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Hurma Meyvesindeki Kalite Kontrol işlemlerinin Yapay Zeka İle Tahminlenmesi

Year 2023, Volume: 9 Issue: 4, 70 - 81, 31.12.2023

Abstract

Meyve ve sebze pazarlarında ürünlerin kalite sınıflandırmasında dış görünüş önemli bir faktördür. Mevcut manuel yöntemler ile tarımda üretilen ürünlerin kontrol aşamalarında mahsulün hastalık, pestisit ve kalite durumları kontrol edilmektedir. Manuel olarak ürünlerin ayrıştırılıp sınıflandırılması uzmanlık gerektirebilir, bu da zaman alıcı ve büyük emek isteyen bir iştir. Günümüzde teknolojinin ilerlemesi ile tarım ve gıda alanında kullanılan yazılım teknikleri de gelişmektedir. Tarım ve gıda alanında üretilen ürünlerin işlenmesi ve pazara sürülmesi aşamalarında yazılım tekniklerinin kullanılması yaygınlaşmaktadır. Gerçekleştirilecek olan çalışma ile meyve ve sebze pazarında önemli bir payı olan Hurma meyvesi ele alınacaktır. Hurma meyvelerinin kalitelerinin sınıflandırılmasında görüntü işleme ve yapay zeka tekniklerinin kullanılması satış sürecinin daha tutarlı ve zaman açısından verimli hale gelmesini sağlayabilir. Çalışma kapsamında özgün olarak hazırlanan veri seti ile çeşitli yapay zeka teknikleri kullanılmıştır. Veri seti 3 farklı sınıftan oluşmaktadır. Bunlar, iyi, kötü ve orta kalite hurma meyvelerinin görüntülerini içermektedir. Özgün veri seti ile MobileNetV2, ResNet50V2, DenseNet201 ve InceptionV3 modelleri eğitilmiştir. Ayrıca çalışmanın ilerki aşamalarında bu alanda kullanılacak olan yazılım teknikleri otomasyon sistemleri ile entegre edilebilir. Yapay zeka ve görüntü işleme tekniklerini kullanan bir otomasyon sistemi meyveleri kalitelerine göre otonom olarak ayırt edebilir. Çalışmanın ilerleyen aşamalarında bu konu üzerinde durulacaktır.

Thanks

Çalışma 5. Uluslararası Mühendislikte Yapay Zeka ve Uygulamalı Matematik Konferans’nda bildiri olarak sunulmuştur.

