Reconhecimento de Plantas Medicinais através de Características das Folhas e Aprendizagem de Máquina
Resumo
Neste trabalho, o problema do reconhecimento automático de espécies de plantas é atacado através de técnicas de Aprendizagem de Máquina e Visão Computacional. O foco é dado para o caso específico das Plantas Medicinais, dado o potencial das mesmas como remedios naturais que previnem e tratam doenças em humanos, com baixo custo e menos efeitos colaterais (quando usadas propriamente) que remédios industrializados. Um sistema de reconhecimento de plantas e uma base de dados são desenvolvidos, baseados em características de textura e forma das folhas. Resultados experimentais indicam uma taxa de acurácia de aproximadamente 98% na base de dados proposta para os melhores classificadores selecionados.
Palavras-chave:
Reconhecimento de Plantas Medicinais, Aprendizagem de Máquina, Visão Computacional, Características das Folhas
Referências
Adinugroho, S. and Sari, Y. A. (2018). Leaves classification using neural network based on ensemble features. In 2018 5th International Conference on Electrical and Electronic Engineering (ICEEE), pages 350–354. 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.
Albregtsen, F. et al. (2008). Statistical texture measures computed from gray level coocurrence matrices. Image processing laboratory, department of informatics, university of oslo, 5.
Britto, L. F. S. and Pacifico, L. D. S. (2018). Plant classification using weighted k-nn variants. In Anais do XV Encontro Nacional de Inteligência Artificial e Computacional , pages 58–69. SBC.
Britto, L. F. S., Pacifico, L. D. S., and Ludermir, T. B. (2019). An automatic leaf classification system based on shape features. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional, pages 1–12. 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.
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.
Kadir, A. (2014). A model of plant identification system using glcm, lacunarity and shen features. arXiv preprint arXiv:1410.0969.
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.
Lee, S. H., Chan, C. S., Wilkin, P., and Remagnino, P. (2015). Deep-plant: Plant identification with convolutional neural networks. In 2015 IEEE international conference on image processing (ICIP), pages 452–456. 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.
Pacifico, L. D. S., Britto, L. F. S., Oliveira, E. G., and Ludermir, T. B. (2019). Automatic classification of medicinal plant species based on color and texture features. In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), pages 741–746. IEEE.
Pacifico, L. D. S., Macario, V., and Oliveira, J. F. L. (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.
Pushpa, B., Anand, C., and Mithun Nambiar, P. (2016). Ayurvedic plant species recognition using statistical parameters on leaf images. International Journal of Applied Engineering Research, 11(7):5142–5147.
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.
Albregtsen, F. et al. (2008). Statistical texture measures computed from gray level coocurrence matrices. Image processing laboratory, department of informatics, university of oslo, 5.
Britto, L. F. S. and Pacifico, L. D. S. (2018). Plant classification using weighted k-nn variants. In Anais do XV Encontro Nacional de Inteligência Artificial e Computacional , pages 58–69. SBC.
Britto, L. F. S., Pacifico, L. D. S., and Ludermir, T. B. (2019). An automatic leaf classification system based on shape features. In Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional, pages 1–12. 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.
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.
Kadir, A. (2014). A model of plant identification system using glcm, lacunarity and shen features. arXiv preprint arXiv:1410.0969.
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.
Lee, S. H., Chan, C. S., Wilkin, P., and Remagnino, P. (2015). Deep-plant: Plant identification with convolutional neural networks. In 2015 IEEE international conference on image processing (ICIP), pages 452–456. 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.
Pacifico, L. D. S., Britto, L. F. S., Oliveira, E. G., and Ludermir, T. B. (2019). Automatic classification of medicinal plant species based on color and texture features. In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS), pages 741–746. IEEE.
Pacifico, L. D. S., Macario, V., and Oliveira, J. F. L. (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.
Pushpa, B., Anand, C., and Mithun Nambiar, P. (2016). Ayurvedic plant species recognition using statistical parameters on leaf images. International Journal of Applied Engineering Research, 11(7):5142–5147.
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
30/06/2020
Como Citar
PACIFICO, Luciano D. S.; BRITTO, Larissa F. S.; LUDERMIR, Teresa B..
Reconhecimento de Plantas Medicinais através de Características das Folhas e Aprendizagem de Máquina. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 14. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 17-24.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2020.11177.
