VegeShop Tool: A Tool for Vegetable Recognition Using DNN

  • Yuki SakaiEmail author
  • Tetsuya Oda
  • Makoto Ikeda
  • Leonard Barolli
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 2)


Deep Learning also called Deep Neural Network (DNN) has a deep hierarchy that connect multiple internal layers for feature detection and recognition learning. In our previous work, we proposed vegetable recognition system which was based on Convolutional Neural Network (CNN). In this paper, we propose a tool called VegeShop for vegetable category recognition which is based on CNN. The user interface serves as e-commerce system for sellers and buyers using Android mobile device. The system can be accessed ubiquitously from any where. Moreover, our system can be applied also for other category recognition.


Deep Neural Network Category recognition CNN 


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  1. 1.
    Raspbian website, Scholar
  2. 2.
    Aapo, H.: Fast and robust fixed-point algorithms for independent component analysis. IEEE Transactions on Neural Networks 10(3), 626–634 (May 1999)Google Scholar
  3. 3.
    Akyildiz, I.F., Kasimoglu, I.H.: Wireless sensor and actor networks: Research challenges. Ad Hoc Networks Journal (Elsevier) 2(4), 351–367 (October 2004)Google Scholar
  4. 4.
    Azad, P., Asfour, T., Dillmann, R.: Combining harris interest points and the sift descriptor for fast scale-invariant object recognition. In: Proceeding of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-2009). pp. 4275–4280 (October 2009)Google Scholar
  5. 5.
    O¨ zgu¨r B. Akan, Akyildiz, I.F.: Event-to-sink reliable transport in wireless sensor networks. IEEE/ACM Transactions on Networking 13(5), 1003–1016 (October 2005)Google Scholar
  6. 6.
    Bay, H., Tuytelaars, T., Gool, L.V.: SURF: Speeded up robust features. Lecture Notes in Computer Science 3951, 404–417 (October 2006)Google Scholar
  7. 7.
    Cun, Y.L.: Generalization and network design strategies. Tech. Rep. CRG-TR-89-4, Department of Computer Science, University of Toronto (June 1989)Google Scholar
  8. 8.
    Fujiyoshi, H.: Gradient-based feature extraction: Sift and hog. Tech. rep., IEICE (August 2007)Google Scholar
  9. 9.
    Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Computation 18(7), 1527–1554 (July 2006)Google Scholar
  10. 10.
    Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Sciense 313(5786), 504–507 (July 2006)Google Scholar
  11. 11.
    Jain, A.K., Mao, J., Mohiuddin, K.M.: Artificial neural networks: a tutorial. Computer 29(3), 31–44 (March 1996)Google Scholar
  12. .
    Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. arXiv preprint arXiv:1408.5093 (2014)Google Scholar
  13. 13.
    Jiang, X., Dawson-Haggerty, S., Dutta, P., Culler, D.: Design and implementation of a highfidelity ac metering network. In: Proceeding of the 8-th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN-2009). pp. 253–264. San Francisco, US (April 2009)Google Scholar
  14. 14.
    Kang, L., Kumar, J., Ye, P., Li, Y., Doermann, D.: Convolutional neural networks for document image classification. In: Proceedings of 22nd International Conference on Pattern Recognition 2014 (ICPR-2014). pp. 3168–3172 (August 2014)Google Scholar
  15. 15.
    Karahan, S., Karaoz, A., Ozdemir, O.F., Gul, A.G., Uludag, U.: On identification from periocular region utilizing sift and surf. In: Proceedings of the 22-nd European Signal Processing Conference (EUSIPCO-2014). pp. 1392–1396 (September 2014)Google Scholar
  16. 16.
    Le, Q.V.: Building high-level features using large scale unsupervised learning. In: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing 2013 (ICASSP-2013). pp. 8595–8598 (May 2013)Google Scholar
  17. 17.
    Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proceedings of the 26th Annual International Conference on Machine Learning. pp. 609–616 (June 2009)Google Scholar
  18. 18.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV-1999). pp. 1150–1157 (September 1999)Google Scholar
  19. 19.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (November 2004)Google Scholar
