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International Conference on AI and Mobile Services

AIMS 2018: Artificial Intelligence and Mobile Services – AIMS 2018 pp 69–82Cite as

Plant Identification Based on Image Set Analysis

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10970))

Abstract

Plant identification is crucial in plant protection, crop breeding and agriculture research area. With the development of information technology, computer vision-based plant identification is an effective and efficient solution. Many tradition plant identification algorithms utilize only one type of plant images, such as images from leaves, flowers and stems etc., which may bring misclassifications. In order to recognize plant accurately, we propose a new plant identification scheme based on image set analysis. In this method, uses image set as a taxonomic unit, each image set consists of multiple images (whole plant, fruits, leaves, flowers, stems, branches, leaves scan), the characteristics of plants are fused together as a basis for plant identification. Compared to the traditional single feature, this study has more characteristics and provides more information in the classification process. A face recognition algorithm based on image collection is used in our research, for example: AHISD/CHISD (Affine/Convex Hull based Image Set Distance), PLRC (Pairwise Linear Regression Classification), SSDML (Set-to-Set Distance Metric Learning), SANP (Sparse Approximated Nearest Point), RNP (Regularized Nearest Points). Data set contains 64,150 plant images, it can be divided into 369 training set (each training set contains 50 pictures) and 914 test sets (each test set contains 50 picture). The results show that CHISD has the highest recognition rate, at 77.02%, which is more suitable for requiring higher accuracy. RNP identifies a plant class took an average of 0.75 s, the algorithm is more suitable for the occasion with higher time requirement. Therefore, the plant identification based on image set is feasible and low cost, which can be extended to agricultural production.

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References

  1. Cevikalp, H., Triggs, B.: Face recognition based on image sets. In: Computer Vision and Pattern Recognition, pp. 2567–2573. IEEE (2010)

    Google Scholar 

  2. Hu, Y., Mian, A.S., Owens, R.: Sparse approximated nearest points for image set classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 121–128. IEEE Computer Society (2011)

    Google Scholar 

  3. Yang, M., Zhu, P., Gool, L.V., et al.: Face recognition based on regularized nearest points between image sets. In: IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, pp. 1–7. IEEE (2013)

    Google Scholar 

  4. Zhu, P., Zhang, L., Zuo, W., et al.: From point to set: extend the learning of distance metrics. In: IEEE International Conference on Computer Vision, pp. 2664–2671. IEEE (2013)

    Google Scholar 

  5. Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  Google Scholar 

  6. Feng, Q., Zhou, Y., Lan, R.: Pairwise linear regression classification for image set retrieval. In: Computer Vision and Pattern Recognition, pp. 4865–4872. IEEE (2016)

    Google Scholar 

  7. Chen, L.: Dual linear regression based classification for face cluster recognition. In: Computer Vision and Pattern Recognition, pp. 2673–2680. IEEE (2014)

    Google Scholar 

  8. Davis, L.S.: Covariance discriminative learning: a natural and efficient approach to image set classification, pp. 2496–2503. In: IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society (2012)

    Google Scholar 

  9. Harandi, Mehrtash T., Salzmann, M., Hartley, R.: From manifold to manifold: geometry-aware dimensionality reduction for SPD matrices. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 17–32. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_2

    Chapter  Google Scholar 

  10. Hayat, M., Bennamoun, M., An, S.: Deep reconstruction models for image set classification. IEEE Trans. Pattern Anal. Mach. Intell. 37(4), 713–727 (2015)

    Article  Google Scholar 

  11. Wang, W., Wang, R., Huang, Z., et al.: Discriminant analysis on Riemannian manifold of Gaussian distributions for face recognition with image sets. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2048–2057. IEEE (2015)

    Google Scholar 

  12. Lu, J., Wang, G., Deng, W., et al.: Multi-manifold deep metric learning for image set classification. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1137–1145. IEEE Computer Society (2016)

    Google Scholar 

  13. Zhang, Z., Luo, P., Loy, C.C., Tang, X.: Joint face representation adaptation and clustering in videos. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 236–251. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_15

