Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content
Springer Nature Link
Log in

The recognition of rice images by UAV based on capsule network

  • Published:
Cluster Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  14. Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. arXiv:1710.09829.2017

  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)

  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)

    Article  Google Scholar 

  17. Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels. Epfl (2010)

  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)

    Article  Google Scholar 

  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)

  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)

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

Download references

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.

Author information

Authors and Affiliations

  1. Wuhan Textile University, Wuhan, China

    Yu Li, Meiyu Qian, Pengfeng Liu, Qian Cai, Junwen Guo, Huan Yan, Fengyuan Yu, Kun Yuan, Juan Yu, Luman Qin, Hongxin Liu, Wan Wu, Peiyun Xiao & Ziwei Zhou

  2. China University of Geosciences, Wuhan, China

    Xiaoying Li

Authors
  1. Yu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Meiyu Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pengfeng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qian Cai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiaoying Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Junwen Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Huan Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Fengyuan Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kun Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Juan Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Luman Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Hongxin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Wan Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Peiyun Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Ziwei Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qian Cai.

Additional information

Yu Li, Meiyu Qian, Pengfeng Liu, Qian Cai and Xiaoying Li are Co-first authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Qian, M., Liu, P. et al. The recognition of rice images by UAV based on capsule network. Cluster Comput 22 (Suppl 4), 9515–9524 (2019). https://doi.org/10.1007/s10586-018-2482-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2482-7

Keywords

Search

Navigation