Different Apple Varieties Classification Using kNN and MLP Algorithms
DOI:
https://doi.org/10.18201/ijisae.2016Special%20Issue-146967Keywords:
Image processing, Apple classification, kNN, MLPAbstract
In this study, three different apple varieties grown in Karaman province are classified using kNN and MLP algorithms. 90 apples in total, 30 Golden Delicious, 30 Granny Smith and 30 Starking Delicious have been used in the study. DFK 23U445 USB 3.0 (with Fujinon C Mount Lens) industrial camera has been used to capture apple images. 4 size properties (diameter, area, perimeter and fullness) and 3 color properties (red, green, blue) have been decided using image processing techniques through analyzing each apple image. A data set which contains 7 physical features for each apple has been obtained. Classification success rates and error rates have been decided changing the neuron numbers in the hidden layers in the classification using MLP model and in different neighbor values in the classification made using kNN algorithm. It is seen that the classification using MLP model is much higher. While the success rate of classification made according to apple type is 98.8889%.Downloads
References
He, Y., Li, X., Shao, Y., (2007). Fast Discrimination of Apple Varieties Using Vis/NIR Spectroscopy, Vol. 10(1). Pages. 9-18.
Ronald, M., Evans, M., (2016). Classification of Selected Apple Fruit Varieties Using Naive Bayes, Indian Journal of Computer Science and Engineering (IJCSE), Vol. 7(1) Pages. 13-19.
Wu, X., Wu, B., Yang, N., (2016). Classification of Apple Varieties Using Near Infrared Reflectance Spectroscopy and Fuzzy Discriminant C-Means Clustering Model, Journal of Food Process Engineering, doi: 10.1111/jfpe.12355.
Shahin, M.A., Tollner, E.W., McClendon, R.W., Arabnia, H.R., (2013). Apple Classification Based on Surface Bruises Using Image Processing and Neural Networks, Transactions of the ASAE, Vol. 45(5). Pages. 1619–1627.
Witten I.H., Frank E., Hall M.A., (2011). Data mining: practical machine learning tools and techniques. Elsevier, London.
Patterson, D., Liu, F., Turner, D., Concepcion, A., Lynch, R., (2008). Performance Comparison of the Data Reduction System. Proceedings of the SPIE Symposium on Defense and Security, Mart, Orlando, FL, pp. 27-34.
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P. & Witten, I. H., (2009). The WEKA Data Mining Software: An Update, SIGKDD Explorations, Vol. 11(1). Pages. 10-18.
Wang, J., Neskovic, P., & Cooper, L. N., (2007). Improving nearest neighbour rule with a simple adaptive distance measure, Pattern Recognition Letters, Vol. 28(2). Pages. 207-213.
Zhou, Y., Li, Y. & Xia, S., (2009). An improved KNN text classification algorithm based on clustering, Journal of computers, Vol. 4(3). Pages. 230-237.
Downloads
Published
How to Cite
Issue
Section
License
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
