Published Paper Details:

Precision agriculture is an evolving approach in modern farming that leverages technology to optimize agricultural processes. Wireless Sensor Networks (WSNs) have emerged as a valuable tool in precision agriculture, enabling real-time monitoring of environmental parameters and crop conditions. Machine learning algorithms, when integrated with WSNs, further enhance the decision-making process by analyzing data collected from the field. This paper provides an overview of the synergy between WSNs and machine learning in precision agriculture, emphasizing their applications, advantages, and challenges. A more productive and sustainable future for agriculture throughout the world depends on the use of sensors and the Internet of Things (IoT). New developments in the Internet of Things (IoT), Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) may be able to help this industry overcome some of its technological, economic, and environmental problems while also creating new possibilities. There is an increasing amount of big data with different modalities and temporal and geographical fluctuations as the number of connected devices grows. To achieve improved decision making, forecasting, and dependable sensor management, a deeper degree of knowledge base and insights must be developed through the intelligent processing and analysis of this massive data. This article provides an extensive overview of the many machine learning applications.