Título: IRRIGATION CONTROL SYSTEM USING MACHINE LEARNING TECHNIQUES APPLIED TO PRECISION AGRICULTURE

Autor(es): CADENA LEMA HECTOR DARIO, DOMINGUEZ LIMAICO HERNAN MAURICIO, MAYA OLALLA EDGAR ALBERTO, NOGALES ROMERO JOSE CAMILO, VASQUEZ AYALA CARLOS ALBERTO, ZAMBRANO VIZUETE OSCAR MARCELO

Fecha de publicación: 02-aug-2022

Resumen: Many technologies are currently assisting in the remote monitoring and analysis of various sensor networks which are intended for inhospitable or difficult to access locations. Therefore, it is necessary to develop several investigations which help to deepen and facilitate such analysis and monitoring. The objective of this research is the development of an electronic system to automate the irrigation process in crops based on artificial neural networks through remote monitoring using low power wide area networks (LPWAN). To this end, the research question is as follows: How does a technology such as LPWANs help the remote monitoring and control of crop irrigation processes (precision agriculture) and how feasible is the application of machine learning by neural networks of a system, for the automatic control of such process? In this context, the analysis of each of the proposed scenarios will allow us to generate an answer to the question posed. The research question is answered through a study in the farm “La Pradera” of the Technical University of the North where field analyses will be carried out in order to develop several scenarios in which the neural networks and LPWAN technologies are implemented for their analysis.

Palabras clave: Internet of Farm Things (IoFT) Wireless sensors network LPWAN Neural networks Irrigation

DOI: https://doi.org/10.1007/978-3-031-11295-9_24

ISSN: 23673370

Tipo publicación: Artículo