Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market
The songbird trade has been identified as a major threat to wild populations, and the bird market has now expanded to online platforms. The study explored the use of machine learning models as a monitoring framework; developed models for taxa identification; applied the best model to understand the...
| Main Authors: | , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Elsevier
2022
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/125941 |
| _version_ | 1855534294349381632 |
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| author | Okarda, B. Muchlish, U. Kusumadewi, S.D. Purnomo, H. |
| author_browse | Kusumadewi, S.D. Muchlish, U. Okarda, B. Purnomo, H. |
| author_facet | Okarda, B. Muchlish, U. Kusumadewi, S.D. Purnomo, H. |
| author_sort | Okarda, B. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | The songbird trade has been identified as a major threat to wild populations, and the bird market has now expanded to online platforms. The study explored the use of machine learning models as a monitoring framework; developed models for taxa identification; applied the best model to understand the current market situation (taxa composition, asking price, and location); and conducted a survey to understand the profile of sellers. The authors found that the machine learning models produced a high level of accuracy in distinguishing relevant ads and identified the songbirds’ taxa. The Support Vector Machine (SVM) was selected as the best model and was used to predict the ad population. The model identified 284,118 songbirds from 247 taxa that were listed online from April 2020 to September 2021. The authors also found that 6.2% of ads listed threatened taxa based on the IUCN Red List. The survey results suggested that songbird sellers are mostly hobbyists or breeders looking for extra income from selling birds. As current studies of the songbird market are mostly conducted offline in the bird markets, transactions by non-bird traders or among hobbyists in the online market are remain underreported. Therefore, monitoring needs to be extended to the online market and to our knowledge, currently there is no applied system or platform is identified for monitoring online songbird market. The result from this study can help fill this gap. Information from the monitoring of the songbird online market in this study may assist stakeholders in formulating corrective action based on the current market situation. |
| format | Journal Article |
| id | CGSpace125941 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1259412025-10-26T13:02:19Z Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market Okarda, B. Muchlish, U. Kusumadewi, S.D. Purnomo, H. wildlife birds trade monitoring The songbird trade has been identified as a major threat to wild populations, and the bird market has now expanded to online platforms. The study explored the use of machine learning models as a monitoring framework; developed models for taxa identification; applied the best model to understand the current market situation (taxa composition, asking price, and location); and conducted a survey to understand the profile of sellers. The authors found that the machine learning models produced a high level of accuracy in distinguishing relevant ads and identified the songbirds’ taxa. The Support Vector Machine (SVM) was selected as the best model and was used to predict the ad population. The model identified 284,118 songbirds from 247 taxa that were listed online from April 2020 to September 2021. The authors also found that 6.2% of ads listed threatened taxa based on the IUCN Red List. The survey results suggested that songbird sellers are mostly hobbyists or breeders looking for extra income from selling birds. As current studies of the songbird market are mostly conducted offline in the bird markets, transactions by non-bird traders or among hobbyists in the online market are remain underreported. Therefore, monitoring needs to be extended to the online market and to our knowledge, currently there is no applied system or platform is identified for monitoring online songbird market. The result from this study can help fill this gap. Information from the monitoring of the songbird online market in this study may assist stakeholders in formulating corrective action based on the current market situation. 2022-11 2022-12-14T07:00:08Z 2022-12-14T07:00:08Z Journal Article https://hdl.handle.net/10568/125941 en Open Access Elsevier Okarda, B., Muchlish, U., Kusumadewi, S.D. and Purnomo, H. 2022. Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market. Global Ecology and Conservation 39: e02280. https://doi.org/10.1016/j.gecco.2022.e02280 |
| spellingShingle | wildlife birds trade monitoring Okarda, B. Muchlish, U. Kusumadewi, S.D. Purnomo, H. Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market |
| title | Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market |
| title_full | Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market |
| title_fullStr | Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market |
| title_full_unstemmed | Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market |
| title_short | Categorizing the songbird market through big data and machine learning in the context of Indonesia’s online market |
| title_sort | categorizing the songbird market through big data and machine learning in the context of indonesia s online market |
| topic | wildlife birds trade monitoring |
| url | https://hdl.handle.net/10568/125941 |
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