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Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models

Yıl 2023, Cilt: 7 Sayı: 2, 79 - 86, 31.12.2023
https://doi.org/10.47897/bilmes.1328313

Öz

The value of the economy provided by pistachios to the countries where they are grown is increasing day by day. From this point of view, the importance of correct classification of pistachios is known. The more accurately the harvested pistachios are classified, the better the monetary return value. In this study, two different classes of pistachios were classified using VGG16 and VGG19 deep learning architectures. There are 2148 pieces of Kirmizi and Siirt Pistachio in the dataset. Experimental studies were carried out with 5-fold crossvalidation. As a result of the experimental studies, the Accuracy value of 0.802117 and the F1-measure value of 0.830593 were obtained from the average of 5 folds from the VGG16 deep learning model. Likewise, the Accuracy value of 0.779404 and the F-measure value as 0.779404 were obtained from the average of 5 folds from the VGG19 deep learning model.

Kaynakça

  • [1] Atay Ü., The Investigation Of Classification Systems Used For Pistahio And Construction Of An Alternetive Classification System, Phd Thesis Harran University, Sanliurfa, 2007.
  • [2] Tunalıoğlu R, and Taşkaya B., “Antepfıstığı”. Tarımsal Ekonomi Araştırma Enstitüsü Dergisi, 2003.
  • [3] Dreher Ml., “Pistachio Nuts: Composition And Potential Health Benefits”, Nutrition Reviews, vol. 70, no. 4, pp. 234–240, 2012
  • [4] Kay Cd, Gebauer Sk, West Sg, and Kris-Etherton Pm., “Pistachios Increase Serum Antioxidants And Lower Serum Oxidized-Ldl İn Hypercholesterolemic Adults”, The Journal Of Nutrition, vol. 140, no. 6, pp. 1093-1098, 2010
  • [5] Casasent Da, Sipe Ma, Schatzki Tf, Keagy Pm, and Lee Lc., “Neural Net Classification Of X-Ray Pistachio Nut Data”, Lwt- Food Science And Technology, vol. 31, no. 2, pp. 122-128, 1998
  • [6] Abbaszadeh M., Rahimifard A., Eftekhari M., Zadeh H.G., Fayazi A., Dini A., and Danaeian M., Deep Learning-Based Classification Of The Defective Pistachios Via Deep Autoencoder Neural Networks., Arxiv:1906.11878, 2019.
  • [7] Rahimzadeh M., Attar A., “Detecting And Counting Pistachios Based On Deep Learning”, Iran J. Comput. Sci., vol. 5, pp. 69–81, 2021
  • [8] Habib M. T., Mia M. J., Uddin M. S., and Ahmed F., “An In-Depth Exploration Of Automated Jackfruit Disease Recognition”, Journal Of King Saud University - Computer And Information Sciences. vol. 34, no. 4, pp. 1200-1209, 2022 Https://Doi.Org/10.1016/J.Jksuci.2020.04.018
  • [9] Assuncao E., Diniz C., Gaspar P. D., and Proenca, H., “Decision-Making Support System For Fruit Diseases Classification Using Deep Learning”, 2020 International Conference On Decision Aid Sciences And Application (Dasa), 2020, https://Doi.Org/10.1109/Dasa51403.2020.9317219
  • [10] Villacrés J. F., and Auat Cheein, F., “Detection And Characterization Of Cherries: A Deep Learning Usability Case Study In Chile”. Agronomy, vol. 10, no. 6, p. 835, 2020, https://Doi.Org/10.3390/Agronomy10060835
  • [11] Vasumathi M. T., and Kamarasan M., “An Effective Pomegranate Fruit Classification Based On Cnn-Lstm Deep Learning Models”, Indian Journal Of Science And Technology, vol. 14, no. 16, pp. 1310–1319, 2021, https://Doi.Org/10.17485/İjst/V14i16.432
  • [12] Simonyan K., and Zisserman A., “Very Deep Convolutional Networks For Large-Scale Image Recognition”, 2014. Web: Https://Arxiv.Org/Abs/1409.1556
  • [13] Doğan F., and Türkoğlu İ., “Derin Öğrenme Algoritmalarının Yaprak Sınıflandırma Başarımlarının Karşılaştırılması”, Sakarya University Journal Of Computer And Information Sciences, vol. 1, pp. 10–21, 2018.
  • [14] Web Site, Hands-On Transfer Learning With Keras And The Vgg16 Model, Https://Www.Learndatasci.Com/Tutorials/Hands-On-Transfer-Learning-Keras/, Accesed Date: 12.12.2022
  • [15] Web Site, Vgg-19 Transfer Learning İle Görüntü Sınıflandırma, Https://Yazilimkaravani.Net/Vgg19-Transfer-Learning-İle-Goruntu-Siniflandirma/, Accesed Date: 12.12.2022.
  • [16] Khattar A., and Quadri S.M.K., “Generalization Of Convolutional Network To Domain Adaptation Network For Classification Of Disaster İmages On Twitter”. Multimed Tools Appl, vol. 81, pp. 30437–30464, 2022, Https://Doi.Org/10.1007/S11042-022-12869-1
  • [17] Singh D, Taspınar Ys, Kursun R, Cınar I, Koklu M, Ozkan Ia, and Lee H-N., “Classification And Analysis Of Pistachio Species With Pre-Trained Deep Learning Models”, Electronics, vol. 11, no. 7, p. 981, 2022, Https://Doi.Org/10.3390/Electronics11070981. (Open Access), Doi: Https://Doi.Org/10.3390/Electronics11070981
  • [18] Ozkan IA., Koklu M. and Saracoglu R., “Classification Of Pistachio Species Using Improved K-Nn Classifier”. Progress In Nutrition, vol. 23, no. 2, 2021, Https://Doi.Org/10.23751/Pn.V23i2.9686. (Open Access), Doı: Https://Doi.Org/10.23751/Pn.V23i2.9686

Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models

Yıl 2023, Cilt: 7 Sayı: 2, 79 - 86, 31.12.2023
https://doi.org/10.47897/bilmes.1328313

Öz

The value of the economy provided by pistachios to the countries where they are grown is increasing day by day. From this point of view, the importance of correct classification of pistachios is known. The more accurately the harvested pistachios are classified, the better the monetary return value. In this study, two different classes of pistachios were classified using VGG16 and VGG19 deep learning architectures. There are 2148 pieces of Kirmizi and Siirt Pistachio in the dataset. Experimental studies were carried out with 5-fold crossvalidation. As a result of the experimental studies, the Accuracy value of 0.802117 and the F1-measure value of 0.830593 were obtained from the average of 5 folds from the VGG16 deep learning model. Likewise, the Accuracy value of 0.779404 and the F-measure value as 0.779404 were obtained from the average of 5 folds from the VGG19 deep learning model.

Kaynakça

  • [1] Atay Ü., The Investigation Of Classification Systems Used For Pistahio And Construction Of An Alternetive Classification System, Phd Thesis Harran University, Sanliurfa, 2007.
  • [2] Tunalıoğlu R, and Taşkaya B., “Antepfıstığı”. Tarımsal Ekonomi Araştırma Enstitüsü Dergisi, 2003.
  • [3] Dreher Ml., “Pistachio Nuts: Composition And Potential Health Benefits”, Nutrition Reviews, vol. 70, no. 4, pp. 234–240, 2012
  • [4] Kay Cd, Gebauer Sk, West Sg, and Kris-Etherton Pm., “Pistachios Increase Serum Antioxidants And Lower Serum Oxidized-Ldl İn Hypercholesterolemic Adults”, The Journal Of Nutrition, vol. 140, no. 6, pp. 1093-1098, 2010
  • [5] Casasent Da, Sipe Ma, Schatzki Tf, Keagy Pm, and Lee Lc., “Neural Net Classification Of X-Ray Pistachio Nut Data”, Lwt- Food Science And Technology, vol. 31, no. 2, pp. 122-128, 1998
  • [6] Abbaszadeh M., Rahimifard A., Eftekhari M., Zadeh H.G., Fayazi A., Dini A., and Danaeian M., Deep Learning-Based Classification Of The Defective Pistachios Via Deep Autoencoder Neural Networks., Arxiv:1906.11878, 2019.
  • [7] Rahimzadeh M., Attar A., “Detecting And Counting Pistachios Based On Deep Learning”, Iran J. Comput. Sci., vol. 5, pp. 69–81, 2021
  • [8] Habib M. T., Mia M. J., Uddin M. S., and Ahmed F., “An In-Depth Exploration Of Automated Jackfruit Disease Recognition”, Journal Of King Saud University - Computer And Information Sciences. vol. 34, no. 4, pp. 1200-1209, 2022 Https://Doi.Org/10.1016/J.Jksuci.2020.04.018
  • [9] Assuncao E., Diniz C., Gaspar P. D., and Proenca, H., “Decision-Making Support System For Fruit Diseases Classification Using Deep Learning”, 2020 International Conference On Decision Aid Sciences And Application (Dasa), 2020, https://Doi.Org/10.1109/Dasa51403.2020.9317219
  • [10] Villacrés J. F., and Auat Cheein, F., “Detection And Characterization Of Cherries: A Deep Learning Usability Case Study In Chile”. Agronomy, vol. 10, no. 6, p. 835, 2020, https://Doi.Org/10.3390/Agronomy10060835
  • [11] Vasumathi M. T., and Kamarasan M., “An Effective Pomegranate Fruit Classification Based On Cnn-Lstm Deep Learning Models”, Indian Journal Of Science And Technology, vol. 14, no. 16, pp. 1310–1319, 2021, https://Doi.Org/10.17485/İjst/V14i16.432
