Reconhecimento de Plantas Medicinais Usando Características de Cor, Textura e Forma
Resumo
A correta identificação da espécie de plantas é uma tarefa importante em diversas áreas. A identificação é ainda mais importante quando o consumo e uso dessas plantas são adotados como forma de prevenção e tratamento de doenças, onde a classificação incorreta pode trazer graves consequências. Neste trabalho, um sistema automático de identificação de espécies de plantas é proposto, utilizando técnicas de aprendizagem de máquina e visão computacional. Para a avaliação experimental, uma base de dados reais de plantas medicinais, contendo características das cores, da forma e da textura das folhas, é proposta. Alguns dos principais classificadores da literatura de Aprendizagem de Máquina foram adotados e avaliados.
Palavras-chave:
Reconhecimento de Plantas Medicinais, Extração de Características, Visão Computacional, Aprendizagem de Máquina
Referências
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Chaki, J., Dey, N., Moraru, L., and Shi, F. (2019). Fragmented plant leaf recognition: Bag-of-features, fuzzy-color and edge-texture histogram descriptors with multi-layer perceptron. Optik, 181:639 – 650.
Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1):21–27.
Criminisi, A., Shotton, J., and Konukoglu, E. (2011). Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning [internet]. Microsoft Research.
Dudani, S. A. (1976). The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics, 1(4):325–327.
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Grabowski, J. (2015). Best practices to follow when taking photos for plant identification. Florida ECS Quick.
Haykin, S. S. (2001). Neural networks: a comprehensive foundation. Tsinghua University Press.
Hu, M.-K. (1962). Visual pattern recognition by moment invariants. IRE transactions on information theory, 8(2):179–187.
Kadir, A. (2014). A model of plant identification system using glcm, lacunarity and shen features. arXiv preprint arXiv:1410.0969.
Kaur, S. and Tulsi, P. K. (2019). Plant species identification based on plant leaf using computer vision and machine learning techniques. Journal of Multimedia Information System, 6:49–60.
Kaya, A., Keceli, A. S., Catal, C., Yalic, H. Y., Temucin, H., and Tekinerdogan, B. (2019). Analysis of transfer learning for deep neural network based plant classification models. Computers and Electronics in Agriculture, 158:20 – 29.
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).
Nguyen, N., Le, V., Le, T., Hai, V., Pantuwong, N., and Yagi, Y. (2016). Flower species identification using deep convolutional neural networks.
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.
Pearline, A., Kumar, S., and Harini, S. (2019). A study on plant recognition using conventional image processing and deep learning approaches. Journal of Intelligent and Fuzzy Systems, 36:1–8.
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., Kumar, P. S., and Ghosh, D. (2017). An efficient low vision plant leaf shape identification system for smart phones. Multimedia Tools and Applications, 76(5):6915–6939.
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.
Raut, S. P. and Bhalchandra, A. S. (2018). Plant recognition system based on leaf image. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), pages 1579–1581.
Rzanny, M., Seeland, M., Wäldchen, J., and Mäder, P. (2017). Acquiring and prepcessing leaf images for automated plant identification: Understanding the tradeoff between effort and information gain. Plant Methods, 13:97.
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.
Seeland, M., Rzanny, M., Alaqraa, N., Wäldchen, J., and Mäder, P. (2017). Plant species classification using flower images—a comparative study of local feature representations. PLOS ONE, 12(2):1–29.
Senterre, B. (2016). Practical plant identification training guide.
Sheahan, C. (2015). Plant guide for canadian serviceberry (amelanchier canadensis). USDA-Natural Resources Conservation Service.
Smith, B. and Chinnappa, C. (2015). Plant Collection, Identification, and Herbarium Procedures, pages 541–572.
Sun, Y., Liu, Y., Wang, G., and Zhang, H. (2017). Deep learning for plant identification in natural environment. Computational intelligence and neuroscience, 2017.
Yigit, E., Sabanci, K., Toktas, A., and Kayabasi, A. (2019). A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture, 156:369 – 377.