References

  • [1] W. M. Amer, “Taxonomic and documentary study of food plants in ancient egypt,” Ph.D. Thesis, Cairo University, Giza, 1994.
  • [2] M. Al-Farsi, C. Alasalvar, A. Morris, M. Baron, and F. Shahidi, “Comparison of antioxidant activity, anthocyanins, carotenoids, and phenolics of three native fresh and sun-dried date (Phoenix dactylifera L.) varieties grown in Oman,” Journal of Agricultural and Food Chemistry, vol. 53, pp. 7586−7599, August 2005. doi: 10.1021/jf050579q
  • [3] E. Yıldız and M. Kaplankıran, “Hatay ili Trabzon hurması seleksiyonunda belirlenen tiplerin özellikleri,” V. Ulusal Bahçe Bitkileri Kongresi, 4-7 Eylül, 2007, Erzurum, Turkey, pp. 266-270.
  • [4] T. Saraçoğlu, “Bazı narenciye türlerinin seçilmiş fiziksel ve hidrodinamik özellikleri,” Anadolu Tarım Bilimleri Dergisi. vol. 32, pp. 206-215. Haziran 2017. doi: 10.7161/omuanajas.303881
  • [5] M.T. Masarirambi, V. Mavuso, V.D. Songwe, T.P. Nkambule and N. Mhazo, “Indigenous postharvest handling and processing of traditional vegetables in Swaziland: A review,” African Journal of Agricultural Research, vol. 5, no. 24, pp.3333-3341, 2010
  • [6] K.G. Liakos, P. Busato, D. Moshou, S. Pearson and D. Bochtis, “Machine learning in agriculture: a review,” Sensors, vol. 18, no.8 pp. 2674, August 2018. doi: 10.3390/s18082674
  • [7] E. Saldana, R. Siche, M. Luján and R. Quevedo, “Computer vision applied to the inspection and quality control of fruits and vegetables,” Brazilian Journal of Food Technology, vol. 16, no. 4, pp. 254–272, December 2013. doi: 10.1590/S1981-67232013005000031
  • [8] H. Armagan, "Color based segmentation with k-means clustering algorithm and numerical analysis of the effect of color spaces on ımage quantities," El-Cezeri, vol. 9, no. 4, pp.1506-1517, December 2022. doi: 10.31202/ecjse.11411 48
  • [9] S. Adige, R. Kurban, A. Durmuş and E. Karaköse, “Görüntü işleme tekniklerinden faydalanarak elma çeşitlerinin türlerine göre sınıflandırılması,” Avrupa Bilim ve Teknoloji Dergisi, no. 37, pp. 131-138, Temmuz 2022. doi: 10.31590/ejosat.1136913
  • [10] M. Dhakate, “BIA diagnosis of pomegranate plant diseases using neural networks,” In: Proceedings of the 5th National Conference on computer vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 16-19 December, 2015, Patna, India [Online]. Available: IEEE Xplore, https://ieeexplore.ieee.org/abstract/document/7490056. [Accessed: 09 July. 2023].
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  • [12] Y. D. Zhang, Z. Dong, X. Chen, W. Jia, S. Du, K. Muhammad and S. H. Wang, “Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation,” Multimedia Tools and Applications, vol. 78, pp. 3613-3632. September 2019. doi: 10.1007/s11042-017-5243-3
  • [13] K. Kayaalp, and S. Metlek, “Classification of robust and rotten apples by deep learning algorithm,” Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 2, pp.112-120, August 2020. doi: 10.35377/saucis.03.02.717452
  • [14] B. Büyükarıkan and E. Ülker, “Aydınlatma özniteliği kullanılarak evrişimsel sinir ağı modelleri ile meyve sınıflandırma,” Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 25 no. 1, pp. 81-100. April 2020. doi: 10.17482/uumfd.628166
  • [15] J. L. Joseph, V.A. Kumar and S.P. Mathew, “Fruit classification using deep learning,” In Innovations in Electrical and Electronic Engineering: Proceedings of ICEEE 2021, vol. 756, pp. 807-817, May 2021. doi: 10.1007/978-981-16-0749-3_62
