Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery
Precision viticulture systems are essential for enhancing traditional intensive viticulture, achieving high-quality results, and minimizing costs. This study explores the integration of Unmanned Aerial Vehicles (UAVs) and artificial intelligence in precision viticulture, focusing on vine detection a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-02-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/3/584 |
| _version_ | 1797318251206672384 |
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| author | Milan Gavrilović Dušan Jovanović Predrag Božović Pavel Benka Miro Govedarica |
| author_facet | Milan Gavrilović Dušan Jovanović Predrag Božović Pavel Benka Miro Govedarica |
| author_sort | Milan Gavrilović |
| collection | DOAJ |
| description | Precision viticulture systems are essential for enhancing traditional intensive viticulture, achieving high-quality results, and minimizing costs. This study explores the integration of Unmanned Aerial Vehicles (UAVs) and artificial intelligence in precision viticulture, focusing on vine detection and vineyard zoning. Vine detection employs the YOLO (You Only Look Once) deep learning algorithm, achieving a remarkable 90% accuracy by analysing UAV imagery with various spectral ranges from various phenological stages. Vineyard zoning, achieved through the application of the K-means algorithm, incorporates geospatial data such as the Normalized Difference Vegetation Index (NDVI) and the assessment of nitrogen, phosphorus, and potassium content in leaf blades and petioles. This approach enables efficient resource management tailored to each zone’s specific needs. The research aims to develop a decision-support model for precision viticulture. The proposed model demonstrates a high vine detection accuracy and defines management zones with variable weighting factors assigned to each variable while preserving location information, revealing significant differences in variables. The model’s advantages lie in its rapid results and minimal data requirements, offering profound insights into the benefits of UAV application for precise vineyard management. This approach has the potential to expedite decision making, allowing for adaptive strategies based on the unique conditions of each zone. |
| first_indexed | 2024-03-08T03:49:43Z |
| format | Article |
| id | doaj.art-516a53130ea1410c94a62139eadec7a7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T03:49:43Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-516a53130ea1410c94a62139eadec7a72024-02-09T15:21:33ZengMDPI AGRemote Sensing2072-42922024-02-0116358410.3390/rs16030584Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle ImageryMilan Gavrilović0Dušan Jovanović1Predrag Božović2Pavel Benka3Miro Govedarica4Faculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, SerbiaFaculty of Agriculture, University of Novi Sad, Trg Dositeja Obradovića 8, 21000 Novi Sad, SerbiaFaculty of Agriculture, University of Novi Sad, Trg Dositeja Obradovića 8, 21000 Novi Sad, SerbiaFaculty of Technical Sciences, University of Novi Sad, Trg Dositeja Obradovića 6, 21000 Novi Sad, SerbiaPrecision viticulture systems are essential for enhancing traditional intensive viticulture, achieving high-quality results, and minimizing costs. This study explores the integration of Unmanned Aerial Vehicles (UAVs) and artificial intelligence in precision viticulture, focusing on vine detection and vineyard zoning. Vine detection employs the YOLO (You Only Look Once) deep learning algorithm, achieving a remarkable 90% accuracy by analysing UAV imagery with various spectral ranges from various phenological stages. Vineyard zoning, achieved through the application of the K-means algorithm, incorporates geospatial data such as the Normalized Difference Vegetation Index (NDVI) and the assessment of nitrogen, phosphorus, and potassium content in leaf blades and petioles. This approach enables efficient resource management tailored to each zone’s specific needs. The research aims to develop a decision-support model for precision viticulture. The proposed model demonstrates a high vine detection accuracy and defines management zones with variable weighting factors assigned to each variable while preserving location information, revealing significant differences in variables. The model’s advantages lie in its rapid results and minimal data requirements, offering profound insights into the benefits of UAV application for precise vineyard management. This approach has the potential to expedite decision making, allowing for adaptive strategies based on the unique conditions of each zone.https://www.mdpi.com/2072-4292/16/3/584neural networksUAVprecision viticultureYOLOK meansremote sensing |
| spellingShingle | Milan Gavrilović Dušan Jovanović Predrag Božović Pavel Benka Miro Govedarica Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery Remote Sensing neural networks UAV precision viticulture YOLO K means remote sensing |
| title | Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery |
| title_full | Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery |
| title_fullStr | Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery |
| title_full_unstemmed | Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery |
| title_short | Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery |
| title_sort | vineyard zoning and vine detection using machine learning in unmanned aerial vehicle imagery |
| topic | neural networks UAV precision viticulture YOLO K means remote sensing |
| url | https://www.mdpi.com/2072-4292/16/3/584 |
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