Smart Irrigation Based on Type of Crop Using IOT and Machine Learning Technologies

Abstract

Water is the main resource for the agriculture sector and a significant amount of water is getting wasted due to the usage of conventional and manual irrigation methods. Hence, it is essential to introduce an intelligent and automated management irrigation system for the optimum and effective utilization of water resources. In this publication work, we proposed a smart irrigation system that works /performs activities with the Internet of Things (IoT) and machine learning technologies to reduce water wastage. The main thought of the proposed methodology is to supply the required amount of water to the user-selected crop variety, depending on the fact that different crops need different quantities of water at various phases of the crop. The proposed system predicts the irrigation needs for selected crops depends on the seed category and phase of the crop. Also, the system uses several sensed ground metrics like soil moisture, crop water level, and humidity along with details about the next weather prediction. The proposed model uniquely determines the required crop water level (CWL) for supplying a minimum quantity of water to the opted crop if precipitation is predicted in the upcoming days. A mobile application (App) is also designed for monitoring as well as visualization of sensed and predicted values of crop water level and soil moisture. Our proposed smart irrigation system achieves better water management compared to the available methods. The entire system is implemented on a pilot scale and the results are fully encouraging.