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Deep Convolutional Neural Networks for Plant Species Characterization Based on Leaf Midrib

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Book cover Computer Analysis of Images and Patterns (CAIP 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11679))

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Abstract

The automatic characterization and classification of plant species is an important task for plant taxonomists. On this work, we propose the use of well-known pre-trained Deep Convolutional Neural Networks (DCNN) for the characterization of plants based on their leaf midrib. The samples studied are microscope images of leaf midrib cross-sections taken from different specimens under varying conditions. Results with traditional handcrafted image descriptors demonstrate the difficulty to effectively characterize these samples. Our proposal is to use DCNN as a feature extractor through Global Average Pooling (GAP) over the raw output of its last convolutional layers without the application of summarizing functions such as ReLU and local poolings. Results indicate considerably performance improvements over previous approaches under different scenarios, varying the image color-space (gray-level or RGB) and the classifier (KNN or LDA). The highest result is achieved by the deeper network analyzed, ResNet (101 layers deep), using the LDA classifier, with \(99.20\%\) of accuracy rate. However, shallower networks such as AlexNet also provide good classification results (\(97.36\%\)), which is still a significant improvement over the best previous result (\(83.67\%\) of combined fractal descriptors).

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Notes

  1. 1.

    www.imageclef.org.

  2. 2.

    www.scg.ifsc.usp.br/dataset/Midrib.php.

References

  1. Backes, A.R., Casanova, D., Bruno, O.M.: Plant leaf identification based on volumetric fractal dimension. Int. J. Pattern Recognit. Artif. Intell. 23(06), 1145–1160 (2009)

    Article  Google Scholar 

  2. Bengio, Y.: Deep learning of representations for unsupervised and transfer learning. In: Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp. 17–36 (2012)

    Google Scholar 

  3. Bruno, O.M., de Oliveira Plotze, R., Falvo, M., de Castro, M.: Fractal dimension applied to plant identification. Inf. Sci. 178(12), 2722–2733 (2008)

    Article  MathSciNet  Google Scholar 

  4. Chen, Q., Abedini, M., Garnavi, R., Liang, X.: IBM research Australia at LifeCLEF2014: plant identification task. In: CLEF (Working Notes), pp. 693–704 (2014)

    Google Scholar 

  5. Cimpoi, M., Maji, S., Kokkinos, I., Vedaldi, A.: Deep filter banks for texturerecognition, description, and segmentation. Int. J. Comput. Vis. 118(1), 65–94 (2016). https://doi.org/10.1007/s11263-015-0872-3

    Article  MathSciNet  Google Scholar 

  6. Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2009, pp. 248–255. IEEE (2009)

    Google Scholar 

  7. Gaston, K.J., O’Neill, M.A.: Automated species identification: why not? Philos. Trans. R. Soc. Lond. B Biol. Sci. 359(1444), 655–667 (2004)

    Article  Google Scholar 

  8. Ghazi, M.M., Yanikoglu, B., Aptoula, E.: Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235, 228–235 (2017)

    Article  Google Scholar 

  9. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 142–158 (2016). https://doi.org/10.1109/TPAMI.2015.2437384

    Article  Google Scholar 

  10. Grinblat, G.L., Uzal, L.C., Larese, M.G., Granitto, P.M.: Deep learning for plant identification using vein morphological patterns. Comput. Electron. Agric. 127, 418–424 (2016)

    Article  Google Scholar 

  11. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)

    Article  MathSciNet  Google Scholar 

  12. Hanson, A., Joel, M., Joy, A., Francis, J.: Plant leaf disease detection using deep learning and convolutional neural network. Int. J. Eng. Sci. 5324 (2017)

    Google Scholar 

  13. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  14. Junior, J.J.d.M.S., Rossatto, D.R., Kolb, R.M., Bruno, O.M.: A computer vision approach to quantify leaf anatomical plasticity: a case study on Gochnatia polymorpha (Less.) Cabrera. Ecol. Inform. 15, 34–43 (2013)

    Google Scholar 

  15. Keating, R.C.: Leaf histology and its contribution to relationships in the Myrtales. Ann. Mo. Bot. Gard., 801–823 (1984)

    Article  Google Scholar 

  16. Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  17. LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  18. Lee, S.H., Chan, C.S., Wilkin, P., Remagnino, P.: Deep-plant: plant identification with convolutional neural networks. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 452–456. IEEE (2015)

    Google Scholar 

  19. Lin, M., Chen, Q., Yan, S.: Network in network. CoRR abs/1312.4400 (2013). http://arxiv.org/abs/1312.4400

  20. Lu, H., Cao, Z., Xiao, Y., Fang, Z., Zhu, Y.: Toward good practices for fine-grained maize cultivar identification with filter-specific convolutional activations. IEEE Trans. Autom. Sci. Eng. 15(2), 430–442 (2018). https://doi.org/10.1109/TASE.2016.2616485

    Article  Google Scholar 

  21. Manjunath, B.S., Ma, W.Y.: Texture features for browsing and retrieval of image data. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 837–842 (1996)

    Article  Google Scholar 

  22. Mokeev, V.V.: On application of convolutional neural network for classification of plant images. In: 2018 Global Smart Industry Conference (GloSIC), pp. 1–6. IEEE (2018)