  20. 20.
    Mikolajczyk, K.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 60(10), 1615–1630 (October 2005)Google Scholar
  21. 21.
    Murillo, A.C., Guerrero, J.J., Sagu¨es, C.: Surf features for efficient robot localization with omnidirectional images. In: Proceedings of the IEEE Robotics and Automation Roma. pp. 3901–3907 (April 2007)Google Scholar
  22. 22.
    Nakano, T., Kida, T.: Two dimensional pattern matching for jpeg images. Tech. rep., IEICE (December 2008)Google Scholar
  23. 23.
    Sainath, T.N., Kingsbury, B., Mohamed, A.R., Dahl, G.E., Saon, G., Soltau, H., Beran, T., Aravkin, A.Y., Ramabhadran, B.: Improvements to deep convolutional neural networks for LVCSR. In: Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding 2013 (ASRU-2013). pp. 315–320 (December 2013)Google Scholar
  24. 24.
    Sakai, Y., Oda, T., Ikeda, M., Barolli, L.: An object tracking system based on sift and surf feature extraction methods. In: Proceedings of the 5th International Workshop on Information Networking and Wireless Communications (INWC-2015). pp. 561–565 (September 2015)Google Scholar
  25. 25.
    Sakai, Y., Oda, T., Ikeda, M., Barolli, L.: A vegetable category recognition system using deep neural network. In: accepted, to appear in Proceedings of the 10th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2016) (July 2016)Google Scholar
  26. 26.
    Sikora, R., Sikora, J., Cardelli, E., Chady, T.: Artificial neural network application for material evaluation by electromagnetic methods. In: Proceedings of International Joint Conference on Neural Networks (IJCNN-1999). vol. 6, pp. 4027–4032 (July 1999)Google Scholar
  27. 27.
    Takaki, S., Yamagishi, J.: Deep auto-encoder based low-dimensional feature extraction using fft spectral envelopes in statistical parametric speech synthesis. IEICE Technical Report 2015-SLP-109(18), 1–6 (November 2015)Google Scholar
  28. 28.
    Tola, E., Lepetit, V., Fua, P.: A fast local descriptor for dense matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2008). pp. 1–8 (June 2008)Google Scholar
  29. 29.
    Tribelhorn, B., Dodds, Z.: Evaluating the roomba: A low-cost, ubiquitous platform for robotics research and education. In: Proceedings of the IEEE International Conference on Robotics and Automation (IEEE ICRA-2007). pp. 1393–1399. Roma, Italy (April 2007)Google Scholar
  30. 30.
    Tsugawa, S., Ohsaki, H.: Community structure and interaction locality in social networks. IPSJ Journal 56(6) (June 2015)Google Scholar
  31. 31.
    Ueda, K., Tamai, M., Yasumoto, K.: A system for daily living activities recognition based on multiple sensing data in a smart home. In: Proceedings of the Multimedia, Distributed, Cooperative, and Mobile Symposium (DICOMO-2014). pp. 1884–1891 (July 2014)Google Scholar
  32. 32.
    Ueki, M.: Human-centric computing to effort. Transactions of the Japan Society of Mechanical Engineers (2013)Google Scholar
  33. 33.
    Uhrig, R.E.: Introduction to artificial neural networks. In: Proceedings of the IEEE 21st International Conference on Industrial Electronics, Control, and Instrumentation (IECON-1995). vol. 1, pp. 33–37 (November 1995)Google Scholar
  34. 34.
    Uijlings, J.R.R., Smeulders, A.W.M., Scha, R.J.H.: Real-time visual concept classification. IEEE Transactions on Multimedia 12(7), 665–681 (October 2010)Google Scholar
  35. 35.
    Yu, Y., Rittle, L.J., Bhandari, V., LeBrun, J.B.: Supporting concurrent applications in wireless sensor networks. In: Proceedings of the 4-th ACM International Conference on Embedded Networked Sensor Systems (ACM SenSys-2006). pp. 139–152. Boulder, US (November 2006)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuki Sakai
    • 1
    Email author
  • Tetsuya Oda
    • 2
  • Makoto Ikeda
    • 2
  • Leonard Barolli
    • 2
  1. 1.Graduate School of EngineeringFukuoka Institute of Technology (FIT)Higashi-kuJapan
  2. 2.Department of Information and Communication EngineeringFukuoka Institute of TechnologyHigashi-kuJapan

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