Crop yield prediction is a valuable tool for agronomists and policymakers. It is also a hard task, especially when dealing with small fields in a subsistence setting. One challenge with many existing datasets is that of location accuracy. GPS locations for fields can end up offset from the true location due to sensor inaccuracies or from locations being collected at the edges of fields rather than the field centres. This makes it harder to connect remote-sensed data to the yield values - a problem faced by participants in our CGIAR Crop Yield Prediction Challenge.
The objective of this competition is to design a method that can help correct these location offsets by finding the most probable field center given an input location. Your model may use any publically available data (subject to approval), including any datasets that can be accessed through tools such as Google Earth Engine. You may NOT use datasets that are not freely available (such as very-high-resolution satellite images) to ensure that we keep the playing field level. For each location, we also provide approximate plot size and measured yield in case these help with creating your solution.
The data for this competition is part of a larger dataset of maize yields collected from East Africa. The top solutions will be incorporated into a research project that aims to correct location errors in this dataset to produce a new high-accuracy plot location and yield dataset that can be used to better understand the agricultural landscape. This, combined with the results of the previous Crop Yield Prediction challenge, will hopefully enable yield prediction at a higher accuracy than previously achieved.
About CGIAR (cgiar.org)
The CGIAR (formerly the Consultative Group for International Agricultural Research) is a consortium of international agricultural research centers scattered across the world who focus on issues related to agricultural productivity, food security, poverty, and the environment. The CGIAR is made up of 15 research centers and operates in dozens of countries across Asia, Africa, and Latin America.
About The Platform for Big Data in Agriculture (bigdata.cgiar.org)
The CGIAR Platform for Big Data in Agriculture is a cross-center platform of the CGIAR with the goal of leveraging and harnessing the power of big data to accelerate and enhance the impact of international agricultural research. This 5-year platform (2017 - 2021) will provide global leadership in organizing open data, convening partners to develop innovative ideas, and demonstrating the power of big data analytics through inspiring projects. It is where information becomes power: power to predict, prescribe, and produce more food, more sustainably. It democratizes decades of agricultural data empowering analysts, statisticians, programmers and more to mine information for trends and quirks, and develop rapid, accurate and compelling recommendations for farmers, researchers and policymakers.
About Lacuna Fund
Lacuna Fund - an initiative co-founded by The Rockefeller Foundation, Google.org, and Canada’s International Development Research Centre and facilitated by Meridian Institute - is the world’s first collaborative effort to provide data scientists, researchers, and social entrepreneurs in low- and middle-income contexts globally with the resources they need to produce labeled datasets that address urgent problems in their communities. With an initial focus on agriculture, languages, and health, Lacuna Fund aims to create the building blocks of labeled training data that allow robust machine learning applications. Funding from the first call for agricultural datasets will support a variety of products, from personalized information on fertilizers and regenerative agriculture practices for farmers, to better information about crop yields and food security to inform decision-makers worldwide.
About One Acre Fund (oneacrefund.org)
One Acre Fund is a non-profit social enterprise that provides smallholder farmers with seeds, fertilizer, and agronomic training on credit. They currently serve over one million farmers across Africa.
About Visual and AI Solutions (VAIS) (linkedin.com/company/visual-and-ai-solutions)
Visual and AI Solutions (VAIS) is a Nile University spinoff and incubated company that is founded by established academics and is the culmination of many years of research and development experience in the areas of computer vision, artificial intelligence and machine/deep learning, big data engineering and wireless sensor networks. VAIS develops novel artificial intelligence and visual computing technologies that can be deployed on mobile/handheld devices and on the cloud to enable smart applications in the domain of agricultural technology (AgriTech). VAIS AgriTech algorithms and modules enable advanced precision agriculture and deliver dependable field analytics to farmers, traders, and insurers through applications such as intelligent ag field scouting, in-situ plant disease detection & diagnosis, and creation of field precision maps. Additional VAIS activities include development of multispectral satellite imagery analytics and AI-based consultancy services.
This challenge is open to all and not restricted to any country.
Teams and collaboration
You may participate in competitions as an individual or in a team of up to four people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the competition, minus the total number of submissions among team members at team formation. Prizes are transferred only to the individual players or to the team leader.
Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be immediately disqualified from the platform.
Code must not be shared privately outside of a team. Any code that is shared, must be made available to all competition participants through the platform. (i.e. on the discussion boards).
