Abstract
The automatic identification of plant leaves is a very important current topic of research in vision systems. Several researchers have tried to solve the problem of identification from plant leaves proposing various techniques. The proposed techniques in the literature have obtained excellent results on data sets where the leaves have dissimilar features to each other. However, in cases where the leaves are very similar to each other, the classification accuracy falls significantly. In this paper, we proposed a system to deal with the performance problem of machine learning algorithms where the leaves are very similar. The results obtained show that combination of different features and features selection process can improve the classification accuracy.
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This study was funded by the Research Secretariat of the Autonomous University of the State of Mexico with the research project 5228/2018/CI.
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Cervantes, J., Garcia Lamont, F., Rodriguez Mazahua, L., Zarco Hidalgo, A., Ruiz Castilla, J.S. (2018). Complex Identification of Plants from Leaves. In: Huang, DS., Gromiha, M., Han, K., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2018. Lecture Notes in Computer Science(), vol 10956. Springer, Cham. https://doi.org/10.1007/978-3-319-95957-3_41
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