Vacuum freeze-drying, which is the transformation from ice to vapor without passing through the liquid phase, has a wide application in the food industry and is now increasingly being used in the agricultural products processing because the absence of heating in this process preserves nutrients and sensory characteristics within the fruits and vegetables.However, the sublimation process makes freeze-drying operation expensive to use.Therefore, it is important to research moisture diffusion and transfer process within fruits and vegetables.This paper provides a new experimental method based on image processing technique to estimate moisture ratio of eggplant samples by analyzing moisture diffusion and transfer process.Eggplant samples were bought from local supermarket, and the experiment was carried out at the mechanics laboratory of Shanxi agricultural university in 2015.After cleaning and peeling, eggplants were cut into 10 mm×10 mm×10 mm cube samples by a sharp self-made knife, 30 regular samples (TA) and another 30 samples (TB) were chosen and frozen in a refrigerator at -40 ℃ for 10~12 h.However, TB would be treated by high pulsed electric field before freezing while TA without any treatments.After that, TA and TB were dried by JDG-0.2 vacuum freeze-drying machine.Meanwhile, section images of TA and TB were captured by a CCD camera with LED light source by 1h interval until drying end for 6h, and the mass of samples were measured simultaneously for calculating moisture ratio.All images, saved in RGB color model, were segmented with K-means clustering method, pseudo-color image processing method and automatic threshold segmentation method.Three cluster images were obtained with K-Means and pseudo-color method, and only No.3 cluster was fit for further segmentation.Automatic threshold segmentation method was able to extract non-freeze-drying area (white pixel) correctly.After thresholding, sobel edge detection method was used to extract the boundary of non-freeze-drying area.In order to obtain a boundary whose width was one pixel, a skeletal image processing method was used for treating the boundary.After that, displacement field was used to express the displacement change of moisture boundary during vacuum freeze-drying process.Geometric center of samples as the origin point of the coordinate system was established, and the dynamic change process of moisture boundary was shown clearly by means of six images overlapping.The Harris corner detection method was used to find the corner points on the boundary, and extract coordinate values of corner points which intersect the coordinate axis, displacement of the two adjacent points was calculated by 1h interval.SAS software was carried out to analyze correlation relationship between the displacement change of moisture boundary and moisture ratio.Regression analysis results showed that significant test probabilities of moisture ratio model was less than 0.000 1, and the coefficients of determination(R2) was 0.999 8, which indicated that the model test was very significant and had quite strong explain ability and high fitting accuracy on original variable.Test results of regressive coefficient showed that the displacement of the four corner points and moisture-ratio-squared of the material were very significant(P<0.000 1).In other words, moisture ratio can be expressed and predicted by the displacement field of the moisture boundary.In summary, the mentioned model not only provides a new monitoring method of moisture ratio, but also gives a foundation of monitoring moisture ratio for other drying processes.