Image Analysis for Efficient Surface Defect Detection of Orange Fruits
This work portrays a novel approach for the improvement of a real-time computerized vision based model for automatic orange fruit peel defect detection. In this paper at first, different filtering methods and wavelet based method has been used to denoise the given input image and performs their comparative study. Based on this study, the wavelet based approach is used for smoothening of the images together with removing the higher energy regions in an image for better defect detection as well as makes the defects more retrievable. Finally, orange fruit skin color defects are identified by using RGB and HSI color spaces. The experimental test results indicate that the designed algorithm is scalable, computationally effective and robust for identification of orange fruit surface defects.
KeywordsImage processing Color spaces Machine vision Background segmentation Defect detection
- 3.Cerruto, E., Failla, S., Schillaci, G.: Identification of blemishes on oranges. In: International Conference on Agricultural Engineering, AgEng 96, Madrid, EurAgEng Paper No. 96F–017 (1996)Google Scholar
- 7.Pearson, T.: Machine vision system for automated detection of stained pistachio nuts. LWT—Food Sci. Tech. 29(3), 203–209 (1996)Google Scholar
- 8.Wulfsohn, D., Sarig, Y., Algazi, R.V.: Defect sorting of dry dates by image analysis. Can. Agric. Eng. 35(2), 133–139 (1993)Google Scholar
- 9.Guyer, D., Uthaisombut, P., Stockman, G.: Tissue reflectance and machine vision for automated sweet cherry sorting. In: Proceedings of the conference SPIE, optics in agriculture, forestry, and biological processing II, pp. 152–165, vol. 2907, Boston (1996)Google Scholar