  • [16] N. Kumari, R.K. Dwivedi, A.K. Bhatt, and R. Belwal, “Automated fruit grading using optimal feature selection and hybrid classification by self-adaptive chicken swarm optimization: grading of mango,” Neural computing and applications, vol. 34, pp. 1-22, 2022. doi: 10.1007/s00521-021-06473-x
  • [17] S. Kesler, A. Karakan, and O. Yüksel, “Alexnet mimarisi ile muz olgunlaşma evrelerinin sınıflandırılması,” Avrupa Bilim ve Teknoloji Dergisi, no. 51, pp. 135-141, Ağustos 2023. doi: 10.31590/ejosat.1252946
  • [18] E. Kahya and F. F. Özdüven, “Robotik hasat sistemlerinde kullanılmak amacıyla lahana ve brokolinin derin öğrenme metodu ile sınıflandırılması,” Turkish Journal of Agriculture-Food Science and Technology, vol. 11 no. 9, pp. 1639-1647, 2023. doi: 10.24925/turjaf.v11i9.1639-1647.6177
  • [19] A. Nasiri, A. Taheri-Garavand, and Y. D. Zhang, “Image-based deep learning automated sorting of date fruit,” Postharvest biology and technology, vol. 153, pp. 133-141, July 2019. doi: 10.1016/j.postharvbio.2019.04.003
  • [20] M. S. Hasan and A. Sattar, “Arabian date classification using CNN algorithm with various pre-trained models” In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 04-06 February 2021, Tirunelveli, India [Online]. Available: IEEE Xplore, https://ieeexplore.ieee.org/abstract/document/9388413. [Accessed: 09 July. 2023].
  • [21] H. Alaskar, S. Alhewaidi, B. Obaid, G. Alzahrani, A. Abdulahi, Z. Sbai, and T. Vaiyapuri, “ Dates fruit classification using convolution neural networks,” In Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, Vol. 3, pp. 757-775, 2022. doi: 10.1007/978-981-16-1781-2_66
  • [22] K. Albarrak, Y. Gulzar, Y. Hamid, A. Mehmood, and A. B. Soomro, “A deep learning-based model for date fruit classification,” Sustainability, vol. 14, no. 10, pp. 6339. 2022. doi: 10.3390/su14106339
  • [23] S. Pa, “An overview on mobilenet: an efficient mobile vision CNN,” medium.com, Jun. 10, 2020. [Online] Avaliable: https://medium.com/@godeep48/an-overview-on-mobilenet-an-efficient-mobile-vision-cnn-f301141db94d
  • [24] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arxiv.org, 17 Apr 2017. [Online]. Available: https://arxiv.org/abs/1704.04861 [Accessed: Dec. 26, 2023].
  • [25] S. Akdağ, “Resnet (residual network) nedir?” medium.com, Aug. 9, 2021, [Online]. Avaliable: https://suhedacilek.medium.com/resnet-residual-network-nedir-49105e642566
  • [26] G. Huang, Z. Liu, L.V.D. Maaten and K. Q. Weinberger, “Densely connected convolutional networks,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 21-26 July 2017, Honolulu, HI, USA [Online]. Available: IEEE Xplore, https://ieeexplore.ieee.org/document/8099726. [Accessed: 09 July. 2023].
  • [27] X. Yu, N. Zeng, S. Liu, and Y. D. Zhang, “Utilization of denseNet201 for diagnosis of breast abnormality,” Machine Vision and Applications, vol. 30, pp. 1135–1144, july 2019. doi: 10.1007/s00138-019-01042-8
  • [28] A. Sevinç and F. Özyurt, “Beton yüzey çatlaklarının tespitinde derin öğrenme mimarilerinin kullanılması,” Uluslararası Yenilikçi Mühendislik Uygulamaları Dergisi, vol. 6 no.2, pp. 318-325, 2022. doi: 10.46460/ijiea.1098046
  • [29] A.H. Hasan, E. Ibra, E. Civil, and M. Cicioğlu, “İnsansız hava araçlarında nesne tespiti ve takibi,” 7. Geleceğin Mühendisleri Uluslararası Öğrenci Sempozyumu, 22-23 June 2023, Zonguldak, Turkey [Online]. Available: researchgate.net, https://www.researchgate.net/publication/373549374_Object_Detection_and_Tracking_in_Unmanned_Aerial_Vehicles. [Accessed: 09 July 2023].

