Estimación automática del estrés hídrico en olivar mediante dendrómetros
DOI:
https://doi.org/10.17979/ja-cea.2025.46.12254Palabras clave:
Aprendizaje automático, Procesamiento de señales, Aprendizaje para el control, Modelado de series temporales, Identificación para el control, Reconocimiento de patrones e inteligencia artificial en la agriculturaResumen
En este trabajo se presenta un método de clasificación basado en aprendizaje automático para estimar el nivel de estrés hídrico en plantas a partir de lecturas de variación del diámetro del tronco, registradas mediante dendrómetros. El modelo se valida con datos experimentales de riego deficitario obtenidos en un olivar en Sevilla, Andalucía, durante cinco años. El objetivo es facilitar la integración de las herramientas propuestas en estrategias avanzadas de riego, reduciendo la dependencia de métodos invasivos o hardware costoso.
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Derechos de autor 2025 Jaime Palomo, Rafael Romero, María V. Cuevas, Teodoro Álamo, David Muñoz de la Peña
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
