The realization of the Internet of Things (IoT) potential in large-scale commercial applications requires integrated IoT platforms that are adaptable to the specific needs of various applications. This paper presents the development of an IoT-based platform for smart irrigation, featuring a flexible architecture that enables the rapid integration of IoT and machine learning (ML) components. The platform facilitates the creation of tailored application solutions, offering a range of customized analytical methods for precision irrigation, thereby advancing machine learning techniques. The proposed system addresses significant water waste associated with conventional irrigation methods by leveraging ML and IoT technologies to automate the irrigation process. Specifically, an IoT-enabled, ML-trained recommendation system is introduced to optimize water consumption with minimal farmer intervention. The system uses IoT sensors to capture precise ground and environmental data, which is then processed on a cloud-based server using machine learning algorithms to provide irrigation recommendations. The paper also explores the application of big data analytics and machine learning in agriculture, focusing on water irrigation management. It emphasizes the need for accurate modeling to estimate water requirements based on climate, soil, and weather conditions. The study reviews the development of a decision support system framework for sustainable water irrigation management using intelligent learning approaches, highlighting the integration of big data technologies and information and communication technologies (ICT) to design advanced analytical modeling applications.

IoT, Cloud Computing, Smart Irrigation Systems, Machine Learning, SVM