Farmdar uses AI and high‑resolution satellite imagery to deliver field‑level crop intelligence—identifying crop type, acreage, variety, sowing dates, yield forecasts, harvest progress, and stress indicators—across millions of hectares. The platform provides an interactive dashboard and API for large agribusinesses to monitor and plan operations with up‑to‑date, actionable data, reducing reliance on manual surveys and improving supply‑chain efficiency.
Funding
Funding not disclosed
Founders
Product
Problem
Agribusinesses often lack timely, accurate, and large‑scale visibility into crop type, acreage, yield forecasts, planting and harvest timelines, and stress conditions, forcing reliance on costly field surveys or outdated data that hampers planning, procurement, and supply‑chain efficiency.
Solution
Farmdar leverages AI models combined with high‑resolution satellite imagery to generate detailed, field‑level crop intelligence across millions of hectares. Its platform continuously monitors crop identification, variety detection, sowing dates, yield predictions, harvest progress, plant health, stress, and moisture levels, updating insights as the season evolves. Clients can visualize data by any geographic boundary—from country to individual field—and access actionable metrics through an interactive dashboard or API integration. By automating data collection and analysis, Farmdar reduces the need for manual surveys, improves forecast accuracy, and supports more efficient decision‑making for pricing, procurement, and resource allocation.
Target Audience
Primary customers are large agribusinesses such as sugar mills, seed, fertilizer and crop‑protection companies, as well as food processors and financial institutions that require scalable crop intelligence for planning, procurement, and risk assessment.
Features
- AI‑driven crop identification with 90‑95% accuracy for major crops, including high‑resolution sugarcane detection
- Yield prediction using deep‑learning models or biomass modeling, refreshed regularly throughout the season
- Harvest monitoring that tracks start dates, cleared area, and unexpected delays in near real‑time
- Variety detection that distinguishes crop cultivars based on growth behavior from space imagery
- Sowing time analysis estimating planting dates via time‑series satellite data
- Plant health and stress monitoring with spatially resolved moisture, nitrogen, and stress indicators
- Productivity zone mapping using five‑year historical satellite data to highlight high, medium, and low performing areas
- Interactive iQ Dashboard with customizable date selection, layer toggling, and color‑coded visualizations for any administrative or grower boundary