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.