Controlling Quality Control Processes In Date Fruit With Artificial Intelligence

Year 2023, Volume: 9 Issue: 4, 70 - 81, 31.12.2023

Abstract

Appearance is an important factor in the quality classification of products in fruit and vegetable markets. Existing manual methods are used to check the disease, pesticide and quality status of crops during the control stages of agricultural production. Manual sorting and grading of products may require expertise, which is time-consuming and labor-intensive. Today, with the advancement of technology, software techniques used in agriculture and food are also developing. The use of software techniques in the processing and marketing of agricultural and food products is becoming widespread. With the study to be carried out, date palm fruit, which has an important share in the fruit and vegetable market, will be discussed. Using image processing and artificial intelligence techniques to classify the quality of date fruits can make the sales process more consistent and time efficient. Within the scope of the study, various artificial intelligence techniques were used with a uniquely prepared dataset. The dataset consists of 3 different classes. These include images of good, bad and medium quality date fruits. MobileNetV2, ResNet50V2, DenseNet201 and InceptionV3 models were trained with the original dataset. In addition, the software techniques to be used in this field can be integrated with automation systems in the future stages of the study. An automation system using artificial intelligence and image processing techniques can autonomously distinguish fruits according to their quality. This will be emphasized in the later stages of the study.

References

  • [1] W. M. Amer, “Taxonomic and documentary study of food plants in ancient egypt,” Ph.D. Thesis, Cairo University, Giza, 1994.
  • [2] M. Al-Farsi, C. Alasalvar, A. Morris, M. Baron, and F. Shahidi, “Comparison of antioxidant activity, anthocyanins, carotenoids, and phenolics of three native fresh and sun-dried date (Phoenix dactylifera L.) varieties grown in Oman,” Journal of Agricultural and Food Chemistry, vol. 53, pp. 7586−7599, August 2005. doi: 10.1021/jf050579q
  • [3] E. Yıldız and M. Kaplankıran, “Hatay ili Trabzon hurması seleksiyonunda belirlenen tiplerin özellikleri,” V. Ulusal Bahçe Bitkileri Kongresi, 4-7 Eylül, 2007, Erzurum, Turkey, pp. 266-270.
  • [4] T. Saraçoğlu, “Bazı narenciye türlerinin seçilmiş fiziksel ve hidrodinamik özellikleri,” Anadolu Tarım Bilimleri Dergisi. vol. 32, pp. 206-215. Haziran 2017. doi: 10.7161/omuanajas.303881
  • [5] M.T. Masarirambi, V. Mavuso, V.D. Songwe, T.P. Nkambule and N. Mhazo, “Indigenous postharvest handling and processing of traditional vegetables in Swaziland: A review,” African Journal of Agricultural Research, vol. 5, no. 24, pp.3333-3341, 2010
  • [6] K.G. Liakos, P. Busato, D. Moshou, S. Pearson and D. Bochtis, “Machine learning in agriculture: a review,” Sensors, vol. 18, no.8 pp. 2674, August 2018. doi: 10.3390/s18082674
  • [7] E. Saldana, R. Siche, M. Luján and R. Quevedo, “Computer vision applied to the inspection and quality control of fruits and vegetables,” Brazilian Journal of Food Technology, vol. 16, no. 4, pp. 254–272, December 2013. doi: 10.1590/S1981-67232013005000031
  • [8] H. Armagan, "Color based segmentation with k-means clustering algorithm and numerical analysis of the effect of color spaces on ımage quantities," El-Cezeri, vol. 9, no. 4, pp.1506-1517, December 2022. doi: 10.31202/ecjse.11411 48
  • [9] S. Adige, R. Kurban, A. Durmuş and E. Karaköse, “Görüntü işleme tekniklerinden faydalanarak elma çeşitlerinin türlerine göre sınıflandırılması,” Avrupa Bilim ve Teknoloji Dergisi, no. 37, pp. 131-138, Temmuz 2022. doi: 10.31590/ejosat.1136913
  • [10] M. Dhakate, “BIA diagnosis of pomegranate plant diseases using neural networks,” In: Proceedings of the 5th National Conference on computer vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 16-19 December, 2015, Patna, India [Online]. Available: IEEE Xplore, https://ieeexplore.ieee.org/abstract/document/7490056. [Accessed: 09 July. 2023].
  • [11] E. Güneş, “Derin öğrenme yaklaşımı ile fındık meyvesinin sınıflandırılması,” Ph.D. Thesis, Marmara Univ., İstanbul, Türkiye, 2022.
  • [12] Y. D. Zhang, Z. Dong, X. Chen, W. Jia, S. Du, K. Muhammad and S. H. Wang, “Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation,” Multimedia Tools and Applications, vol. 78, pp. 3613-3632. September 2019. doi: 10.1007/s11042-017-5243-3
  • [13] K. Kayaalp, and S. Metlek, “Classification of robust and rotten apples by deep learning algorithm,” Sakarya University Journal of Computer and Information Sciences, vol. 3, no. 2, pp.112-120, August 2020. doi: 10.35377/saucis.03.02.717452
