Data-driven water need estimation for IoT-based smart irrigation: A survey

Precision irrigation plays an important socio-economic and environmental role in our society, reducing water and electricity consumption and increasing food production. An essential task for achieving the full potential of precision irrigation is Water Need Estimation (WNE), as it determines how much, when, and where to irrigate. WNE has traditionally been a complex and almost unattainable process that involves obtaining reliable data, dealing with uncertainties caused by environmental and technical conditions, and considering plant, soil, and water interactions. Particularly, data-driven WNE approaches have been gaining momentum as the Internet of Things (IoT) quickly evolves, providing large volumes of data. This paper reviews the state of the art and future challenges in WNE, focusing on promising data-driven approaches, and discussing trends and research challenges.