Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensors
The ability to detect and respond to vineyard spatial variation can lead to improved management—a practice known as precision viticulture. The goal of this study was to determine if remote sensors can enhance precision viticulture applications by detecting vineyard spatial variation. The hypothesis...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Canadian Science Publishing
2024-01-01
|
| Series: | Drone Systems and Applications |
| Subjects: | |
| Online Access: | https://cdnsciencepub.com/doi/10.1139/dsa-2023-0024 |
| _version_ | 1797242652796649472 |
|---|---|
| author | Briann Dorin Andrew G. Reynolds Hyun-Suk Lee Marilyne Carrey Adam Shemrock Mehdi Shabanian |
| author_facet | Briann Dorin Andrew G. Reynolds Hyun-Suk Lee Marilyne Carrey Adam Shemrock Mehdi Shabanian |
| author_sort | Briann Dorin |
| collection | DOAJ |
| description | The ability to detect and respond to vineyard spatial variation can lead to improved management—a practice known as precision viticulture. The goal of this study was to determine if remote sensors can enhance precision viticulture applications by detecting vineyard spatial variation. The hypothesis was that differences in vine spectral reflectance, as detected by remote sensors, would be associated with variations in viticultural variables due to known relationships with vine size, structure, and pigmentation. Riesling grapevines were geolocated within six commercial vineyards across Niagara, Ontario. Water status, vine size, winter hardiness, virus titer, yield components, and berry composition were measured on these vines. Remote sensing technologies subsequently collected multispectral data by unmanned aerial vehicles and by proximal sensing technology (GreenSeeker™), which were transformed into the Normalized Difference Vegetation Index (NDVI). Direct relationships between NDVI and vine size, water status, yield, berry weight, and titratable acidity were observed, as well as inverse relationships between NDVI and Brix and potentially volatile terpenes. Remote sensing demonstrated the ability to detect vineyard areas differing in measures of vine health, yield, and berry composition in certain sites and years; however, more research is needed to determine when these technologies should be used for precision viticulture applications. |
| first_indexed | 2024-03-08T14:43:25Z |
| format | Article |
| id | doaj.art-ee87b2958d3c4ccfb1308009fa5e7da0 |
| institution | Directory Open Access Journal |
| issn | 2564-4939 |
| language | English |
| last_indexed | 2024-04-24T18:42:38Z |
| publishDate | 2024-01-01 |
| publisher | Canadian Science Publishing |
| record_format | Article |
| series | Drone Systems and Applications |
| spelling | doaj.art-ee87b2958d3c4ccfb1308009fa5e7da02024-03-27T11:16:49ZengCanadian Science PublishingDrone Systems and Applications2564-49392024-01-011211810.1139/dsa-2023-0024Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensorsBriann Dorin0Andrew G. Reynolds1Hyun-Suk Lee2Marilyne Carrey3Adam Shemrock4Mehdi Shabanian5Department of Biological Sciences, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON L2S 3A1, CanadaDepartment of Biological Sciences, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON L2S 3A1, CanadaDepartment of Biological Sciences, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON L2S 3A1, CanadaEnvironmental Sustainability Research Centre, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON L2S 3A1, CanadaAirTech UAV Solutions Inc., Inverary, ON, CanadaDepartment of Molecular and Cellular Biology, University of Guelph, 50 Stone Rd E, Guelph, ON N1G 2W1, CanadaThe ability to detect and respond to vineyard spatial variation can lead to improved management—a practice known as precision viticulture. The goal of this study was to determine if remote sensors can enhance precision viticulture applications by detecting vineyard spatial variation. The hypothesis was that differences in vine spectral reflectance, as detected by remote sensors, would be associated with variations in viticultural variables due to known relationships with vine size, structure, and pigmentation. Riesling grapevines were geolocated within six commercial vineyards across Niagara, Ontario. Water status, vine size, winter hardiness, virus titer, yield components, and berry composition were measured on these vines. Remote sensing technologies subsequently collected multispectral data by unmanned aerial vehicles and by proximal sensing technology (GreenSeeker™), which were transformed into the Normalized Difference Vegetation Index (NDVI). Direct relationships between NDVI and vine size, water status, yield, berry weight, and titratable acidity were observed, as well as inverse relationships between NDVI and Brix and potentially volatile terpenes. Remote sensing demonstrated the ability to detect vineyard areas differing in measures of vine health, yield, and berry composition in certain sites and years; however, more research is needed to determine when these technologies should be used for precision viticulture applications.https://cdnsciencepub.com/doi/10.1139/dsa-2023-0024remote sensingproximal sensingprecision viticultureunmanned aerial vehicles |
| spellingShingle | Briann Dorin Andrew G. Reynolds Hyun-Suk Lee Marilyne Carrey Adam Shemrock Mehdi Shabanian Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensors Drone Systems and Applications remote sensing proximal sensing precision viticulture unmanned aerial vehicles |
| title | Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensors |
| title_full | Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensors |
| title_fullStr | Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensors |
| title_full_unstemmed | Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensors |
| title_short | Detecting cool-climate Riesling vineyard variation using unmanned aerial vehicles and proximal sensors |
| title_sort | detecting cool climate riesling vineyard variation using unmanned aerial vehicles and proximal sensors |
| topic | remote sensing proximal sensing precision viticulture unmanned aerial vehicles |
| url | https://cdnsciencepub.com/doi/10.1139/dsa-2023-0024 |
| work_keys_str_mv | AT brianndorin detectingcoolclimaterieslingvineyardvariationusingunmannedaerialvehiclesandproximalsensors AT andrewgreynolds detectingcoolclimaterieslingvineyardvariationusingunmannedaerialvehiclesandproximalsensors AT hyunsuklee detectingcoolclimaterieslingvineyardvariationusingunmannedaerialvehiclesandproximalsensors AT marilynecarrey detectingcoolclimaterieslingvineyardvariationusingunmannedaerialvehiclesandproximalsensors AT adamshemrock detectingcoolclimaterieslingvineyardvariationusingunmannedaerialvehiclesandproximalsensors AT mehdishabanian detectingcoolclimaterieslingvineyardvariationusingunmannedaerialvehiclesandproximalsensors |