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dc.contributor.author | Díaz, Francisco Javier | es_ES |
dc.contributor.author | Ahmad, Ali | es_ES |
dc.contributor.author | Parra, Lorena | es_ES |
dc.contributor.author | Sendra, Sandra | es_ES |
dc.contributor.author | Lloret, Jaime | es_ES |
dc.date.accessioned | 2024-07-01T18:36:03Z | |
dc.date.available | 2024-07-01T18:36:03Z | |
dc.date.issued | 2024-02 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/205606 | |
dc.description.abstract | [EN] Studying soil composition is vital for agricultural and edaphology disciplines. Presently, colorimetry serves as a prevalent method for the on-site visual examination of soil characteristics. However, this technique necessitates the laboratory-based analysis of extracted soil fragments by skilled personnel, leading to substantial time and resource consumption. Contrastingly, sensor techniques effectively gather environmental data, though they mostly lack in situ studies. Despite this, sensors offer substantial on-site data generation potential in a non-invasive manner and can be included in wireless sensor networks. Therefore, the aim of the paper is to develop a low-cost red, green, and blue (RGB)-based sensor system capable of detecting changes in the composition of the soil. The proposed sensor system was found to be effective when the sample materials, including salt, sand, and nitro phosphate, were determined under eight different RGB lights. Statistical analyses showed that each material could be classified with significant differences based on specific light variations. The results from a discriminant analysis documented the 100% prediction accuracy of the system. In order to use the minimum number of colors, all the possible color combinations were evaluated. Consequently, a combination of six colors for salt and nitro phosphate successfully classified the materials, whereas all the eight colors were found to be effective for classifying sand samples. The proposed low-cost RGB sensor system provides an economically viable and easily accessible solution for soil classification. | es_ES |
dc.description.sponsorship | This work is partially funded by the "Programa Estatal de I + D + i Orientada a los Retosde la Sociedad, en el marco del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 2017-2020" project PID2020-114467RR-C33/AEI/10.13039/501100011033 and by "Proyectos Estrate-gicos Orientados a la Transicion Ecologica y a la Transicion Digital" project TED2021-131040B-C31. This study also forms part of the ThinkInAzul program and was supported by MCIN with funding from the European Union NextGenerationEU (PRTR-C17.I1) and from Generalitat Valenciana(THINKINAZUL/2021/002). | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Sensors | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Soil fertilizer | es_ES |
dc.subject | Dryland agriculture | es_ES |
dc.subject | WSN | es_ES |
dc.subject | Agricultural practices | es_ES |
dc.subject | Soil properties | es_ES |
dc.subject | Salinity | es_ES |
dc.subject | Optical sensor | es_ES |
dc.subject.classification | INGENIERÍA TELEMÁTICA | es_ES |
dc.title | Low-Cost Optical Sensors for Soil Composition Monitoring | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/s24041140 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114467RR-C33/ES/RED HETEROGENEA INTELIGENTE DE SENSORES INALAMBRICOS PARA MONITORIZAR Y ESTIMAR EL CONTENIDO DE RESINA DE CISTUS LADANIFER/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/GVA//THINKINAZUL%2F2021%2F002/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MINECO//TED2021-131040B-C31/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/MICINN//PRTR-C17.I1/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Instituto de Investigación para la Gestión Integral de Zonas Costeras - Institut d'Investigació per a la Gestió Integral de Zones Costaneres | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia | es_ES |
dc.description.bibliographicCitation | Díaz, FJ.; Ahmad, A.; Parra, L.; Sendra, S.; Lloret, J. (2024). Low-Cost Optical Sensors for Soil Composition Monitoring. Sensors. 24(4). https://doi.org/10.3390/s24041140 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/s24041140 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 24 | es_ES |
dc.description.issue | 4 | es_ES |
dc.identifier.eissn | 1424-8220 | es_ES |
dc.identifier.pmid | 38400299 | es_ES |
dc.identifier.pmcid | PMC10892096 | es_ES |
dc.relation.pasarela | S\520396 | es_ES |
dc.contributor.funder | Generalitat Valenciana | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.contributor.funder | Ministerio de Ciencia e Innovación | es_ES |
dc.contributor.funder | Ministerio de Economía y Competitividad | es_ES |