Plant Species Classification Using Extreme Learning Machine

• Larissa Britto Universidade Federal Rural de Pernambuco
• Luciano Pacífico Universidade Federal Rural de Pernambuco

DOI: https://doi.org/10.5753/eniac.2019.9268

Resumo

Plants play an important role in nature, but correct plant species identification is still a challenging task for non-specialized people. Many works have been proposed towards the development of automatic plant species recognition systems through Machine Learning methods, but most of them lack the proper experimental analysis. In this work, we evaluate the performance of a general-purpose Artificial Neural Network to perform plant classification task: the Extreme Learning Machine (ELM).We compare ELM with several classifiers from plant recognition literature by means of three real-world data sets obtained from different image processing and feature extraction processes. A statistical hypothesis test is employed to perform proper experimental evaluation.

Palavras-chave: Reconhecimento Automático de Espécies de Plantas, Extremen Learning Machine, Extração de Características

Referências

Agarwal, G., Belhumeur, P., Feiner, S., Jacobs, D., Kress,W. J., Ramamoorthi, R., Bourg, N. A., Dixit, N., Ling, H., Mahajan, D., et al. (2006). First steps toward an electronic field guide for plants. Taxon, 55(3):597–610.

Akusok, A., Björk, K.-M., Miche, Y., and Lendasse, A. (2015). High-performance extreme learning machines: a complete toolbox for big data applications. IEEE Access, 3:1011–1025.

Anderson, E. (1935). The irises of the gaspe peninsula. Bulletin of American Iris Society, 59:2–5.

Asuncion, A. and Newman, D. (2007). Uci machine learning repository.

Bellman, R. E. (1957). Dynamic programming. Princeton University Press.

Britto, L. F. and Pacifico, L. D. (2018). Plant classification using weighted k-nn variants. In Anais do XV Encontro Nacional de Inteligˆencia Artificial e Computacional, pages 58–69. SBC.

Buitinck, L., Louppe, G., Blondel, M., Pedregosa, F., Mueller, A., Grisel, O., Niculae, V., Prettenhofer, P., Gramfort, A., Grobler, J., Layton, R., VanderPlas, J., Joly, A., Holt, B., and Varoquaux, G. (2013). API design for machine learning software: experiences from the scikit-learn project. In ECML PKDD Workshop: Languages for Data Mining and Machine Learning, pages 108–122.

Cerutti, G., Tougne, L., Mille, J., Vacavant, A., and Coquin, D. (2013). Understanding leaves in natural images–a model-based approach for tree species identification. Computer Vision and Image Understanding, 117(10):1482–1501.

Charytanowicz, M., Niewczas, J., Kulczycki, P., Kowalski, P. A., Łukasik, S., and ˙ Zak, S. (2010). Complete gradient clustering algorithm for features analysis of x-ray images. In Information technologies in biomedicine, pages 15–24. Springer.

Cope, J. S., Corney, D., Clark, J. Y., Remagnino, P., and Wilkin, P. (2012). Plant species identification using digital morphometrics: A review. Expert Systems with Applications, 39(8):7562–7573.

Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1):21–27.

De Stefano, C., Fontanella, F., and Di Freca, A. S. (2012). A novel naive bayes voting strategy for combining classifiers. In Frontiers in Handwriting Recognition (ICFHR), 2012 International Conference on, pages 467–472. IEEE.

Demˇsar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7:1–30.

Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of human genetics, 7(2):179–188.

Friedman, M. (1937). The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the american statistical association, 32(200):675–701.

Haykin, S. S. (2001). Neural networks: a comprehensive foundation. Tsinghua University Press.

Huang, G.-B., Zhu, Q.-Y., and Siew, C.-K. (2006). Extreme learning machine: theory and applications. Neurocomputing, 70(1):489–501.

Jamil, N., Hussin, N. A. C., Nordin, S., and Awang, K. (2015). Automatic plant identification: Is shape the key feature? Procedia Computer Science, 76:436–442.

Jin, T., Hou, X., Li, P., and Zhou, F. (2015). A novel method of automatic plant species identification using sparse representation of leaf tooth features. PloS one, 10(10):e0139482.

Kumar, N., Belhumeur, P. N., Biswas, A., Jacobs, D. W., Kress, W. J., Lopez, I. C., and Soares, J. V. (2012). Leafsnap: A computer vision system for automatic plant species identification. In Computer Vision–ECCV 2012, pages 502–516. Springer.

