Localized Data Work as a Precondition for Data-Centric ML: A Case Study
  of Full Lifecycle Crop Disease Identification in Ghana
Darlington Akogo Issah Samori Cyril Akafia Harriet Fiagbor Andrews Kangah Donald Kwame Asiedu Kwabena Fuachie Luis Oala
The Ghana Cashew Disease Identification with Artificial Intelligence (CADI
AI) project demonstrates the importance of sound data work as a precondition
for the delivery of useful, localized datacentric solutions for public good
tasks such as agricultural productivity and food security. Drone collected data
and machine learning are utilized to determine crop stressors. Data, model and
the final app are developed jointly and made available to local farmers via a
desktop application.