Alimboyong, C., Hernandez, A., and Medina, R. (2018). Classification of plant seedling images using deep learning. pages 1839–1844.
Barré, P., Stöver, B. C., Müller, K. F., and Steinhage, V. (2017). Leafnet: A computer vision system for automatic plant species identification. Ecological Informatics, 40:50 – 56.
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.
Chaki, J., Dey, N., Moraru, L., and Shi, F. (2019). Fragmented plant leaf recognition: Bag-of-features, fuzzy-color and edge-texture histogram descriptors with multi-layer perceptron. Optik, 181:639 – 650.
Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE transactions on information theory, 13(1):21–27.
Criminisi, A., Shotton, J., and Konukoglu, E. (2011). Decision forests for classification, regression, density estimation, manifold learning and semi-supervised learning [internet]. Microsoft Research.
Dudani, S. A. (1976). The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics, 1(4):325–327.
Goëau, H., Bonnet, P., and Joly, A. (2016). Plant identification in an open-world (lifeclef 2016). In CLEF.
Grabowski, J. (2015). Best practices to follow when taking photos for plant identification. Florida ECS Quick.
Haykin, S. S. (2001). Neural networks: a comprehensive foundation. Tsinghua University Press.
Hu, M.-K. (1962). Visual pattern recognition by moment invariants. IRE transactions on information theory, 8(2):179–187.
Kadir, A. (2014). A model of plant identification system using glcm, lacunarity and shen features. arXiv preprint arXiv:1410.0969.
Kaur, S. and Tulsi, P. K. (2019). Plant species identification based on plant leaf using computer vision and machine learning techniques. Journal of Multimedia Information System, 6:49–60.
Kaya, A., Keceli, A. S., Catal, C., Yalic, H. Y., Temucin, H., and Tekinerdogan, B. (2019). Analysis of transfer learning for deep neural network based plant classification models. Computers and Electronics in Agriculture, 158:20 – 29.
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).
Nguyen, N., Le, V., Le, T., Hai, V., Pantuwong, N., and Yagi, Y. (2016). Flower species identification using deep convolutional neural networks.
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.
Pearline, A., Kumar, S., and Harini, S. (2019). A study on plant recognition using conventional image processing and deep learning approaches. Journal of Intelligent and Fuzzy Systems, 36:1–8.
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., Kumar, P. S., and Ghosh, D. (2017). An efficient low vision plant leaf shape identification system for smart phones. Multimedia Tools and Applications, 76(5):6915–6939.
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.
Raut, S. P. and Bhalchandra, A. S. (2018). Plant recognition system based on leaf image. In 2018 Second International Conference on Intelligent Computing and Control Systems (ICICCS), pages 1579–1581.
Rzanny, M., Seeland, M., Wäldchen, J., and Mäder, P. (2017). Acquiring and prepcessing leaf images for automated plant identification: Understanding the tradeoff between effort and information gain. Plant Methods, 13:97.
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.
Seeland, M., Rzanny, M., Alaqraa, N., Wäldchen, J., and Mäder, P. (2017). Plant species classification using flower images—a comparative study of local feature representations. PLOS ONE, 12(2):1–29.
Senterre, B. (2016). Practical plant identification training guide.
Sheahan, C. (2015). Plant guide for canadian serviceberry (amelanchier canadensis). USDA-Natural Resources Conservation Service.
Smith, B. and Chinnappa, C. (2015). Plant Collection, Identification, and Herbarium Procedures, pages 541–572.
Sun, Y., Liu, Y., Wang, G., and Zhang, H. (2017). Deep learning for plant identification in natural environment. Computational intelligence and neuroscience, 2017.
Yigit, E., Sabanci, K., Toktas, A., and Kayabasi, A. (2019). A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture, 156:369 – 377.
Publicado
20/10/2020
Como Citar
BRITTO, Larissa; PACÍFICO, Luciano; SILVA, Matheus; LUDERMIR, Teresa.
Reconhecimento de Plantas Medicinais Usando Características de Cor, Textura e Forma. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 17. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 674-685.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2020.12169.
