This document provides an overview of the "Harnessing Machine Learning to Estimate Aquaculture Production and Value Chain Performance in Bangladesh" project. The project was funded by USAID Innovation Lab for Fish and implemented from April 2020 to September 2022 by Michigan State University, BAU, WorldFish, and CIAT. The project aimed to survey aquaculture farmers, fish traders, and suppliers; use machine learning on remote sensing images combined with survey data to estimate economic impacts; and build capacity through extension videos. Key components included surveying 1100 participants, using machine learning to analyze remote sensing data, and disseminating short extension videos on social media.

1. Photo Credit Goes Here
WELCOME & INTRODUCTION
Ben Belton
Photo: MM Haque
The Harnessing Machine Learning Project Outreach Workshop with Development Partners
August 4, 2022

2. PROJECT OVERVIEW
• “Harnessing Machine Learning to Estimate Aquaculture
Production and Value Chain Performance in Bangladesh”
• Funded by USAID Innovation Lab for Fish (Mississippi State
University)
• April 2020 – September 2022
• Implemented by Michigan State University, BAU, WorldFish,
CIAT
• Worked in major aquaculture districts in FTF zone of influence in
SW (Khulna, Satkhira, Bagerhat, Jashore, Barisal, Bhola,
Gopalganj)

3. PROJECT COMPONENTS
• Component 1 Survey a representative sample of 1100, farmers,
fish traders, feed suppliers & hatcheries in S&SW BD
• Component 2 Use machine learning to extract and analyze data
on aquaculture from remotely sensed images; Combine with
survey data to estimate aquaculture’s contributions to the
regional economy and nutrition; Develop interactive online
decision support tool
• Component 3 Capacity building: Produce & disseminate short
extension videos via social media to promote new and emerging
technologies identified during the survey; Remote sensing
training

4. PROJECT OVERVIEW
• Innovative approaches:
 First to combine remote sensing and machine learning
with value chain surveys to estimate the economic
impacts of aquaculture – hope to provide proof of
concept for adoption by government agencies
 Very rigorous approach to survey design &
implementation to ensure data quality &
representativeness
 Develop low-cost extension videos for dissemination
through social media to replicate & accelerate farmer-
farmer technology diffusion

5. TODAY’S AGENDA
• Machine learning supported estimates of
aquaculture area, production, contributions to
regional employment & economy
• Survey methodology
• Remote sensing & machine learning techniques
• Key findings from farm, fish trader and feed
supplier surveys

6. www.feedthefuture.gov