Localized Data Work as a Precondition for Data-Centric ML: A Case Study of Full Lifecycle Crop Disease Identification in Ghana

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 collect...

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Bibliographic Details
Main Authors:	Akogo, Darlington, Samori, Issah, Akafia, Cyril, Fiagbor, Harriet, Kangah, Andrews, Asiedu, Donald Kwame, Fuachie, Kwabena, Oala, Luis
Format:	Journal Article
Language:	English
Published:	04-07-2023
Subjects:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning
Online Access:	Get full text
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Summary:	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.
DOI:	10.48550/arxiv.2307.01767