AI-Driven Techniques for Real-Time Crop Monitoring in Smart Agriculture

Abstract—The application of Artificial Intelligence (AI) with IoT-based sensors for real-time crop monitoring using center pivot sprinkler systems to achieve smart agriculture. The proposed system integrates deep learning algorithms, computer vision techniques, and center pivot irrigation to manage key agricultural parameters, including irrigation levels, water consumption, soil conditions, wind speed, humidity, and weather forecasts. It also provides plant health analysis, disease detection, and fertilizer recommendations to enhance crop productivity. Data from cameras and environmental sensors is processed through AI models to generate actionable insights, supporting precision farming and resource optimization. Although initial implementation costs may be high, the long-term benefits—such as improved crop planning, reduced manual labor, higher yields, minimized losses, and enhanced sustainability—make this approach cost-effective and essential for modern agriculture. This AI-driven framework addresses current challenges, ensuring efficient resource use and sustainable food production. Keywords—Artificial Intelligence, Smart Agriculture, Precision Farming, Computer Vision, IoT Sensors, Crop Monitoring, Sustainable Agriculture.