Projects / AI Innovation Challenge

Determining the Water Retention Capacity of Naturally Fertilized Soil Through Machine Learning

Challenge Completed!


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Develop a tool to estimate soil water retention and water holding capacity with the use of manure, biochar, and compost. In this 8-week challenge, you will join a collaborative team of 50 AI engineers from all around the world.

The problem

It’s critical to take into account the issue of excess manure produced globally by animals as we work to address the pressing issue of climate catastrophe and adopt regenerative farming techniques. The soil, a non-renewable natural resource, is no longer healthy as a result of human dependence on synthetic fertilizers. We should think about the original fertilizer found in nature instead. It’s important to remember that the bulk of farms—roughly 84%—are modest enterprises with an area of fewer than two hectares. On the other hand, over 50 hectare farms make up just 1% of all farms. Nonetheless, the majority of the greenhouse gas emissions brought on by manure storage and application to the soil are attributable to these larger farms. In actuality, although synthetic fertilizers account for almost 20% of worldwide direct agricultural emissions, manure only accounts for 7% of those emissions. Manure emissions must be decreased through improved management and efficiency in order to counteract this. By promoting the use of manure and organic soil amendments, we can effectively address the issue of excess manure while reducing reliance on synthetic fertilizers and ultimately mitigating greenhouse gas emissions from agriculture. In order to do this, in this Omdena-Nitrolytics project, will be working on this subject using a data-centric approach.

The project goals

We aim to develop a tool to evaluate soil water retention and water-holding capacity when manure, charcoal, and compost are utilized. The main goals of this Omdena-Nitrolytics Challenge are:

  • Relevant data collection through different possible sources.
  • Demonstrate organic fertilizer use and how it improves water retention capacity.
  • Give farmers insights into crop water demands in real or near real-time to maximize water conservation.
  • Demonstrate enhanced capabilities with biochar application, and support intelligent irrigation.

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

First Omdena Project?

Join the Omdena community to make a real-world impact and develop your career

Build a global network and get mentoring support

Earn money through paid gigs and access many more opportunities



Your Benefits

Address a significant real-world problem with your skills

Get hired at top companies by building your Omdena project portfolio (via certificates, references, etc.)

Access paid projects, speaking gigs, and writing opportunities



Requirements

Good English

A very good grasp in computer science and/or mathematics

Student, (aspiring) data scientist, (senior) ML engineer, data engineer, or domain expert (no need for AI expertise)

Programming experience with Python

Understanding of Machine Learning and/or Data Science



This challenge is hosted with our friends at


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