Segmentação Automática de Doenças em Imagens de Plantas através de Algoritmos Evolucionários
Resumo
Neste trabalho, abordagens para a segmentação de doenças em imagens de folhas são apresentadas, como parte de um sistema automático de classificação de doenças em plantas. Para isso, Algoritmos Evolucionários (EAs), que sao meta-heurísticas de busca global, são adaptados como técnicas não supervisionadas de agrupamento de dados, no intuito de lidar com o problema. Quatro algoritmos foram selecionados e testados através do uso de doze imagens, que apresentam diferentes graus de doença, de modo a avaliar o quão robusto são tais modelos na solução do problema. A análise experimental revelou que as abordagens utilizadas são capazes de realizar de forma satisfatória a tarefa de segmentação das doenças nas imagens avaliadas.
Palavras-chave:
Doenças em Plantas, Segmentação de Images, Algoritmos Evolucionários, Algoritmos de Agrupamento
Referências
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Asuncion, A. and Newman, D. (2007). Uci machine learning repository.
Barbedo, J. G. A., Koenigkan, L. V., Halfeld-Vieira, B. A., Costa, R. V., Nechet, K. L., Godoy, C. V., Junior, M. L., Patricio, F. R. A., Talamini, V., Chitarra, L. G., et al. (2018). Annotated plant pathology databases for image-based detection and recognition of diseases. IEEE Latin America Transactions, 16(6):1749–1757.
Chen, C.-Y. and Ye, F. (2004). Particle swarm optimization algorithm and its application to clustering analysis. In Networking, Sensing and Control, 2004 IEEE International Conference on, volume 2, pages 789–794. IEEE.
Demsar, J. (2006). Statistical comparisons of classifiers over multiple data sets. The Journal of Machine Learning Research, 7:1–30.
Eiben, A. E. and Smith, J. E. (2010). Introduction to evolutionary computing, volume 2. Springer Berlin.
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.
He, S., Wu, Q. H., and Saunders, J. (2009). Group search optimizer: an optimization algorithm inspired by animal searching behavior. IEEE Transactions on Evolutionary Computation, 13(5):973–990.
Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1):66–72.
Hruschka, E. R., Campello, R. J., Freitas, A. A., et al. (2009). A survey of evolutionary algorithms for clustering. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 39(2):133–155.
Kennedy, J. and Eberhart, R. (1995). Particle swarm optimization. In Neural Networks, 1995. Proceedings., IEEE International Conference on, volume 4, pages 1942–1948. IEEE.
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.
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.
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. and Ludermir, T. B. (2014). A group search optimization method for data clustering. In Intelligent Systems (BRACIS), 2014 Brazilian Conference on, pages 342–347. IEEE.
Pacifico, L. D. S. and Ludermir, T. B. (2018). Hybrid k-means and improved group search optimization methods for data clustering. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–8. IEEE.
Pacifico, L. D. S. and Ludermir, T. B. (2019). Hybrid k-means and improved self-adaptive particle swarm optimization for data clustering. In 2019 International Joint Conference on Neural Networks (IJCNN), pages 1–7. IEEE.
Prajapati, H. B., Shah, J. P., and Dabhi, V. K. (2017). Detection and classification of rice plant diseases. Intelligent Decision Technologies, 11(3):357–373.
Sawarkar, V. and Kawathekar, S. (2018). A review: Rose plant disease detection using image processing. IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN, pages 2278–0661.
Solanke, S., Mehare, P., Shinde, S., Ingle, V., and Zope, S. (2018). Iot based crop disease detection and pesting for greenhouse-a review. In 2018 3rd International Conference for Convergence in Technology (I2CT), pages 1–4. IEEE.
Storn, R. and Price, K. (1995). Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. international computer science institute, berkeley. Technical report, CA, 1995, Tech. Rep. TR-95–012.
Tanmayee, P. (2017). Rice crop monitoring system—a lot based machine vision approach. In 2017 International Conference on Nextgen Electronic Technologies: Silicon to Software (ICNETS2), pages 26–29. IEEE.
Wong, M. T., He, X., and Yeh, W.-C. (2011). Image clustering using particle swarm optimization. In Evolutionary Computation (CEC), 2011 IEEE Congress on, pages 262–268. IEEE.
Publicado
30/06/2020
Como Citar
PACIFICO, Luciano D. S..
Segmentação Automática de Doenças em Imagens de Plantas através de Algoritmos Evolucionários. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 14. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 25-32.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2020.11178.
