A Methodology Based on Machine Learning and Soft Computing to Design More Sustainable Agriculture Systems

Cadenas Figuerero, J.M.; Garrido Carrera, María del Carmen; Martínez España, R.

Publication:
A Methodology Based on Machine Learning and Soft Computing to Design More Sustainable Agriculture Systems

dc.contributor.author	Cadenas Figuerero, J.M.
dc.contributor.author	Garrido Carrera, María del Carmen
dc.contributor.author	Martínez España, R.
dc.contributor.department	Ingeniería de la Información y las Comunicaciones
dc.date.accessioned	2024-01-24T16:59:16Z
dc.date.available	2024-01-24T16:59:16Z
dc.date.issued	2023
dc.description	©2023. This manuscript version is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the Published, version of a Published Work that appeared in final form in Sensors. To access the final edited and published work see https://doi.org/10.3390/s23063038	es
dc.description.abstract	Advances in new technologies are allowing any field of real life to benefit from using these ones. Among of them, we can highlight the IoT ecosystem making available large amounts of information, cloud computing allowing large computational capacities, and Machine Learning techniques together with the Soft Computing framework to incorporate intelligence. They constitute a powerful set of tools that allow us to define Decision Support Systems that improve decisions in a wide range of real-life problems. In this paper, we focus on the agricultural sector and the issue of sustainability. We propose a methodology that, starting from times series data provided by the IoT ecosystem, a preprocessing and modelling of the data based on machine learning techniques is carried out within the framework of Soft Computing. The obtained model will be able to carry out inferences in a given prediction horizon that allow the development of Decision Support Systems that can help the farmer. By way of illustration, the proposed methodology is applied to the specific problem of early frost prediction. With some specific scenarios validated by expert farmers in an agricultural cooperative, the benefits of the methodology are illustrated. The evaluation and validation show the effectiveness of the proposal.	es
dc.format	application/pdf	es
dc.format.extent	16	es
dc.identifier.citation	Sensors, 23(6), 3038. 2023
dc.identifier.doi	https://doi.org/10.3390/s23063038
dc.identifier.issn	1424-8220
dc.identifier.uri	http://hdl.handle.net/10201/137716
dc.language	eng	es
dc.publisher	MDPI	es
dc.relation	This work is part of the project of I+D+i PID2020-112675RB-C44, funded by MCIN/AEI/ 10.13039/501100011033. Type of project: National	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights	Atribución 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.subject	Sustainable agriculture	es
dc.subject	Time series forecast	es
dc.subject	Soft Computing	es
dc.subject	IoT	es
dc.title	A Methodology Based on Machine Learning and Soft Computing to Design More Sustainable Agriculture Systems	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es