As the 2nd largest provider of carbohydrates in Africa, cassava is a key food security crop grown by small-holder farmers because it can withstand harsh conditions. At least 80% of small-holder farmer households in Sub-Saharan Africa grow cassava and viral diseases are the major sources of poor yields.
In this competition, we introduce a dataset of 5 fine-grained cassava disease categories with labeled training images collected during a regular survey in Uganda, mostly crowdsourced from farmers taking images of their gardens, and annotated by experts the National Crops Resources Research Institute (NaCRRI) in collaboration with the AI lab in Makarere University, Kampala.
The dataset consists of leaf images of the cassava plant. The goal is to learn a model to classify a given image into these 4 disease categories or a 5th category indicating a healthy leaf, using both labeled and unlabeled images in the training data.
Ernest Mwebaze, Timnit Gebru
We thank the different experts and collaborators from NaCRRI for assisting in preparing this dataset.