Cassava Disease Detection at Scale
Tunde Fashola Industries
Sponsor & Co-designer
Plain language
Cassava blight and mosaic disease destroy an estimated 30% of Nigeria's cassava yield annually — equivalent to $3.5 billion in losses. Smallholder farmers in rural areas lack access to agronomists. By the time disease is visible to the naked eye, it has already spread to neighbouring plots.
SE approach applied
Designed an end-to-end ML pipeline: image dataset curation (12,000 annotated leaf images), CNN architecture selection and fine-tuning for low-compute mobile inference, human-machine interface design for feature phone compatibility, and deployment systems architecture for offline-first operation.
Disciplines Applied
Impact & results
87% disease detection accuracy with a model under 3MB — operable on ₦12,000 smartphones. Deployed across 3 states through partnership with Borno State Agricultural Development Programme. 14,000 farmers registered in the first six months.
Tunde Fashola Industries
Sponsor & Co-designer
Published by
ASES · UNILAG
Association of Systems Engineering Students