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...
Guardado en:
Autores principales: | , , , , |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
International Viticulture and Enology Society
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/771aaf7ab26946399ca3d5092cd370b7 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
id |
oai:doaj.org-article:771aaf7ab26946399ca3d5092cd370b7 |
---|---|
record_format |
dspace |
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 |
work_keys_str_mv |
AT sergiovelez estimationofleafareaindexinvineyardsbyanalysingprojectedshadowsusinguavimagery AT carlospobleteecheverria estimationofleafareaindexinvineyardsbyanalysingprojectedshadowsusinguavimagery AT joseantoniorubio estimationofleafareaindexinvineyardsbyanalysingprojectedshadowsusinguavimagery AT rubenvacas estimationofleafareaindexinvineyardsbyanalysingprojectedshadowsusinguavimagery AT enriquebarajas estimationofleafareaindexinvineyardsbyanalysingprojectedshadowsusinguavimagery |
_version_ |
1718405420248203264 |