Enhancement-based background separation techniques for fruit grading and sorting

Abstract

Image processing plays a remarkable role in the automation of fruit grading and sorting. While grading the fruit, accurate extraction of fruit object from the image (background separation) is the chief concern. For extraction of fruit, appropriate segmentation technique is employed; and to accomplish it accurately, enhancement must be performed prior to segmentation. However, majority of the researchers emphasised over fruit segmentation alone. This communication is intended to show the potential of enhancement techniques when combined with fruit image segmentation. Besides, it presents a comparative analysis of enhancement-based background separation techniques for fruit grading and sorting. For this purpose, four main techniques, namely, contrast limited adaptive histogram equalisation (CLAHE) method, Gaussian filter, median filter and Wiener filter were utilised for enhancement and basic global thresholding, adaptive thresholding, Otsu thresholding and Otsu-HSV thresholding were applied for segmentation. 16 sub-models were developed by combining each enhancement method with every segmentation technique. Afterwards, the image quality of the sub-models was validated using quantitative as well as qualitative analyses. Test results demonstrate that CLAHE/Otsu-HSV model outperformed the others for fruit grading and sorting.