Abstract
Agathosma Buchu plants are a holistic healing system used as alternative medicine in dealing with numerous diseases and also for cooking oil production. There are two types namely, Betulina and Crenulata. The plants are difficult to separate if mixed up after harvest. Furthermore, the high rate of the plants’ cultivation poses challenges in separating them for specific functions. Hence, other identification methods are crucial. This paper presents an implementation of machine learning algorithms based on spectroscopic imagery properties for automatic recognition of the plants’ species. Image Local Polynomial Approximation method is used for the image processing to reduce classification error and dimensionality of classification challenges. To demonstrate the efficacies of the processed dataset, K-Nearest Neighbour, Naïve Bayes, Decision Tree, and Neural Network classifiers were used for the classification procedures in different data mining tools. The classifiers’ performances are valuable for decision-makers to consider tradeoffs in method accuracy versus method complexity.
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Acknowledgement
Our appreciation goes to Allen Harris the owner of the Buchu moon farm, near Cape Town, South Africa, for his support, taking us through his farm and giving us Buchu specimens used for this research. This work is based on the research supported wholly by the National Research Foundation of South Africa (Grant specific unique reference number (UID) 85745). The Grant holder acknowledges that opinions, findings and conclusions or recommendations expressed in any publication generated by the NRF supported research are that of the author(s), and that the NRF accepts no liability whatsoever in this regard.
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Abe, B.T., Jordaan, J. (2019). Separability Method for Homogeneous Leaves Using Spectroscopic Imagery and Machine Learning Algorithms. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_29
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DOI: https://doi.org/10.1007/978-3-030-22808-8_29
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