Novel Feature Extraction for Pineapple Ripeness Classification

• Autorzy: Wang Hui Hui , Chai Sze Ye

Identyfikatory

DOI

10.26636/jtit.2022.156021

• Języki publikacji: EN

Abstrakty

EN

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 pineap ples into three main groups: unripe, ripe, and fully ripe. The accuracy of 86.05% has been achieved during experiments.

Słowa kluczowe

EN

image processing technique; pineapple; ripeness grading

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2022
• Tom: nr 1
• Strony: 14--22
• Opis fizyczny: Bibliogr. 6 poz., rys., tab.

Twórcy

autor

Wang Hui Hui

• Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia

• https://orcid.org/0000-0002-5511-1993

autor

Chai Sze Ye

• Universiti Malaysia Sarawak, 94300 Kota Samarahan, Sarawak, Malaysia

• https://orcid.org/0000-0002-4921-470X

Bibliografia

• [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/
• [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.
• [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 (DOI: 10.37865/jafe.2020.0011).
• [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 (DOI: 10.5281/zenodo.1077855).
• [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: 10.4028/www.scientific.net/amr.418-420.1739).
• [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

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).