The startup develops an artificial intelligence and machine learning platform tailored for Norwegian farmers to enhance grain production through a specialized neural network model and crop classification system. By providing precise yield data and calculations in a centralized interface, the platform enables farmers to monitor crop growth and make informed decisions based on localized agricultural conditions.
Funding
$3.5M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Farmers often lack access to precise, real-time data regarding field conditions, crop health, and yield potential, hindering informed decision-making and efficient resource allocation. Traditional methods for assessing these factors can be time-consuming, costly, and lack the granularity needed for precision agriculture.
Solution
DigiFarm provides an AI-powered platform that delivers real-time data and analytics to optimize agricultural practices. Using deep neural networks and high-resolution satellite imagery, the platform accurately detects field boundaries, classifies crops, and assesses field performance. DigiFarm's solutions enable precision farming by providing insights into vegetation indices, sustainability metrics, and historical trends, allowing farmers and agricultural companies to make data-driven decisions to improve yields, reduce costs, and enhance sustainability. The platform's API-first approach allows seamless integration with existing agtech solutions, empowering businesses to leverage advanced analytics without extensive infrastructure investments.
Target Audience
DigiFarm targets farmers, agricultural companies, crop insurance providers, national paying agencies, and financial institutions seeking to optimize crop production, improve resource allocation, and enhance sustainability through precision agriculture techniques.
Features
- High-resolution field delineation using deep neural networks and satellite imagery, achieving up to 96% Intersection over Union (IoU) accuracy
- Crop classification with over 92% accuracy, identifying various crop types based on extensive ground-truth data
- S2 Time Series analysis, providing long-term and in-season cloud-filtered vegetation indices (EVI, NDVI, NDMI, MI+)
- Sustainability Index (DFSI) based on a proprietary algorithm, offering insights into the sustainability of crop production
- Deep Resolution Imagery, enhancing Sentinel-2 satellite imagery from 10 meters to 1 meter per pixel
- API access for seamless integration with existing agricultural software and FMS platforms
- Historical data access, providing agri data back to 2016, including seeded acres and vegetation indices
- Geospatial data delivery via GeoJSON, enabling easy integration with mapping and GIS applications