AI-Based Autonomous Paddy Farm using Smart and Precise Irrigation System

With the rampant growth of the global population and the increasing need for agricultural products, farmers confront a critical hurdle of ensuring an adequate supply of water resources for irrigation to meet these escalating demands. Amid limited water resources, farmers must embrace innovative solutions and efficient water management practices in the era of Agriculture 4.0 turning this concept into a tangible reality to meet the growing demand for efficient farming practices. By integrating Artificial Intelligence (AI) and the Internet of Things (IoT) to gather and analyze agricultural data, we now have the capability to make more accurate and informed decisions in agriculture. In this, an innovative and adaptable irrigation system is introduced which prioritizes efficiency and cost-effectiveness by minimizing resource consumption. This strategy leverages the capabilities of Machine Learning (ML) algorithms to elevate the level of agricultural intelligence. By employing a suite of sensors, including those for soil moisture, temperature, and rainfall, within an environment conducive to prolonged plant growth, the collected data to construct SVM models findings indicate that SVM outperforms other models (LR, NN, NB) with an impressive recognition rate of 98.5% and a minimal root mean square error (RMSE) of 0.10. Through this system, crop irrigation is achieved with a high level of precision, entirely automated, and without the need for human intervention. As a result, this technology leads to a substantial reduction in water consumption, up to 50%.