Characterization of vineyard training systems based on remote sensing and crop indices

Autores
Capraro, Flavio; Pacheco, Daniela; Campillo, Pedro
Año de publicación
2024
Idioma
español castellano
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In the context of precision viticulture, this work presents the implementation of remote sensing techniques to analyze the spatial variability of a vineyard (Vitis vinifera L.). This work seeks to continue a preliminary investigation conducted in 2020; this time, the study area within the vineyard was expanded, and the campaigns of 2023 and 2024 were considered. This trial was conducted in a vineyard located in the province of San Juan, Argentina. The vineyard was divided into three blocks (replicates), and within each block, three training systems were randomly implemented: Free Cordon, Minimal Pruning and Box Pruning. The analysis was mainly based on extracting information from various vineyard maps constructed from high-resolution (2.5 cm pixel size) multispectral and thermographic images. These images were captured using special cameras mounted on an unmanned aerial vehicle (UAV). Vegetation indices NDVI and NDRE were calculated from the orthomosaics. The spatial distribution of each index and the crop temperature (Tc) were studied, and measurements were subsequently recorded in plants within each training system. Based on these measurements, significant differences were identified among the three training systems. The results demonstrated the usefulness of the high-resolution images acquired to assess the vineyard's condition at the plant level, allowing the producer to manage each training system specifically.
EEA San Juan
Fil: Capraro, Flavio. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Pacheco, Daniela Elizabeth. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina.
Fil: Campillo, Pedro. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fuente
VII Congreso Bienal ARGENCON. 18 al 20 de septiembre 2024. San Nicolás de los Arroyos. Argentina
Materia
Imagen Multiespectral
Vitis vinífera
Vid
Agricultura Digital
Agricultura de Precisión
Digital Agriculture
Precision Agriculture
Vehículo Aéreo No Tripulado
Multispectral Imagery
Unmanned Aerial Vehicles
Grapevines
Remote Sensing
Teledetección
Imágenes Termográficas
Nivel de accesibilidad
acceso restringido
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/23992
Acceder
id INTADig_0faeb9e0b9b38924493ac5476a14ef0f
oai_identifier_str oai:localhost:20.500.12123/23992
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Characterization of vineyard training systems based on remote sensing and crop indicesCapraro, FlavioPacheco, DanielaCampillo, PedroImagen MultiespectralVitis viníferaVidAgricultura DigitalAgricultura de PrecisiónDigital AgriculturePrecision AgricultureVehículo Aéreo No TripuladoMultispectral ImageryUnmanned Aerial VehiclesGrapevinesRemote SensingTeledetecciónImágenes TermográficasIn the context of precision viticulture, this work presents the implementation of remote sensing techniques to analyze the spatial variability of a vineyard (Vitis vinifera L.). This work seeks to continue a preliminary investigation conducted in 2020; this time, the study area within the vineyard was expanded, and the campaigns of 2023 and 2024 were considered. This trial was conducted in a vineyard located in the province of San Juan, Argentina. The vineyard was divided into three blocks (replicates), and within each block, three training systems were randomly implemented: Free Cordon, Minimal Pruning and Box Pruning. The analysis was mainly based on extracting information from various vineyard maps constructed from high-resolution (2.5 cm pixel size) multispectral and thermographic images. These images were captured using special cameras mounted on an unmanned aerial vehicle (UAV). Vegetation indices NDVI and NDRE were calculated from the orthomosaics. The spatial distribution of each index and the crop temperature (Tc) were studied, and measurements were subsequently recorded in plants within each training system. Based on these measurements, significant differences were identified among the three training systems. The results demonstrated the usefulness of the high-resolution images acquired to assess the vineyard's condition at the plant level, allowing the producer to manage each training system specifically.EEA San JuanFil: Capraro, Flavio. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaFil: Pacheco, Daniela Elizabeth. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina.Fil: Campillo, Pedro. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; ArgentinaIEEE2025-09-30T10:34:29Z2025-09-30T10:34:29Z2024-09-18info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://hdl.handle.net/20.500.12123/23992https://ieeexplore.ieee.org/document/10735888979-8-3503-6593-1https://doi.org/10.1109/ARGENCON62399.2024.10735888VII Congreso Bienal ARGENCON. 18 al 20 de septiembre 2024. San Nicolás de los Arroyos. Argentinareponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaspainfo:eu-repograntAgreement/INTA/2023-PE-L01-I002, Aportes para la innovación y el desarrollo en los territorios a través del fortalecimiento de la viticulturainfo:eu-repo/semantics/restrictedAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2025-10-16T09:32:36Zoai:localhost:20.500.12123/23992instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2025-10-16 09:32:36.885INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Characterization of vineyard training systems based on remote sensing and crop indices
title Characterization of vineyard training systems based on remote sensing and crop indices
spellingShingle Characterization of vineyard training systems based on remote sensing and crop indices
Capraro, Flavio
Imagen Multiespectral
