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01370nam a2200181Ia 4500 |
| 001 |
10.33640-2405-609X.3154 |
| 008 |
220427s2021 CNT 000 0 und d |
| 020 |
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|a 2405609X (ISSN)
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| 245 |
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|a Artificial Intelligence for Para Rubber Identification Combining Five Machine Learning Methods
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| 260 |
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|b University of Kerbala
|c 2021
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.33640/2405-609X.3154
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|a This study aims to identify Para rubber species using a combination of five machine learning techniques to classify leaf images. The learning process is defined using a dataset for each classification method. Approximately 1,472 leaf images are prepared consisting of various sizes, shapes, quality provided for the model. The classification indicators are defined with the help of an algorithm to identify at least three of the top five potential classification outcomes. The algorithm accurately predicts 100% of the five classification methods. Methods can provide precise and rapid classification of large quantities, without the need for image preprocessing prior to classification. © 2021 University of Kerbala. All rights reserved.
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|a Classification
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| 650 |
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|a Machine Learning (ML)
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| 650 |
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|a Para rubber
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| 650 |
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|a Programming
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| 700 |
1 |
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|a Yaiprasert, C.
|e author
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| 773 |
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|t Karbala International Journal of Modern Science
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