Crop Yield Data
Field-level bushels-per-acre by hybrid and practice -- the ground truth that satellite-based yield prediction models need to calibrate against.
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Find Me This Data →Overview
What Is Crop Yield Data?
Crop yield data represents field-level measurements of bushels-per-acre by hybrid and agricultural practice—the empirical ground truth that calibrates satellite-based and machine learning yield prediction models. This data forms the foundation for precision agriculture analytics, enabling researchers and agritech companies to train predictive systems on real-world performance across diverse soil, climate, and management conditions. Raw crop yield data typically includes state, county, FIPS codes, and detailed crop statistics that require preprocessing to standardize formats for analysis and model training.
Market Data
$0.99 billion
ML Crop Yield Prediction Market Size (2025)
Source: Research and Markets
$1.24 billion
ML Crop Yield Prediction Market Forecast (2026)
Source: Research and Markets
$2.95 billion
Projected Market Size (2030)
Source: Research and Markets
25%
CAGR (2025–2026)
Source: Research and Markets
24.2%
CAGR (Forecast to 2030)
Source: Research and Markets
Who Uses This Data
What AI models do with it.do with it.
Yield Forecasting Consulting
Service providers use field-level yield data to deliver yield forecasting consulting, helping farmers and agribusinesses anticipate production outcomes and optimize planning.
Soil Health and Fertility Analysis
Crop yield correlations with soil properties enable consultants to provide soil health assessments and fertility recommendations tied to historical performance.
Machine Learning Model Training
Agritech companies and researchers calibrate satellite imagery, drone data, and climate models against actual yield measurements to build accurate predictive systems.
Weather Impact Analysis and Risk Mitigation
Historical yield data linked to weather patterns enables risk assessment and farm decision-making frameworks for crop insurance and input optimization.
What Can You Earn?
What it's worth.worth.
Research Report (ML Crop Yield Prediction Market)
€4,034 / $4,490 / £3,518
One-time purchase price for comprehensive market intelligence report covering services and technology providers.
Data Licensing and Custom Analysis
Varies
Pricing depends on data scope (regional vs. national), granularity (field-level vs. county), and customization requirements.
Consulting and Expert Support
Varies
Bi-annual updates, customization, and expert consultant support available with service agreements.
What Buyers Expect
What makes it valuable.valuable.
Data Completeness and Accuracy
Buyers require verified field-level data with minimal missing values, including hybrid identifiers, acreage, management practices, and exact yield measurements.
Standardized Format
Data must be preprocessed and transformed into uniform, machine-learning-friendly formats with properly structured columns for production, yield, and descriptive fields (FIPS, county, state).
Contextual Metadata
Yield data should include corresponding soil properties, climate observations, and historical weather datasets to enable multivariate model training.
Geographic and Temporal Coverage
Buyers value datasets spanning multiple countries and regions with consistent, multi-year observations to capture yield variability across diverse agroecological zones.
Companies Active Here
Who's buying.buying.
Develops and integrates AI and cloud analytics platforms for crop yield prediction and precision farming insights.
Leverages crop yield data to optimize agricultural input recommendations and support precision farming strategies.
Acquired Trimble Agriculture in 2024 to integrate machine learning-based crop yield prediction into agricultural machinery and farm management systems.
Provides yield prediction and crop analytics platforms using field data and environmental inputs.
Uses aerial imagery combined with field-level yield data to deliver precision farming insights and crop performance analysis.
FAQ
Common questions.questions.
What exactly is field-level crop yield data?
Field-level crop yield data consists of measured bushels-per-acre by specific crop hybrid and agricultural practice for individual fields. It includes metadata such as FIPS codes, state and county identifiers, and soil/climate context. This data serves as the empirical ground truth that machine learning models use to calibrate predictions from satellite imagery and environmental sensors.
Why do machine learning companies need crop yield data?
ML crop yield prediction models require real-world yield measurements to train and validate algorithms. Without this ground truth, satellite imagery, drone data, and climate models cannot be accurately calibrated. Yield data enables companies to develop predictive systems that forecast production, optimize farm inputs, and assess climate impact.
What preprocessing is required before yield data can be used?
Raw crop yield data must be cleaned and standardized, including verification of completeness, handling of missing values, and transformation into consistent formats. Data is often restructured from multi-column formats into clean, machine-learning-friendly structures with separate columns for yield, production, and descriptive information.
How fast is the crop yield prediction market growing?
The machine learning crop yield prediction market is growing at approximately 24–25% CAGR. It was valued at $0.99 billion in 2025, is expected to reach $1.24 billion in 2026, and is projected to grow to $2.95 billion by 2030, driven by adoption of AI systems, cloud analytics, satellite imagery integration, and real-time environmental monitoring.
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