Yield Estimation & Harvest Planning

The Problem

Accurate pre-harvest yield estimation is critical for logistics planning, labor scheduling, cold chain coordination, and market commitments. Traditional estimation methods — manual sampling, historical averages, expert judgment — are imprecise and labor-intensive. Overestimation leads to wasted logistics capacity; underestimation means missed market windows.

How Computer Vision Solves It

Computer vision counts and measures produce directly from drone and in-row camera imagery. Fruit detection models identify and size individual fruits on the tree, grain models estimate density from canopy imagery, and vegetable models assess maturity stage. Aggregated across fields, these measurements produce yield forecasts with far greater accuracy than sampling methods.

The Challenge with Current Solutions

Produce is often occluded by foliage. Size estimation from 2D imagery requires calibration. Maturity assessment varies by variety. Weather and lighting conditions during image capture affect accuracy.

What vfrog Brings That's Different

vfrog trains counting and sizing models on your specific varieties and growing conditions. Synthetic data covers occlusion patterns and maturity stages. Models calibrate to your field camera setup for consistent size estimation. Forecasts improve each season as harvest actuals feed back into model training.

Key Benefits

• Accurate pre-harvest yield forecasts
• Better logistics and labor planning
• Reduced waste from overestimation
• Fewer missed market windows
• Variety-specific maturity assessment
• Improving accuracy season over season