Iot-Integrated Drip Irrigation Scheduling Using Weather Forecasts And Soil Moisture Data For Efficient Water Management

Abstract

The need for productive and eco-friendly farming has pushed the demand for applying IoT, Machine Learning (ML), and predictive analytics in smart farming. Weather forecasting using traditional methods is not reliable, and irrigation scheduling and resource planning are not efficient, resulting in low crop yield and water wastage. In this paper, it is suggested to implement an IoT-based sensor network, predictive analytics, and machine learning models based on real-time decision and monitoring for smart precision agriculture. The system employs the machine learning methodologies for the estimation of soil moisture and prediction of weather conditions for efficient irrigation control and crop health for optimal efficiency. A ML-based virtual soil moisture sensor and a Prophet-based Weather forecasting model are integrated to maximize resource allocation. Experimental analysis yields enhanced precision in weather forecasting and soil moisture and tremendous water savings while attaining the highest amount of crop growth. Comparison with the traditional system confirms the effectiveness and scalability of the proposed solution. The proposed model offers a better solution for precision farming with positive impacts on sustainable agriculture saving the environment, reducing ecological destruction, and enhancing the efficiency of agricultural processes. The upcoming projects will incorporate expanding the capability of the system by implementing blockchain for security and making the model more versatile to accommodate various climatic conditions.