Projects / AI Innovation Project

Farming Habitat Classification: AI-Powered Solutions for Sustainable Agriculture

Project completed!


Featured Image

Background

The challenge of farming habitat classification stems from the reliance on governmental datasets that often use estimated calculations, leaving farmers without accurate insights to enhance their practices. This lack of actionable data prevents farmers from adapting to meet environmental and compliance standards effectively. Origin Chain Networks (OCN), a tech startup, sought to address this issue by promoting a bottom-up, farmer-first approach to data ownership and enabling accurate farming habitat classification through innovative mobile farming solutions.

Objective

The primary objectives of this farming habitat classification project were to:

  • Develop an open-source Earth Observation reference dataset for farming habitat classification, covering both commercial and peripheral habitats.
  • Validate self-reported farming data by comparing it with independent datasets to ensure trust and integrity.
  • Empower farmers with tools to understand and manage the environmental impacts of their practices while meeting compliance requirements.

Approach

The farming habitat classification challenge was undertaken over two months by 50 AI changemakers, following these steps:

  1. Data Sources: Open-source satellite imagery was utilized to test and derive meaningful farming habitat classifications.
  2. Classification Categories:
    • Commercial Farming Habitats: Including crops, grasslands, commercial forestry, glasshouse production, livestock rearing, and horticulture.
    • Peripheral Habitats: Covering waterways, wetlands, hedgerows, native woodlands, and leisure gardens.
  3. Machine Learning Techniques: AI algorithms were employed to create accurate and reliable classifications.
  4. Data Visualization: A public-access methodology was developed, allowing stakeholders to view, evaluate, and provide feedback on the farming habitat classification dataset.

Results and Impact

The farming habitat classification project achieved the following:

  • Comprehensive Classifications: Validated classifications for a wide range of farming habitats, from commercial crops to peripheral ecosystems, enhancing the quality of environmental impact assessments.
  • Annotated Dataset: A robust, open-source dataset for farming habitat classification that supports transparency and collaboration among stakeholders.
  • Empowered Farmers: By providing actionable insights, farmers can better understand their practices’ environmental impact and make strategic decisions to align with EU Green Deal compliance.
  • Trust and Transparency: Enhanced integrity of self-reported data, addressing issues like trustless reporting, reputation management, and brand protection.

This farming habitat classification initiative strengthened the connection between farmers, policymakers, and stakeholders, contributing to a more sustainable agricultural ecosystem.

Future Implications

The farming habitat classification findings offer significant potential for future developments:

  • Policy Development: Data-driven farming habitat classification insights can guide agricultural policies to prioritize both sustainability and productivity.
  • Further Research: Expanding AI-based farming habitat classification methodologies to include additional ecosystems and farming practices.
  • Market Opportunities: Creating new markets for farm-level data, ensuring farmers benefit from their contributions to the data revolution.

This project highlights the pivotal role of farming habitat classification in transforming agriculture into a more transparent, sustainable, and farmer-centric industry.

This challenge has been hosted with our friends at
Origin Chain Networks


3D Imagery Analysis & Segmentation
3D Imagery Analysis & Segmentation
Street-Level Imagery Analysis
Street-Level Imagery Analysis
Thumbnail Image
Accurately Identifying Crop Types Using Remote Sensing and Machine Learning

Become an Omdena Collaborator

media card
Visit the Omdena Collaborator Dashboard Learn More