estimation of apple volume and its shape indentation using image processing technique and neural network

physical properties of agricultural products such as volume are the most important parameters influencing grading and packaging systems. they should be measured accurately as they are considered for any good system design. image processing and neural network techniques are both non-destructive and useful methods which are recently used for such purpose. in this study, the images of apples were captured from a constant distance and then were processed in matlab software and the edges of apple images were extracted. the interior area of apple image was divided into some thin trapezoidal elements perpendicular to longitudinal axis. total volume of apple was estimated by the summation of incremental volumes of these elements revolved around the apple’s longitudinal axis. the picture of half cut apple was also captured in order to obtain the apple shape’s indentation volume, which was subtracted from the previously estimated total volume of apple. the real volume of apples was measured using water displacement method and the relation between the real volume and estimated volume was obtained. the t-test and bland-altman indicated that the difference between the real volume and the estimated volume was not significantly different (p>0.05) i.e. the mean difference was 1.52 cm3 and the accuracy of measurement was 92%. utilizing neural network with input variables of dimension and mass has increased the accuracy up to 97% and the difference between the mean of volumes decreased to 0.7 cm3.