Our project is a Smart Agriculture System designed to aid farmers in decision-making. It consists of two modules: the Crop Monitoring System and the Crop Recommendation System. The Crop Monitoring System uses sensors through Arduino, ESP32 and LoRaWAN to collect soil parameters like moisture, temperature, and pH, a total of 9 parameters of soil and air. The information is then displayed on a web interface for remote access. The Crop Recommendation System is focused on making the best crop recommendations based on weather forecasts, however. We make weather condition predictions such as temperature, humidity, and rain using the SARIMA model. A Random Forest Classifier makes crop recommendations based on this predicted data, which are suitable for Mianwali conditions and can give good yields, although they may not be generally grown there. This framework aims to boost farm productivity by combining IoT and machine learning techniques.
Tools: Arduino IDE, Python,MQTT Server
Department: Department of Electrical Engineering
Project Poster
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