Patch-Based Model for the Classification of Soybean Leaf Diseases
Resumo
The disease detection is vital to increase the productivity and quality of soybean cultivation and this detection is usually carried out in a laboratory, which is time consuming and costly. To overcome these issues, there is a growing demand for technologies that aim at a faster detection and classification of diseases. In this context, this work proposes the extraction of several patches from a leaf image and combining a convolutional neural network with a support vector machine, we present a complete model for the classification of soybean leaf diseases. In this approach, an image dataset with evidence of diseases commonly observed in soybean crops was analyzed and our experiments achieved precisions greater than 90%.
Palavras-chave:
Soybean leaf diseases, Classification, Convolutional Neural Network
Referências
EMBRAPA, “Soja em números (safra 2020/21),” tech. rep., Londrina, 2021.
A. A. B. Junior, M. H. Hirakuri, J. C. Franchini, H. Debiasi, and R. H. Ribeir, “Análise da área, produção e produtividade da soja no brasil em duas décadas (1997-2016),” Londrina, 2017.
J. F. J. Grigolli, “Manejo de doenças na cultura da soja,” Tecnologia & Produção Soja 2014/2015, 2015.
R. D. L. Pires, D. N. Gonçalves, J. P. M. Oruê, W. E. S. Kanashiro, J. F. Rodrigues, B. B. Machado, and W. N. Gonçalves, “Local descriptors for soybean disease recognition,” Computers and Electronics in Agriculture, vol. 125, pp. 48-55, 2016.
A. dos Santos Ferreira, “Redes neuras convolucionais profundas na detecção de plantas daninhas em lavouras de soja,” Master's thesis, Universidade Federal Do Mato Grosso Do Sul, 2017.
A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, “A robust deep-learningbased detector for real-time tomato plant diseases and pests recognition,” Sensors (Basel, Switzerland), vol. 17, 2017.
S. M. Hassan, A. K. Maji, M. Jasinski, Z. Leonowicz, and E. Jasinska, “Identification of plant-leaf diseases using cnn and transfer-learning approach,” Electronics, vol. 10, no. 12, 2021.
J. G. A. Barbedo and C. V. Godoy, “Automatic classification of soybean diseases based on digital images of leaf symptoms,” in X congresso Brasileiro De Agroinformática, 2015.
A. Karlekar and A. Seal, “Soynet: Soybean leaf diseases classification,” Comput. Electron. Agric., vol. 172, p. 105342, 2020.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” 2015.
A. A. B. Junior, M. H. Hirakuri, J. C. Franchini, H. Debiasi, and R. H. Ribeir, “Análise da área, produção e produtividade da soja no brasil em duas décadas (1997-2016),” Londrina, 2017.
J. F. J. Grigolli, “Manejo de doenças na cultura da soja,” Tecnologia & Produção Soja 2014/2015, 2015.
R. D. L. Pires, D. N. Gonçalves, J. P. M. Oruê, W. E. S. Kanashiro, J. F. Rodrigues, B. B. Machado, and W. N. Gonçalves, “Local descriptors for soybean disease recognition,” Computers and Electronics in Agriculture, vol. 125, pp. 48-55, 2016.
A. dos Santos Ferreira, “Redes neuras convolucionais profundas na detecção de plantas daninhas em lavouras de soja,” Master's thesis, Universidade Federal Do Mato Grosso Do Sul, 2017.
A. Fuentes, S. Yoon, S. C. Kim, and D. S. Park, “A robust deep-learningbased detector for real-time tomato plant diseases and pests recognition,” Sensors (Basel, Switzerland), vol. 17, 2017.
S. M. Hassan, A. K. Maji, M. Jasinski, Z. Leonowicz, and E. Jasinska, “Identification of plant-leaf diseases using cnn and transfer-learning approach,” Electronics, vol. 10, no. 12, 2021.
J. G. A. Barbedo and C. V. Godoy, “Automatic classification of soybean diseases based on digital images of leaf symptoms,” in X congresso Brasileiro De Agroinformática, 2015.
A. Karlekar and A. Seal, “Soynet: Soybean leaf diseases classification,” Comput. Electron. Agric., vol. 172, p. 105342, 2020.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” 2015.
Publicado
22/11/2021
Como Citar
S., Gustavo Vigilato G.; CAVALCANTI, Pablo G..
Patch-Based Model for the Classification of Soybean Leaf Diseases. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 17. , 2021, Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 113-117.
DOI: https://doi.org/10.5753/wvc.2021.18899.