Vitis vinífera
Vid
Agricultura Digital
Agricultura de Precisión
Digital Agriculture
Precision Agriculture
Vehículo Aéreo No Tripulado
Multispectral Imagery
Unmanned Aerial Vehicles
Grapevines
Remote Sensing
Teledetección
Imágenes Termográficas
title_short Characterization of vineyard training systems based on remote sensing and crop indices
title_full Characterization of vineyard training systems based on remote sensing and crop indices
title_fullStr Characterization of vineyard training systems based on remote sensing and crop indices
title_full_unstemmed Characterization of vineyard training systems based on remote sensing and crop indices
title_sort Characterization of vineyard training systems based on remote sensing and crop indices
dc.creator.none.fl_str_mv Capraro, Flavio
Pacheco, Daniela
Campillo, Pedro
author Capraro, Flavio
author_facet Capraro, Flavio
Pacheco, Daniela
Campillo, Pedro
author_role author
author2 Pacheco, Daniela
Campillo, Pedro
author2_role author
author
dc.subject.none.fl_str_mv Imagen Multiespectral
Vitis vinífera
Vid
Agricultura Digital
Agricultura de Precisión
Digital Agriculture
Precision Agriculture
Vehículo Aéreo No Tripulado
Multispectral Imagery
Unmanned Aerial Vehicles
Grapevines
Remote Sensing
Teledetección
Imágenes Termográficas
topic Imagen Multiespectral
Vitis vinífera
Vid
Agricultura Digital
Agricultura de Precisión
Digital Agriculture
Precision Agriculture
Vehículo Aéreo No Tripulado
Multispectral Imagery
Unmanned Aerial Vehicles
Grapevines
Remote Sensing
Teledetección
Imágenes Termográficas
dc.description.none.fl_txt_mv In the context of precision viticulture, this work presents the implementation of remote sensing techniques to analyze the spatial variability of a vineyard (Vitis vinifera L.). This work seeks to continue a preliminary investigation conducted in 2020; this time, the study area within the vineyard was expanded, and the campaigns of 2023 and 2024 were considered. This trial was conducted in a vineyard located in the province of San Juan, Argentina. The vineyard was divided into three blocks (replicates), and within each block, three training systems were randomly implemented: Free Cordon, Minimal Pruning and Box Pruning. The analysis was mainly based on extracting information from various vineyard maps constructed from high-resolution (2.5 cm pixel size) multispectral and thermographic images. These images were captured using special cameras mounted on an unmanned aerial vehicle (UAV). Vegetation indices NDVI and NDRE were calculated from the orthomosaics. The spatial distribution of each index and the crop temperature (Tc) were studied, and measurements were subsequently recorded in plants within each training system. Based on these measurements, significant differences were identified among the three training systems. The results demonstrated the usefulness of the high-resolution images acquired to assess the vineyard's condition at the plant level, allowing the producer to manage each training system specifically.
EEA San Juan
Fil: Capraro, Flavio. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
Fil: Pacheco, Daniela Elizabeth. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria San Juan; Argentina.
Fil: Campillo, Pedro. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina
description In the context of precision viticulture, this work presents the implementation of remote sensing techniques to analyze the spatial variability of a vineyard (Vitis vinifera L.). This work seeks to continue a preliminary investigation conducted in 2020; this time, the study area within the vineyard was expanded, and the campaigns of 2023 and 2024 were considered. This trial was conducted in a vineyard located in the province of San Juan, Argentina. The vineyard was divided into three blocks (replicates), and within each block, three training systems were randomly implemented: Free Cordon, Minimal Pruning and Box Pruning. The analysis was mainly based on extracting information from various vineyard maps constructed from high-resolution (2.5 cm pixel size) multispectral and thermographic images. These images were captured using special cameras mounted on an unmanned aerial vehicle (UAV). Vegetation indices NDVI and NDRE were calculated from the orthomosaics. The spatial distribution of each index and the crop temperature (Tc) were studied, and measurements were subsequently recorded in plants within each training system. Based on these measurements, significant differences were identified among the three training systems. The results demonstrated the usefulness of the high-resolution images acquired to assess the vineyard's condition at the plant level, allowing the producer to manage each training system specifically.
publishDate 2024
dc.date.none.fl_str_mv 2024-09-18
2025-09-30T10:34:29Z
2025-09-30T10:34:29Z
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/23992
https://ieeexplore.ieee.org/document/10735888
979-8-3503-6593-1
https://doi.org/10.1109/ARGENCON62399.2024.10735888
url http://hdl.handle.net/20.500.12123/23992
https://ieeexplore.ieee.org/document/10735888
https://doi.org/10.1109/ARGENCON62399.2024.10735888
identifier_str_mv 979-8-3503-6593-1
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv info:eu-repograntAgreement/INTA/2023-PE-L01-I002, Aportes para la innovación y el desarrollo en los territorios a través del fortalecimiento de la viticultura
dc.rights.none.fl_str_mv info:eu-repo/semantics/restrictedAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv restrictedAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv VII Congreso Bienal ARGENCON. 18 al 20 de septiembre 2024. San Nicolás de los Arroyos. Argentina
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
_version_ 1846143595217682432
score 13.22299

Publicaciones similares