ICAR Research Data Repository for Knowledge Management
Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh
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View Archive Info| Field | Value | |
| Title |
Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh
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| Creator |
Belton, Benjamin
Haque, Mohammad Mahfujul Ali, Hazrat Nejadhashemi, Amir Pouyan Hernandez, Ricardo Khondker, Murshed-e-Jahan Ferriby, Hannah |
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| Subject |
climate change
food systems deltas |
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| Description |
The presentation detailed the use of machine learning techniques to extract information from freely available satellite images and estimate the area of waterbodies used for aquaculture in seven districts in southern Bangladesh, one of country’s most important aquaculture zones producing fish for domestic markets and crustaceans for export. The research combined machine learning derived estimates of aquaculture farm area per district with data from statistically representative farm surveys to estimate farm size, productivity, and total output, economic value of production, on-farm employment generation by gender, and demand for formulated and non-formulated feeds.
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| Date |
2022-12-01
2023-01-13T14:09:31Z 2023-01-13T14:09:31Z |
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| Type |
Presentation
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| Identifier |
Belton, B., Haque, M.M., Ali, H., Nejadhashemi, A.P., Hernandez, R.A., Khondker, M. and Ferriby, H. 2022. Harnessing Machine Learning to Estimate Aquaculture’s Contributions to the Economy of Southwest Bangladesh. Presented at World Aquaculture Society, Singapore, 1 December 2022. Penang, Malaysia: WorldFish.
https://hdl.handle.net/10568/127061 |
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| Language |
en
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| Rights |
Other
Open Access |
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| Format |
application/pdf
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| Publisher |
WorldFish
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