Classification of Raisin Grains Using Machine Vision and Artificial Intelligence Methods
Abstract
In this study, machine vision system was developed in order to distinguish between two different variety of raisins (Kecimen and Besni) grown in Turkey. Firstly, a total of 900 pieces raisin grains were obtained, from an equal number of both varieties. These images were subjected to various preprocessing steps and 7 morphological feature extraction operations were performed using image processing techniques. In addition, minimum, mean, maximum and standard deviation statistical information was calculated for each feature. The distributions of both raisin varieties on the features were examined and these distributions were shown on the graphs. Later, models were created using LR, MLP, and SVM machine learning techniques and performance measurements were performed. The classification achieved 85.22% with LR, 86.33% with MLP and 86.44% with the highest classification accuracy obtained in the study with SVM. Considering the number of data available, it is possible to say that the study was successful.
Keywords
Image processing , Morphological features , Machine learning , Feature extraction
References
- Karimi, N., et al., Modelling of raisin berries by some physical and statistical characteristics. Int. Agrophys, 2011. 25: p. 141-147.
- Semerci, A., et al., Türkiye bağcılığının genel durumu. Mustafa Kemal Üniversitesi Ziraat Fakültesi Dergisi 2015. 20(2).
- Karimi, N., R.R. Kondrood, and T. Alizadeh, An intelligent system for quality measurement of Golden Bleached raisins using two comparative machine learning algorithms. Measurement, 2017. 107: p. 68-76.
- Mollazade, K., M. Omid, and A. Arefi, Comparing data mining classifiers for grading raisins based on visual features. Computers electronics in agriculture, 2012. 84: p. 124-131.
- Okamura, N.K., M. Delwiche, and J. Thompson, Raisin grading by machine vision. Transactions of the ASAE, 1993.
- Omid, M., et al., Implementation of an efficient image processing algorithm for grading raisins. International Journal of Signal Image Processing, 2010. 1(1): p. 31-34.
- Yu, X., et al., Raisin quality classification using least squares support vector machine (LSSVM) based on combined color and texture features. Food bioprocess technology, 2012. 5(5): p. 1552-1563.
- Angadi, S.A. and N. Hiregoudar, A Cost Effective Algorithm for Grading Raisins Using Image Processing. International Journal of Recent Trends in Engineering Research Volume, 2016. 2: p. 2455-1457.
- MathWorks. Image Processing Toolbox. 2020 [Accessed: May. 05, 2020]; Available from: https://ch.mathworks.com/help/images/morphological-filtering.html.
- Çataloluk, H., Gerçek tıbbi veriler üzerinde veri madenciliği yöntemlerini kullanarak hastalık teşhisi. 2012, Bilecik Üniversitesi, Fen Bilimleri Enstitüsü.
