ABSTRACT
The Wireless Sensor Network (WSN)-controlled irrigation system integrated with Machine Learning (ML) and Genetic Algorithm (GA) demonstrates efficient water management in agriculture. This is implemented using the wireless sensor nodes that are placed in the agricultural field to monitor various environmental factors which involve temperature, moisture and humidity. They help in the continuous collection of data and transmit them wirelessly. This helps in real-time monitoring and control. The automatic decision-making process of the irrigation system is done using machine learning techniques. They help in analysing the huge amount of data in the WSN. The ML algorithms help to detect various abnormalities and trends that help to provide optimized irrigation schedules based on the particular requirements of the crops. This intelligent system helps in the prediction of optimal irrigation timings and water quantities. The accurate irrigation strategies are obtained using a genetic algorithm. This is evolved using selection, crossover and mutation processes. These parameters help to improve the water usage efficiency with enhanced crop yield. This helps to ensure that the right amount of water at the right time is irrigated without wastage. Thus the integration of ML in a WSN-controlled irrigation system helps to overcome various constraints of the agricultural environments through providing adaptive solutions.
