Browsing by Author "Kabwangu, Muhamed"

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    Automated hydroponic fodder growth chamber with live monitoring and AI mold detection.
    (Busitema University, 2026-07) Kabwangu, Muhamed; Talemwa, Dickson
    Livestock production heavily depends on the consistent availability of high quality fodder and hydroponic fodder systems have become a sustainable solution for efficient animal feed production. However, these systems are often affected by challenges such as mold contamination and the lack of continuous monitoring, which can reduce fodder quality and result in losses. Traditional manual inspection methods are time consuming, inconsistent and unable to provide real-time monitoring of system conditions. [1] This project presents the design and implementation of an Automated Hydroponic Fodder Grow Chamber with AI-Based Mold Detection and Real-Time Web Monitoring System to address these challenges. The system automates the monitoring of key environmental parameters such as temperature, humidity and pH level using sensors connected to a microcontroller. A camera-based Artificial Intelligence model was integrated to detect mold growth on hydroponically produced rice fodder. The AI model processes images frames using computer vision techniques and classifies the fodder as either healthy or mold-infected. The results are displayed in real time on a web-based dashboard together with sensor data for easy monitoring. The integration of automation, artificial intelligence and web technologies enables continuous monitoring, reduces manual inspection efforts and improves the accuracy and speed of detecting mold contamination. This leads to improved fodder quality, reduced losses and more efficient livestock feed production.