An Intelligent IoT and Machine Learning-Based Smart Irrigation System Using LoRaWAN

Abstract

In this paper, we introduce the development of a smart agriculture system which unifies LoRaWAN-based Internet of Things (IoT) devices and machine learning in order to improve monitoring, cultivation, and watering processes in modernized agricultural activities. Based on Milesight IoT sensors and gateways, it monitors environmental data such as soil condition, air temperature, humidity, and wind speed 24/7 and transmits this information over a LoRaWAN network to a central cloud platform. The data is harvested by a Python backend and stored in an interactive Streamlit dashboard for real-time visualization and farm control. For efficient water usage, an irrigation prediction Random Forest Classifier was built based on a curated data set of 1,248 records and tested using unseen data. The model's accuracy approaches the perfect, with conductivity as the most important feature, correlated with the soil moisture. Cross-validation supported strong generalization with an average accuracy of 81.6%. The presented system highlights how integration of IoT and machine learning minimizes resource wastage, reduces manual interaction, and promotes sustainable robust agricultural practices. The implications of this study are to promote the development of -its-kind and scalable IoT-based smart farming systems in farms, so that farm resources can be more effectively utilized based on real-time dynamic data analytics