ABSTRACT
This research aims to create a machine learning and Internet of Things -based strategy for smart irrigation systems, which would allow for the individualized monitoring and management of the process. Efficient and responsible use of water in agriculture should be a top priority for resource preservation and the sustainable development of crops. Current approaches to irrigation are generally inaccurate, imprecise, and do not involve real-time monitoring, which leads to the unnecessary waste of water, and poor agricultural yields. To build a new, more reliable, and efficient solution, this study proposes a novel approach, which combines machine learning technologies with Internet-of-Things technologies. The latter involves the use of multiple sensors measuring temperature, humidity soil properties, and other parameters over a real-time basis. This site aims to upgrade such systems using machine learning technologies, which develop and train models that would monitor these data and make respective irrigation judgments. As a result, the system would allow for real-time monitoring conserving water through optimized irrigation practices: a plan for each type of crop would be unique and based on their respective yields and the state of soil into which they are sewn. Machine learning algorithms are used to recognize the patterns and make predictions on when next to implement the irrigation. The IoT infrastructure allows for all sensors, microcontrollers, and irrigation pumps to communicate with one another, no transmission of data is missed. The latter ones go on to make forecasts using machine learning and automatically adjust the water supply and irrigation schedules. This process is monitored and managed remotely by the customers with the use of mobile or computer applications. The smart irrigation system is likely to benefit from the proposed machine learning and IoT-based 79approach resulting in the hardly optimal water use–more yield per the amount of crop grown, and remote-monitorability.
