Abstract

The percentage of moisture content is one of the most important indexes in maize quality evaluation. Maize with high moisture content will not stay for extended periods; hence it is important to have accurate prediction of moisture content. As aflatoxin contamination in maize is of major concern, the necessity for suitable methods to predict moisture content with less time and higher accuracy assumes greater importance. Hence, Three Term Back propagation network is proposed as a prediction tool for moisture content on maize. The new model is an improvement on Two Term Back propagation by the addition of an extra parameter, the proportional factor, which increases the convergence speed and reduces learning stalls in the conventional neural network. The experimental results are conducted using semi-annual datasets obtained from a maize thermal dryer. The results show that the proposed model outperforms Two Term Back propagation and other prediction methods like empirical correlation, analytical models (tank in series) and genetic algorithm which were used as prediction tools. Quantitatively, Three Term Back Propagation Neural Network obtained a higher precision result with a Mean Absolute Deviation (MAD) and Mean Absolute Percentage Error (MAPE) of 0.00145 and 0.00001 respectively.