2026-05-16T02:32:58Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/100622025-01-30T01:05:35Zcom_10259.4_2506com_10259.4_2505com_10259.4_106com_10259_2604col_10259_7491

Vélez Martín, Sergio 31deed37-f210-4d3c-8107-3297a0706165 600 Poblete Echeverría, Carlos eea14787-0570-4be8-b105-ff0c775b59de 600 Rubio Cano, José Antonio 4c17ffdd-943c-4af6-b07d-59cc7ab4f84a 600 Vacas Izquierdo, Rubén 5c52d7ab-e2da-4a67-a76a-eb6a14a4564b 600 Barajas, Enrique 143decd5-69b1-4d67-b51c-b63435757da0 600 2025-01-29T09:10:11Z 2025-01-29T09:10:11Z 2021-11 http://hdl.handle.net/10259/10062 10.20870/oeno-one.2021.55.4.4639 2494-1271 A few decades ago, farmers could precisely monitor their croplands just by walking over the fields, but this task becomes more difficult as farm size increases. Precision viticulture can help better understand the vineyard and measure some key structural parameters, such as the Leaf Area Index (LAI). Remote Sensing is a typical approach to monitoring vegetation which measures the spectral information directly emitted and reflected from vegetation. This study explores a new method for estimating LAI which measures the projected shadows of plants using UAV (unmanned aerial vehicle) imagery. A flight mission over a vineyard was scheduled in the afternoon (15:30 to 16:00 solar time), which is the optimal time for the projection of vine shadows on the ground. Real LAI was measured destructively by removing all the vegetation from the area. Then, the projected shadows in the image were detected using machine learning methods (k-means and random forest) and analysed at pixel level using a customised R code. A strong linear relationship (R² = 0.76, RMSE = 0.160 m² m-2 and MAE = 0.139 m² m-2) was found between the shaded area and the LAI per vine. This is a quick and simple method, which is non-destructive and gives accurate results; moreover, flights can be scheduled during other periods of the day than solar noon, such as in the morning or afternoon, thus enabling pilots to extend their working day. Therefore, it may be a viable option for determining LAI in vineyards trained on Vertical Shoot Positioned (VSP) systems. This study was possible thanks to the financial support of Junta de Castilla y León (Spain), the project INIA RTA2014- 00077-C02, FPI-INIA2016-017 and FEDER funding. application/pdf eng International Viticulture and Enology Society OENO One. 2021, V. 55, n. 4, p. 159-180 https://doi.org/10.20870/oeno-one.2021.55.4.4639 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Leaf area index Shadow detection Image analysis Precision agriculture Machine learning Spatial variability Random forest classification Agricultura Viticultura Agriculture Viticulture Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion OENO One 55 4 159 180 THUMBNAIL velez-oenoo_2021.pdf.jpg velez-oenoo_2021.pdf.jpg IM Thumbnail image/jpeg 2711 https://riubu.ubu.es/bitstream/10259/10062/4/velez-oenoo_2021.pdf.jpg 71909243e5ec335e46aed0e49c6ab28d MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/10062/3/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/10062/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL velez-oenoo_2021.pdf velez-oenoo_2021.pdf application/pdf 7747504 https://riubu.ubu.es/bitstream/10259/10062/1/velez-oenoo_2021.pdf 46424c9e530cba2099dcc7cd6f8e20bb MD5 1 10259/10062 oai:riubu.ubu.es:10259/10062 2025-01-30 02:05:35.053 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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