Um Sistema Automático de Classificação de Folhas Baseado em Características da Forma
Resumo
As plantas são peças indispensáveis para nossos ecossistemas, provendo recursos insubistituíveis para os animais. Porém, a identificação de espécies de plantas é uma tarefa difícil, e a maioria das pessoas não esté apta ao reconhecimento correto de muitas espécies de plantas. Neste trabalho, uma abordagem para o reconhecimento de espécies de plantas é desenvolvida, baseada na extração de características de forma das imagens de suas folhas. Uma base de dados de plantas medicinais é proposta, e quatro classificadores são testados como módulo de reconhecimento do sistema automático proposto. Os resultados experimentais mostram que os melhores classificadores são capazes de obter uma acurácia média acima de 98% na base de dados proposta.
Palavras-chave:
Classificação Automática de Espécies de Plantas, Reconhecimento da Folha, Visão Computacional, Extração de Características
Referências
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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.
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.
Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7:1–30.
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.
Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics, 11(1):86–92.
Hu, J., Chen, Z., Yang, M., Zhang, R., and Cui, Y. (2018). A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal Processing Letters, 25(6):853–857.
Hu, M.-K. (1962). Visual pattern recognition by moment invariants. IRE transactions on information theory, 8(2):179–187.
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.
Jose, C.-R., Erick, M.-M., and Herve, G. (2018). Hidden biases in automated imagebased plant identification. In 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pages 1–9. IEEE.
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.
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.
Nemenyi, P. (1962). Distribution-free multiple comparisons. In Biometrics, volume 18, page 263. INTERNATIONAL BIOMETRIC SOC 1441 I ST, NW, SUITE 700, WASHINGTON, DC 20005-2210.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1):62–66.
Pacifico, L. D., Macario, V., and Oliveira, J. F. (2018). 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. and Singh, P. P. (2017). Medicinal plant leaf information extraction using deep features. In Region 10 Conference, TENCON 2017-2017 IEEE, pages 2722–2726. IEEE.
Rahmani, M. E., Amine, A., and Hamou, M. R. (2015). Plant leaves classification. ALLDATA 2015, 82.
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.
Sabu, A., Sreekumar, K., and Nair, R. R. (2017). Recognition of ayurvedic medicinal plants from leaves: A computer vision approach. In Image Information Processing (ICIIP), 2017 Fourth International Conference on, pages 1–5. 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.
Sun, Y., Liu, Y., Wang, G., and Zhang, H. (2017). Deep learning for plant identification in natural environment. Computational intelligence and neuroscience, 2017.
Venkataraman, D. and Mangayarkarasi, N. (2016). Computer vision based feature extraction of leaves for identification of medicinal values of plants. In Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on, pages 1–5. IEEE.
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.
Britto, L. F. and Pacifico, L. D. (2018). Plant classification using weighted k-nn variants. In Anais do XV Encontro Nacional de Inteligência 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.
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.
Demšar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7:1–30.
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.
Friedman, M. (1940). A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics, 11(1):86–92.
Hu, J., Chen, Z., Yang, M., Zhang, R., and Cui, Y. (2018). A multiscale fusion convolutional neural network for plant leaf recognition. IEEE Signal Processing Letters, 25(6):853–857.
Hu, M.-K. (1962). Visual pattern recognition by moment invariants. IRE transactions on information theory, 8(2):179–187.
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.
Jose, C.-R., Erick, M.-M., and Herve, G. (2018). Hidden biases in automated imagebased plant identification. In 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI), pages 1–9. IEEE.
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.
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.
Nemenyi, P. (1962). Distribution-free multiple comparisons. In Biometrics, volume 18, page 263. INTERNATIONAL BIOMETRIC SOC 1441 I ST, NW, SUITE 700, WASHINGTON, DC 20005-2210.
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics, 9(1):62–66.
Pacifico, L. D., Macario, V., and Oliveira, J. F. (2018). 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. and Singh, P. P. (2017). Medicinal plant leaf information extraction using deep features. In Region 10 Conference, TENCON 2017-2017 IEEE, pages 2722–2726. IEEE.
Rahmani, M. E., Amine, A., and Hamou, M. R. (2015). Plant leaves classification. ALLDATA 2015, 82.
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.
Sabu, A., Sreekumar, K., and Nair, R. R. (2017). Recognition of ayurvedic medicinal plants from leaves: A computer vision approach. In Image Information Processing (ICIIP), 2017 Fourth International Conference on, pages 1–5. 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.
Sun, Y., Liu, Y., Wang, G., and Zhang, H. (2017). Deep learning for plant identification in natural environment. Computational intelligence and neuroscience, 2017.
Venkataraman, D. and Mangayarkarasi, N. (2016). Computer vision based feature extraction of leaves for identification of medicinal values of plants. In Computational Intelligence and Computing Research (ICCIC), 2016 IEEE International Conference on, pages 1–5. IEEE.
Publicado
15/10/2019
Como Citar
BRITTO, Larissa; PACÍFICO, Luciano; LUDERMIR, Teresa.
Um Sistema Automático de Classificação de Folhas Baseado em Características da Forma. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 16. , 2019, Salvador.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2019
.
p. 1008-1019.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2019.9353.
