Design and Development of A Smart AI-Integrated Solar Drying System

Abstract

This paper presents the design and development of a simple AI-enhanced solar dryer through the integration of Machine Learning (ML) techniques into the drying process of food and bio-based materials. Traditional drying systems often rely on conventional physics-based models, which may have limitations in accurately predicting drying behavior under varying environmental conditions. By incorporating AI and data-driven machine learning approaches, the proposed system improves prediction accuracy, enhances process efficiency, and enables intelligent real-time monitoring and control. The study explores the application of machine learning models for analyzing key drying parameters such as temperature, humidity, airflow, and moisture content, allowing for adaptive and optimized drying performance. Techniques such as nonlinear control and multi-objective optimization are employed to improve energy utilization, reduce drying time, and maintain product quality. The integration of sensor technologies with AI software tools further strengthens the system’s ability to respond dynamically to environmental changes and operational demands. In addition, this research highlights the potential of developing open-source datasets and smart analytical frameworks to support the continuous improvement of machine learning algorithms for solar drying applications. The convergence of artificial intelligence, sensor-based monitoring, process analysis, and software engineering provides a promising pathway toward the development of intelligent, cost-effective, and sustainable solar drying systems suitable for industrial and agricultural applications.