Shahrul Fazly Man@Sulaiman
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Shahrul Fazly Man@Sulaiman
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Shahrul Fazly, Man@Sulaiman
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  • Publication
    AI Assisted and IOT Based Fertilizer Mixing System
    (Universiti Malaysia Perlis, 2024-06-03)
    Wan Mohd Faizal Wan Nik
    ;
    Shahrul Fazly Man@Sulaiman
    ;
    Muhammad Imran Ahmad
    ;
    Shafie Omar
    ;
    Tan Shie Chow
    ;
    Mohd Nazri Abu Bakar
    ;
    Fadhilnor Abdullah
    ;
    Muhammad Khamil Akbar
    Agriculture techniques, particularly fertilizer mixing, have significant impacts on crop productivity. Introducing IoT technology to agriculture can enhance productivity, and machine learning offers a mechanism to gain insights from data, making agricultural practices more efficient. This research aims to design an AI-assisted and IoT-based fertilizer mixing system for greenhouses. This system utilizes sensor data and AI algorithms, specifically the Support Vector Machine (SVM), to optimize fertilizer application. Results from the SVM classifier showed a 100% accuracy rate for temperature and humidity, 65% accuracy for phosphorus, 86% for nitrogen, and 100% for potassium. These findings demonstrate the potential of the proposed system to improve fertilizer efficiency while reducing labor and resource waste.
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