Fruit monitoring system using multi-layered neural network

  • Authors

    • Rui Xu Daegu Cauthlic University
    • Tae Hyun Cho Kyungpook National University
    • Chang Kil Kim Kyungpook National University
    • Bonghwan Kim Kyungpook National University
    • In Soo Lee Kyungpook National University
    2018-07-11
    https://doi.org/10.14419/ijet.v7i3.11862
  • Fruit Monitoring System, GUI, Image Processing, Neural Network.

  • Abstract

    A fruit monitoring system based on image processing technology and multi-layer neural network is proposed. The advantage of the proposed fruit monitoring system allows it to be remotely controlled by PCs and the graphical user interface (GUI) program by LabVIEW which has been designed for more intuitive and convenient operation of this system. In addition, the neural network can reduce nonlinearity of the system compared to the calculation based system. Therefore, experienced workers and novices can easily judge the ripeness of the fruits using the GUI program without necessarily going to the orchards. In this study, the color is used as a criterion to judge the maturity of tomatoes. Ripe tomatoes will appear to be red, while the unripe tomatoes will be green in color. The region of interest (ROI) function and Canny edge detection are applied to crop the image and remove the background, then the pixel data obtained are to supply the use of neural network. After that the maturity level of tomatoes is judged by the neural network. In laboratory test, 50 experiments have been down, 48 of which were successful, 2 of which failed, so the recognition rate was 96%. The experiments of this fruit monitoring system in the greenhouse on real growing tomatoes has been conducted. Therefore, 10 experiments on the red and green tomatoes has been conducted, respectively. As a result, the recognition rate of the red tomatoes is 100%, and recognition rate of the green tomatoes is 90%. The experimental results show that the proposed mobile fruit monitoring system has a very high recognition rate of accuracy.

     

     

  • References

    1. [1] Seok, H. J.; Lee, J. H.; Lee, S. H.; Lee, E. P.; Oh, B. H.; Rui X.;, Lee, I. S. Implementation of an Image Processing System Based on Mobile Robot for Fruit State Monitoring, Proceedings of the 2016 KIIT Summer Conference, Korea, 2016, pp.394-396. (In Korean).

      [2] Yang, J. H.; Ha, J. S. Estimation of Future Death Burden of High Temperatures from Climate Change, Korean society of Environmental Health, 2013, 39(1), pp. 19-31. (In Korean).

      [3] Morimoto, T. ; Takeuchi, T. ; Miyata, H. ; Hashimoto, Y. Pattern recognition of fruit shape based on the concept of chaos and neural networks, Computers and Electronics in Agriculture, 2000, 26(2), pp. 171-186. https://doi.org/10.1016/S0168-1699(00)00070-3.

      [4] Nakano, K. Application of neural networks to the color grading of apples, Computers and Electronics in Agriculture, 1997, 18(2-3), pp. 105-116. https://doi.org/10.1016/S0168-1699(97)00023-9.

      [5] Zhao, M.; Hou, W. Method of apple automatic grading based on neural network, Journal of Nanjing Forestry University (Natural Sciences Edition), 2009, 33(1), pp. 136-138.

      [6] Ji, H.; Yuan, J. The application Study of Apple Color Grading by Particle Swarm Optimization Neural Networks, Intelligent Control and Automation, Proceedings in the Sixth World Congress, 2006, pp. 2651-2654.

      [7] Hu, H.; Deng, J.; Zhang, T. Study on color Sorting for Apples Based on Hamming Neural Networks, Journal of Agricultural Mechanization Research, 2006, 11.

      [8] Kim, U. C.; Lee, S. W.; Choi, Y. Y.; Kim, G. L.; Lee, Y. I. On the self-driving of a model car using image processing, Institute of Control, Robotics and Systems, 2011, 2011(5), pp. 660-663. (In Korean).

      [9] Jeong, J. S.; Jang, Y. M.; Park, Park, E. C. H.; Cho, S. B. Vehicle License Plate Recognition System using a Noise-cancelling based on Square Feature and OpenCV, The Institute of Electronics Engineers of Korea, 2015, 2, pp. 784-786. (In Korean).

      [10] Johnson, J.; Picton, P. Concepts in artificial intelligence, Butterworth-Heinemann LTD, 1995, Ch(4), pp. 95-96.

      [11] Schurmann J, Pattern classification, a unified view of statistical and neural approaches, John Wiley and Sons, New York, 1996.

      [12] Kohonen T, Self-organizing maps, Springer, Berlin, 1997. https://doi.org/10.1007/978-3-642-97966-8.

