A data-driven bibliometric review on precision irrigation

Abstract

In recent years, there has been an increasing demand for water to meet the world's growing population. At the same time, increasing drought phenomena reduce water availability. At present, traditional irrigation systems used by farmers allow them to supply the necessary amount of water to crops, but they often result in over-irrigation. The latter over time could lead to a loss of water resources. On the other hand, especially in case of water shortage, inefficient use of water may fail to meet irrigation needs. To remedy this problem, precision irrigation methods are being developed to dose the amount of water needed by crops without wasting both raw material and economics, in order to produce more with less. Specifically, the main technologies used in precision irrigation are IoT-based sensors and machine learning algorithms. In this sense, a data-driven bibliometric review on precision irrigation was conducted from 2001 to 2023; the analysis evidenced 10 main clusters discussed considering the very recent literature after 2020. Among the clusters extracted through CiteSpace are those concerning UAVs (unmanned aerial vehicles), machine vision, WSN sensors (wireless sensor network) and energy savings. This shows that these topics concerning a new way of seeing precision irrigation, placing greater attention to new technologies and algorithms. The focus is on recent research related to monitoring and advanced control concepts for precision irrigation. It is expected that this revision work will serve as a useful reference to improve the reader's knowledge of advanced monitoring and control opportunities linked to precision irrigation.

Keywords

• IoT
• Machine learning
• Smart irrigation
• New advancements
• Water resources