iCassava 2019Fine-Grained Visual Categorization Challenge

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.

[1]  Michael Biehl,et al.  Prototype-Based Classification for Image Analysis and Its Application to Crop Disease Diagnosis , 2016, WSOM.

[2]  Jayme Garcia Arnal Barbedo,et al.  Digital image processing techniques for detecting, quantifying and classifying plant diseases , 2013, SpringerPlus.

[3]  Jennifer R. Aduwo,et al.  Automated Vision-Based Diagnosis of Cassava Mosaic Disease , 2010, ICDM.

[4]  Darko Stefanovic,et al.  Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification , 2016, Comput. Intell. Neurosci..

[5]  T. Alicai,et al.  Changes in the incidence and severity of Cassava mosaic virus disease, varietal diversity and cassava production in Uganda , 2001 .

[6]  Rory Hillocks,et al.  The association between root necrosis and above‐ground symptoms of brown streak virus infection of cassava in southern Tanzania , 1996 .

[7]  I. Abdullahi,et al.  Effects of cassava genotype, climate and the Bemisia tabaci vector population on the development of African cassava mosaic geminivirus (ACMV) , 2003 .

[8]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Thomas Villmann,et al.  Divergence-based classification in learning vector quantization , 2011, Neurocomputing.

[10]  Marcel Salathé,et al.  Using Deep Learning for Image-Based Plant Disease Detection , 2016, Front. Plant Sci..

[11]  Gration M. Rwegasira,et al.  Response of Selected Cassava Varieties to the Incidence and Severity of Cassava Brown Streak Disease in Tanzania , 2012 .