  Viral diseases are major sources of poor yields for cassava, the 2nd largest
provider of carbohydrates in Africa.At least 80% of small-holder farmer
households in Sub-Saharan Africa grow cassava. Since many of these farmers have
smart phones, they can easily obtain photos of dis-eased and healthy cassava
leaves in their farms, allowing the opportunity to use computer vision
techniques to monitor the disease type and severity and increase yields.
How-ever, annotating these images is extremely difficult as ex-perts who are
able to distinguish between highly similar dis-eases need to be employed. We
provide a dataset of labeled and unlabeled cassava leaves and formulate a
Kaggle challenge to encourage participants to improve the performance of their
algorithms using semi-supervised approaches. This paper describes our dataset
and challenge which is part of the Fine-Grained Visual Categorization workshop
at CVPR2019.
