Harvest Yield Maps
Buy and sell harvest yield maps data. Combine harvester yield data with GPS coordinates showing bushels per acre across fields. Crop prediction AI trains on yield maps.
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Find Me This Data →Overview
What Is Harvest Yield Maps?
Harvest yield maps combine GPS-tagged harvester data with spatial measurements to create detailed records of crop production across fields, typically measured in bushels per acre or fruit counts. These maps are generated by instrumenting harvesting machines—particularly grain combines—to measure the mass or volume of harvested crop immediately after harvest, or in some cases afterward during post-harvest processing. Yield maps provide farmers with detailed accounts of crop production variability and potential revenue, enabling improved input management, on-farm experimentation, and profitability analysis. The data is increasingly valuable for training crop prediction AI models that require spatial yield patterns to improve forecasting accuracy.
Market Data
Widely available for maize, soybean, and grain; emerging for horticultural crops
Primary Crops Mapped
Source: ResearchGate
In-field instrumentation of harvesting machines during or immediately after harvest
Dominant Collection Method
Source: ResearchGate
Machine vision models achieve MAPE of 23-26% accuracy at tree level for yield prediction
Emerging Technology Performance
Source: ResearchGate
Who Uses This Data
What AI models do with it.do with it.
Variable Rate Application
Farmers and precision ag platforms use yield maps to create variable rate application tasks, optimizing input distribution based on demonstrated productivity zones across fields.
Pre-Harvest Prediction
For crops like citrus, yield maps inform sampling-based predictions before harvest, allowing growers to estimate total block yield by counting fruits in representative trees.
AI Model Training
Crop prediction algorithms train on historical yield maps combined with GPS coordinates to improve forecast accuracy and identify production patterns.
Farm Management & Experimentation
Yield maps enable on-farm experimentation and profitability mapping, helping farmers understand field variability and refine agronomic decisions.
What Can You Earn?
What it's worth.worth.
Field-Level Datasets
Varies
Pricing depends on field size, crop type, spatial resolution, and buyer demand for historical yield records.
Horticultural Yield Maps
Varies
Premium pricing for orchard and berry crop yield data, given scarcity of commercial solutions in these segments.
AI Training Datasets
Varies
Bulk historical yield map collections command higher rates when combined with GPS coordinates and multi-year temporal data.
What Buyers Expect
What makes it valuable.valuable.
Accurate GPS Coordinates
Spatial data must be precisely georeferenced to enable field-level variability mapping and integration with farm management systems.
Timestamped Harvest Data
Yield measurements require clear temporal documentation to account for the aggregation effects of harvesting processes and machine dynamics.
Calibrated Sensor Output
Yield data from harvester sensors must be validated against actual harvest weight or volume; different sensing methods (impact sensors, weighing) may require crop-specific adjustments.
Multi-Year Historical Records
AI training applications require temporal consistency; maps from multiple seasons and field conditions improve model robustness.
Companies Active Here
Who's buying.buying.
Precision agronomy platform integrating machine data from CLAAS equipment for yield visualization, analysis, and variable rate task creation.
Global agricultural machinery and precision ag technology leader investing in technician training and equipment connectivity for data capture.
Farm machinery manufacturer providing digital farm and fleet management platform that receives and integrates yield data from field equipment.
FAQ
Common questions.questions.
When are yield maps typically collected?
The dominant method collects yield data in-field during harvest by instrumenting harvesting machines, particularly grain combines. Some systems also measure yield after harvest but before transportation. The challenge is that yield information is not available until during or after harvest, limiting pre-harvest decision-making.
What crops can be yield-mapped?
Yield mapping is widely established for field crops like maize, soybean, and grain. However, few commercial solutions exist for horticultural crops such as berries, field vegetables, or orchards, creating opportunities for new data collection methods and higher-value datasets.
How accurate are machine vision-based yield predictions?
Recent machine vision models for yield prediction achieve Mean Absolute Percentage Errors (MAPE) ranging from 23-26% at the tree level, with performance varying by model type and crop conditions. Deep learning approaches show promise but require training on diverse conditions.
Why do buyers want yield maps combined with GPS data?
GPS-tagged yield maps enable spatial variability analysis and integration with farm management systems for variable rate applications. The combination allows AI models to identify production patterns tied to specific field locations and soil conditions, improving forecast accuracy.
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