Faculty of Computing

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Author: search.filters.author.Karunarathna H.B.T.N.Has files: Yes

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    A Data-Driven Approach to Nutrient Deficiency Detection and Yield Optimization in Hydroponic Farming Using IoT Technology
    (Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Wimansha R.A.I; Senarathne D.M.N.P.S; Rathnaweera W.L.B; Karunarathna H.B.T.N.; Chandrasiri, S; De Silva, A
    This study explores the use of IoT sensors, such as the ESP32, for leaf pigmentation analysis to improve the accuracy of identifying nutritional deficiencies and optimizing resource allocation in hydroponic farming. The results demonstrate that a data-driven approach can significantly enhance decision-making for hydroponic farmers, contributing to improved food security and the promotion of sustainable agricultural practices. Cross-validation across multiple crop varieties and growing cycles confirmed model generalizability. Scenario simulation capabilities were implemented to allow growers to evaluate hypothetical environmental and nutritional interventions prior to deployment, with sensitivity analysis revealing key leverage points for yield optimization. Implementation of the system in three commercial hydroponic facilities demonstrated average yield improvements of 17.3 percent while reducing resource inputs by 9.6 percent. These findings suggest that data-driven predictive modeling can significantly enhance precision agriculture practices in controlled environment growing systems, thereby improving agricultural sustainability, enhancing food security, and optimizing economic outcomes for hydroponic operations.