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Cluster Computing

, Volume 22, Supplement 4, pp 9515–9524 | Cite as

The recognition of rice images by UAV based on capsule network

  • Yu Li
  • Meiyu Qian
  • Pengfeng Liu
  • Qian CaiEmail author
  • Xiaoying Li
  • Junwen Guo
  • Huan Yan
  • Fengyuan Yu
  • Kun Yuan
  • Juan Yu
  • Luman Qin
  • Hongxin Liu
  • Wan Wu
  • Peiyun Xiao
  • Ziwei Zhou
Article

Abstract

It is important to recognize the rice image captured by unmanned aerial vehicle (UAV) for monitoring the growth of rice and preventing the diseases and pests. Aiming at the image recognition, we use rice images captured by UAV as our data source, the structure of capsule network (CapsNet) is built to recognize rice images in this paper. The images are preprocessed through histogram equalization method into grayscale images and through superpixel algorithm into the superpixel segmentation results. The both results are output into the CapsNet. The function of CapsNet is to perform the reverse analysis of rice images. The CapsNet consists of five layers: an input layer, a convolution layer, a primary capsules layer, a digital capsules layer and an output layer. The CapsNet trains classification and predicts the output vector based on routing-by-agreement protocol. Therefore, the features of rice image by UAV can be precisely and efficiently extracted. The method is more convenient than the traditional artificial recognition. It provides the scientific support and reference for decision-making process of precision agriculture.

Keywords

Image recognition Capsule network Feature extraction Routing-by-agreement protocol 

Notes

Acknowledgement

This paper is acknowledged by the National Natural Science Foundation of China (Grant No. 51502209), the Government Support Enterprise Development Funding of Hubei Province (Grant No. 16441), the Three-dimensional Textiles Engineering Research Center of Hubei Province, the Anqing Technology Transfer Center of Wuhan Textile University.

References

  1. 1.
    Pádua, L., Adão, T., Hruška, J., et al.: Very high resolution aerial data to support multi-temporal precision agriculture information management. Proc. Comput. Sci. 121, 407–414 (2017)CrossRefGoogle Scholar
  2. 2.
    Schmidt, D.F., Botwinick, J.: UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability. Photogrammetrie-Fernerkundung-Geoinformation 6(6), 551–562 (2013)Google Scholar
  3. 3.
    Bendig, J., Yu, K., Aasen, H., et al.: Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 39, 79–87 (2015)CrossRefGoogle Scholar
  4. 4.
    Shen, K., Li, W., Pei, Z., et al.: Crop area estimation from UAV transect and MSR image data using spatial sampling method. Proc. Environ. Sci. 26, 95–100 (2015)CrossRefGoogle Scholar
  5. 5.
    Chang, A., Jung, J., Maeda, M.M., et al.: Crop height monitoring with digital imagery from unmanned aerial system (UAS). Comput. Electron. Agric. 141, 232–237 (2017)CrossRefGoogle Scholar
  6. 6.
    Bendig, J., Yu, K., Aasen, H., et al.: Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 39, 79–87 (2015)CrossRefGoogle Scholar
  7. 7.
    Xiang, H., Tian, L.: Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosys. Eng. 108(2), 174–190 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Gibson-Poole, S., Humphris, S., Toth, I.: Identification of the onset of disease within a potato crop using a UAV equipped with unmodified and modified commercial off-the-shelf digital cameras. Adv. Anim. Biosci. 8, 2812–2816 (2017)CrossRefGoogle Scholar
  9. 9.
    Barbedo, J.G.A.: A review on the main challenges in automatic plant disease identification based on visible range images. Biosys. Eng. 144, 52–60 (2016)CrossRefGoogle Scholar
  10. 10.
    Latte, M.V., Shidnal, S., Anami, B.S., et al.: A combined HSV and GLCM approach for paddy variety identification from crop images. Int. J. Signal Process. Image Process. Pattern Recognit. 8 (2015)Google Scholar
  11. 11.
    Dorj, U.O., Lee, M., Yun, S.S.: An yield estimation in citrus orchards via fruit detection and counting using image processing. Comput. Electron. Agric. 140, 103–112 (2017)CrossRefGoogle Scholar
  12. 12.
    Grinblat, G.L., Uzal, L.C., Larese, M.G., et al.: Deep learning for plant identification using vein morphological patterns. Comput. Electron. Agric. 127, 418–424 (2016)CrossRefGoogle Scholar
  13. 13.
    dos Santos Ferreira, A., Freitas, D.M., da Silva, G.G.: Weed detection in soybean crops using ConvNets. Comput. Electron. Agric. 143, 314–324 (2017)CrossRefGoogle Scholar
  14. 14.
    Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. arXiv:1710.09829.2017
  15. 15.
    Shin, M., Kim, M., Kwon, D.S.: Baseline CNN structure analysis for facial expression recognition. In: Robot and Human Interactive Communication (RO-MAN), 2016 25th IEEE International Symposium on, pp. 724–729 (2016)Google Scholar
  16. 16.
    García-Santillán, I.D., Pajares, G.: On-line crop/weed discrimination through the Mahalanobis distance from images in maize fields. Biosyst. Eng. 166, 28–43 (2018)CrossRefGoogle Scholar
  17. 17.
    Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels. Epfl (2010)Google Scholar
  18. 18.
    Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  19. 19.
    Hsu, C.Y., Ding, J.J.: Efficient image segmentation algorithm using SLIC superpixels and boundary-focused region merging, pp. 1–5. In: Communications and Signal Processing. IEEE (2013)Google Scholar
  20. 20.
    Dubey, S.R., Jalal, A.S.: Detection and classification of apple fruit diseases using complete local binary patterns. In: Third International Conference on Computer and Communication Technology, pp. 346–351. IEEE Computer Society (2012)Google Scholar
  21. 21.
    Omrani, E., Khoshnevisan, B., Shamshirband, S., et al.: Potential of radial basis function-based support vector regression for apple disease detection. Measurement 55(9), 512–519 (2014)CrossRefGoogle Scholar
  22. 22.
    Wen, C., Wu, D., Hu, H., et al.: Pose estimation-dependent identification method for field moth images using deep learning architecture. Biosys. Eng. 136, 117–128 (2015)CrossRefGoogle Scholar
  23. 23.
    Grinblat, G.L., Uzal, L.C., Larese, M.G., et al.: Deep learning for plant identification using vein morphological patterns. Comput. Electron. Agric. 127, 418–424 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yu Li
    • 1
  • Meiyu Qian
    • 1
  • Pengfeng Liu
    • 1
  • Qian Cai
    • 1
    Email author
  • Xiaoying Li
    • 2
  • Junwen Guo
    • 1
  • Huan Yan
    • 1
  • Fengyuan Yu
    • 1
  • Kun Yuan
    • 1
  • Juan Yu
    • 1
  • Luman Qin
    • 1
  • Hongxin Liu
    • 1
  • Wan Wu
    • 1
  • Peiyun Xiao
    • 1
  • Ziwei Zhou
    • 1
  1. 1.Wuhan Textile UniversityWuhanChina
  2. 2.China University of GeosciencesWuhanChina

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