Published in

Advances in Environmental Engineering and Green Technologies, p. 113-145, 2023

DOI: 10.4018/978-1-6684-6791-6.ch006

Links

Tools

Export citation

Search in Google Scholar

Artificial Intelligence, Internet of Things, and Machine-Learning

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Red circle
Preprint: archiving forbidden
Green circle
Postprint: archiving allowed
Red circle
Published version: archiving forbidden
Data provided by SHERPA/RoMEO

Abstract

Water scarcity has been escalating both in terms of frequency and severity, owing to climate change and global warming. Furthermore, water is a vital source that is at the core of crucial sectors like agriculture. Yet, this source is labeled scarce, and its distribution is uneven globally. For the aforementioned reasons, achieving a rational use of water is of utmost importance. In this framework, computational intelligence like artificial intelligence (AI), the internet of things (IoT), and machine-learning, has been gaining momentum for implementing smart irrigation and precision agriculture. Thus, the chapter surveys a selection of recent studies, corroborating AI, IoT, and machine-learning as promising approaches to advance agriculture. The chapter also sheds light on the notion of virtual water, proposes strategies to deal with water scarcity, and highlights the essential components to achieve effective smart irrigation, thereby switching toward an innovative model of sustainable digital agriculture.