Abstract
The classification methods are diverse and variety from one field of study to another. Among botanists, plants classification is done manually. This task is difficult, and results are not satisfactory. However, artificial intelligence, which is a new field of computer science, advocates automatic classification methods. It uses well-trained algorithms facilitating the classification activity for very efficient results. However, depending on the classification criterion, some algorithms are more efficient than others. Through our article, we classify plants according to their type: trees, shrubs and herbaceous plants by comparing two types of learning meaning the supervised and unsupervised learning. For each type of learning, we use these corresponding algorithms which are K-Means algorithms and decision trees. Thus we developed two classification models with each of these algorithms. The performance indicators of these models revealed different figures. We have concluded that one of these algorithms is more effective than the other in grouping our plants by similarity.
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Trey, Z.F.O., Goore, B.T., Konan, B.M. (2020). Classification of Plant Species by Similarity Using Automatic Learning. In: Zitouni, R., Agueh, M., Houngue, P., Soude, H. (eds) e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 311. Springer, Cham. https://doi.org/10.1007/978-3-030-41593-8_14
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DOI: https://doi.org/10.1007/978-3-030-41593-8_14
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