MSc in Information Technology

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/2484

Students enrolled in the MSc in Information Technology programme are required to submit a thesis as a compulsory component of their degree requirements. This collection features merit-based theses submitted by postgraduate students specialising in Information Technology. Abstracts are available for public viewing, while the full texts can be accessed on-site within the library.
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Theses and Dissertations of the Sri Lanka Institute of Information Technology (SLIIT) are licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License .

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Author: search.filters.author.De Silva, G.P.S.NHas files: Yes

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    AI-Driven Nutrient Management in Hydroponics for Urban Agriculture Enhancing Food Security through Technology
    (SLIIT, 2024-12) De Silva, G.P.S.N
    This research investigates the integration of artificial intelligence (AI) into hydroponic farming systems to tackle challenges in urban agriculture, particularly food security and resource optimization. Urban expansion and shrinking arable land necessitate innovative agricultural solutions, and hydroponics—a soilless cultivation method—is increasingly recognized for its efficiency and scalability in urban environments. By leveraging AI and Internet of Things (IoT) technologies, this study develops an automated nutrient management system that optimizes critical parameters such as pH, electrical conductivity (EC), and nutrient concentrations (NPK: Nitrogen, Phosphorus, Potassium) to enhance plant growth and resource efficiency. The experimental design includes two hydroponic systems: an AI-driven system and a manual control setup, both operating under identical conditions. The AI-driven system utilizes real-time sensor data, processed by machine learning models, to automate nutrient adjustments. Data collected from sensors, including pH, EC, and temperature, is transmitted via AWS IoT Core and stored in DynamoDB for real-time monitoring and historical analysis. The system's performance is visualized through an Angular-based dashboard, enabling continuous monitoring and decision-making. Results demonstrate that the AI-driven system significantly outperforms manual nutrient management in terms of plant growth, resource efficiency, and environmental stability. Plants grown in the automated system exhibited a 48% increase in weight and improved root development compared to those grown in the manual system. The automated system 4 also maintained optimal pH (6.3–6.7) and EC (1.8–2.4) levels with minimal deviations, reducing nutrient waste and ensuring precise dosing. This research contributes to the field of smart agriculture by showcasing the transformative potential of AI and IoT technologies in hydroponic farming. The findings emphasize the viability of AI-driven systems to enhance the sustainability, scalability, and efficiency of hydroponics for urban agriculture. The integration of advanced