In Cassava Leaf Disease Classification Competition, we introduce a dataset of 21,367 labeled images collected during a regular survey in Uganda.
Most images were crowdsourced from farmers taking photos of their gardens, and annotated by experts at the National Crops Resources Research Institute (NaCRRI) in collaboration with the AI lab at Makerere University, Kampala. This is in a format that most realistically represents what farmers would need to diagnose in real life.
As the second-largest provider of carbohydrates in Africa, cassava is a key food security crop grown by smallholder farmers because it can withstand harsh conditions. At least 80% of household farms in Sub-Saharan Africa grow this starchy root, but viral diseases are major sources of poor yields. With the help of data science, it may be possible to identify common diseases so they can be treated.
Existing methods of disease detection require farmers to solicit the help of government-funded agricultural experts to visually inspect and diagnose the plants. This suffers from being labor-intensive, low-supply and costly. As an added challenge, effective solutions for farmers must perform well under significant constraints, since African farmers may only have access to mobile-quality cameras with low-bandwidth.
Your task is to classify each cassava image into four disease categories or a fifth category indicating a healthy leaf. With your help, farmers may be able to quickly identify diseased plants, potentially saving their crops before they inflict irreparable damage.
The Makerere Artificial Intelligence (AI) Lab is an AI and Data Science research group based at Makerere University in Uganda. The lab specializes in the application of artificial intelligence and data science – including for example, methods from machine learning, computer vision and predictive analytics to problems in the developing world. Their mission is: “To advance Artificial Intelligence research to solve real-world challenges.”
We thank the different experts and collaborators from National Crops Resources Research Institute (NaCRRI) for assisting in preparing this dataset.
Leaderboard Prizes: Awarded based on private leaderboard ranking.
- 1st Place – $8,000
- 2nd Place – $4,000
- 3rd Place – $3,000
TPU Star Prizes: Awarded to the most knowledgeable and helpful TPU experts in the community.
- Three $1,000 Prizes
“TPU Star” prizes will be evaluated by experts from Google, on the following criteria:
- Forum and notebook discussion/comment contributions
- Quality of code samples in public notebooks and/or forums
- Thoughtful analysis and explainability of code content
You may become eligible for these prizes either of two ways:
- Kaggle-identified: Kaggle will query all participants in the competition contributing upvoted public notebook(s) and making discussion contribution(s).
- Self-nominated: Submit this form by February 28, 2021 to self-nominate for consideration. At a minimum, you must have shared a public notebook which uses Kaggle’s TPU integration on this competition’s dataset.
Be aware that because this is a code competition with a hidden test set, internet and TPUs cannot be enabled on your submission notebook. Therefore TPUs will only be available for training models. For a walk-through on how to train on TPUs and run inference/submit on GPUs, see our TPU Docs.
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