Estimation of Cardamom Capsule Size and Surface Area using Digital Image Processing Technique

Abstract

Manual grading and sorting of cardamom spices require a long time and considerable resources. The introduction of machine vision technology can substantially increase the timeliness of grading operation and reduce the associated drudgery. This research was carried out to contribute towards the use of machine vision technology in the grading of cardamom spices. In this regard, the utility of images captured by mobile devices was assessed using digital image processing techniques. The color images of cardamom capsules were acquired using an Apple iPhone 7 in the first place. The geometric features (major diameter, minor diameter, surface area, and perimeter) of the samples were calculated using MATLAB algorithms. The pixelated units were converted into SI units (mm). The predicted values of the parameters were compared with the actual values. The goodness of fit was assessed using the coefficient of determination (R2), which was found to be 0.92, 0.88, 0.95, and 0.97 for the major diameter, minor diameter, surface area, and perimeter of the samples, respectively. In terms of mean absolute percentage error (MAPE), the accuracy of the model was found to be 95.64%, 94.74%, 95.32%, and 97.81% for the predicting major diameter, minor diameter, surface area, and perimeters of cardamom capsules, respectively. These results indicate that mobile images could be successfully incorporated in machine vision technology for the effective grading of cardamom capsules.