Projects / AI Innovation Challenge

Applying Remote Sensing and Computer Vision for Farming Habitat Classification

Project completed!

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Origin Chain Networks (OCN) is a tech startup with the mission to forge a future of food we can trust. Their mission is to promote fairness and incentivise participation with a bottom-up, farmer-first data ownership model and an accessible mobile farming solution.

In this two-month Omdena Challenge, 50 AI changemakers built an open-source Earth Observation reference dataset for classifying commercial crops and peripheral habitats on-farm that can be used by food industry bodies to contextualize on-farm data that is self-reported by farmers.

The problem 

The collation of national datasets for farm-level environmental impacts is normally conducted at a governmental level and is usually determined using general calculations based on estimated performance. This information is never utilized by farmers in order to adapt or change for improved performance. Changing the flow of information from the bottom up will disrupt and dramatically improve the quality and accuracy of this information. This is reliant on engaging the farming community to deliver on required data. However, if this is achieved successfully there will be a novel and new market for the farm-level data. Farmers will be at the center of this data revolution and should see the benefits both in the supply chain and through government supports. 

By ameliorating, valorizing, and assuring the integrity of self-reported data against the public, the independent dataset we can solve the issue of trustless reporting, reputation management, and brand protection on the part of all actors. 

The project outcomes

The Origin Chain Networks *Agri-trust mobile farming service helps farmers to digitize and report compliance-based field data. Most recently, in the EU, with the advent of the Green Deal, compliance requirements encompass environmental measures as well as commercial and food safety objectives.

This challenge focus on land quality classifications: 

  • Commercial farming habitats including crops, grasslands, commercial forestry, large scale glasshouse, and polytunnel production as well as livestock-rearing in sheds (poultry, eggs, livestock) and horticulture under glasshouses and polytunnels.
  • Peripheral habitats including waterways and (non-commercial) habitats on farms. These may include uplands, wetlands, hedgerows, native woodlands, and leisure gardens.

The deliverables of the project are as follows:

  • Testing what (if any) useful crop and peripheral vegetation classifications can be derived from open-source satellite imagery.
  • Creating an annotated data set of commercial crops, grassland for livestock, and peripheral habitat classification
  • We require a data visualization methodology that allows for public access to the outcome where farmers and other stakeholders can view and provide feedback on the classification dataset. 

Ultimately, Origin Chain Networks can help farmers to improve strategic decision-making by enabling a broad overview and understanding of the impact of commercial and peripheral landscape practices, incentivizing and accelerating the adoption of positive environmental actions.

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Good English

A good/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 C/C++, C#, Java, Python, Javascript or similar

Understanding of CV, ML and/or Deep learning algorithms

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