Liu, J.-C., Chiang, C.-Y., and Chen, S. (2016). Image-based plant recognition by fusion of multimodal information. In Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), 2016 10th International Conference on, pages 5–11. IEEE.

Mallah, C., Cope, J., and Orwell, J. (2013). Plant leaf classification using probabilistic integration of shape, texture and margin features. Signal Processing, Pattern Recognition and Applications, 5(1).

Mallah, C. D. and Orwell, J. (2013). Probabilistic classification from a k-nearestneighbour classifier. Computational Research, 1(1):1–9.

Mitchell, T. M. et al. (1997). Machine learning. wcb.

Nemenyi, P. (1962). Distribution-free multiple comparisons. Biometrics, 18(2):263.

Pacifico, L. D., Ludermir, T. B., and Oliveira, J. F. (2018a). Evolutionary elms with alternative treatments for the population out-bounded individuals. In 2018 7th Brazilian Conference on Intelligent Systems (BRACIS), pages 151–156. IEEE.

Pacifico, L. D., Macario, V., and Oliveira, J. F. (2018b). Plant classification using artificial neural networks. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–6. IEEE.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.

Prasad, S., Kudiri, K. M., and Tripathi, R. (2011). Relative sub-image based features for leaf recognition using support vector machine. In Proceedings of the 2011 International Conference on Communication, Computing & Security, pages 343–346. ACM.

Rahma, O., Wikaya, S., Prawito, and Badri, C. (2017). Electroencephalogram analysis with extreme learning machine as a supporting tool for classifying acute ischemic stroke severity. 2017 IEEE International Seminar on Sensors, Instrumentation, Measurement and Metrology (ISSIMM), 1:180–186.

Rahmani, M. E., Amine, A., and Hamou, M. R. (2015). Plant leaves classification. ALLDATA 2015, 82.

Rankothge, W., Dissanayake, D., Gunathilaka, U., Gunarathna, S., Mudalige, C., and Thilakumara, R. (2013). Plant recognition system based on neural networks. In Advances in Technology and Engineering (ICATE), 2013 International Conference on, pages 1–4. IEEE.

Sabu, A. and Sreekumar, K. (2017). Literature review of image features and classifiers used in leaf based plant recognition through image analysis approach. In Inventive Communication and Computational Technologies (ICICCT), 2017 International Conference on, pages 145–149. IEEE.

Sahay, A. and Chen, M. (2016). Leaf analysis for plant recognition. In Software Engineering and Service Science (ICSESS), 2016 7th IEEE International Conference on, pages 914–917. IEEE.

Serre, D. (2002). Matrices: Theory and Applications. Springer.

Sethulekshmi, A. and Sreekumar, K. (2014). Ayurvedic leaf recognition for plant classification.

Journal of Computer Science and Information Technologies, 5(6):8061–8066.

Shen, C., Zhang, S.-F., Zhai, J.-H., Luo, D.-S., and Chen, J.-F. (2018). Imbalanced data classification based on extreme learning machine autoencoder. In 2018 International Conference on Machine Learning and Cybernetics (ICMLC), volume 2, pages 399–404. IEEE.

Song, Y., He, B., Zhao, Y., Li, G., Sha, Q., Shen, Y., Yan, T., Nian, R., and Lendasse, A. (2019). Segmentation of sidescan sonar imagery using markov random fields and extreme learning machine. IEEE Journal of Oceanic Engineering, 44(2):502–513.

Sönmez, Y., Tuncer, T., G¨okal, H., and Avcı, E. (2018). Phishing web sites features classification based on extreme learning machine. In 2018 6th International Symposium on Digital Forensic and Security (ISDFS), pages 1–5. IEEE.

Sun, Y., Liu, Y., Wang, G., and Zhang, H. (2017). Deep learning for plant identification in natural environment. Computational intelligence and neuroscience, 2017.

Vijendran, S. and Dubey, R. (2019). Deep online sequential extreme learning machines and its application in pneumonia detection. In 2019 8th International Conference on Industrial Technology and Management (ICITM), pages 311–316. IEEE.

Xu, X., Deng, J., Coutinho, E., Wu, C., Zhao, L., and Schuller, B. W. (2019). Connecting subspace learning and extreme learning machine in speech emotion recognition. IEEE Transactions on Multimedia, 21(3):795–808.

Zhai, C.-M. and Du, J.-X. (2008). Applying extreme learning machine to plant species identification. In 2008 International Conference on Information and Automation, pages 879–884. IEEE.