Possibility of using different predictive models to estimate pistachio seed mass and determine suitable model

Understanding the physical properties of pistachio kernel are necessary for the proper design of equipments for handling, transporting, processing, drying, sorting, grading and storage this crop. In this study, different predictive tools include artificial neural network, genetic algorithm, response surface and liner regression were used to predict mass of pistachio seed. Result demonstrated that all models have a high degree of accuracy to estimate mass of pistachio seed, so that, models of perseptron neural network, radial basis function, linear regression, linear regression coupled with genetic algorithm and response surface conjugated with neural network were able to predict pistachio seed mass with R2 value 0.9949, 0.914, 0.986, 0.995 and 0.945, respectively. Generally, comparison of the models showed that synthetic model of linear regression-genetic algorithm with R2 value equal 0.995 has the highest accuracy.