Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch

Sensors (Switzerland)

Volumn 12, Issue 10, 2012, Pages 14179-14195

  Fadilah, Norasyikin ^a   Mohamad Saleh, Junita ^a   Halim, Zaini Abdul ^a   Ibrahim, Haidi ^a   Ali, Syed Salim Syed ^b  

^a SCHOOL OF CHEMICAL ENGINEERING   (Malaysia)

^b Pusat Perkhidmatan Pertanian Tun Razak   (Malaysia)

Author keywords

Artificial neural network; Digital image processing; Oil palm fresh fruit bunch; Principal component analysis

Indexed keywords

CLASSIFICATION ACCURACY; COLOR FEATURES; COLOR VISION SYSTEM; DIGITAL IMAGE PROCESSING TECHNIQUE; FRESH FRUITS; OIL PALM FFB; OIL PRODUCTION; REDUCTION TECHNIQUES;

COLOR VISION; COMPUTER VISION; FRUITS; IMAGE PROCESSING; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS;

PALM OIL;

PALM OIL; VEGETABLE OIL;

AGRICULTURE; ARECACEAE; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; CLASSIFICATION; COLOR; EQUIPMENT; FOOD HANDLING; FOOD INDUSTRY; FRUIT; IMAGE PROCESSING; METHODOLOGY; PRINCIPAL COMPONENT ANALYSIS;

AGRICULTURE; ARECACEAE; COLOR; FOOD HANDLING; FOOD INDUSTRY; FRUIT; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; PLANT OILS; PRINCIPAL COMPONENT ANALYSIS;

EID: 84868257617     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s121014179     Document Type: Article

References (30)

