Machine Learning Techniques for Accurate Agriculture through Wireless Sensor Networks: A Comprehensive Review

Abstract

Improving agricultural output while decreasing environmental impact requires widespread use of sensor technology and the
IoT. New developments in the Internet of Things (IoT), Wireless Sensor Networks (WSN), and ICT may help solve some of
the sector’s technological, economic, and environmental problems. Big data with many modalities and geographical and temporal
changes is increasing due to the proliferation of linked devices. For improved decision-making, more accurate forecasting,
and dependable sensor management, intelligent processing and analysis of this massive data is essential. This article provides a
thorough analysis of how various machine learning algorithms have been used for agricultural ecosystem sensor data analytics.
In addition, the article delves into a case study of a prototype smart farm that integrates food, energy, and water (FEW) systems
via the use of the Internet of Things (IoT).