Smart Agriculture: Leveraging IoT and Machine Learning for Sustainable Farming

Abstract

The increasing global demand for food, along with the challenges posed by climate change and limited natural resources, calls for a shift from conventional farming to more intelligent, data-centric methods. This study investigates the use of Internet of Things (IoT) devices, cloud computing, and Machine Learning (ML) algorithms to support sustainable agricultural practices. A dataset containing 10,001 entries—including variables such as environmental conditions, soil nutrients, and crop data—was analysed to forecast crop yield. Multiple regression models were tested, with the Random Forest Regressor delivering the highest accuracy at 98.48%, significantly outperforming baseline models like Linear Regression, which scored 76.42%. The integration of cloud services facilitates scalable, real-time data handling and allows efficient processing of sensor data alongside predictive modelling. This research highlights the effectiveness of ensemble learning methods and connected infrastructure in delivering actionable insights for precision agriculture. In order to increase productivity and ensure sustainable resource use, the suggested framework encourages more intelligent choices in areas such as crop planning, soil management, and yield enhancement.