AI Assisted and IOT Based Fertilizer Mixing System


  • Wan Mohd Faizal Wan Nik Universiti Malaysia Perlis
  • Shahrul Fazly Man Universiti Malaysia Perlis
  • Muhammad Imran Ahmad Universiti Malaysia Perlis
  • Shafie Omar Universiti Malaysia Perlis
  • Tan Shie Chow Universiti Malaysia Perlis
  • Mohd Nazri Abu Bakar Universiti Malaysia Perlis
  • Fadhilnor Abdullah Universiti Malaysia Perlis
  • Muhammad Khamil Akbar Universiti Malaysia Perlis



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.


IoT, SVM classifier, Fertilizer


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How to Cite

Wan Mohd Faizal Wan Nik, Shahrul Fazly Man, Muhammad Imran Ahmad, Shafie Omar, Tan Shie Chow, Mohd Nazri Abu Bakar, Fadhilnor Abdullah, & Muhammad Khamil Akbar. (2024). AI Assisted and IOT Based Fertilizer Mixing System. Advanced and Sustainable Technologies (ASET), 3(1), 38–45.