Precision Agriculture in the Digital Age: IoT and Machine Learning Synergy for Improved Agricultural Productivity

Abstract

This study explores the integration of Internet of Things (IoT) devices and machine learning (ML) to enhance agricultural productivity. By leveraging IoT sensors deployed across agricultural farms, real-time data on environmental and agronomic factors such as temperature, humidity, soil moisture, and crop health is collected. The dataset, spanning multiple farms over two years, also incorporates satellite imagery for comprehensive crop health analysis. Using machine learning algorithms, the data is analyzed to predict key variables, enabling informed decision-making. This approach aims to optimize resource management, improve crop yields, and address challenges like climate change. The study highlights the synergistic potential of IoT and ML in transforming modern agriculture into a more efficient and sustainable industry.