    Chapter  Google Scholar 

  14. Chi, Z., Li, H., Chao, W.: Plant species recognition based on bark patterns using novel Gabor filter banks. In: International Conference on Neural Networks and Signal Processing, vol. 2, pp. 1035–1038. IEEE (2003)

    Google Scholar 

  15. Saitoh, T., Kaneko, T.: Automatic recognition of wild flowers. In: International Conference on Pattern Recognition, Proceedings, vol. 2, pp. 507–510. IEEE (2000)

    Google Scholar 

  16. Seng, W.C., Mirisaee, S.H.: A new method for fruits recognition system. In: International Conference on Electrical Engineering and Informatics, pp. 130–134. IEEE (2009)

    Google Scholar 

  17. Lunau, K.: Innate flower recognition in bumblebees (Bombus terrestris, B. lucorum; Apidae): optical signals from stamens as landing reaction releasers. Ethology 88(3), 203–214 (1991)

    Article  Google Scholar 

  18. Goëau, H., Bonnet, P., Joly, A.: LifeCLEF Plant Identification Task 2015. CEUR-WS (2015)

    Google Scholar 

  19. Clarke, G., Hargreaves, S., Rutherford, C., et al.: State of the World’s Plants 2016 (2016)

    Google Scholar 

  20. Nguyen, Q.K., Le, T.L., Pham, N.H.: Leaf based plant identification system for Android using SURF features in combination with bag of words model and supervised learning. In: International Conference on Advanced Technologies for Communications, pp. 404–407. IEEE (2014)

    Google Scholar 

  21. Wu, S.G., Bao, F.S., Xu, E.Y., et al.: A leaf recognition algorithm for plant classification using probabilistic neural network. In: IEEE International Symposium on Signal Processing and Information Technology, pp. 11–16. IEEE (2008)

    Google Scholar 

  22. Carranza-Rojas, J., Mata-Montero, E.: Combining leaf shape and texture for costa rican plant species identification. CLEI Electron. J. 19(1), 7:1–7:29 (2016)

    Article  Google Scholar 

  23. Herdiyeni, Y., Kusmana, I.: Fusion of local binary patterns features for tropical medicinal plants identification. In: International Conference on Advanced Computer Science and Information Systems, pp. 353–357 (2013)

    Google Scholar 

  24. Gu, X., Du, J.-X., Wang, X.-F.: Leaf recognition based on the combination of wavelet transform and gaussian interpolation. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 253–262. Springer, Heidelberg (2005). https://doi.org/10.1007/11538059_27

    Chapter  Google Scholar 

  25. Chaki, J., Parekh, R.: Plant leaf recognition using shape based features and neural network classifiers. Int. J. Adv. Comput. Sci. Appl. 2(10), 41–47 (2011)

    Google Scholar 

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Authors and Affiliations

  1. College of Engineering Information Engineering, Northwest A&F University, Yangling, 712100, China

    Liu Tao & Cai Cheng

Authors
  1. Liu Tao
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  2. Cai Cheng
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Corresponding author

Correspondence to Cai Cheng .

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Editors and Affiliations

  1. University of Stuttgart, Stuttgart, Germany

    Marco Aiello

  2. Tsinghua University, Beijing, China

    Yujiu Yang

  3. Peking University, Beijing, China

    Yuexian Zou

  4. Kingdee International Software Group Co., Ltd., Shenzhen, China

    Liang-Jie Zhang

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Tao, L., Cheng, C. (2018). Plant Identification Based on Image Set Analysis. In: Aiello, M., Yang, Y., Zou, Y., Zhang, LJ. (eds) Artificial Intelligence and Mobile Services – AIMS 2018. AIMS 2018. Lecture Notes in Computer Science(), vol 10970. Springer, Cham. https://doi.org/10.1007/978-3-319-94361-9_6

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  • DOI: https://doi.org/10.1007/978-3-319-94361-9_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94360-2

  • Online ISBN: 978-3-319-94361-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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