Araştırma Makalesi
Weight and Diameter Estimation Using Image Processing and Machine Learning Techniques on Apple Images
Öz
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.
Anahtar Kelimeler
Kaynakça
- 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.
Yıl 2017,
Cilt: 9 Sayı: 3, 147 - 154, 26.12.2017
Öz
Kaynakça
- 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.
Toplam 14 adet kaynakça vardır.
Ayrıntılar
| Bölüm | Makaleler |
|---|---|
| Yazarlar | |
| Yayımlanma Tarihi | 26 Aralık 2017 |
| Gönderilme Tarihi | 8 Ekim 2017 |
| Yayımlandığı Sayı | Yıl 2017 Cilt: 9 Sayı: 3 |
Cited By
Area Based Diameter Measurement Algorithm for Industrial Machine Vision Applications
Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
https://doi.org/10.28948/ngumuh.1069442
Alüminyum Ekstrüzyon Profillerinin Hassas Kesit Ölçümlerinin Görüntü İşleme Teknolojisi İle Gerçekleştirilmesi
European Journal of Science and Technology
Mehmet YOLDAŞ
https://doi.org/10.31590/ejosat.803308
Tüm hakları saklıdır. Kırıkkale Üniversitesi, Mühendislik Fakültesi.