CLASSIFICATION OF DYNAMIC EGG WEIGHTS USING FEATURE EXTRACTION METHODS

Yıl 2020, Cilt: 21 Sayı: 4, 499 - 513, 28.12.2020

Gülin Elibol Seçil , Mehmet Yumurtacı , Semih Ergin , İsmail Yabanova

https://doi.org/10.18038/estubtda.658077

Cited By: 1

Öz

In this study, a feature vector is determined in order to classify chicken eggs into four different weight groups by using the dynamic weighing system and then the success rate of different classifiers in the process of weight classification are analyzed. The dynamic weighing system is made of three components; mechanic system, electronic control board, and software. Firstly, a data set is created on the basis of analogue egg weight data obtained from the dynamic weighing system. From the obtained data set, three different feature vectors are extracted by using Time-domain, Power Spectral Density (PSD) and Discrete Wavelet Transform (DWT)-based methods. The extracted feature vectors are then applied to Linear Bayes Normal Classifier, Fisher’s Linear Discriminant Analysis (FLDA), Support Vector Machine (SVM), Decision Tree (DT) and K-Nearest Neighborhood (k-NN) classifiers respectively and egg weight classes are determined. A five-fold cross validation is carried out in order to confidentially test the performance of classification. As can be seen from the experimental results, both feature vectors and classifiers are highly successful in determining the weight classes of eggs. It is observed that the most successful features are the entropy values of DWT with a classification rate of 97.01% for k-NN classifier.

Anahtar Kelimeler

Dynamic weighing system, Measurement, Signal processing, Feature extraction, Egg classifying

