Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery

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...

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Autores principales: Sergio Vélez, Carlos Poblete-Echeverría, José Antonio Rubio, Rubén vacas, Enrique Barajas
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Lenguaje:EN
Publicado: International Viticulture and Enology Society 2021
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Acceso en línea:https://doaj.org/article/771aaf7ab26946399ca3d5092cd370b7
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spelling oai:doaj.org-article:771aaf7ab26946399ca3d5092cd370b72021-12-01T07:14:37ZEstimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery10.20870/oeno-one.2021.55.4.46392494-1271https://doaj.org/article/771aaf7ab26946399ca3d5092cd370b72021-11-01T00:00:00Zhttps://oeno-one.eu/article/view/4639https://doaj.org/toc/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. Sergio VélezCarlos Poblete-EcheverríaJosé Antonio RubioRubén vacasEnrique BarajasInternational Viticulture and Enology Societyarticleleaf area indexshadow detectionimage analysisprecision agriculturemachine learningspatial variabilityAgricultureSBotanyQK1-989ENOENO One, Vol 55, Iss 4 (2021)
institution DOAJ
collection DOAJ
language EN
topic leaf area index
shadow detection
image analysis
precision agriculture
machine learning
spatial variability
Agriculture
S
Botany
QK1-989
spellingShingle leaf area index
shadow detection
image analysis
precision agriculture
machine learning
spatial variability
Agriculture
S
Botany
QK1-989
Sergio Vélez
Carlos Poblete-Echeverría
José Antonio Rubio
Rubén vacas
Enrique Barajas
Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery
description 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.
format article
author Sergio Vélez
Carlos Poblete-Echeverría
José Antonio Rubio
Rubén vacas
Enrique Barajas
author_facet Sergio Vélez
Carlos Poblete-Echeverría
José Antonio Rubio
Rubén vacas
Enrique Barajas
author_sort Sergio Vélez
title Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery
title_short Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery
title_full Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery
title_fullStr Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery
title_full_unstemmed Estimation of Leaf Area Index in vineyards by analysing projected shadows using UAV imagery
title_sort estimation of leaf area index in vineyards by analysing projected shadows using uav imagery
publisher International Viticulture and Enology Society
publishDate 2021
url https://doaj.org/article/771aaf7ab26946399ca3d5092cd370b7
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AT joseantoniorubio estimationofleafareaindexinvineyardsbyanalysingprojectedshadowsusinguavimagery
AT rubenvacas estimationofleafareaindexinvineyardsbyanalysingprojectedshadowsusinguavimagery
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