1. Brosnan, T.; Sun, D.W. Improving quality inspection of food products by computer vision-A review. J. Food Eng. 2004, 61, 3-16.
2. Corley, R.H.V.; Tinker, P.B. The Oil Palm; Blackwell Science Ltd: Oxford, UK, 2003.
3. Vaughan, J.; Nicholson, B.; Geissler, C.; Dowle, E.; Rice, E. The New Oxford Book of Food Plants; Oxford University Press: London, UK, 2009.
4. Hitam, A.H.; Yusof, A.M. Mechanization in Oil Palm Plantations. In Advances in Oil Palm Research; Basiron, Y., Jalani, B., Chan, K., Eds.; Malaysian Palm Oil Board: Ministry of Primary Industries, Malaysia, 2000; Volume 1, pp. 653-696.
5. Jalil, A. Grading of FFB for Palm Oil Mills in Malaysia. In Proceedings of 1994 PORIM National Palm Oil Milling and Refining Technology Conference, Kuala Lumpur, Malaysia, 7-8 July 1994.
6. Malaysian Palm Oil Board. Oil Palm Fruit Grading Manual; Malaysian Palm Oil Board: Kajang,Malaysia, 2003.
7. Ismail, W.I.W.; Bardaie, M.Z.; Hamid, A.M.A. Optical properties for mechanical harvesting of oil palm FFB. J. Oil Palm Res. 2000, 12, 38-45.
8. Ghazali, K.H.; Samad, R.; Arshad, N.W.; Karim, R.A. Image Processing Analysis of Oil Palm Fruits for Automatic Grading. In Proceedings of the International Conference on Instrumentation,Control & Automation, Bandung, Indonesia, 20 October 2009.
9. Choong, T.; Abbas, S.; Shariff, A.; Halim, R.; S., M.H.; Yunus, R.; Ali, S.; ahmadun, F.R. Digital image processing of palm oil fruits. Int. J. Food Eng. 2006, 2, 1-4.
10. Alfatni, M.S.M.; Shariff, A.R.M.; Shafri, H.Z.M.; Saaed, O.M.B.; Eshanta, O.M. Oil palm fruit bunch grading system using red, green and blue digital number. J. Appl. Sci. 2008, 8, 1444-1452.
11. Hudzari, R.; Ishak, W.W.; Noorman, M. Parameter acceptance of software development for oil palm fruit maturity prediction. J. Softw. Eng. 2010, 4, 244-256.
12. Jaffar, A.; Jaafar, R.; Jamil, N.; Low, C.Y.; Abdullah, B. Photogrammetric grading of oil palm fresh fruit bunches. Int. J. Mech. Mechatron. Eng. 2009, 9, 18-24.
13. Guan, L.C. Stepwise Discriminant Analysis on Oil Palm Fruit's Hues for Ripeness Grading Using Machine Vision System. M.Sc. Thesis, School of Electrical and Electronic Engineering, Universiti Sains Malaysia, Pulau Pinang, Malaysia, 2005.
14. Tan, Y.A.; Low, K.W.; Lee, C.K.; Low, K.S. Imaging technique for quantification of oil palm fruit ripeness and oil content. Eur. J. Lipid Sci. Technol. 2010, 112, 838-843.
15. Ismail, W.I.W.; Razali, M.H. Hue Optical Properties to Model Oil Palm Fresh Fruit Bunches Maturity Index. In Proceedings of World Multi-Conference on Systemics, Cybernetics and Pattern Recognition, Orlando, FL, USA, 2010; pp. 168-173.
16. Zakaria, A.; Shakaff, A.Y.M.; Masnan, M.J.; Ahmad, M.N.; Adom, A.H.; Jaafar, M.N.; Ghani, S.A.; Abdullah, A.H.; Aziz, A.H.A.; Kamarudin, L.M.; et al. A biomimetic sensor for the classification of honeys of different floral origin and the detection of adulteration. Sensors 2011,11, 7799-7822.
17. Zhang, Y.; Wu, L. Crop classification by forward neural network with adaptive chaotic particle swarm optimization. Sensors 2011, 11, 4721-4743.
18. Junkwon, P.; Takigawa, T.; Okamoto, H.; Hasegawa, H.; Koike, M.; Sakai, K.; Siruntawineti, J.;Chaeychomsri, W.; Sanevas, N.; Tittinuchanon, P.; et al. Potential application of color and hyperspectral images for estimation of weight and ripeness of oil palm (Elaeis guineensis Jacq.var. tenera). Agric. Inf. Res. 2009, 18, 72-81.
19. Jamil, N.; Mohamed, A.; Abdullah, S. Automated grading of palm oil Fresh Fruit Bunches (FFB) using neuro-fuzzy technique. In Proceedings of the International Conference of Soft Computing and Pattern Recognition, SOCPAR'09, Malacca, Malaysia, 31 December 2009; pp. 245-249.
20. May, Z.; Amaran, M.H. Automated oil palm fruit grading system using artificial intelligence. Int.J. Eng. Sci. 2011, 11, 30-35.
21. Chinchuluun, R.; Lee, W.S.; Bhorania, J.; Pardalos, P.M. Clustering and classification algorithms in food and agricultural applications: A survey. In Advances in Modeling Agricultural Systems;Springer: Yardley, PA, USA, 2009; Volume 25, pp. 433-454.
22. Gonzalez, R.; Woods, R. Digital Image Processing, 3rd ed.; Prentice-Hall: Upper Saddle River,NJ, USA, 2010.
23. Abdullah, M.; Guan, L.; Mohamed, A.; Noor, M. Color vision system for ripeness inspection of oil palm elaeis guineensis. J. Food Process. Pres. 2002, 26, 213-235.
24. Ayrulu-Erdem, B.; Barshan, B. Leg motion classification with artificial neural networks using wavelet-based features of gyroscope signals. Sensors 2011, 11, 1721-1743.
25. Baladŕon, C.; Aguiar, J.M.; Calavia, L.; Carro, B.; Śanchez-Esguevillas, A.; Herńandez, L. Performance study of the application of artificial neural networks to the completion and prediction of data retrieved by underwater sensors. Sensors 2012, 12, 1468-1481.
26. Jeon, H.Y.; Tian, L.F.; Zhu, H. Robust crop and weed segmentation under uncontrolled outdoor illumination. Sensors 2011, 11, 6270-6283.
27. Rafiq, M.; Bugmann, G. Neural network design for engineering applications. Comput. Struct.2001, 79, 1541-1552.
28. Abdullah, M.; Mohamad-Saleh, J.; Fathinul-Syahir, A.; Mohd-Azemi, B. Discrimination and classification of fresh-cut starfruits (Averrhoa carambola L.) using automated machine vision system. J. Food Eng. 2006, 76, 506-523.
29. Priddy, K.L.; Keller, P.E. Artificial Neural Networks: An Introduction; SPIE Publications:Bellingham, DC, USA, 2005.
30. Jollife, I. Principal Component Analysis; Springer: Berlin/Heidelberg, Germany, 2002.