Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

TLDR

Freshwater is vital for irrigation and plant nutrition, yet agriculture consumes about 70 % of available freshwater, making responsible management with smart technologies essential. The study investigates how integrating various machine learning models can optimize irrigation decisions and examines digital farming solutions that enable remote monitoring and control. The authors review current machine learning techniques and their deployment for sustainable irrigation, and describe mobile and web frameworks that facilitate smart irrigation management. The paper discusses the challenges of implementing these technologies and outlines future research directions.

Abstract

Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.