Novel Feature Extraction for Pineapple Ripeness Classification
DOI:
https://doi.org/10.26636/jtit.2022.156021Keywords:
image processing technique, pineapple, ripeness gradingAbstract
A novel feature extraction method has been proposed to improve the accuracy of the pineapple ripeness classification process. The methodology consists of six stages, namely: image acquisition, image pre-processing, color extraction, feature selection, classification and evaluation of results. The red element in the RGB model is selected as the threshold value parameter. The ripeness of pineapples is determined based on the percentage share of yellowish scales visible in images presenting the front and the back side of the fruit. The prototype system is capable of classifying pineapples into three main groups: unripe, ripe, and fully ripe. The accuracy of 86.05% has been achieved during experiments.
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[1] C. Edward, „Uggah: Sarawak aims to be top producer of pineapples" [Online]. Available: http://www.theborneopost.com/2016/08/09/uggah-sarawak-aims-to-be-top-producer-of-pineapples/
View in Google Scholar
[2] J. Sung, J. H. Suh, A. H. Chambers, J. Crane, and Y. Wang, „Relationship between sensory attributes and chemical composition of different mango cultivars", J. of Agricultural and Food Chemistry, vol. 67, no. 18, pp. 5177-5188, 2019. DOI: https://doi.org/10.1021/acs.jafc.9b01018
View in Google Scholar
[3] M. Mohd Ali, N. Hashim, S. Abd Aziz, and O. Lasekan, „An overview of non-destructive approaches for quality determination In pineapples", J. of Agricultural and Food Engineer., vol. 1, pp. 1-7, 2020.
View in Google Scholar
[4] J. I. Asnor, S. Rosnah, Z. W. H. Wan, and H. A. B. Badrul, „Pineapple maturity recognition using RGB extraction", Int. J. of Electrical, Computer, Energetic, Electronic and Commun. Engineer., vol. 7, no. 6, pp. 597-600, 2013.
View in Google Scholar
[5] S. Mohammad, K. H. Ghazali, N. Che Zan, S. S. Mohd Radzi, and R. Abdul Karim, „Classification of fresh N36 pineapple crop using image processing technique", Advanced Materials Res., vol. 418-420, pp. 1739-1743, 2011. DOI: https://doi.org/10.4028/www.scientific.net/AMR.418-420.1739
View in Google Scholar
[6] J. I. Asnor, S. Rosnah, Z.W. H.Wan, and H. A. B. Badrul, „Ripeness level classification for pineapple using RGB and HSI colour maps", J. of Theoretical and Applied Informat. Technol., vol. 57, no. 3, pp. 587-593, 2013 [Online]. Available: http://www.jatit.org/volumes/Vol57No3/33Vol57No3.pdf
View in Google Scholar
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