{"id":{"repo_id":"brock","oai_identifier":"oai:brocku.scholaris.ca:10464/9406"},"canonical_url":"https://search.dev.ndltd.org/etd/brock/oai:brocku.scholaris.ca:10464/9406","repository":{"repo_id":"brock","name":"Brock University","base_url":"https://brocku.scholaris.ca/server/oai/request"},"display":{"title":"Utilisation of Proximal Sensing Technology to Map Variability in Ontario Vineyards","abstract":"Precision agriculture is a term used to refer to a suite of technologies used for the optimisation of production in agronomic crops. The overall goal of this study was to determine whether high resolution proximally sensed observations acquired by the GreenSeeker™ technology could be correlated with soil moisture, vine water status, yield components and grape composition, and whether temporally consistent relationships could be established. The research was carried out on three experimental sites involving two Riesling, two Cabernet franc and two Pinot noir blocks throughout the Niagara Region of Ontario (Canada). A grid of geolocated sentinel vines was chosen for each vineyard block. Data were collected three times during the growing season between fruit set and veraison [soil moisture, leaf water potential (ψ)], at harvest (yield components, berry composition) and in winter [vine size, winter hardiness (LT50)]. GreenSeeker™ observations were likewise collected from lag phase to just prior to harvest, through the calculation of Normalized Difference Vegetation Index (NDVI). Thereafter, relationships between vine water status, yield components and berry composition variables as well as data from the GreenSeeker™ were validated. Overall, higher NDVI values were associated with yield components and vine size, while lower NDVI values were correlated with better berry composition, suggesting that GreenSeeker™ is a practical tool for vineyard vegetative growth surveys, and for grape composition inferences. Clustering associations were made through k-means statistical analysis in conjunction with Moran's I spatial autocorrelation index; soil moisture followed by the NDVI had the strongest clustering patterns. The outcomes from proximal sensing technology allow opportunities to stream and compliment present agricultural practices towards higher accuracy and efficacy by means of exploiting the observed variation.","abstract_html":"Precision agriculture is a term used to refer to a suite of technologies used for the optimisation of production in agronomic crops. The overall goal of this study was to determine whether high resolution proximally sensed observations acquired by the GreenSeeker™ technology could be correlated with soil moisture, vine water status, yield components and grape composition, and whether temporally consistent relationships could be established. The research was carried out on three experimental sites involving two Riesling, two Cabernet franc and two Pinot noir blocks throughout the Niagara Region of Ontario (Canada). A grid of geolocated sentinel vines was chosen for each vineyard block. Data were collected three times during the growing season between fruit set and veraison [soil moisture, leaf water potential (ψ)], at harvest (yield components, berry composition) and in winter [vine size, winter hardiness (LT50)]. GreenSeeker™ observations were likewise collected from lag phase to just prior to harvest, through the calculation of Normalized Difference Vegetation Index (NDVI). Thereafter, relationships between vine water status, yield components and berry composition variables as well as data from the GreenSeeker™ were validated. Overall, higher NDVI values were associated with yield components and vine size, while lower NDVI values were correlated with better berry composition, suggesting that GreenSeeker™ is a practical tool for vineyard vegetative growth surveys, and for grape composition inferences. Clustering associations were made through k-means statistical analysis in conjunction with Moran's I spatial autocorrelation index; soil moisture followed by the NDVI had the strongest clustering patterns. The outcomes from proximal sensing technology allow opportunities to stream and compliment present agricultural practices towards higher accuracy and efficacy by means of exploiting the observed variation.","abstract_has_math":false,"creators":["Kotsaki, Eleni"],"institution":"Brock University","degree_name":"M.Sc. Biological Sciences","degree_level":"Masters","degree_discipline":"Faculty of Mathematics and Science","degree_department":"Department of Biological Sciences","school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-06-27T19:44:23Z","date_published":"2016-06-27T19:44:23Z","updated_at":"2026-07-24T01:23:05Z","subjects":["Precision viticulture, proximal sensing technology, NDVI, spatial variability, temporal stability"],"languages":["eng"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10464/9406","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.department","label":"Department","values":["Department of Biological Sciences"]},{"key":"dc:creator","label":"Author","values":["Kotsaki, Eleni"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2016-06-27T19:44:23Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2016-06-27T19:44:23Z"]},{"key":"dc:date.issued","label":"Date","values":["2016-06-27T19:44:23Z"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Faculty of Mathematics and Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.Sc. 