Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Cover of the book

2020 | OriginalPaper | Chapter

Fruit Classification for Retail Stores Using Deep Learning

Authors : Jose Luis Rojas-Aranda, Jose Ignacio Nunez-Varela, J. C. Cuevas-Tello, Gabriela Rangel-Ramirez

Published in: Pattern Recognition

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Payment of fruits or vegetables in retail stores normally require them to be manually identified. This paper presents an image classification method, based on lightweight Convolutional Neural Networks (CNN), with the goal of speeding up the checkout process in stores. A new dataset of images is introduced that considers three classes of fruits, inside or without plastic bags. In order to increase the classification accuracy, different input features are added into the CNN architecture. Such inputs are, a single RGB color, the RGB histogram, and the RGB centroid obtained from K-means clustering. The results show an overall 95% classification accuracy for fruits with no plastic bag, and 93% for fruits in a plastic bag .

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
next chapter Towards Dendrite Spherical Neurons for Pattern Classification
Footnotes
Literature
1.
Abadi, M., Agarwal, A., Barham, P., Goodfellow, I., et. al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). https://​www.​tensorflow.​org/​, software available from tensorflow.org
2.
Bargoti, S., Underwood, J.: Deep fruit detection in orchards. In: 2017 IEEE International Conference on Robotics and Automation (ICRA), pp. 3626–3633 (2017)
3.
Femling, F., Olsson, A., Alonso-Fernandez, F.: Fruit and vegetable identification using machine learning for retail applications. In: 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 9–15. IEEE (2018)
4.
Hameed, K., Chai, D., Rassau, A.: A comprehensive review of fruit and vegetable classification techniques. Image Vis. Comput. 80, 24–44 (2018)CrossRef
5.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
6.
Hossain, M.S., Al-Hammadi, M., Muhammad, G.: Automatic fruit classification using deep learning for industrial applications. IEEE Trans. Ind. Inf. 15(2), 1027–1034 (2018)CrossRef
7.
Katarzyna, R., Paweł, M.: A vision-based method utilizing deep convolutional neural networks for fruit variety classification in uncertainty conditions of retail sales. Appl. Sci. 9(19), 3971 (2019)CrossRef
8.
Koirala, A., Walsh, K.B., Wang, Z., McCarthy, C.: Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of ‘MangoYOLO’. Precis. Agric. 20(6), 1107–1135 (2019)CrossRef
9.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advanced in Neural Information Processing Systems, vol. 25, pp. 1097–1105 (2012)
10.
Mureşan, H., Oltean, M.: Fruit recognition from images using deep learning. Acta Universitatis Sapientiae Informatica 10(1), 26–42 (2018)CrossRef
11.
Rahnemoonfar, M., Sheppard, C.: Deep count: fruit counting based on deep simulated learning. Sensors 17(4), 905 (2017)CrossRef
12.
Russakovsky, O., Deng, J., et al.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)MathSciNetCrossRef
13.
Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., McCool, C.: A fruit detection system using deep neural networks. Sensors 16(8), 1222 (2016)CrossRef
14.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
15.
Shin, H.C., Roth, E.A.: Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans. Med. Imaging 35(5), 1285–1298 (2016)CrossRef
16.
Sze, V., Chen, Y.H., Yang, T.J., Emer, J.S.: Efficient processing of deep neural networks: a tutorial and survey. Proc. IEEE 105(12), 2295–2329 (2017)CrossRef
17.
Tan, L., Jiang, J.: Fundamentals of analog and digital signal processing. AuthorHouse (2007)
Metadata
Title
Fruit Classification for Retail Stores Using Deep Learning
Authors
Jose Luis Rojas-Aranda
Jose Ignacio Nunez-Varela
J. C. Cuevas-Tello
Gabriela Rangel-Ramirez
Copyright Year
2020
Book
Pattern Recognition

Print ISBN: 978-3-030-49075-1

Electronic ISBN: 978-3-030-49076-8

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-49076-8_1

Premium Partner

    Image Credits