Precision irrigation management using machine learning and digital farming solutions

• Main Authors: Abioye, Emmanuel Abiodun, Hensel, Oliver, Esau, Travis J., Elijah, Olakunle, Zainal Abidin, Mohamad Shukri, Ayobami, Ajibade Sylvester, Yerima, Omosun, Nasirahmadi, Abozar
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.utm.my/id/eprint/100608/1/MohamadShukri2022_PrecisionIrrigationManagementUsingMachineLearning.pdf; http://eprints.utm.my/id/eprint/100608/; http://dx.doi.org/10.3390/agriengineering4010006

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.