Kaynakça

• Asadi V, Raoufat MH, Nassiri SM. Multiple regression analysis results of EGG weight estimation using machine vision technique. In: International Conference on Agricultural Engineering - AgEng 2010: towards environmental Technologies; 6-8 September 2010; Clermont-Ferrand, France, 1-10.
• Asadi V, Raoufat MH, Nassiri SM. Fresh egg mass estimation using machine vision technique, Int. Agrophys. 2012; 26(3): 229-234.
• Asadi V, Raoufat M.H, Estimation of egg weight by machine vision and neural networks technique. International Journal Natural and Engineering Sciences (IJNES) 2010; 4(2): 1-4.
• King’ori AM. Poultry egg external characteristics: egg weight, shape and shell colour. Res. J. Poultry Sci. 2012; 5(2): 14-17.
• Rashidi M, Gholami M. Prediction of egg mass based on geometrical attributes. Agr. Biol. J. N. Am. 2011; 2(4): 638-644.
• Niedźwiecki M, Meller M, Pietrzak P. System identification based approach to dynamic weighing revisited. Mechanical Systems and Signal Processing 2016; 80: 582–599.
• Boschetti G, Caracciolo R, Richiedei D, Trevisani A. Model-based dynamic compensation of load cell response in weighing machines affected by environmental vibrations. Mechanical Systems and Signal Processing 2013; 34(1- 2): 116-130.
• Yamazaki T, Ono T. Dynamic problems in measurement of mass-related quantities. In: SICE, 2007 Annual Conference; 17-20 Sept. 2007; Takamatsu, Japan, 1183-1188.
• Rui Z, Wen-hong L, Yin-jing G. A vehicle weigh-in-motion system based on hopfield neural network adaptive filter. In: 3rd International Communication and Mobile Computing Conference; 12-14 April 2010; Shenzhen, China Shenzhen, 123-127.
• Xiao J, Lv P. Application of wavelet transform in weigh-in-motion. In: International Workshop on Intelligent Systems and Applications; 23-24 May 2009; Wuhan, China, 1-4. Halimic M, Halimic A, Zugail S, Huneitti Z. Intelligent signal processing for electro-mechanical systems. In: Proceeding of the 5th International Symposium on Mechatronics and its Applications; 27-29 May 2008; 1-5.
• Xiaoyan C, Zhenliang L. An intelligent dynamic weighing controller. In: IEEE International Conference on Automation and Logistics; 27-29 May 2008; 1609-1612.
• Bahar H.B, Horrocks DH. Dynamic weight estimation using an artificial neural network. Artif. Intell. Eng. 1998; 12(1-2): 135-139.
• Halimic M, Balachandran W, Enab Y. Fuzzy logic estimator for dynamic weighing system. In: Proceedings of IEEE 5th International Fuzzy Systems; 11 Sept. 1996; New Orleans, LA, USA, 2123-2129.
• Yasin SMTA, White NM. Application of artificial neural networks to intelligent weighing systems. IEE Proceedings - Science, Measurement and Technology 1999; 146(6): 265-269.
• Soltani M, Omid M, Alimardani R. Egg volume prediction using machine vision technique based on pappus theorem and artificial neural network. J. Food Sci. Technol. 2015; 52(5): 3065–3071.
• Waranusast R, Intayod P, Makhod D. Egg size classification on android mobile devices using image processing and machine learning. In: 2016 Fifth ICT International Student Project Conference (ICT-ISPC); 27-28 May 2016; Nakhon Pathom, Thailand, 170-173.
• Alikhanov D, Penchev S, Georgieva Ts, Moldajanov A, Shynybaj Z, Daskalov P. Indirect method for egg weight measurement using image processing. International Journal of Emerging Technology and Advanced Engineeering 2015; 5(11): 30-34.
• Javadikia P, Dehrouyeh MH, Naderloo L, Rabbani H, Lorestani AN. Measuring the weight of egg with image processing and ANFIS model. In: SEMCCO 2011; 19-21 December; Visakhapatnam, India; 407–416.
• Sun L, Yuan L, Cai J, Lin H, Zhao J. Egg freshness on-line estimation using machine vision and dynamic weighing. Food Anal. Methods 2015; 8: 922–928.
• Omid M, Soltani M, Dehrouyeh MH, Mohtasebi SS, Ahmadi H. An expert egg grading system based on machine vision and artificial intelligence techniques. J. Food Eng. 2013; 118: 70–77.
• Garcia S, Luengo J, Herrera F. Data preprocessing in data mining. Springer, Intelligent Systems Reference Library 2015; 72.
• Malik K, Sadawarti H, Kalra G. Comparative analysis of outlier detection techniques. Int. J. Computer Appl. 2014; 97: 12–21.
• Hira, ZM, Gillies DF. A review of feature selection and feature extraction methods applied on microarray data. Adv. Bioinformatics 2015; 2015: 1-13. Dougherty G. Pattern Recognition and Classification. Springer New York, 2013.
• Welch’s power spectral density estimate. Available at: https://www.mathworks.com/help/signal/ref/pwelch.html; Accessed: 21-Apr.-2019.
• Reconstruct wavelet packet coefficients. Available at: https://www.mathworks.com/help/wavelet/ref/wprcoef.html; Accessed: 21-Apr.-2019.
• PRTools A MATLAB toolbox for pattern recognition. Available at: http://prtools.tudelft.nl/; Accessed: 21-Apr.-2019.
• Elibol G, Ergin S. The assessment of time-domain features for detecting symptoms of diabetic retinopathy”, International Journal of Intelligent Systems and Applications in Engineering 2016; 4: 136-140.
• Guyon I, Gunn S, Nikravesh M, Zadeh LA. Feature Extraction Foundations and Applications. Springer -Verlag Berlin Heidelberg, 2006.
• Cover T, Hart P. Nearest neighbor pattern classification. IEEE Trans. Inform. Theor. 1967; 13(1): 21-27.
• Bolon-Canedo V, Sanchez-Marono N, Alonso-Betanzos A. Feature Selection for High-dimensional Data. Springer International Publishing, 2015.
• Bishop CM. Pattern Recognition and Machine Learning. Springer, 2006.
• Yabanova İ. Dynamic mass measurement and appropriate filter analysis. IU-JEEE 2016; 16(2): 3033-3036.
• Türk Gıda Kodeksi Yumurta Tebliği, Gıda Tarım Hayvancılık Bakanlığı, Resmi Gazete 2014, Sayı: 29211.