Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

TLDR

IoT, wireless sensor networks, and ICT advancements are driving agriculture toward greater productivity and sustainability by generating large, multimodal, spatially and temporally varied data that requires intelligent processing for improved decision‑making, forecasting, and sensor management. This review examines the application of machine‑learning algorithms to sensor data analytics in agriculture. It surveys how these algorithms are employed to analyze agricultural sensor data. The review also presents a case study of an IoT‑based, data‑driven smart farm prototype that integrates food, energy, and water systems.

Abstract

The use of sensors and the Internet of Things (IoT) is key to moving the world’s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system.