The Zindi user who sets up a team is the default Team Leader. The Team Leader can invite other data scientists to their team. Invited data scientists can accept or reject invitations. Until a second data scientist accepts an invitation to join a team, the data scientist who initiated a team remains an individual on the leaderboard. No additional members may be added to teams within the final 5 days of the competition or the last hour of a hackathon, unless otherwise stated in the competition rules
A team can be disbanded if it has not yet made a submission. Once a submission is made individual members cannot leave the team.
All members in the team receive points associated with their ranking in the competition and there is no split or division of the points between team members.
Datasets and packages
The solution must use publicly-available, open-source packages only. Your models should not use any of the metadata provided.
You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.
If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission.
You may use pretrained models as long as they are openly available to everyone.
The data used in this competition is the sole property of Zindi and the competition host. You may not transmit, duplicate, publish, redistribute or otherwise provide or make available any competition data to any party not participating in the Competition (this includes uploading the data to any public site such as Kaggle or GitHub). You may upload, store and work with the data on any cloud platform such as Google Colab, AWS or similar, as long as 1) the data remains private and 2) doing so does not contravene Zindi’s rules of use.
You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition 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.
Submissions and winning
You may make a maximum of 10 submissions per day. Your highest-scoring solution on the private leaderboard at the end of the competition will be the one by which you are judged.
You may make a maximum of 300 submissions for this competition.
Zindi maintains a public leaderboard and a private leaderboard for each competition. The Public Leaderboard includes approximately 50% of the test dataset. While the competition is open, the Public Leaderboard will rank the submitted solutions by the accuracy score they achieve. Upon close of the competition, the Private Leaderboard, which covers the other 50% of the test dataset, will be made public and will constitute the final ranking for the competition.
Note that to count, your submission must first pass processing. If your submission fails during the processing step, it will not be counted and not receive a score; nor will it count against your daily submission limit. If you encounter problems with your submission file, your best course of action is to ask for advice on the Competition’s discussion forum.
If you are in the top 20 at the time the leaderboard closes, we will email you to request your code. On receipt of email, you will have 48 hours to respond and submit your code following the submission guidelines detailed below. Failure to respond will result in disqualification.
If your solution places 1st, 2nd, or 3rd on the final leaderboard, you will be required to submit your winning solution code to us for verification, and you thereby agree to assign all worldwide rights of copyright in and to such winning solution to Zindi.
If two solutions earn identical scores on the leaderboard, the tiebreaker will be the date and time in which the submission was made (the earlier solution will win).
If the error metric requires probabilities to be submitted, do not set thresholds (or round your probabilities) to improve your place on the leaderboard. In order to ensure that the client receives the best solution Zindi will need the raw probabilities. This will allow the clients to set thresholds to their own needs.
The winners will be paid via bank transfer, PayPal, or other international money transfer platform. International transfer fees will be deducted from the total prize amount, unless the prize money is under $500, in which case the international transfer fees will be covered by Zindi. In all cases, the winners are responsible for any other fees applied by their own bank or other institution for receiving the prize money. All taxes imposed on prizes are the sole responsibility of the winners. The top 3 winners or team leaders will be required to present Zindi with proof of identification, proof of residence and a letter from your bank confirming your banking details. Winners will be paid in USD or the currency of the competition. If your account cannot receive US Dollars or the currency of the competition then your bank will need to provide proof of this and Zindi will try to accommodate this.
You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.
Zindi is committed to providing solutions of value to our clients and partners. To this end, we reserve the right to disqualify your submission or not award prizes on the grounds of usability or value. This includes but is not limited to the use of data leaks or any other practices that we deem to compromise the inherent value of your solution. Any solution that is based on a constant value (zeros for example) won't be eligible for a prize unless it provides a strong justification why is it a viable solution to be used in real life setup.
Zindi also reserves the right to disqualify you and/or your submissions from any competition if we believe that you violated the rules or violated the spirit of the competition or the platform in any other way. The disqualifications are irrespective of your position on the leaderboard and completely at the discretion of Zindi.
Reproducibility of submitted code
Consequences of breaking any rules of the competition or submission guidelines:
Monitoring of submissions
The evaluation metric for this competition is Mean Absolute Error, measured in kilometers.
For each field you must submit a displacement vector from the field center. The starter notebook shows how to create an appropriate submission file and how to score your model locally on the train set.
The submission file should take the following format:
Field_ID X Y
id_e7032b10 0.95 0.13
id_ae7cb51e 0.45 0.12
id_e59f7730 0.12 0.94
There are FIVE winners for this competition.
Additional conditions to note:
Competition closes on 4 July 2021.
Final submissions must be received by 11:59 PM GMT.
We reserve the right to update the contest timeline if necessary.