Artificial intelligence for smart irrigation: Reducing water consumption and improving agricultural output

Abstract

The study investigates the application of artificial intelligence for optimizing irrigation systems in agriculture, aiming to reduce water losses and improve production efficiency. Traditional irrigation methods and their drawbacks, particularly in water-scarce regions, are analyzed. Existing approaches to using artificial intelligence in agricultural technologies for predicting water needs and regulating irrigation are examined. A mathematical model based on machine learning algorithms is developed to predict the optimal water volume required for irrigation of agricultural crops. Key factors affecting water consumption, such as temperature, soil moisture, and precipitation, are identified. The study finds that using the proposed model reduces water usage by 15% while maintaining stable crop yields. The results of testing the model on an experimental plot in Lebap region of Turkmenistan demonstrate its effectiveness in real conditions. It is substantiated that the implementation of such intelligent irrigation management systems can significantly improve the sustainability of agriculture in the face of climate change.