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Classification of Apple Varieties by Types Using Image Processing Techniques

Year 2022, Issue: 37, 131 - 138, 15.07.2022
https://doi.org/10.31590/ejosat.1136913

Abstract

With the developing technology, the concepts of "Artificial Intelligence" and "Deep Learning", which we have been hearing frequently in recent years, have many application areas. These methods, which imitate human intelligence, learn the data sets they receive from their environment through experience, just like humans. In this study, apple varieties grown in Yahyalı district of Kayseri were classified according to their type variables. Since the apple fruit is picked in September, it was obtained at the harvest time when the apples were plucked from the tree. The biggest problem of the farmers producing apples is the classification of apples according to their varieties without a handprint and as soon as possible. In this study, a total of 120 images were taken from 20 Golden, 20 Argentina, 20 Buckeye Gala, 20 Galaval, 20 Superchief and 20 Joremin apple varieties. The images were taken with a Canon EOS 70D DSLR camera at the same angle and the same size, on a fixed background. R2021a version of MATLAB program was used for image processing. Deep learning algorithms were used to classify apple varieties according to their types. AlexNet and GoogleNet deep learning algorithms, which are among the most basic architectures used in solving classification problems, are used. The study was carried out in both AlexNet and GoogleNet methods at 10 epochs and sgdm training algorithm. Learning rates are taken as 0.0001 and 0.0003 for AlexNet and GoogleNet, respectively. 70% of the images were used for training and 30% for testing, and the total data set consists of 120 images, 20 of each type. AlexNet architecture has 83.33% success rate, 1 minute 52 seconds. The classification success rate of the GoogleNet architecture is 91.67%, and it performed the most successful classification process for 2 minutes and 14 seconds.

References

  • Kaur, C., & Kapoor, H. C. (2001). Antioxidants in fruits and vegetables–the millennium’s health. International journal of food science & technology, 36(7), 703-725.
  • Ahmad, R., Hussain, B., & Ahmad, T. (2021). Fresh and dry fruit production in himalayan Kashmir, sub-Himalayan Jammu and trans-himalayan Ladakh, India. Heliyon, 7(1), e05835.
  • Raikar, M. M., Meena, S. M., Kuchanur, C., Girraddi, S., & Benagi, P. (2020). Classification and Grading of Okra-ladies finger using Deep Learning. Procedia Computer Science, 171, 2380-2389.
  • Liu, Y., Zhang, Z., Liu, X., Wang, L., & Xia, X. (2021). Deep learning-based image classification for online multi-coal and multi-class sorting. Computers & Geosciences, 157, 104922.
  • Deepak, S., & Ameer, P. M. (2020). Retrieval of brain MRI with tumor using contrastive loss based similarity on GoogLeNet encodings. Computers in Biology and Medicine, 125, 103993.
  • Luo, T., Zhao, J., Gu, Y., Zhang, S., Qiao, X., Tian, W., & Han, Y. (2021). Classification of weed seeds based on visual images and deep learning. Information Processing in Agriculture.
  • PAN, S. Q., QIAO, J. F., Rui, W. A. N. G., YU, H. L., Cheng, W. A. N. G., TAYLOR, K., & PAN, H. Y. (2022). Intelligent diagnosis of northern corn leaf blight with deep learning model. Journal of Integrative Agriculture, 21(4), 1094-1105.
  • Kumar, L. S., Hariharasitaraman, S., Narayanasamy, K., Thinakaran, K., Mahalakshmi, J., & Pandimurugan, V. (2022). AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images. Materials Today: Proceedings, 51, 58-65.
  • Behera, S. K., Rath, A. K., & Sethy, P. K. (2021). Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Information Processing in Agriculture, 8(2), 244-250.
  • Gao, Z., Shao, Y., Xuan, G., Wang, Y., Liu, Y., & Han, X. (2020). Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning. Artificial Intelligence in Agriculture, 4, 31-38.
  • Loddo, A., Loddo, M., & Di Ruberto, C. (2021). A novel deep learning based approach for seed image classification and retrieval. Computers and Electronics in Agriculture, 187, 106269.
  • Sachdeva, G., Singh, P., & Kaur, P. (2021). Plant leaf disease classification using deep Convolutional neural network with Bayesian learning. Materials Today: Proceedings, 45, 5584-5590.
  • Abade, A., Ferreira, P. A., & de Barros Vidal, F. (2021). Plant diseases recognition on images using convolutional neural networks: A systematic review. Computers and Electronics in Agriculture, 185, 106125.
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
  • Adger, W. N., Huq, S., Brown, K., Conway, D., & Hulme, M. (2003). Adaptation to climate change in the developing world. Progress in development studies, 3(3), 179-195.
  • Muhammad, N. A., Nasir, A. A., Ibrahim, Z., & Sabri, N. (2018). Evaluation of CNN, Alexnet and GoogleNet for fruit recognition. Indonesian Journal of Electrical Engineering and Computer Science, 12(2), 468-475.
  • Bai, Y., Wan, H., & Bai, C. (2017). Study on human behavior classification in still images based on GoogLeNet. Comput. Knowl. Technol, 13(18), 186-188.

