The Use of Artificial Intelligence to Predict Irrigation Needs

Abstract

The use of artificial intelligence (AI) in farming could completely change how irrigation is managed, solving the growing problem of not having enough water, and increasing food yields. This essay looks at how AI methods, such as machine learning algorithms and prediction analytics, can be used to accurately identify the need for watering. AI models can look at complicated trends and give accurate advice on when to water crops by using a variety of data sources, including soil moisture levels, weather forecasts, satellite images, and past crop performance. Real-time data collection through IoT devices and remote sensor technologies is emphasized in the study. These technologies make AI forecasts more accurate. We look at case studies of AI-driven irrigation systems that have been used and shown to make big differences in how much water is used and how healthy the crops are. The data show that AI can increase farming output while lowering water use by up to 30%. Additionally, AI makes it easier to create flexible watering plans that can adapt to changing weather conditions, lowering the risks that come with droughts or too much rain. Problems with the quality of the data, the ease of understanding the models, and the addition of AI solutions to current farming systems are also talked about.