Feyza GÜRBÜZ, İkbal ESKI, Bünyamin DEMİR, Zeynel Abidin KUŞ

Data Mining Approach For Prediction Of Fruit Color Properties

Renk, birçok taze meyve ve sebzenin kalitesini ve tüketici tercihlerini belirleyen önemli bir özelliktir. Meyvelerin renk ölçümünde, uniform renk ölçeği nedeniyle CIE L*a*b* en çok kullanılan renk uzayıdır. Bu çalışmada elma çeşitlerinin renk özelliklerine ait ham veriler ilk aşamada test ve eğitim verileri olarak iki kısma ayrılmış, eğitim verileri üzerinde analizler yapılmış ve test verileri ise testlerde kullanılmıştır. Find laws algoritması uygulanarak elde edilen kurallar Color index (CI), hue angle (h*) and Chroma (C*) değerlerini tahmin etmek için kullanılmıştır. İkinci aşamada ise ham veriler cluster analizine tabi tutularak Strict ve Liberal seçenekleri ile sınıflandırılmıştır. Find laws algoritması her bir sınıfa tek tek uygulanıp, her bir CI, h*, C* parametreleri için elde edilen 7 farklı tahmin kuralı R2 değerlerine göre karşılaştırılarak en yüksek doğruluğa sahip kurallar tespit edilmiştir

Meyve Renk Özelliklerini Tahmin Etmek İçin Veri Madenciliği Yaklaşımı

Color is an important feature that dictates the quality and consumer preferences of many fresh fruits and vegetables. In color measurement of fruits, the CIE L*a*b* color space is widely used since it is a uniform color scale. In this study, raw data for the color features of apple varieties were divided into two parts as test and train data in the first stage, analyses were performed on train data and tests were performed on test data. The rules obtained by applying the Find laws algorithm were used to estimate the color index (CI), hue angle (h *) and Chroma (C *) values. In the second stage, raw data were classified by Strict and Liberal options of cluster analysis. Find Laws algorithm was applied to each cluster and 7 different prediction rules were obtained for CI, h*and C* parameters. R2 values of the rules were compared and the rules with the most accurate outcomes were identified

