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. Dronecollected 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. Cashew is a significant cash crop in Ghana (Rabany et al., 2015), with small and medium farmers relying on it for income. Cashew cultivation is concentrated in specific regions of Ghana. However, farmers face challenges including insect, plant disease and abiotic stress factors that reduce their Figure 1: A visual summary of the application lifecycle: yields (ICAR; Jayaprakash et al., 2023; Mensah et al., 2023; data work (data collection with farmers, data annotation Timothy et al., 2021). To address these issues, the Cashew and labelling), model work (model training and fine-tuning), Disease Identification With Artificial Intelligence (CADI and UI application (software deployment and release to AI) project was launched to provide a data-centric solution.