Farmdar Products | AI Crop Monitoring & Yield Prediction Tools
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AI and space technology driven large scale crop data
Features
- Crop identification
- Crop identification
- Crop identification
- Crop identification
- Crop identification
- Crop identification
- Crop identification
- Crop identification
Specifications
- 80% - 95% accuracy
- Visualised by country, state, province, districts or customised zones & territories
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Crop Stage Detection
CropScan uses AI and satellite intelligence to identify and track field-level crop stage detection across large areas throughout the growing cycle. This enables agribusinesses to understand crop progression, monitor growth variability, and plan interventions or sales and marketing activity with greater accuracy. Stage detection updates provide continuous visibility to support timely agronomic and business decisions.
Empty Arable Land
CropScan uses AI and satellite intelligence to automatically identify and classify empty arable land across large regions. This enables agribusinesses to understand where cultivable land remains, reduce reliance on manual surveys, and make informed decisions around sales planning, business expansion, crop conversions, and resource allocation.
Yield Prediction
We forecast yields early and often using a combination of space-based data and weather inputs. CropScan offers two methods: deep learning where historical data exists and biomass modeling where it doesn't. These estimates help clients plan procurement, pricing and supply chain operations with greater accuracy.
Crop Detection
CropScan uses space technology and AI to automatically identify crop types across vast areas. This enables agribusinesses, mills and governments to gain clear visibility on what is growing where, eliminating guesswork and reducing reliance on field surveys. Our detection is updated regularly to track changes across the season.
Harvest Monitoring
With CropScan you can monitor harvest progress in near real time. Our system detects when harvesting begins, tracks how much area has been cleared and flags unexpected delays. This helps procurement teams plan better logistics, avoid mill downtime and respond quickly to disruptions.
Variety Detection
Different crop varieties perform differently but often look the same from the ground. CropScan uses AI trained on space technology imagery to differentiate between crop varieties based on growth behavior, allowing clients to map high-performing areas and make informed decisions for future sowing.
Sowing Time Analysis
Knowing when fields were planted helps predict harvest windows, understand performance and detect anomalies. CropScan estimates sowing dates using time-series analysis from space technology, giving agribusinesses the ability to monitor planting trends, plan resources and reduce surprises at the end of the season.
Crop Stage Detection
Empty Arable Land
Yield Prediction
Crop Detection
Firebird Field Delineation
Firebird is Farmdar’s AI-powered field delineation module that creates accurate digital field boundaries at scale, supporting cleaner field records and stronger downstream analytics.
Key capabilities
- Large-scale field boundary extraction with high consistency
- Supports faster onboarding of growers and field programs
- Creates cleaner geo-referenced boundaries for planning and execution
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Plant Health
Monitor overall crop health
- Identify areas of good and poor plant health
- Focus efforts on specific areas rather than a blanket approach
- Track and improve health of crops resulting in better quality and yields
- Remote monitoring of farms
Plant Stress
Identifies existing and potential crop stress
- Identify which crops are under stress and which ones are likely to be under stress in the future
- Focus efforts on specific areas rather than a blanket approach
Productivity Zones
Identify historically productive zones
- AI-backed using 5-year historical satellite imagery
- Identifies productivity zones by high, medium and low ranges
- Isolate areas for intervention
- Select the most productive fields intelligently
- Allows farmers to apply P&K intelligently
Nitrogen Zones
Identifies precise nitrogen requirement of crops
- Identify areas of high, medium and low Nitrogen requirement
- Based on AI and satellite imagery analysis of plant chlorophyl content
- Enables reduction of environmental impact of Nitrogen
Moisture Report
Track early germination performance
- Identify fields with healthy vs weak crop establishment early in the season
- Enable targeted field follow-up for replanting, advisory, and intervention
- Improve in-season decision quality by linking emergence performance with yield planning
Soil Organic Matter
Identify zones of high and low organic matter
- In the SOM analysis, black represents areas where organic matter is good, while grey and white represents the areas with medium and low organic matter quality
- High yield productivity area corresponds with good SOM
- Identifying zones of low, medium and high SOM allows focused areas for soil improvement efforts
Your business challenges are unique, your solutions should be too.
Share your goals and we'll show how Farmdar's AI-powered crop intelligence can deliver measurable impact for your operation.