Satellite Crop Yield Mapping
What We Do
We use maching learning methods (AI) to analyze satellite images and create maps showing the relative yield of different areas in agricultural fields. Think of it as creating an image of a field that shows which parts of the field are performing better or worse compared to others.
How It Works
Our machine learning system learns to recognize patterns in satellite imagery that indicate crop yield using a convolutional neural network (CNN), trained on crop yield data from over 2500 fields.
When analyzing a new image, our system will:
- Examines visual indicators like plant color, density, and growth patterns
- Compares each area to identify relative performance differences
- Creates a color-coded map showing productivity zones
How to use these maps
Our Machine Learning system can provide a high level overview on which areas of your field are performing well, and which areas are performing marginally, without needing to do on-the-ground field analysis.