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.
Highlights
Introduction published maps and institutional affilGlobally, the agricultural sector utilizes about 85% of the available freshwater due to increasing population growth, creating the need for an increase in food production [1]
A major driver regarding the attainment of sustainable precision irrigation has been the integration of smart technology, such as machine learning, Internet of Things (IoT), the web, and the mobile framework
Some of the findings from this study suggest that sustainable precision irrigation management plays an important role in enhancing the attainment of food security and the prevention of water scarcity
Summary
Machine learning is a branch of artificial intelligence that allows computers to learn without being explicitly programmed [31]. Farmers make the decision to irrigate based on their previous experience; with advancements in machine learning, irrigation decisions can be better informed using the concept of predicting the water needs of crops based on the forecast of weather and soil conditions. Regression models depict the relationship between two variables, while classifications in supervised learning algorithms are preset. These classifications are created in a finite set, defined by humans, which means that a specific segment of data will be labeled with these classifications. The most commonly used types of supervised learning algorithms (K nearest neighbor (KNN), support vector machine (SVM), decision trees (DT), random forest (RF), etc.) are employed to optimize irrigation volume, timing, scheduling, soil moisture prediction, and weather predictions, to guide irrigation decisions [25]. The different types of supervised learning algorithms are discussed in the subsection
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