The standard method of shrinkage measurement consists of immersion of the product in a fluid in order to calculate the volume changes before and after drying. It is destructive and time-consuming and also is not a practical method to be used in online drying monitoring systems. To date, it has been tried for measuring shrinkage based on passive stereo vision. But no report has been provided so far on the accuracy of this technique and its comparison with conventional method of measuring volumetric shrinkage. On the other hand, because of the small size of dried foodstuff products, it does not seem that the stereo vision to be able to extract high detail point clouds from the surface of objects. Therefore, this research was conducted in order to study the potential use of 3D laser scanning for measurement of the volumetric shrinkage of some horticultural products during drying process. To this end, a calibrated 3D laser imaging system was applied in order to precisely scan the surface of some small size horticultural products (including plum, fig, date, and button mushroom), which take non-symmetric form during the drying process. 2D image of samples was also taken to predict the volumetric shrinkage by various texture analysis methods. Drying was carried out by a convective dryer. The results indicated a significant superiority of 3D laser imaging compared to 2D imaging. The value of correlation coefficient and mean absolute percentage error of multilayer perceptron artificial neural networks models created based on selected spatial features of point clouds in predicting volumetric shrinkage for plum, fig, date, and mushroom was obtained 0.90 and 19.48, 0.95 and 14.25, 0.78 and 23.54, and 0.87 and 9.47, respectively.
