AI-Optimized Irrigation for Sustainable Agriculture

This paper presents an innovative AI-enhanced irrigation system designed to optimize water management in agriculture. The system integrates advanced technologies such as IoT, sensor networks, and artificial intelligence algorithms to achieve precise and efficient irrigation scheduling. Leveraging real-time data from sensors including soil moisture, temperature, and humidity, combined with historical weather forecasts, the system employs a dynamic algorithm to make informed irrigation decisions. Experimental evaluation conducted over a week-long period using a garden rose as a test subject demonstrated the system's ability to maintain optimal soil moisture levels within the range of 60-75%, while significantly reducing water consumption compared to conventional methods. Simulation results further validated the system's effectiveness in predicting soil moisture levels and optimizing irrigation scheduling. Key metrics including enhanced crop output, reduced water usage, and adherence to sustainable farming practices were used to assess the superiority of the proposed model. Overall, the AI-enhanced irrigation system presents a promising solution for sustainable agriculture, offering improved water conservation, enhanced crop productivity, and efficient resource utilization.