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The overall goal of this study was to determine whether high resolution proximally sensed observations acquired by the GreenSeeker™ technology could be correlated with soil moisture, vine water status, yield components and grape composition, and whether temporally consistent relationships could be established. The research was carried out on three experimental sites involving two Riesling, two Cabernet franc and two Pinot noir blocks throughout the Niagara Region of Ontario (Canada). A grid of geolocated sentinel vines was chosen for each vineyard block. Data were collected three times during the growing season between fruit set and veraison [soil moisture, leaf water potential (ψ)], at harvest (yield components, berry composition) and in winter [vine size, winter hardiness (LT50)]. GreenSeeker™ observations were likewise collected from lag phase to just prior to harvest, through the calculation of Normalized Difference Vegetation Index (NDVI). Thereafter, relationships between vine water status, yield components and berry composition variables as well as data from the GreenSeeker™ were validated. Overall, higher NDVI values were associated with yield components and vine size, while lower NDVI values were correlated with better berry composition, suggesting that GreenSeeker™ is a practical tool for vineyard vegetative growth surveys, and for grape composition inferences. Clustering associations were made through k-means statistical analysis in conjunction with Moran's I spatial autocorrelation index; soil moisture followed by the NDVI had the strongest clustering patterns. The outcomes from proximal sensing technology allow opportunities to stream and compliment present agricultural practices towards higher accuracy and efficacy by means of exploiting the observed variation."]},{"key":"dc:title","label":"Title","values":["Utilisation of Proximal Sensing Technology to Map Variability in Ontario Vineyards"]}]}],"canonical_facts":{"dc:contributor.department":["Department of Biological Sciences"],"dc:creator":["Kotsaki, Eleni"],"dc:date.accessioned":["2016-06-27T19:44:23Z"],"dc:date.available":["2016-06-27T19:44:23Z"],"dc:date.issued":["2016-06-27T19:44:23Z"],"dc:description.abstract":["Precision agriculture is a term used to refer to a suite of technologies used for the optimisation of production in agronomic crops. The overall goal of this study was to determine whether high resolution proximally sensed observations acquired by the GreenSeeker™ technology could be correlated with soil moisture, vine water status, yield components and grape composition, and whether temporally consistent relationships could be established. The research was carried out on three experimental sites involving two Riesling, two Cabernet franc and two Pinot noir blocks throughout the Niagara Region of Ontario (Canada). A grid of geolocated sentinel vines was chosen for each vineyard block. Data were collected three times during the growing season between fruit set and veraison [soil moisture, leaf water potential (ψ)], at harvest (yield components, berry composition) and in winter [vine size, winter hardiness (LT50)]. GreenSeeker™ observations were likewise collected from lag phase to just prior to harvest, through the calculation of Normalized Difference Vegetation Index (NDVI). Thereafter, relationships between vine water status, yield components and berry composition variables as well as data from the GreenSeeker™ were validated. Overall, higher NDVI values were associated with yield components and vine size, while lower NDVI values were correlated with better berry composition, suggesting that GreenSeeker™ is a practical tool for vineyard vegetative growth surveys, and for grape composition inferences. Clustering associations were made through k-means statistical analysis in conjunction with Moran's I spatial autocorrelation index; soil moisture followed by the NDVI had the strongest clustering patterns. The outcomes from proximal sensing technology allow opportunities to stream and compliment present agricultural practices towards higher accuracy and efficacy by means of exploiting the observed variation."],"dc:identifier.uri":["http://hdl.handle.net/10464/9406"],"dc:language.iso":["eng"],"dc:subject":["Precision viticulture, proximal sensing technology, NDVI, spatial variability, temporal stability"],"dc:title":["Utilisation of Proximal Sensing Technology to Map Variability in Ontario Vineyards"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Faculty of Mathematics and Science"],"thesis:degree_level":["Masters"],"thesis:degree_name":["M.Sc. Biological Sciences"],"thesis:institution_name":["Brock University"]},"updated_at":"2026-07-24T01:23:05Z"}