Abstract
With the increase in the world population, it is necessary to increase agricultural production. The technology in the field aims to assist producers, agriculture with greater productivity without forgetting to care for the environment. One of the problems encountered by farmers is plant diseases, which can cause great damage to their crops. Thus, the use of automatic disease detection techniques by means of a computational method can be an alternative to solve this problem. However, the problem in using automatic techniques is the lack of data and that the use of methods to augment existing bases is a challenge. The objective of this work is to verify the efficiency of the SLIC together with the CNNs, using the SLIC as a preprocessing technique and the CNNs as a classification method. Finally, the selected results are not motivating with the use of SLIC at the expense of using the original images.
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Trindade, L.D.G., Basso, F.P., de Macedo Rodrigues, E., Bernardino, M., Welfer, D., Müller, D. (2021). Analysis of the Superpixel Slic Algorithm for Increasing Data for Disease Detection Using Deep Learning. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_45
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