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- W2965088510 abstract "Viral diseases are major sources of poor yields for cassava, the 2nd largest provider of carbohydrates in this http URL 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." @default.
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- W2965088510 date "2019-08-08" @default.
- W2965088510 modified "2023-09-25" @default.
- W2965088510 title "iCassava 2019Fine-Grained Visual Categorization Challenge" @default.
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