  • [12] Simonyan K., and Zisserman A., “Very Deep Convolutional Networks For Large-Scale Image Recognition”, 2014. Web: Https://Arxiv.Org/Abs/1409.1556
  • [13] Doğan F., and Türkoğlu İ., “Derin Öğrenme Algoritmalarının Yaprak Sınıflandırma Başarımlarının Karşılaştırılması”, Sakarya University Journal Of Computer And Information Sciences, vol. 1, pp. 10–21, 2018.
  • [14] Web Site, Hands-On Transfer Learning With Keras And The Vgg16 Model, Https://Www.Learndatasci.Com/Tutorials/Hands-On-Transfer-Learning-Keras/, Accesed Date: 12.12.2022
  • [15] Web Site, Vgg-19 Transfer Learning İle Görüntü Sınıflandırma, Https://Yazilimkaravani.Net/Vgg19-Transfer-Learning-İle-Goruntu-Siniflandirma/, Accesed Date: 12.12.2022.
  • [16] Khattar A., and Quadri S.M.K., “Generalization Of Convolutional Network To Domain Adaptation Network For Classification Of Disaster İmages On Twitter”. Multimed Tools Appl, vol. 81, pp. 30437–30464, 2022, Https://Doi.Org/10.1007/S11042-022-12869-1
  • [17] Singh D, Taspınar Ys, Kursun R, Cınar I, Koklu M, Ozkan Ia, and Lee H-N., “Classification And Analysis Of Pistachio Species With Pre-Trained Deep Learning Models”, Electronics, vol. 11, no. 7, p. 981, 2022, Https://Doi.Org/10.3390/Electronics11070981. (Open Access), Doi: Https://Doi.Org/10.3390/Electronics11070981
  • [18] Ozkan IA., Koklu M. and Saracoglu R., “Classification Of Pistachio Species Using Improved K-Nn Classifier”. Progress In Nutrition, vol. 23, no. 2, 2021, Https://Doi.Org/10.23751/Pn.V23i2.9686. (Open Access), Doı: Https://Doi.Org/10.23751/Pn.V23i2.9686
Toplam 18 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Derin Öğrenme
Bölüm Makaleler
Yazarlar

Emre Avuçlu 0000-0002-1622-9059

Yayımlanma Tarihi 31 Aralık 2023
Kabul Tarihi 1 Ağustos 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 7 Sayı: 2

Kaynak Göster

APA Avuçlu, E. (2023). Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models. International Scientific and Vocational Studies Journal, 7(2), 79-86. https://doi.org/10.47897/bilmes.1328313
AMA Avuçlu E. Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models. ISVOS. Aralık 2023;7(2):79-86. doi:10.47897/bilmes.1328313
Chicago Avuçlu, Emre. “Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models”. International Scientific and Vocational Studies Journal 7, sy. 2 (Aralık 2023): 79-86. https://doi.org/10.47897/bilmes.1328313.
EndNote Avuçlu E (01 Aralık 2023) Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models. International Scientific and Vocational Studies Journal 7 2 79–86.
IEEE E. Avuçlu, “Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models”, ISVOS, c. 7, sy. 2, ss. 79–86, 2023, doi: 10.47897/bilmes.1328313.
ISNAD Avuçlu, Emre. “Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models”. International Scientific and Vocational Studies Journal 7/2 (Aralık 2023), 79-86. https://doi.org/10.47897/bilmes.1328313.
JAMA Avuçlu E. Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models. ISVOS. 2023;7:79–86.
MLA Avuçlu, Emre. “Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models”. International Scientific and Vocational Studies Journal, c. 7, sy. 2, 2023, ss. 79-86, doi:10.47897/bilmes.1328313.
Vancouver Avuçlu E. Classification of Pistachio Images Using VGG16 and VGG19 Deep Learning Models. ISVOS. 2023;7(2):79-86.


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