LAUSR

ABSTRACT In engineering and agriculture, morphological properties are the important characters in studying physical properties of the products. The use of image processing and machine vision technique to measure appearance and morphological properties of objects has become very common in recent years. The purpose of this study was to predict the diameters, area and perimeter of spherical objects using image processing and nonlinear multivariate modeling. The hardware part of the system included an imaging station, a back lighting source, a camera, a frame grabber and a personal computer. The distance from the objects to the camera was considered to determine the characteristics. An algorithm in MATLAB software was developed to model the characteristics based on number of pixels and the distance from the object to the camera. The results showed that the system can determine the perimeter, projected area and the major and minor diameters with high accuracy (>99%) and prediction error of 0.1113, 0.1989, 2.0721 and 0.6953%, respectively. Based on the modeling results, the procedure is a promising technique to measure the morphological properties of the fruits and vegetables.