Machine learning to identify olive-tree cultivars

Mendes, João; Lima, José; Costa, Lino; Rodrigues, Nuno; Brandão, Diego; Leitão, Paulo; Pereira, Ana I.

http://hdl.handle.net/10198/27263

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Abstract(s)

The identification of olive-tree cultivars is a lengthy and expensive process, therefore, the proposed work presents a new strategy for identifying different cultivars of olive trees using their leaf and machine learning algorithms. In this initial case, four autochthonous cultivars of the Trás-os-Montes region in Portugal are identified (Cobrançosa, Madural, Negrinha e Verdeal). With the use of this type of algorithm, it is expected to replace the previous techniques, saving time and resources for farmers. Three different machine learning algorithms (Decision Tree, SVM, Random Forest) were also compared and the results show an overall accuracy rate of the best algorithm (Random Forest) of approximately 93%.

Keywords

Machine learning Identification Leaf Cultivars Varieties

URI

http://hdl.handle.net/10198/27263

Citation

Mendes, João; Lima, José; Costa, Lino; Rodrigues, Nuno G.; Brandão, Diego; Leitão, Paulo; Pereira, Ana I. (2022). Machine learning to identify olive-tree cultivars. In 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022. Bragança