Making decisions for frost prediction in agricultural crops in a softcomputing framework

Cadenas Figueredo, J.M.; Garrido Carrera, María del Carmen; Martínez España, R.; Guillén-Navarro, M.A.

Publication:
Making decisions for frost prediction in agricultural crops in a softcomputing framework

dc.contributor.author	Cadenas Figueredo, J.M.
dc.contributor.author	Garrido Carrera, María del Carmen
dc.contributor.author	Martínez España, R.
dc.contributor.author	Guillén-Navarro, M.A.
dc.contributor.department	Ingeniería de la Información y las Comunicaciones
dc.date.accessioned	2024-01-24T16:40:01Z
dc.date.available	2024-01-24T16:40:01Z
dc.date.issued	2020
dc.description	© 2020. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ This document is the Accepted version of a Published Work that appeared in final form in Computers and Electronics in Agriculture. To access the final edited and published work see https://doi.org/10.1016/j.compag.2020.105587	es
dc.description.abstract	Nowadays, there are many areas of daily life that can obtain benefit from technological advances and the large amounts of information stored. One of these areas is agriculture, giving place to precision agriculture. Frosts in crops are among the problems that precision agriculture tries to solve because produce great economic losses to farmers. The problem of early detection of frost is a process that involves a large amount of wheather data. However, the use of these data, both for the classification and regression task, must be carried out in an adequate way to obtain an inference with quality. A preprocessing of them is carried out in order to obtain a dataset grouping attributes that refer to the same measure in a single attribute expressed by a fuzzy value. From these fuzzy time series data we must use techniques for data analysis that are capable of manipulating them. Therefore, first a regression technique based on k-nearest neighbors in a Soft Computing framework is proposed that can deal with fuzzy data, and second, this technique and others to classification are used for the early detection of a frost from data obtained from different weather stations in the Region of Murcia (south-east Spain) with the aim of decrease the damages that these frosts can cause in crops. From the models obtained, an interpretation of the provided information is performed and the most relevant set of attributes is obtained for the anticipated prediction of a frost and of the temperature value. Several experiments are carried out on the datasets to obtain the models with the best performance in the prediction validating the results by means of a statistical analysis.	es
dc.format	application/pdf	es
dc.format.extent	23	es
dc.identifier.citation	Computers and Electronics in Agriculture, v. 175. 2020
dc.identifier.doi	https://doi.org/10.1016/j.compag.2020.105587
dc.identifier.issn	0168-1699
dc.identifier.issn	1872-7107
dc.identifier.uri	http://hdl.handle.net/10201/137715
dc.language	eng	es
dc.publisher	ScienceDirect	es
dc.relation	This work is supported by the project "Hacia la continuidad de servicios emergentes a partir de objetos inteligentes basados en IoT" (ref: TIN2017-86885-R). Project type: National. Finantial entity: Ministerio de Economía, Industria y Competitividad.	es
dc.rights	info:eu-repo/semantics/openAccess	es
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internacional	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	Precision agriculture	es
dc.subject	Crop frost	es
dc.subject	Data analysis	es
dc.subject	Fuzzy data	es
dc.subject	Fuzzy classification and regression	es
dc.title	Making decisions for frost prediction in agricultural crops in a softcomputing framework	es
dc.type	info:eu-repo/semantics/article	es
dspace.entity.type	Publication	es