Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh
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 dom...
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Ponencia |
| Lenguaje: | Inglés |
| Publicado: |
WorldFish
2022
|
| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/127061 |
| _version_ | 1855540964152573952 |
|---|---|
| author | Belton, Ben Haque, Mohammad Mahfujul Ali, Hazrat Nejadhashemi, Amir Pouyan Hernández, Ricardo Khondker, Murshed-E-Jahan Ferriby, Hannah |
| author_browse | Ali, Hazrat Belton, Ben Ferriby, Hannah Haque, Mohammad Mahfujul Hernández, Ricardo Khondker, Murshed-E-Jahan Nejadhashemi, Amir Pouyan |
| author_facet | Belton, Ben Haque, Mohammad Mahfujul Ali, Hazrat Nejadhashemi, Amir Pouyan Hernández, Ricardo Khondker, Murshed-E-Jahan Ferriby, Hannah |
| author_sort | Belton, Ben |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| 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. |
| format | Ponencia |
| id | CGSpace127061 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | WorldFish |
| publisherStr | WorldFish |
| record_format | dspace |
| spelling | CGSpace1270612025-01-09T16:58:44Z Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh Belton, Ben Haque, Mohammad Mahfujul Ali, Hazrat Nejadhashemi, Amir Pouyan Hernández, Ricardo Khondker, Murshed-E-Jahan Ferriby, Hannah climate change food systems deltas 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. 2022-12-01 2023-01-13T14:09:31Z 2023-01-13T14:09:31Z Presentation https://hdl.handle.net/10568/127061 en Open Access application/pdf WorldFish 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. |
| spellingShingle | climate change food systems deltas Belton, Ben Haque, Mohammad Mahfujul Ali, Hazrat Nejadhashemi, Amir Pouyan Hernández, Ricardo Khondker, Murshed-E-Jahan Ferriby, Hannah Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh |
| title | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh |
| title_full | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh |
| title_fullStr | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh |
| title_full_unstemmed | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh |
| title_short | Harnessing Machine Learning to Estimate Aquaculture’s Contributions to The Economy of Southwest Bangladesh |
| title_sort | harnessing machine learning to estimate aquaculture s contributions to the economy of southwest bangladesh |
| topic | climate change food systems deltas |
| url | https://hdl.handle.net/10568/127061 |
| work_keys_str_mv | AT beltonben harnessingmachinelearningtoestimateaquaculturescontributionstotheeconomyofsouthwestbangladesh AT haquemohammadmahfujul harnessingmachinelearningtoestimateaquaculturescontributionstotheeconomyofsouthwestbangladesh AT alihazrat harnessingmachinelearningtoestimateaquaculturescontributionstotheeconomyofsouthwestbangladesh AT nejadhashemiamirpouyan harnessingmachinelearningtoestimateaquaculturescontributionstotheeconomyofsouthwestbangladesh AT hernandezricardo harnessingmachinelearningtoestimateaquaculturescontributionstotheeconomyofsouthwestbangladesh AT khondkermurshedejahan harnessingmachinelearningtoestimateaquaculturescontributionstotheeconomyofsouthwestbangladesh AT ferribyhannah harnessingmachinelearningtoestimateaquaculturescontributionstotheeconomyofsouthwestbangladesh |