Estimation Equilibrium Moisture Content in Agriculture Product Using Neural Network Method

ABSTRACT: The equilibrium moisture content (EMC) is an important parameter for several post-harvesting operations such as processing, packaging storage and drying processes. EMC is affected by different parameters Such as temperature , relative humidity, type of the product and ingredient of them. Empirical models and Artificial Neural Networks (ANNs) were utilized for the prediction of Equilibrium Moisture Content (EMC) in agriculture product. Many empirical models including GAB, Smith, Henderson, Chung-Post, Modified Halsey, Chen-Clayton, Oswin, Halsey and D’Arsy-watt, Vemuganti were applied for this estimation. Artificial neural networks (ANNs) method is the approach used in this paper to estimate EMC too. A Generalized Regression Neural Network (GRRN) was used to achieve the better results. Comparing the data obtained using this model with Vemuganti model showed the desirable result. The accuracy of the predicted rates for EMC was 92.6% to 99.7%.