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Low-Cost Optical Sensors for Soil Composition Monitoring

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Low-Cost Optical Sensors for Soil Composition Monitoring

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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


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