Develop a model that can take in real-time data and monitor nitrogen flow in agricultural systems remotely. In this 8-week challenge, you will join a collaborative team of 50 AI engineers from all around the world.

The problem

Our planet is facing unprecedented environmental and climate challenges, with our food system contributing significantly to those challenges. Synthetic nitrogen fertiliser (N) has long been a cornerstone of modern agriculture however its excessive use has contributed to a host of problems such as soil degradation, water pollution, and climate change while locking in farmers as part of the pursuit of yield and revenue.

To move away from the dependency and negative impacts of synthetic fertiliser, we must first understand the complex movements of nitrogen in the agricultural system. By studying the many forms of nitrogen and the ways in which it is lost through volatilization, leaching, and runoff, we can begin to better understand so that we can decrease the environmental impact of synthetic nitrogen fertilisers. This is a monumental task that requires a multidisciplinary approach and cutting-edge AI technology to achieve impact at scale.

At the forefront of this revolution are data scientists passionate about using artificial intelligence and remote sensing to support the transition to more sustainable agricultural practices. They will work to unlock the secrets of the nitrogen movement and develop innovative solutions that help farmers ‘lose less and use less’ Nitrogen. By harnessing the power of technology and collaborating with farmers and other stakeholders, we can create a brighter future for our planet and a more sustainable and resilient food system.

Join us on this critical mission – together, we can help transform the way we grow our food and leave a positive legacy for future generations.

The project goals

This Omdena & Agreed Earth Challenge will focus on the creation of a nitrogen flow model that can characterize the flow of nitrogen losses after the application of fertilizer on a farm. The goals in this challenge can be broken down as following: 

• Take remote sensing data, geological survey data, published emissions data, other publicly available data sets, and relevant open-source models to model the flow of nitrogen at the farm level.
• Leverage remote sensing, and inference parameters, and create a scalable model.
• Use AI to minimize required inputs with the goal of remotely monitoring farm-level changes in real-time.

Why join? The uniqueness of Omdena AI Innovation Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works