AGRINNOVACIÓN 4.0: Methodological classification tool to determine production areas of short-cycle crops
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| Auteurs: | , , , , |
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| Format: | artículo original |
| Statut: | Versión publicada |
| Date de publication: | 2022 |
| Description: | The National Plan for the Improvement of Productivity and Sustainability of the Agricultural Sector aims to be applied in a staggered manner to the entire country, under the name of AGRINNOVACION 4.0 to promote economic recovery and job creation after the COVID-19 pandemic. The objective of this work is to analyze geospatial information of the producers of the AGRINNOVACIÓN 4.0 program using the free Google Earth Engine (GEE) platform, in order to establish the base of the digital agricultural cadastre of the North Zone of Cartago and have a system of geographic information for the application of high-precision technologies, as a basis for the identification model of productive areas with short-cycle crops developed in the North Zone of Cartago. A data acquisition methodology was generated using geographic information systems and machine learning techniques (Random Forest), with good fitting results. For the area under study, it is imperative that the information affected by cloud cover be reduced to make the classification of lands for horticultural use as accurate as possible. The tool is replicable and constitutes a support in the success of the plan for the later stages. |
| Pays: | Portal de Revistas TEC |
| Institution: | Instituto Tecnológico de Costa Rica |
| Repositorio: | Portal de Revistas TEC |
| Langue: | Español |
| OAI Identifier: | oai:ojs.pkp.sfu.ca:article/6059 |
| Accès en ligne: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/6059 |
| Mots-clés: | Google Earth Engine machine learning GIS Irrigation satellital imagery digital farming aprendizaje automático SIG riego imagen satelital agricultura digital |