This research focuses on the design and development of a prototype Artificial Intelligence of Things (AIoT) system for monitoring and controlling irrigation using weather information. The system consists of four main components:

• Weather Station: Includes various sensors such as air temperature, relative humidity, wind speed, and sunlight duration to collect real-time weather data.
• Controller Unit: Equipped with machine learning algorithms to estimate reference evapotranspiration (ETo) and calculate the plant’s water requirement by integrating the crop coefficient (Kc) with other plant-related data.
• User Interface (UI) and Display: Allows farmers or users to input relevant information, such as plant type, soil type, irrigation system type, number of water emitters, planting distance, and growth stages.
• Irrigation Unit: Responsible for controlling the water supply and managing the irrigation emitters to ensure efficient water distribution based on the calculated requirements.