Fruits Recognition based on Texture Features and K-Nearest Neighbor
Keywords:Fruits recognition, Gray-level Co-occurrence Matrix, k-Nearest Neighbor, texture features.
Malaysia is well-known for its variety of fruits available in the country such as pineapple, guava, durian, apple, and watermelon. Therefore, it is important for us to get to know more about fruits so that we can take advantage of all the benefits that each fruit can offer. However, problems may arise where a person may know nothing about a particular fruit apart from only having an image of it. Most of the fruit encyclopedias nowadays still rely on text as search input. Furthermore, various features are commonly utilised for representation which can lead to high computational complexity. Therefore, to overcome these problems, a content-based texture-only fruits recognition that accepts an image as input instead of text is proposed. A framework which extracts five texture features (homogeneity, energy, entropy, correlation, and contrast) based on Gray-level Co-occurrence Matrix (GLCM) descriptor is constructed. k-Nearest Neighbour (k-NN) is used at the classifier model to determine the type of fruits. The conducted empirical study has shown that the proposed work has the ability to effectively recognize fruit images with 100% accuracy.
 Ding C, Choi J, Tao D & Davis L (2016), Multi-directional multi-level dual-cross patterns for robust face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(3), 518-531.
 Ding C & Tao D (2015), Robust face recognition via multimodal deep face representation. IEEE Transactions on Multimedia, 17(11), 2049-2058.
 Boles A & Rad P, Voice biometrics: Deep learning-based voiceprint authentication system. Proceedings of the 2017 12th Conference on System of Systems Engineering (SoSE), Waikoloa, Hawaii, USA, June 18-21, 2017, IEEE, 1-6.
 Kumar P, Saini R, Roy PP & Pal U (2018), A lexicon-free approach for 3D handwriting recognition using classifier combination. Pattern Recognition Letters, 103, 1-7.
 Kahou, Samira Ebrahimi, Xavier Bouthillier, Pascal Lamblin, Caglar Gulcehre, Vincent Michalski, Kishore Konda, SÃ©bastien Jean et al. (2016), Emonets: Multimodal deep learning approaches for emotion recognition in video. Journal on Multimodal User Interfaces, 10(2), 99-111.
 Escalera S, Athitsos V & Guyon I, Challenges in multi-modal gesture recognition. Gesture Recognition, Springer, Cham, 2017, 1-60.
 Llano EG, VÃ¡zquez MSG, Vargas JMC, Fuentes LMZ & Acosta AAR (2018), Optimized robust multi-sensor scheme for simultaneous video and image iris recognition. Pattern Recognition Letters, 101, 44-51.
 Seng WC & Mirisaee SH, A new method for fruits recognition system. Proceedings of the 2009 International Conference on Electrical Engineering and Informatics, Selangor, Malaysia, August 5-7, 2009, IEEE, 130-134.
 Ebert C & Louridas P (2016), Machine learning. IEEE Software, 33(5), 110-115.
 Muhtaseb A, Sarahneh S & Tamimi H, Fruit / vegetable recognition. Proceedings of the 2012 Student Innovation Conference, Hebron, Palestine, June, 2012.
 Mohana SH & Prabhakar CJ (2014), A novel technique for grading of dates using shape and texture. An International Journal (MLAIJ), 1(2), 15-29.
 Nasirahmadi A & Ashtiani SHM (2017), Bag-of-Feature model for sweet and bitter almond classification. Biosystems Engineering, 156, 51-60.
 Mohanaiah P, Sathyanarayana P & Gurukumar L (2013), Image texture feature extraction using GLCM approach. International Journal of Scientific and Research Publications, 3(5), 1-5.
 Semary NA, Tharwat A, Elhariri E & Hassanien AE, Fruit-based tomato grading system using features fusion and support vector machine. Intelligent Systems' 2014, 2015, Springer, Cham, 401-410.
View Full Article:
How to Cite
LicenseAuthors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under aÂ Creative Commons Attribution Licensethat allows others to share the work with an acknowledgement of the work''s authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal''s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (SeeÂ The Effect of Open Access).