    Google Scholar 

  23. Mortensen, A.K., Dyrmann, M., Karstoft, H., Jørgensen, R.N., Gislum, R., et al.: Semantic segmentation of mixed crops using deep convolutional neural network. In: CIGR-AgEng Conference, 26–29 June 2016, Aarhus, Denmark. Abstracts and Full papers, pp. 1–6. Organising Committee, CIGR 2016 (2016)

    Google Scholar 

  24. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  Google Scholar 

  25. Ojansivu, Ville, Heikkilä, Janne: Blur insensitive texture classification using local phase quantization. In: Elmoataz, Abderrahim, Lezoray, Olivier, Nouboud, Fathallah, Mammass, Driss (eds.) ICISP 2008. LNCS, vol. 5099, pp. 236–243. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69905-7_27

    Chapter  Google Scholar 

  26. Plotze, R.d.O., et al.: Leaf shape analysis using the multiscale Minkowski fractal dimension, a new morphometric method: a study with Passiflora (Passifloraceae). Can. J. Bot. 83(3), 287–301 (2005)

    Article  Google Scholar 

  27. Reyes, A.K., Caicedo, J.C., Camargo, J.E.: Fine-tuning deep convolutional networks for plant recognition. In: CLEF (Working Notes) (2015)

    Google Scholar 

  28. Ripley, B.D.: Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge (1996)

    Book  Google Scholar 

  29. Seeland, M., Rzanny, M., Boho, D., Wäldchen, J., Mäder, P.: Image-based classification of plant genus and family for trained and untrained plant species. BMC Bioinform. 20(1), 4 (2019)

    Article  Google Scholar 

  30. da Silva, N.R., Florindo, J.B., Gómez, M.C., Rossatto, D.R., Kolb, R.M., Bruno, O.M.: Plant identification based on leaf midrib cross-section images using fractal descriptors. PLoS ONE 10(6), e0130014 (2015)

    Article  Google Scholar 

  31. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014)

  32. Singh, A.K., Ganapathysubramanian, B., Sarkar, S., Singh, A.: Deep learning for plant stress phenotyping: trends and future perspectives. Trends Plant Sci. (2018)

    Google Scholar 

  33. Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deepneural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016 (2016)

    Google Scholar 

  34. Sünderhauf, N., McCool, C., Upcroft, B., Perez, T.: Fine-grained plant classification using convolutional neural networks for feature extraction. In: CLEF (Working Notes), pp. 756–762 (2014)

    Google Scholar 

  35. Szegedy, C., et al.: Going deeper with convolutions. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015

    Google Scholar 

  36. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

    Google Scholar 

  37. Ubbens, J., Cieslak, M., Prusinkiewicz, P., Stavness, I.: The use of plant models in deep learning: an application to leaf counting in rosette plants. Plant Methods 14(1), 6 (2018)

    Article  Google Scholar 

  38. Wäldchen, J., Mäder, P.: Plant species identification using computer vision techniques: a systematic literature review. Arch. Comput. Methods Eng. 25(2), 507–543 (2018)

    Article  MathSciNet  Google Scholar 

  39. Yigit, E., Sabanci, K., Toktas, A., Kayabasi, A.: A study on visual features of leaves in plant identification using artificial intelligence techniques. Comput. Electron. Agric. 156, 369–377 (2019)

    Article  Google Scholar 

  40. Zhang, L., Zhou, Z., Li, H.: Binary Gabor pattern: an efficient and robust descriptor for texture classification. In: 2012 19th IEEE International Conference on Image Processing (ICIP), pp. 81–84. IEEE (2012)

    Google Scholar 

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Acknowledgements

Leonardo F. S. Scabini acknowledges support from CNPq (Grant number #142438/2018-9). Rayner M. Condori acknowledges support from FONDECYT, an initiative of the National Council of Science, Technology and Technological Innovation-CONCYTEC (Peru). Odemir M. Bruno acknowledges support from CNPq (Grant #307797/2014-7 and Grant #484312/2013-8) and FAPESP (grant #14/08026-1 and #16/18809-9).

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Authors and Affiliations

  1. São Carlos Institute of Physics, University of São Paulo, São Carlos, SP, Brazil

    Leonardo F. S. Scabini, Isabella C. L. Munhoz & Odemir M. Bruno

  2. Institute of Mathematics and Computer Science, University of São Paulo, São Carlos, SP, Brazil

    Rayner M. Condori

Authors
  1. Leonardo F. S. Scabini
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  2. Rayner M. Condori
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  3. Isabella C. L. Munhoz
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  4. Odemir M. Bruno
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Corresponding author

Correspondence to Leonardo F. S. Scabini .

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Editors and Affiliations

  1. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Mario Vento

  2. Department of Computer and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano (SA), Italy

    Gennaro Percannella

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Scabini, L.F.S., Condori, R.M., Munhoz, I.C.L., Bruno, O.M. (2019). Deep Convolutional Neural Networks for Plant Species Characterization Based on Leaf Midrib. In: Vento, M., Percannella, G. (eds) Computer Analysis of Images and Patterns. CAIP 2019. Lecture Notes in Computer Science(), vol 11679. Springer, Cham. https://doi.org/10.1007/978-3-030-29891-3_34

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  • DOI: https://doi.org/10.1007/978-3-030-29891-3_34

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