Agscore
Agscore is a predictive agronomic science platform that integrates over 100 variables and 200,000 audited data points per field to deliver field-level recommendations for cultivar selection, planting windows, and risk management. The system simulates pre-planting scenarios, monitors fields in-season with real climate data, and compares predicted versus actual yields post-harvest, achieving over 92% accuracy in soybean and 93% in off-season corn across more than 6,000 validated areas.
- Agriculture Technology
- Artificial Intelligence
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Agronomic planning often relies on manually crossing fragmented data from soil, climate, genetics, and production history that live in different places, consuming valuable planning time. Decisions based on farm-wide averages ignore field-level variability in soil, cultivar, and risk, while unpredictable weather and dynamic markets shrink the window to define cultivars, planting windows, and planting order.
Solution
Agscore provides a predictive agronomic science platform that integrates more than 100 variables and 200,000 audited data points per field to deliver field-level recommendations rather than average-based decisions. The system simulates pre-planting scenarios per field—comparing cultivars, analyzing risk across planting windows, and estimating productive potential—before the first seed is planted. During the season, it updates management zone maps with real data and continuous climate monitoring, then compares predicted versus actual results after harvest to improve the next cycle. Each prediction is generated by explainable AI (XAI/SHAP), making the technical justification behind every recommendation auditable and understandable.
Target Audience
Primary customers are agronomists, rural producers, and agricultural operations that need field-level, data-driven recommendations for cultivar selection, planting windows, and risk management across soybean and off-season corn crops.
Features
- Machine Learning, Deep Learning, explainable AI (XAI/SHAP), geointelligence, and MLOps as five core technology pillars
- Integration of +100 variables per field spanning soil, climate, genetics, management, and region
- Pre-planting scenario simulation for cultivar selection, planting-window risk analysis, and productive-potential comparison per field
- In-season monitoring with management zone maps updated using real field and climate data
- Post-harvest comparison of predicted versus realized productivity per field and cultivar, feeding model learning
- Flexible data intake; no specific formatting required, as the team organizes incoming data before processing