Towards Intelligent Agriculture: ML Algorithm Analysis in IoT-Based Smart Irrigation Systems

Smart irrigation systems have emerged as a vital innovation in modern agriculture, addressing the challenges of water scarcity, energy efficiency, and crop yield optimization. This study presents a comparative analysis of multiple smart irrigation system projects, focusing on their technological frameworks, performance metrics, and cost effectiveness. This research focuses on systems including IoT devices, automation, machine learning, and sensor-based technologies to enhance water management. Particular emphasis is placed on soil moisture monitoring, energy consumption control, scalability, and adaptability to diverse climatic conditions. This analysis also considers the practical implications of cleansing methods in agriculture, evaluating the advantages and disadvantages of the approaches. Overall, the findings aim to support economic considerations of these systems for small and large-scale farms. This analysis is intended to aid decision making on smart sustainable irrigation for different types of farming, across different farming contexts.