Integrating IoT and Machine Learning for Smart Irrigation Systems in Agriculture

The purpose of this exploratory article is to investigate the possibility of utilizing Internet of Things (IoT) technology and machine literacy algorithms to develop intelligent irrigation systems for aquaculture agriculture. In many cases, traditional irrigation methods make the assumption that they are ideal and effective, which ultimately results in the loss of water and poor crop yields. By utilizing Internet of Things biases such as soil humidity monitors and rainfall stations, in conjunction with machine learning algorithms for data analysis and decision-making, intelligent irrigation systems can firmly adapt to soddening programs that are based on real-time environmental circumstances and crop requirements. Because of this integration, farmers canmaximize crop yield, reduce the amount of resources that are wasted, and perfect their water management. This research assesses the usefulness of the Internet of Things (IoT) machine literacy emulsion in perfecting irrigation techniques and agrarian sustainability. As a result, it contributes to developments in perfection husbandry and environmental conservation. The evaluation is carried out through empirical investigations and trials