Agriculture is essential for sustaining human life. As the global population is expected to reach 10 billion by the mid-21st century, ensuring food security presents significant challenges. Traditional agricultural practices, which have historically met the dietary needs of the population, may no longer be sufficient to support such a large number of individuals. Modern agriculture enhances productivity by integrating IoT and machine learning technologies. In recent years, Iraq has experienced significant climate changes, reducing the availability of groundwater crucial for irrigation. Despite a long-standing water agreement with Turkey, Iraq continues to face water scarcity issues. This research demonstrates that implementing intelligent irrigation systems can conserve water and enhance agricultural productivity in the region. Although research shows that 61% of farming studies focus on crop management, less than 10% address irrigation strategies. Effective irrigation management, however, significantly influences crop yields. In our approach, we manage the irrigation of various crops, including strawberries, vegetables, and tomatoes, using IoT-enabled devices and sensors such as temperature, humidity, light intensity, and irrigation sensors. Devices such as Arduino Uno and Ethernet Shield collect data and transmit it to an edge server for processing. During our research, we engineered an advanced irrigation system tailored to various crops. This system employs machine learning techniques, specifically multi-class classification algorithms, to create a sophisticated irrigation schedule that optimizes water usage across different types of crops. By integrating these cutting-edge technologies, our study aims to enhance agricultural efficiency and resource management, By using machine learning algorithms such as Random Forest, Support Vector Machines, Logistic Regression, and KNN, we can predict irrigation needs with an accuracy exceeding 95%. This data-driven strategy allows us to create precise irrigation schedules, improving both irrigation management and crop yields. The edge server sends data to a local web server and the ThingSpeak cloud.
