Characterization of vineyard training systems based on remote sensing and crop indices
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 w...
| Autores principales: | , , |
|---|---|
| Formato: | info:ar-repo/semantics/documento de conferencia |
| Lenguaje: | Español |
| Publicado: |
IEEE
2025
|
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12123/23992 https://ieeexplore.ieee.org/document/10735888 https://doi.org/10.1109/ARGENCON62399.2024.10735888 |
| _version_ | 1855038856998420480 |
|---|---|
| author | Capraro, Flavio Pacheco, Daniela Campillo, Pedro |
| author_browse | Campillo, Pedro Capraro, Flavio Pacheco, Daniela |
| author_facet | Capraro, Flavio Pacheco, Daniela Campillo, Pedro |
| author_sort | Capraro, Flavio |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/documento de conferencia |
| id | INTA23992 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Español |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | IEEE |
| publisherStr | IEEE |
| record_format | dspace |
| spelling | INTA239922025-09-30T10:40:03Z Characterization of vineyard training systems based on remote sensing and crop indices Capraro, Flavio Pacheco, Daniela Campillo, Pedro 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 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 2025-09-30T10:34:29Z 2025-09-30T10:34:29Z 2024-09-18 info:ar-repo/semantics/documento de conferencia info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion 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 spa 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 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) application/pdf IEEE VII Congreso Bienal ARGENCON. 18 al 20 de septiembre 2024. San Nicolás de los Arroyos. Argentina |
| spellingShingle | 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 Capraro, Flavio Pacheco, Daniela Campillo, Pedro 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 |
| 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_short | 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 |
| 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 |
| url | http://hdl.handle.net/20.500.12123/23992 https://ieeexplore.ieee.org/document/10735888 https://doi.org/10.1109/ARGENCON62399.2024.10735888 |
| work_keys_str_mv | AT capraroflavio characterizationofvineyardtrainingsystemsbasedonremotesensingandcropindices AT pachecodaniela characterizationofvineyardtrainingsystemsbasedonremotesensingandcropindices AT campillopedro characterizationofvineyardtrainingsystemsbasedonremotesensingandcropindices |