Abstract
Population growth and the increasing impact of climate change significantly increase the demands on the efficiency and sustainability of agricultural production. In this context, the concept of “smart agriculture,” based on the application of Internet of Things (IoT) technologies and artificial intelligence (AI) methods, is considered a promising approach for predicting crop yields and optimizing agricultural processes. This paper proposes an integrated smart agriculture system that combines data collection using IoT devices and intelligent data analysis based on AI algorithms. IoT sensors provide continuous monitoring of key agro-ecological parameters, including air temperature, humidity, soil moisture, precipitation, and nutrient levels. The collected data is transmitted to a centralized platform, where preprocessing, normalization, and feature extraction are performed. Machine and deep learning methods are used to predict crop yields, enabling the identification of nonlinear relationships between environmental factors and crop productivity.Experimental results demonstrate that using AI models based on IoT data provides higher forecasting accuracy than traditional statistical methods. The proposed approach helps improve resource efficiency, reduce production risks, and support informed management decisions. These results confirm the high potential of IoT and artificial intelligence for sustainable agricultural development and food security.