iCassava 2019 Fine-Grained Visual Categorization Challenge

Code

• github.com/nii4u/Cassava-Disease-Classification
  none★ 0
• github.com/dcpatton/cassava_disease_classification
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• github.com/Sunil4423/-cassava_disease_classification
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• github.com/mjvakili/cassava_disease_classification
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• github.com/riron1206/kaggle_Cassava
  pytorch★ 0

Abstract

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.

Tasks

Reproductions