Classification of Coffee Biotic Stresses Using Convolutional Neural Networks and Enhanced Image Preprocessing Techniques
Abstract
Coffee is a big commodity in Brazil, however it’s production is threatened by several biotic stresses. The objective of this paper is the classification of these biotic stresses that attack the leaf, such as leaf miner, rust, cercospora, and brown leaf spot, comparing several methods of image preprocessing to reach a better performance and finding the best one for this task. Using neural networks, part of artificial intelligence, we obtained good results using the networks ResNet50, MobileNetV2 and AlexNet using images with filters such as clahe, gaussian, wavelet and Graythresh. The networks achieved good result, varying from 93% to 98% using certain filters. This paper can aid farmers in the classification of these biotic stresses and can also help future researchers in this area, demonstrating the impact of each filter used in this research and it’s benefits in this type of task.
Keywords:
Coffee, Artificial intelligence, Image pre-processing, Neural Networks
References
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S. Mishra, R. Sachan, and D. Rajpal, “Deep convolutional neural network based detection system for real-time corn plant disease recognition,” Procedia Computer Science, vol. 167, pp. 2003–2010, 2020, International Conference on Computational Intelligence and Data Science. [Online]. Available: [link]
N. Bevers, E. J. Sikora, and N. B. Hardy, “Soybean disease identification using original field images and transfer learning with convolutional neural networks,” Computers and Electronics in Agriculture, vol. 203, p. 107449, 2022. [Online]. Available: [link]
D. Tedesco-Oliveira, R. Pereira da Silva, W. Maldonado, and C. Zerbato, “Convolutional neural networks in predicting cotton yield from images of commercial fields,” Computers and Electronics in Agriculture, vol. 171, p. 105307, 2020. [Online]. Available: [link]
J. Kranz, Measuring Plant Disease. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988, pp. 35–50. [Online]. DOI: 10.1007/978-3-642-95534-1_4
J. G. Esgario, R. A. Krohling, and J. A. Ventura, “Deep learning for classification and severity estimation of coffee leaf biotic stress,” Computers and Electronics in Agriculture, vol. 169, p. 105162, 2020. [Online]. Available: [link]
J. G. Esgario, P. B. de Castro, L. M. Tassis, and R. A. Krohling, “An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning,” Information Processing in Agriculture, vol. 9, no. 1, pp. 38–47, 2022. [Online]. Available: [link]
S. A. A. I. K. M. Arif Wani, Farooq Ahmad Bhat, Advances in Deep Learning. Singapore: Springer Singapore, 2019.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems, F. Pereira, C. Burges, L. Bottou, and K. Weinberger, Eds., vol. 25. Curran Associates, Inc., 2012. [Online]. Available: [link]
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 2015.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 1–9.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, N. Navab, J. Hornegger, W. M. Wells, and A. F. Frangi, Eds. Cham: Springer International Publishing, 2015, pp. 234–241.
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia, “Pyramid scene parsing network,” 2017.
L. X. Boa Sorte, C. T. Ferraz, F. Fambrini, R. dos Reis Goulart, and J. H. Saito, “Coffee leaf disease recognition based on deep learning and texture attributes,” Procedia Computer Science, vol. 159, pp. 135–144, 2019, Knowledge-Based and Intelligent Information Engineering Systems: Proceedings of the 23rd International Conference KES2019. [Online]. Available: [link]
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” 2019.
J. Jepkoech, D. M. Mugo, B. K. Kenduiywo, and E. C. Too, “Arabica coffee leaf images dataset for coffee leaf disease detection and classification,” Data in Brief, vol. 36, p. 107142, 2021. [Online]. Available: [link]
J. P. D’Haeyer, “Gaussian filtering of images: A regularization approach,” Signal Processing, vol. 18, no. 2, pp. 169–181, 1989. [Online]. Available: [link]
S. Pizer, R. Johnston, J. Ericksen, B. Yankaskas, and K. Muller, “Contrast-limited adaptive histogram equalization: speed and effectiveness,” in [1990] Proceedings of the First Conference on Visualization in Biomedical Computing, 1990, pp. 337–345.
N. Otsu, “A threshold selection method from gray-level histograms,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
Published
2024-11-06
How to Cite
YAMAKAWA, Daniel Imai; SOUSA, Pedro Moises de; MELO, Pedro Henrique Aparecido Damaso de.
Classification of Coffee Biotic Stresses Using Convolutional Neural Networks and Enhanced Image Preprocessing Techniques. In: WORKSHOP ON INFORMATION SYSTEMS (WSIS), 15. , 2024, Rio Paranaíba/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 51-56.
DOI: https://doi.org/10.5753/wsis.2024.33672.
