Smart irrigation system using IoT and machine learning methods

Water loss and improper scheduling are problems with traditional irrigation techniques, making it difficult to meet the growing demand for food production while also preserving precious water resources. To address these challenges, this paper introduces a cutting-edge smart irrigation system that leverages the power of the Internet of Things (IoT), data analysis, and machine learning to determine the most efficient way to apply and schedule water for irrigation. The cornerstone of this innovative system is the utilization of a carefully curated dataset, "Crop Irrigation Scheduling," sourced from Kaggle. This dataset comprises six crucial attributes: Crop Type, Crop Days, Soil Moisture, Temperature, Humidity, and Irrigation. These attributes, described in detail in the dataset’s metadata, provide the foundational information required for precise irrigation management. The system operates seamlessly by deploying IoT sensors to collect real-time data from the field. This data is then preprocessed to ensure its quality and consistency. Subsequently, a machine learning model is trained using this dataset to make intelligent irrigation decisions in real time. The model’s predictions are seamlessly integrated with control mechanisms that govern the irrigation process. Moreover, the system offers an intuitive API for easy data access and management, allowing agricultural professionals to monitor and adjust irrigation strategies effortlessly. Experimental findings validate the system’s utility, with particular emphasis on the decision tree model, which demonstrates the best balance of accuracy, precision, recall, and F1 score. This system maximizes crop yield and minimizes water waste, contributing to a more sustainable and productive agricultural ecosystem.