PDF

___

Ahmad, A., 2016. Sarosh Hashmi, K-Harmonic means type clustering algorithm for mixed datasets, Applied Soft Computing 48 39–49.
Armano, G., Farmani M.R. 2016. Multiobjective clustering analysis using particle swarm optimization, Expert Systems With Applications 55, 184–193.
Cebulj, A., Cunja, V., Mikulic-Petkovsek, M., Veberic, R. 2017. Importance of metabolite distribution in apple fruit. Scientia Horticulturae, 214, 214-220.
Cheng, H., Yang, S., Cao, J., 2013. Dynamic genetic algorithms for the dynamic load balanced clustering problem in mobile ad hoc networks. Expert Systems with Applications, 40 (4), 1381–1392.
Chowdhury, D.R., Ojha, S., 2017. An Empirical Study on Mushroom Disease Diagnosis: A Data Mining Approach. International Research Journal of Engineering and Technology (IRJET), 04(01), 529-534
Fairchild, M. D. 2013. Color appearance models. John Wiley & Sons. Germšek, B., Rozman, Č., Unuk, T., 2017. Forecasting Apple Fruit Color Intensity with Machine Learning Methods. ErwerbsObstbau, 59(2), 109-118.
Gonzalez-Sanchez, A., Frausto-Solis, J., Ojeda-Bustamante, W., 2014. Predictive ability of machine learning methods fo r massive crop yield prediction. Spanish Journal of Agricultural Research, 12(2), 313-328.
Gürbüz, F., Özbakır, L., Yapıcı, H., 2011. Data mining and preprocessing application on component reports of an airline company in Turkey. Expert Systems with Applications, 38, 6618–6626.
Han, J., Kamber, M., 2000. Data mining: concepts and techniques, the Morgan Kaufmann Series in data management systems. Morgan Kaufmann.
Isaza, C., Anaya, K., de Paz, J.Z., Vasco-Leal, J.F., HernandezRios, I., Mosquera-Artamonov, J.D., 2017. Image analysis and data mining techniques for classification of morphological and color features for seeds of the wild castor oil plant (Ricinus communis L.). Multimedia Tools and Applications, 1-18.
Jain, A.K., Dubes R.C., 1988. Algorithms for Clustering Data, Prentice-Hall, Inc., 1988.
Kao, Y.T., Zahara, E., Kao, I.W, 2008. A hybridized approach to data clustering. Expert Systems with Applications, 34 (3), 1754–1762. doi: 10.1016/j.eswa.2007. 01.028.
Kus, Z.A., Demir, B., Eski, I., Gurbuz, F., & Ercisli, S., 2017. Estimation of the Colour Properties of Apples Varieties Using Neural Network. Erwerbs-Obstbau, 59(4), 291-299.
Leung, Y., Zhang, J.S., Xu, Z.B., 2000. Clustering by scale-space filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (12), 1396–1410. doi: 10.1109/34.895974 .
Majumdar, J., Naraseeyappa, S., Ankalaki, S., 2017. Analysis of agriculture data using data mining techniques: application of big data. Journal of Big Data, 4(1), 20.
Maskan, M., 2001. Kinetics of colour change of kiwifruits during hot air and microwave drying. Journ al of food engineering, 48(2), 169-175.
McGuire, R.G., 1992. Reporting of objective color measurements. HortScience 27(12): 1254-1255.
McLellan, M.R., Lind, L.R., Kime, R.W., 1995. Hue angle determination and statistical analysis for multiquadrant hunter L, a, b data. J Food Qual 18(3):235–240
Nguyen, C.D., Cios, K.J., 2008. Gakrem: a novel hybrid clustering algorithm. Information Sciences, 178 (22), 4205–4227. doi: 10.1016/j.ins.2008.07.016.
Ortiz, A., Le Meurlay, D., Lara, I., Symoneaux, R., Madieta, E., Mehinagic, E., 2017. The effects of sous-vide cooking parameters on texture and cell wall modifications in two apple cultivars: A response surface methodology approach. Food Science and Technology International, 23(2), 99-109.
Pantazi, X.E., Moshou, D., Alexandridis, T., Whetton, R. L., Mouazen, A.M., 2016. Wheat yield prediction using machine learning and advanced sensing techniques. Computers and Electronics in Agriculture, 121, 57-65.
PolyAnalyst, 2007. User Manuel of PolyAnalyst 6.5, April 2007.
Qiu, H., Xu, Y., Gao, L., Li, X., Chi, L., 2016. Multi-stage design space reduction and metamodeling optimization method based on self-organizing maps and fuzzy clustering. Expert Systems with Applications, 46, 180–195.
Ramesh, D., Vardhan, B.V., 2013. Data mining techniques and applications to agricultural yield data. International Journal of Advanced Research in Computer and Communication Engineering, 2(9), 3477-80.
Rabinovich, A. T., 2009. Studies on apple peel color regulation. University of Minnesota.(pp.1-65)
Saha, S., Alok, A.K., Ekbal, A., 2016. Brain image segmentation using semi- supervised clustering. Expert Systems with Applications, 52, 50–63.
Sahoo, A.K., Zuo, M.J., Tiwari, M., 2012. A data clustering algorithm for stratified data partitioning in artificial neural network. Expert Systems with Applications, 39 (8), 7004– 7014.
Thong, N.T., 2015. HIFCF: An effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. Expert Systems With Applications, 42(7), 3682–3701.
Trinderup, C. H., Dahl, A., Jensen, K., Carstensen, J.M., Conradsen, K., 2015. Comparison of a multispectral vision system and a colorimeter for the assessment of meat color. Meat science, 102, 1-7.
Viscarra Rossel, R.A., Minasny, B., Roudier, P., McBratney, A.B., 2006. Color space models for soil science. Geoderma 133:320–337
Vlontzos, G., Pardalos, P.M., 2017. Data mining and optimisation issues in the food industry. International Journal of Sustainable Agricultural Management and Informatics, 3(1), 44-64.
Veenadhari, S., Misra, B., Singh, C.D., 2014. Machine learning approach for forecasting crop yield based on climatic parameters. In Computer Communication and Informatics (ICCCI), 2014 International Conference on (pp. 1-5). IEEE.
Wu, D., Sun, D.W., 2013. Colour measurements by computer vision for food quality control–A review. Trends in Food Science & Technology, 29(1), 5-20.
Zhang, B., Dai, D., Huang, J., Zhou, J., Gui, Q., Dai, F., 2017. Influence of physical and biological variability and solution methods in fruit and vegetable quality nondestructive inspection by using imaging and near-infrared spectroscopy techniques: A review. Critical reviews in food science and nutrition, 1-20.