Ahmad, Ali

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Ahmad
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Ali
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Author: Díaz Blasco, Francisco Javier ×

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    Low-Cost Optical Sensors for Soil Composition Monitoring
    (MDPI AG, 2024-02) Díaz Blasco, Francisco Javier; Ahmad, Ali; Parra, Lorena; Sendra Compte, Sandra; Lloret Mauri, Jaime; Dpto. de Comunicaciones; Escuela Politécnica Superior de Gandia; Instituto de Investigación para la Gestión Integrada de Zonas Costeras; Generalitat Valenciana; Agencia Estatal de Investigación; Ministerio de Ciencia e Innovación; Ministerio de Economía y Competitividad
    [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.