Dataset Details
Title:
Amini Cocoa Contamination Challenge
Details:
Cocoa is a key economic driver for millions of smallholder farmers in Africa, particularly in countries like Ghana and Côte d'Ivoire, which together account for over 60% of global cocoa production. Cocoa Swollen Shoot Virus Disease (CSSVD) and other cocoa diseases threaten not only individual livelihoods but also the broader agricultural economy. Traditional disease identification methods—such as visual inspection by agricultural officers—are slow, costly, and often inaccessible to farmers in remote areas.
The objective of this challenge is to develop machine learning models to accurately predict all diseases present in images of cocoa. Participants are tasked with creating models that can a) generalise well, even when encountering new diseases not seen in the training set, and b) operate efficiently on edge devices such as the entry-level smartphones used by most subsistence farmers in Africa.
By developing machine learning models capable of accurately identifying multiple cocoa diseases using smartphones, this challenge will empower farmers with real-time, AI-driven diagnostics accessible through their mobile phones. The ability to detect new and emerging diseases beyond those seen in the training set will improve early intervention strategies, minimising crop losses and reducing reliance on reactive pesticide use.
The data for this challenge is provided by Makerere AI Lab, Marconi Machine Learning Lab and Makerere University Research Innovations Fund (RIF).