Localized Data Work as a Precondition for Data-Centric ML: A Case Study of Full Lifecycle Crop Disease Identification in Ghana
This work addresses agricultural productivity and food security for local farmers in Ghana, but it is incremental as it applies existing methods to a new context.
The CADI AI project tackled crop disease identification in Ghana by developing a localized data-centric solution using drone data and machine learning, resulting in a desktop application for farmers.
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.