The Feasibility study of the application of dielectric technique for prediction the moisture content of olive based on artificial neural network and support vector regression
Mahdi Rashvand, Mahmoud Soltani Firouz
Olive is one of the most important agriculture crops in the world, which is harvested in different stages of growth for various uses. One of the ways to detect the adequate time of processing of olives is to determine its moisture content. In this study, to determine the moisture content of olive, the dielectric technique was used in seven periods of harvesting and three different varieties of olive including Oily, Mary and Fishemi. . The dielectric properties of the olive fruits were measured using an electronic device in the rang of 0.1-30 MHz . Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods were applied to develop the prediction models by using the obtained data acquired by the system. The best results (R = 0.999 and MSE = 0.014) were obtained by the ANN model with a topology of 384-12-1 (384 features in the input vector, 12 neurons in the hidden layer and 1 output). The results obtained indicated an acceptable accuracy of the dielectric technique combined with ANN model.
Capacitive Sensor; Data Mining; Estimation; Moisture; Olive