  • [14] B. Büyükarıkan and E. Ülker, “Aydınlatma özniteliği kullanılarak evrişimsel sinir ağı modelleri ile meyve sınıflandırma,” Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, vol. 25 no. 1, pp. 81-100. April 2020. doi: 10.17482/uumfd.628166
  • [15] J. L. Joseph, V.A. Kumar and S.P. Mathew, “Fruit classification using deep learning,” In Innovations in Electrical and Electronic Engineering: Proceedings of ICEEE 2021, vol. 756, pp. 807-817, May 2021. doi: 10.1007/978-981-16-0749-3_62
  • [16] N. Kumari, R.K. Dwivedi, A.K. Bhatt, and R. Belwal, “Automated fruit grading using optimal feature selection and hybrid classification by self-adaptive chicken swarm optimization: grading of mango,” Neural computing and applications, vol. 34, pp. 1-22, 2022. doi: 10.1007/s00521-021-06473-x
  • [17] S. Kesler, A. Karakan, and O. Yüksel, “Alexnet mimarisi ile muz olgunlaşma evrelerinin sınıflandırılması,” Avrupa Bilim ve Teknoloji Dergisi, no. 51, pp. 135-141, Ağustos 2023. doi: 10.31590/ejosat.1252946
  • [18] E. Kahya and F. F. Özdüven, “Robotik hasat sistemlerinde kullanılmak amacıyla lahana ve brokolinin derin öğrenme metodu ile sınıflandırılması,” Turkish Journal of Agriculture-Food Science and Technology, vol. 11 no. 9, pp. 1639-1647, 2023. doi: 10.24925/turjaf.v11i9.1639-1647.6177
  • [19] A. Nasiri, A. Taheri-Garavand, and Y. D. Zhang, “Image-based deep learning automated sorting of date fruit,” Postharvest biology and technology, vol. 153, pp. 133-141, July 2019. doi: 10.1016/j.postharvbio.2019.04.003
  • [20] M. S. Hasan and A. Sattar, “Arabian date classification using CNN algorithm with various pre-trained models” In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), 04-06 February 2021, Tirunelveli, India [Online]. Available: IEEE Xplore, https://ieeexplore.ieee.org/abstract/document/9388413. [Accessed: 09 July. 2023].
  • [21] H. Alaskar, S. Alhewaidi, B. Obaid, G. Alzahrani, A. Abdulahi, Z. Sbai, and T. Vaiyapuri, “ Dates fruit classification using convolution neural networks,” In Proceedings of Sixth International Congress on Information and Communication Technology: ICICT 2021, Vol. 3, pp. 757-775, 2022. doi: 10.1007/978-981-16-1781-2_66
  • [22] K. Albarrak, Y. Gulzar, Y. Hamid, A. Mehmood, and A. B. Soomro, “A deep learning-based model for date fruit classification,” Sustainability, vol. 14, no. 10, pp. 6339. 2022. doi: 10.3390/su14106339
  • [23] S. Pa, “An overview on mobilenet: an efficient mobile vision CNN,” medium.com, Jun. 10, 2020. [Online] Avaliable: https://medium.com/@godeep48/an-overview-on-mobilenet-an-efficient-mobile-vision-cnn-f301141db94d
  • [24] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto and H. Adam, “Mobilenets: Efficient convolutional neural networks for mobile vision applications,” arxiv.org, 17 Apr 2017. [Online]. Available: https://arxiv.org/abs/1704.04861 [Accessed: Dec. 26, 2023].
  • [25] S. Akdağ, “Resnet (residual network) nedir?” medium.com, Aug. 9, 2021, [Online]. Avaliable: https://suhedacilek.medium.com/resnet-residual-network-nedir-49105e642566
  • [26] G. Huang, Z. Liu, L.V.D. Maaten and K. Q. Weinberger, “Densely connected convolutional networks,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 21-26 July 2017, Honolulu, HI, USA [Online]. Available: IEEE Xplore, https://ieeexplore.ieee.org/document/8099726. [Accessed: 09 July. 2023].
  • [27] X. Yu, N. Zeng, S. Liu, and Y. D. Zhang, “Utilization of denseNet201 for diagnosis of breast abnormality,” Machine Vision and Applications, vol. 30, pp. 1135–1144, july 2019. doi: 10.1007/s00138-019-01042-8
  • [28] A. Sevinç and F. Özyurt, “Beton yüzey çatlaklarının tespitinde derin öğrenme mimarilerinin kullanılması,” Uluslararası Yenilikçi Mühendislik Uygulamaları Dergisi, vol. 6 no.2, pp. 318-325, 2022. doi: 10.46460/ijiea.1098046
  • [29] A.H. Hasan, E. Ibra, E. Civil, and M. Cicioğlu, “İnsansız hava araçlarında nesne tespiti ve takibi,” 7. Geleceğin Mühendisleri Uluslararası Öğrenci Sempozyumu, 22-23 June 2023, Zonguldak, Turkey [Online]. Available: researchgate.net, https://www.researchgate.net/publication/373549374_Object_Detection_and_Tracking_in_Unmanned_Aerial_Vehicles. [Accessed: 09 July 2023].
There are 29 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Research Articles
Authors

Bekir Aksoy 0000-0001-8052-9411

Mehmet Yücel 0000-0002-4100-5831

Hamdi Sayın 0000-0002-0826-8517

Nergiz Aydın 0000-0002-3921-3295

Özge Ekrem 0000-0001-9142-405X

Publication Date December 31, 2023
Submission Date November 20, 2023
Acceptance Date December 23, 2023
Published in Issue Year 2023 Volume: 9 Issue: 4

Cite

IEEE B. Aksoy, M. Yücel, H. Sayın, N. Aydın, and Ö. Ekrem, “Hurma Meyvesindeki Kalite Kontrol işlemlerinin Yapay Zeka İle Tahminlenmesi”, GJES, vol. 9, no. 4, pp. 70–81, 2023.

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