Optimizing Irrigation Scheduling through Artificial Intelligence Techniques

Abstract

Planning irrigation well is a key part of using water more efficiently, getting better food yields, and keeping resources safe in farming. Traditional methods don't always take into account how the climate and land are changing, which wastes water. This study looks into how Artificial Intelligence (AI) tools, such as machine learning (ML), deep learning, and optimisation methods, can be used to make scheduling watering better. AI models can very accurately guess how much water crops will need by combining weather forecasts, data on soil wetness, and information about the crops themselves. AI-based models can also learn from past data and change watering plans in real time to adapt to changing conditions, making sure that the right amount of water is distributed. This study also looks into how AI can be used with Internet of Things (IoT) devices to make decisions and keep an eye on data all the time. This would allow automated watering practices that are based on data. The results show that AI-driven watering systems spend a lot less water, make crops more productive, and help make farming more environmentally friendly. This study lays the groundwork for creating smarter irrigation systems that use AI to help farmers deal with the growing problems of climate change and water shortages.