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| dc.contributor.author | Tennakoon, Sachini | |
| dc.contributor.author | Rupasinghe, Sulochana | |
| dc.date.accessioned | 2025-04-23T05:42:10Z | |
| dc.date.available | 2025-04-23T05:42:10Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | Tennakoon, S. and Rupasinghe, S. (2022) ‘Comprehensive Review on Tea Clone Classification Systems’, in 2022 2nd Asian Conference on Innovation in Technology (ASIANCON). 2022 2nd Asian Conference on Innovation in Technology (ASIANCON), pp. 1–5. Available at: https://doi.org/10.1109/ASIANCON55314.2022.9909158. | en_US |
| dc.identifier.uri | https://ieeexplore.ieee.org/document/9909158 | |
| dc.identifier.uri | http://dlib.iit.ac.lk/xmlui/handle/123456789/2260 | |
| dc.description.abstract | A wide range of tea clones have been developed over the years. As each tea clone produces a distinct quality of tea, it is critical to identify them in the field. Tea clones may have extremely similar characteristics, making it difficult for tea farmers and tea estate owners to distinguish them manually. This problem can be resolved by using machine learning to create an application that recognizes tea clones automatically. This paper conducts a comparative review of existing tea clone classification systems, followed by a study that identifies research gaps and potential future works. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | IEEE | en_US |
| dc.subject | Tea clone identification | en_US |
| dc.subject | Image classification | en_US |
| dc.subject | Machine learning | en_US |
| dc.title | Comprehensive Review on Tea Clone Classification Systems | en_US |
| dc.type | Article | en_US |
