iCassava 2019 Fine-Grained Visual Categorization Challenge
This work addresses a domain-specific problem for small-holder farmers in Sub-Saharan Africa by providing a tool to monitor cassava diseases, but it is incremental as it builds on existing computer vision techniques.
The paper tackles the problem of identifying viral diseases in cassava leaves to improve crop yields in Africa by introducing a dataset and Kaggle challenge for fine-grained visual categorization, using semi-supervised approaches to address annotation difficulties.
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.