Abdul Wahid, Jumadi Mabe Parenreng, Aswandi, Ananta Dwi Prayoga Alwi, Satria Gunawan Zain, Wirawan Setialeksana
Farmers and agricultural newcomers often rely on intuition for plant nutrient management, leading to inaccuracies. This study presents a real-time soil nutrient monitoring and plant recommendation system using IoT and machine learning. The prototype integrates an ESP32 microcontroller, NPK sensor, soil pH sensor, and DHT11 with an application and a machine learning model. The model employs a hard voting method combining random forest, LightGBM, and support vector machine algorithms, deployed via Flask API and hosted on Vercel. Testing showed the system effectively monitors soil nutrients in real-time, with a pH sensor error margin of ±0.51 and NPK sensor readings displaying a non-linear increase in levels. The hard voting method achieved 98.5% accuracy, with recommendation processing taking less than one second (371.17ms) and CPU usage at 7%. System performance depends on internet speed, while recommendation accuracy is influenced by temperature and humidity. This system offers a practical solution for precision agriculture by optimizing nutrient application and plant recommendations. © 2024 IEEE.
Universitas Negeri Makassar, Informatics and Computer Engineering Department, Makassar, Indonesia; Politeknik Negeri Lhokseumawe, Network Engineering Technology Study Program, Lhokseumawe, Indonesia
