Image Analysis Techniques for Ripeness Detection of Palm Oil Fresh Fruit Bunches
DOI:
https://doi.org/10.11113/elektrika.v18n3.192Keywords:
image processing, classification, bag of visualword, SVMAbstract
Being one of the biggest producers and exporters of palm oil and palm oil products, Malaysia has an important role to play in fulfilling the growing global need for oils and fats sustainably. Quality is an important factor that ensuring palm oil industries fulfill the demands of palm oil product. There has significant relationship between the quality of the palm oil fruits and the content of its oil. Ripe FFB gives more oil content, while unripe FFB give the least content. Overripe FFB shows that the content of oil is deteriorates. There have 4 classes of ripeness stages involves in this paper which are ripe, unripe, underipe and overripe. The proposes approach in this paper uses color features and bag of visual word for classifying oil palm fruit ripeness stages. Experiments conducted in this paper consisted of smartphone camera for image acquisition, python and matlab software for image pre processing and Support Vector Machine for classification. A total of 400 images is taken in a few plant in north Malaysia. Experiments involved on a dataset of 360 images for training for four classes and 40 images for testing. The average accuracy for the 4 classes of the FFB by color features is 57% while the accuracy for ripeness classification by using bag of visual word is 70%.
References
W. I. W. Ishak and R. M. Hudzari, “Image based modeling for oil palm fruit maturity prediction,†J. Food Agric. Environ., vol. 8, no. 2, 1, pp. 469–476, 2010.
Y. A. Tan, K. W. Low, C. K. Lee, and K. S. Low, “Imaging technique for quantification of oil palm fruit ripeness and oil content,†Eur. J. Lipid Sci. Technol., vol. 112, no. 8, pp. 838–843, 2010.
Z. B. Mat Sharif, N. B. Mohd Taib, M. S. Bin Yusof, M. Z. Bin Rahim, A. L. Bin Mohd Tobi, and M. S. Bin Othman, “Study on Handing Process and Quality Degradation of Oil Palm Fresh Fruit Bunches (FFB),†IOP Conf. Ser. Mater. Sci. Eng., vol. 203, no. 1, 2017.
M. Makky and P. Soni, “Development of an automatic grading machine for oil palm fresh fruits bunches (FFBs) based on machine vision,†Comput. Electron. Agric., vol. 93, pp. 129–139, 2013.
Malaysian Palm Oil Board, “MANUAL PENGGREDAN BUAH KELAPA SAWIT,†2017. [Online]. Available: http://wskj.mpob.gov.my/pnp/gredffb.htm. [Accessed: 23-Jul-2018].
N. Fadilah, J. Mohamad-Saleh, Z. A. Halim, H. Ibrahim, and S. S. S. Ali, “Intelligent color vision system for ripeness classification of oil palm fresh fruit bunch,†Sensors (Switzerland), vol. 12, no. 10, pp. 14179–14195, 2012.
T. Brosnan and D. Sun, “Inspection and grading of agricultural and food products by computer v ision systems * a re v iew,†vol. 36, pp. 193–213, 2002.
W. Ishak and W. Ismail, “Hue Optical Properties to Model Oil Palm Fresh Fruit Bunches Maturity Index,†pp. 15–17, 2010.
Z. May and M. H. Amaran, “Automated Oil Palm Fruit Grading System using Artificial Intelligence,†Int. J. Video Image Process. Netw. Secur. IJVIPNS-IJENS, vol. 11, no. June, pp. 30–35, 2011.
N. Fadilah and J. Mohamad-Saleh, “Color feature extraction of oil palm fresh fruit bunch image for ripeness classification,†13th Int. Conf. Appl. Comput. Appl. Comput. Sci., pp. 51–55, 2014.
D. Zhang, D. Lee, B. J. Tippetts, and K. D. Lillywhite, “Date maturity and quality evaluation using color distribution analysis and back projection,†J. Food Eng., vol. 131, pp. 161–169, 2014.
R. P. Salunkhel and A. A. Patif, “Image Processing for Mango Ripening Stage Detection : RGB and HSV method,†pp. 362–365, 2015.
A. Nasirahmadi and S. M. Ashtiani, “ScienceDirect Bag-of-Feature model for sweet and bitter almond classification,†Biosyst. Eng., vol. 156, pp. 51–60, 2017.
S. Jana, S. Basak, and R. Parekh, 2017 : Automatic Fruit Recognition from Natural Images using Color and Texture Features. 2017.
M. Z. Abdullah, L. C. Guan, A. M. D. Mohamed, and M. A. M. Noor, “Color Vision System for Ripeness Inspection of,†J. Food Process. Preserv., vol. 26, no. 604, pp. 213–235, 2002.
Downloads
Published
How to Cite
Issue
Section
License
Copyright of articles that appear in Elektrika belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
