Deadline: 15th December 2024 by 11:59 PM GMT.
The objective of this challenge is to develop robust machine learning models that can accurately predict all diseases present in images of corn, pepper, and tomato crops. Participants are tasked with creating models that can
a) generalise well, even when encountering new diseases not seen in the training set, and b) operate efficiently on edge devices such as the entry-level smartphones used by most subsistence farmers in Africa.
By harnessing the power of machine learning, they aim to develop advanced solutions for detecting and identifying multiple diseases in three vital crops: corn, pepper, and tomatoes. The models and solutions developed in this challenge will support accurate and timely disease detection, enhance agricultural productivity and sustainability, and ensure food security for millions of people.
1st place: USD4000
2nd place: USD2500
3rd place: USD1500
In addition to financial prizes, the creators of the winning models will be invited to collaborate with RAIL to publish the results in a scientific journal, as well as work together on paths towards implementing the solutions developed.
- Languages and tools:Â You may only use open-source languages and tools in building models for this challenge.
- Who can compete:Â This challenge is only open to citizens of African countries.
- Submission Limits: 5 submissions per day, 200 submissions overall.
- Team size: Max team size of 4
- Public-Private Split: Zindi maintains a public leaderboard and a private leaderboard for each challenge. The Public Leaderboard includes approximately 30% of the test dataset. The private leaderboard will be revealed at the close of the challenge and contains the remaining 70% of the test set.
- Data Sharing: CC-BY SA 4.0 license
- Platform abuse:Â Multiple accounts, or sharing of code and information across accounts not in teams, or any other forms of platform abuse are not allowed, and will lead to disqualification.
- Code Review: Top 10 on the private leaderboard will receive an email requesting their code at the close of the challenge. You will have 48 hours to submit your code.
Datasets and packages
The solution must use publicly-available, open-source packages only.
You may use only the datasets provided for this challenge. Automated machine learning tools such as automl are not permitted.
You may use pretrained models as long as they are openly available to everyone.
You are allowed to access, use and share challenge data for any commercial,. non-commercial, research or education purposes, under a CC-BY SA 4.0 license.
You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the challenge data, and work with Zindi to rectify any unauthorised transmission or access.
Your solution must not infringe the rights of any third party and you must be legally entitled to assign ownership of all rights of copyright in and to the winning solution code to Zindi.
For more information and application.