Publication: A Methodology Based on Machine Learning and Soft Computing to Design More Sustainable Agriculture Systems
Authors
Cadenas Figuerero, J.M. ; Garrido Carrera, María del Carmen ; Martínez España, R.
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Publisher
MDPI
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DOI
https://doi.org/10.3390/s23063038
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info:eu-repo/semantics/article
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
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.
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Citation
Sensors, 23(6), 3038. 2023
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