Onur CÖMERT, Mahmut HEKİM, Kemal ADEM

Weight and Diameter Estimation Using Image Processing and Machine Learning Techniques on Apple Images

Classification of table fruits according to size is traditionally hand made. But human factors are the cause of faulty classifications. Automatically performing this process with the machines is important in terms of speeding up the process, reducing costs, and minimizing errors. In this study, weight and diameter estimations were made on "Starking" type apples using image processing techniques. Firstly 50 photographs were taken with NIR camera and 830nm long pass filter. Afterwards, edge detection algorithms and morphological operations were performed on the images to obtain the boundaries of the images. Diameter and area information obtained from the binary image were used as attributes. These attributes were given as input to Linear Regression method and estimated. As a result, 93% of the diameters of the apples and 96.5% of the weights could be estimated.

Keywords:

Starking Apple, Image Processing,

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Anonymous, “All in 1 LED Lighting Solutions Guide. PhilipsLumileds”, https://web.archive.org/web/20130314111003/http://www.philipslumileds.com/uploads/221/PG01-pdf (24.04.2017). 2012.
Anonymous, “Dünya meyve suyu sektörüne bakış”. MEYED/Meyve Suyu Endüstrisi Derneği, http://www.meyed.org.tr/userfiles/file/sektor_istatistikleri/dunya_meyve_suyu_sektorune_bakis_akdag.pdf (08.04.2016), 2016.
Anonymous, “Light-emitting diode”. Wikipedia, https://en.wikipedia.org/wiki/Light-emitting_diode#cite_note-56 (24.04.2017), 2017a.
Anonymous, “Halogen Lamp”. Wikipedia, https://en.wikipedia.org/wiki/Halogen_lamp (24.04.2017), 2017b.
Er, O., Cetişli, B., Sofu, M. M. and Kayacan, M. C., “Gerçek Zamanlı Otomatik Elma Tasnifleme”. Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 17(2), 31-38. 2013.
Kondo, N., “Automation on fruit and vegetable grading system and food traceability”. Trends in Food Science Technology, 21, (145-152). 2010.
Sofu, M. M., Er, O., Kayacan, M. C. and Cetişli, B., “Elmaların Görüntü İşleme Yöntemi ile Sınıflandırılması ve Leke Tespiti”, Gıda Teknolojileri Elektronik Dergisi, 8(1), 12-25. 2013.
Tonguç, G. and Yakut, A. K., “Fruit grading using digital image processing”. Tarım Makinaları Bilimi Dergisi, 5(1), 93–101. 2009.
Unay, D., “A stem-and/calyx recognition system based on pattern recognition for ‘jonagold’ apples”. Tech. Rep., TCTS Labs., Faculte Poly- technique de Mons. 2005.
Unay, D. and Gosselin, B., “Artifical Neural Network-Based Segmentation and Apple Grading”. IEEE International Conference on Image Processing, 2, 630-633. 2005.
Unay, D. and Gosselin, B., “Thresholding-Based Segmentation And Apple Grading By Machine Vision”. Tech. Rep., TCTS Labs., Faculte Poly- technique de Mons. 2005.
Xiaobo, Z., Jiewen, Z. and Yanxiao, L., “Apple color grading based on organization feature parameters”. Pattern Recognition Letters, 28, 2046–2053. 2007.
Chapra, S. C. and Canale, R. P. “Yazılım Ve Programlama Uygulamalarıyla Mühendisler İçin Sayısal Yöntemler”. Literatür Yayıncılık, ISBN: 975-8431-83-8. 2003.
Singh, K.P., Basant, A., Malik, A. and Jain, G. “Artificial neural network modeling of the river water quality-A case study”, Ecological Modelling, 220(6), 888-895. 2009.