Abstract: Using data-driven technologies, modern agriculture promotes sustainability while increasing farming efficiency. Intelligent farming solutions, like the one suggested in this paper, are now possible thanks to developments in machine learning (ML) and the internet of things (IoT). Using an Internet of Things framework, it integrates AI-based crop recommendations with real-time soil monitoring. Together with a temperature and pH sensor, a multi-electrode NPK sensor measures the three main nutrients found in soil: nitrogen (N), phosphorus (P), and potassium (K). An ESP32 microcontroller receives data through an RS485-to-USB interface, processes it, and shows it on a 20x4 LCD I2C screen. Real-time weather data from API integrations is also added. Reliance on conventional lab testing is decreased by cloud-based machine learning models that evaluate environmental variables and soil health to recommend appropriate crops. Because of the system's remote accessibility, farmers—even in remote locations with little technical know-how—to make wise choices. Additional sensors (such as light and moisture sensors) and weather-adaptive predictive models are examples of future improvements that could improve the accuracy of the system and encourage sustainable farming methods.
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DOI:
10.17148/IJARCCE.2025.14407