Görüntü İşleme Tekniklerinden Faydalanarak Elma Çeşitlerinin Türlerine Göre Sınıflandırılması

Year 2022, Issue: 37, 131 - 138, 15.07.2022
https://doi.org/10.31590/ejosat.1136913

Abstract

Gelişen teknoloji ile birlikte son dönemlerde sıkça duymaya başladığımız “Yapay Zekâ” ve “Derin Öğrenme” kavramlarının pek çok uygulama alanları mevcuttur. İnsan zekâsını taklit eden bu yöntemler çevresinden aldığı veri setlerini tıpkı insanlar gibi deneyim yoluyla öğrenir. Bu çalışmada Kayseri’nin Yahyalı ilçesinde yetişen elma çeşitlerinin cinslerine göre sınıflandırılması yapılmıştır. Elma meyvesi Eylül ayında toplandığı için elmaların ağaçtan koparıldığı hasat zamanında elde edilmiştir. Elma üretimi yapan çiftçilerin en büyük problemleri el izi olmadan ve en kısa sürede elmaların çeşitlerine göre sınıflandırılmasıdır. Bu çalışmada, 20 Golden, 20 Arjantin, 20 Buckeye Gala, 20 Galaval, 20 Superchief ve 20 Joremin elma türlerinden toplam 120 görüntü alınmıştır. Görüntüler sabit bir arka fonda aynı açı ve aynı büyüklükte Canon EOS 70D DSLR marka fotoğraf makinası ile çekilmiştir. Görüntü işlemek için MATLAB programının R2021a sürümünden faydalanılmıştır. Elma çeşitlerinin türlerine göre sınıflandırılması için derin öğrenme algoritmalarından yararlanılmıştır. Sınıflandırma problemlerinin çözümünde kullanılan en temel mimarilerinden olan AlexNet ve GoogleNet derin öğrenme algoritmaları kullanılmıştır. Çalışma AlexNet ve GoogleNet yöntemlerinin her ikisinde de 10 epoch değerinde ve sgdm eğitim algoritmasında gerçekleştirilmiştir. Öğrenme oranları AlexNet ve GoogleNet için sırasıyla 0.0001 ve 0.0003 olarak ele alınmıştır. Görüntülerin %70’i eğitim %30’u test amacıyla kullanılmış olup toplam veri seti her çeşitte 20 adet olmak üzere 120 tane görselden oluşmaktadır. AlexNet mimarisi %83.33 başarı oranı, 1 dakika 52 saniyedir. GoogleNet mimarisinin sınıflandırma başarı oranı %91,67’ dir, 2 dakika 14 saniye süre ile en başarılı sınıflandırma işlemini gerçekleştirmiştir.

