Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.12348/5526
Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh
Abstract
- 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.
- External link to download this item: https://cgspace.cgiar.org/handle/10568/127061
Collections
- Sustainable aquaculture [2702]
View/ Open
Date
- 2022
Author
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Belton, B.
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Haque, A.M.
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Ali, H.
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Nejadhashemi, A.
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Hernandez, R.
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Khondker, M.
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Ferriby, H.
Author(s) ORCID(s)
- A.B.M. Mahfuzul Haquehttps://orcid.org/0000-0002-5334-5630
- Hazrat Alihttps://orcid.org/0000-0002-6303-1336
- Murshed-E-Jahan Khondkerhttps://orcid.org/0000-0001-9933-8631
Subject(s)
AGROVOC Keywords
Type
- Presentation
Publisher
- Mississippi State University, Feed the Future Innovation Lab for Fish (MS State - Fish Innovation Lab)