Prediction of Drying Efficiency in Cabinet Solar Dryers for Medicinal Plants Using Artificial Neural Networks

This study presents an artificial neural network (ANN)-based predictive model for evaluating the drying efficiency of a cabinet-type solar dryer used for dehydrating Plantago major leaves under natural climatic conditions. The performance of solar drying systems is strongly affected by nonlinear and time-varying factors such as solar irradiance, drying-chamber temperature, and ambient relative humidity, which limits the accuracy of conventional modeling approaches. To address this challenge, a multilayer feedforward ANN was developed using solar irradiance, chamber temperature, and relative humidity as input variables and drying efficiency as the output. Experimental data comprising 120 samples were collected during summer conditions and divided into training, validation, and testing subsets. The ANN was trained using the Levenberg–Marquardt algorithm and demonstrated strong predictive performance, achieving an overall correlation coefficient of R = 0.9556 and a low mean squared error of 1.22×10−4 The results confirm that the proposed ANN model can reliably capture the nonlinear drying behavior and accurately predict drying efficiency, providing a practical tool for real-time performance evaluation and supporting the development of intelligent monitoring and control strategies for cabinet-type solar drying systems.