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Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery

Olive has a notable importance in countries of Mediterranean basin and its profitability depends on several factors such as actual yield, production cost or product price. Actual “on year” Yield (AY) is production (kg tree(−1)) in “on years”, and this research attempts to relate it with geometrical...

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Autores principales: Sola-Guirado, Rafael R., Castillo-Ruiz, Francisco J., Jiménez-Jiménez, Francisco, Blanco-Roldan, Gregorio L., Castro-Garcia, Sergio, Gil-Ribes, Jesus A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579829/
https://www.ncbi.nlm.nih.gov/pubmed/28758945
http://dx.doi.org/10.3390/s17081743
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author Sola-Guirado, Rafael R.
Castillo-Ruiz, Francisco J.
Jiménez-Jiménez, Francisco
Blanco-Roldan, Gregorio L.
Castro-Garcia, Sergio
Gil-Ribes, Jesus A.
author_facet Sola-Guirado, Rafael R.
Castillo-Ruiz, Francisco J.
Jiménez-Jiménez, Francisco
Blanco-Roldan, Gregorio L.
Castro-Garcia, Sergio
Gil-Ribes, Jesus A.
author_sort Sola-Guirado, Rafael R.
collection PubMed
description Olive has a notable importance in countries of Mediterranean basin and its profitability depends on several factors such as actual yield, production cost or product price. Actual “on year” Yield (AY) is production (kg tree(−1)) in “on years”, and this research attempts to relate it with geometrical parameters of the tree canopy. Regression equation to forecast AY based on manual canopy volume was determined based on data acquired from different orchard categories and cultivars during different harvesting seasons in southern Spain. Orthoimages were acquired with unmanned aerial systems (UAS) imagery calculating individual crown for relating to canopy volume and AY. Yield levels did not vary between orchard categories; however, it did between irrigated orchards (7000–17,000 kg ha(−1)) and rainfed ones (4000–7000 kg ha(−1)). After that, manual canopy volume was related with the individual crown area of trees that were calculated by orthoimages acquired with UAS imagery. Finally, AY was forecasted using both manual canopy volume and individual tree crown area as main factors for olive productivity. AY forecast only by using individual crown area made it possible to get a simple and cheap forecast tool for a wide range of olive orchards. Finally, the acquired information was introduced in a thematic map describing spatial AY variability obtained from orthoimage analysis that may be a powerful tool for farmers, insurance systems, market forecasts or to detect agronomical problems.
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spelling pubmed-55798292017-09-06 Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery Sola-Guirado, Rafael R. Castillo-Ruiz, Francisco J. Jiménez-Jiménez, Francisco Blanco-Roldan, Gregorio L. Castro-Garcia, Sergio Gil-Ribes, Jesus A. Sensors (Basel) Article Olive has a notable importance in countries of Mediterranean basin and its profitability depends on several factors such as actual yield, production cost or product price. Actual “on year” Yield (AY) is production (kg tree(−1)) in “on years”, and this research attempts to relate it with geometrical parameters of the tree canopy. Regression equation to forecast AY based on manual canopy volume was determined based on data acquired from different orchard categories and cultivars during different harvesting seasons in southern Spain. Orthoimages were acquired with unmanned aerial systems (UAS) imagery calculating individual crown for relating to canopy volume and AY. Yield levels did not vary between orchard categories; however, it did between irrigated orchards (7000–17,000 kg ha(−1)) and rainfed ones (4000–7000 kg ha(−1)). After that, manual canopy volume was related with the individual crown area of trees that were calculated by orthoimages acquired with UAS imagery. Finally, AY was forecasted using both manual canopy volume and individual tree crown area as main factors for olive productivity. AY forecast only by using individual crown area made it possible to get a simple and cheap forecast tool for a wide range of olive orchards. Finally, the acquired information was introduced in a thematic map describing spatial AY variability obtained from orthoimage analysis that may be a powerful tool for farmers, insurance systems, market forecasts or to detect agronomical problems. MDPI 2017-07-30 /pmc/articles/PMC5579829/ /pubmed/28758945 http://dx.doi.org/10.3390/s17081743 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Sola-Guirado, Rafael R.
Castillo-Ruiz, Francisco J.
Jiménez-Jiménez, Francisco
Blanco-Roldan, Gregorio L.
Castro-Garcia, Sergio
Gil-Ribes, Jesus A.
Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery
title Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery
title_full Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery
title_fullStr Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery
title_full_unstemmed Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery
title_short Olive Actual “on Year” Yield Forecast Tool Based on the Tree Canopy Geometry Using UAS Imagery
title_sort olive actual “on year” yield forecast tool based on the tree canopy geometry using uas imagery
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579829/
https://www.ncbi.nlm.nih.gov/pubmed/28758945
http://dx.doi.org/10.3390/s17081743
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