prediction. Overall, this study demonstrates that UAV-based multispectral imaging is a valuable tool for monitoring Chl status and estimating yield potential during the growing season."],"dc:description.degree":["Master of Science"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45157"],"dc:identifier.uri":["https://hdl.handle.net/10919/140555"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["In Copyright"],"dc:rights.uri":["http://rightsstatements.org/vocab/InC/1.0/"],"dc:subject":["Unmanned Aerial Vehicle (UAV)","Chlorophyll","SPAD","Growth stage","Precision Agriculture","Regression models","Vegetation Index (VI)","Nutrient Management","Soil Fertility"],"dc:title":["Predicting Corn Response to Variable Synthetic Fertilizer Treatments Using UAV-Derived Imagery"],"dc:type":["Thesis"],"thesis:degree_discipline":["Crop and Soil Environmental Sciences"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:20:02Z"}