References

  • Kaur, C., & Kapoor, H. C. (2001). Antioxidants in fruits and vegetables–the millennium’s health. International journal of food science & technology, 36(7), 703-725.
  • Ahmad, R., Hussain, B., & Ahmad, T. (2021). Fresh and dry fruit production in himalayan Kashmir, sub-Himalayan Jammu and trans-himalayan Ladakh, India. Heliyon, 7(1), e05835.
  • Raikar, M. M., Meena, S. M., Kuchanur, C., Girraddi, S., & Benagi, P. (2020). Classification and Grading of Okra-ladies finger using Deep Learning. Procedia Computer Science, 171, 2380-2389.
  • Liu, Y., Zhang, Z., Liu, X., Wang, L., & Xia, X. (2021). Deep learning-based image classification for online multi-coal and multi-class sorting. Computers & Geosciences, 157, 104922.
  • Deepak, S., & Ameer, P. M. (2020). Retrieval of brain MRI with tumor using contrastive loss based similarity on GoogLeNet encodings. Computers in Biology and Medicine, 125, 103993.
  • Luo, T., Zhao, J., Gu, Y., Zhang, S., Qiao, X., Tian, W., & Han, Y. (2021). Classification of weed seeds based on visual images and deep learning. Information Processing in Agriculture.
  • PAN, S. Q., QIAO, J. F., Rui, W. A. N. G., YU, H. L., Cheng, W. A. N. G., TAYLOR, K., & PAN, H. Y. (2022). Intelligent diagnosis of northern corn leaf blight with deep learning model. Journal of Integrative Agriculture, 21(4), 1094-1105.
  • Kumar, L. S., Hariharasitaraman, S., Narayanasamy, K., Thinakaran, K., Mahalakshmi, J., & Pandimurugan, V. (2022). AlexNet approach for early stage Alzheimer’s disease detection from MRI brain images. Materials Today: Proceedings, 51, 58-65.
  • Behera, S. K., Rath, A. K., & Sethy, P. K. (2021). Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Information Processing in Agriculture, 8(2), 244-250.
  • Gao, Z., Shao, Y., Xuan, G., Wang, Y., Liu, Y., & Han, X. (2020). Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning. Artificial Intelligence in Agriculture, 4, 31-38.
  • Loddo, A., Loddo, M., & Di Ruberto, C. (2021). A novel deep learning based approach for seed image classification and retrieval. Computers and Electronics in Agriculture, 187, 106269.
  • Sachdeva, G., Singh, P., & Kaur, P. (2021). Plant leaf disease classification using deep Convolutional neural network with Bayesian learning. Materials Today: Proceedings, 45, 5584-5590.
  • Abade, A., Ferreira, P. A., & de Barros Vidal, F. (2021). Plant diseases recognition on images using convolutional neural networks: A systematic review. Computers and Electronics in Agriculture, 185, 106125.
  • Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., ... & Rabinovich, A. (2015). Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1-9).
  • Adger, W. N., Huq, S., Brown, K., Conway, D., & Hulme, M. (2003). Adaptation to climate change in the developing world. Progress in development studies, 3(3), 179-195.
  • Muhammad, N. A., Nasir, A. A., Ibrahim, Z., & Sabri, N. (2018). Evaluation of CNN, Alexnet and GoogleNet for fruit recognition. Indonesian Journal of Electrical Engineering and Computer Science, 12(2), 468-475.
  • Bai, Y., Wan, H., & Bai, C. (2017). Study on human behavior classification in still images based on GoogLeNet. Comput. Knowl. Technol, 13(18), 186-188.
There are 17 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Sevim Adige 0000-0002-8693-1126

Rifat Kurban 0000-0002-0277-2210

Ali Durmuş 0000-0001-8283-8496

Ercan Karaköse 0000-0001-5586-3258

Early Pub Date June 30, 2022
Publication Date July 15, 2022
Published in Issue Year 2022 Issue: 37

Cite

APA Adige, S., Kurban, R., Durmuş, A., Karaköse, E. (2022). Görüntü İşleme Tekniklerinden Faydalanarak Elma Çeşitlerinin Türlerine Göre Sınıflandırılması. Avrupa Bilim Ve Teknoloji Dergisi(37), 131-138. https://doi.org/10.31590/ejosat.1136913