      [13] Fausett L, Fundamental of neural netwroks, Prentice Hall, 1994.

      [14] Timmermans, A. J. M. Hulzebosch, A.A. Computer vision system for on-line sorting of pot plants using an artificila neural network classifier, Computer and Electronics in Agriculture, 1996, 15(1), pp. 41-55. https://doi.org/10.1016/0168-1699(95)00056-9.

      [15] Nakano, K. Application of neural networks to the color grading of apples, Computers and Electronics in Agriculture, 1997, 18(2-3), pp. 105–116. https://doi.org/10.1016/S0168-1699(97)00023-9.

      [16] Schmoldt, D.L.; Li, P.; Abbott, A. L. Machine vision using artificial neural networks with local 3D neighbourhoods, Computer and Electronics in Agriculture, 1997, 16(3), pp. 255-271. https://doi.org/10.1016/S0168-1699(97)00002-1.

      [17] Wang, D.; Dowell, F. E.; Lacey, R. E. Single wheat kernel color classification using neural networks, Transactions of the ASAE-American Society of Agricultural Engineers, 1999, 42(1), pp. 233-240. https://doi.org/10.13031/2013.13200.

      [18] Yang, C.-C.; Prasher, S.O.; Lacroix, R.; Sreekanth, S.; Madani, A.; Masse, L. Artificial neural network model for subsurface-drained farmlands, Journal of Irrigation and Drainage Engineering, 1997, 123(4), pp. 285-292. https://doi.org/10.1061/(ASCE)0733-9437(1997)123:4(285).

      [19] Yang, C.-C.; Pasher, S.O.; Mehuys, G. R.; An artificial neural network to estimate soil temperature, Canadian Journal of Soil Science, 1997, 77(3), pp. 421-429. https://doi.org/10.4141/S96-062.

      [20] SHT-11 Data Sheet http://www.sensirion.co.kr/.

      [21] Oh, B. H.; Kim, T. H.; Lee, I. S. Implementation of Safety Buoy System Using PIR Sensors, Journal of Korean Institute of Information Technology, 2015, 13(4), pp. 9-15. (In Korean). https://doi.org/10.14801/jkiit.2015.13.4.9.

      [22] LEE, I. S. Diagnostic system development for state monitoring of induction motor and oil level in press process system, Korean Institute of Intelligent Systems, 2002, 19(5), pp. 706-712. (In Korean).

      [23] Lee, K. H.; Kim, I. J.; Choi, J. Y.; Lee, S. K. Design of Real-Time Power System Simulator for Education using LabVIEW, The Korean Institute of Illuminating and electrical Installation Engineers, 2012, 24(6), pp. 177-182. (In Korean).

      [24] Park, H. S. Vehicle Tracking System using HSV Color Space at nighttime, Korea Information Electron Communication Technology, 2015, 8(4), pp. 270-274. (In Korean). https://doi.org/10.17661/jkiiect.2015.8.4.270.

      [25] Kim, J. H.; Jeon, S. H.; Park, K. H.; Kim, Y. H. The Method of the Identity-Discriminant using HSV Color Model for Moving Object Tracking, Korean Institute of Information Technology, 2014, 5, pp. 292-295. (In Korean).

      [26] Choi, J. H.; Ko, K. J.; Han, D. I. Hardware Design and Implementation of Advanced Edge-Detection engine based on Canny Edge Filter for Robustness to Quantization Noise, The HCI Society of Korea, 2011, 11(1), pp. 582-584. (In Korean).

      [27] Rumelhart, D. E. McClelland, J. L. Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1986, 1, MIT Press, Reading, MA.

      [28] Xu, R.; Seok, H. J.; Lee, J. H.; Lee, S. H.; Lee, E. P.; Cho, T. H.; Lee, I. S. The Mobile Fruit Monitoring System Using Image Processing Technology and Temperature/Humidity Sensors, Korean Institute of Information Technology, 2017, 15(5), pp. 37-45. (In Korean). https://doi.org/10.14801/jkiit.2017.15.5.37.

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  • How to Cite

    Xu, R., Hyun Cho, T., Kil Kim, C., Kim, B., & Soo Lee, I. (2018). Fruit monitoring system using multi-layered neural network. International Journal of Engineering & Technology, 7(3), 1439-1445. https://doi.org/10.14419/ijet.v7i3.11862

    Received date: 2018-04-21

    Accepted date: 2018-06-27

    Published date: 2018-07-11