Adaptive Region Growing for Automated Oil Palm Fruit Quality Recognition
Besides rubber and rice, oil palm or Elaeis Guineensis remains as one of the most important plantation crops in Malaysia. Unfortunately, the lack of experience in oil palm fruit grading among the plucking farmers results in wrong estimation when harvesting. This affects production, negatively. Meanwhile, region growing conventional image segmentation techniques need manually or fixed initial seed selection which, actually, increases the computational cost, as well as, implementation time. Hence, the main goal of this study is to improve the seed region growing algorithm in order to gain higher accuracy in segmenting color information for oil palm fruit image. This study presents n-Seed Region Growing (n-SRG) for color image segmentation by choosing adaptive numbers of seed. The data sample consists of 80 images which comprises and two ripeness classes (ripe and unripe).The proposed work has out-performed the k-mean clustering method with 86% and 80% of average accuracy rates correspondingly.
Keywordscolor image segmentation seed region growing automated visual inspection
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- 1.Pippet, J.: Oil palm in Rural Development Series hand book, 57 p. Dept. of Agriculture, Port Moresby (1987)Google Scholar
- 2.Kurniawakti, N.N., Sheikh Abdullah, S.N.H., Abdullah, S., Abdullah, S.: Texture Analysis for Diagnosing Paddy Diseases. In: International Conference on Electrical Engineering and Informatics, vol. I, pp. 23–27 (2009)Google Scholar
- 4.Jaffar, A., Jaafar, R., Jamil, N., Low, C.Y., Abdullah, B.: Photogrammetri grading of oil palm fresh fruit bunches. Int. J. Mech. Mechatron. Eng. 9, 18–24 (2009)Google Scholar
- 7.Jamil, N., Mohamed, A., Abdullah, S.: Automated grading of palm oil fresh bunches (FFB) using neuro-fuzzy technique. In: Soft Computing and Pattern Recognition, SOCPAR 2009, pp. 245–249 (2009)Google Scholar
- 8.May, Z., Amaran, M.H.: In Automated Ripeness Assessment of Oil Palm Fruit Using RGB and Fuzzy Logic Technique. In: Mathematical Methods and Techniques in Engineering and Environmental Science, Universiti Teknologi PETRONAS, pp. 52–59 (2011)Google Scholar
- 9.Roseleena, J., Nursuriati, J., Ahmed, J., Low, C.Y.: Assessment of palm oil fresh fruit bunches using photogrammetric grading system. International Food Research Journal 18(3), 999–1005 (2011)Google Scholar
- 10.Tang, J.: Color Image segmentation algorithm based on region growing. In: International Conference on Computer Engineering and Technology, vol. 6, pp. 634–637 (2010)Google Scholar
- 11.Verma, O.P., Hanmandlu, M., Susan, S., Kulkarni, M., Jain, P.K.: A Simple Single Seeded Region Growing Algorithm for Color Image Segmentation using Adaptive Thresholding. In: International Conference on Communication Systems and Network Technologies, pp. 500–503 (2011)Google Scholar
- 12.Sanders, I.: Seeded region growing using multiple seed points. In: Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp. 177–182 (2005)Google Scholar