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Equation-based Analytics in Precision Agriculture

1 mins read |

Equation-based analytics module has been released by the GeoPard team and is part of the core of the Platform.

Predefined GeoPard formulas (catalog of 50 templates and growing) are the next big step for the platform to empower farmers, agronomists and spatial data analytics with the solution to derive actionable analytics for each square meter.

Formulas could be configured to be executed automatically for any field.

Equations are based on multi-year independent agronomic university and industry research.

Potassium Removal based on Yield data

Use Cases (see examples below):

  • Nitrogen Uptake in absolute numbers using Yield and Protein data
  • Nitrogen Use Efficiency (NUE) and Excess calculations with Yield and Protein data layers
  • Lime recommendations based on pH data from soil sampling or soil scanners
  • Sub-field (zones or pixel-level ROI maps)
  • Micro and Macro nutrients fertilization recommendations based on Soil sampling, Field Potential, Topography, and Yield data
  • Carbon modeling
  • Change detection and alerting (calculate difference between Sentinel-2, Landsat8-9 or Planet imagery)
  • Soil and grain moisture modeling
  • Calculation of dry yield out of wet yield datasets
  • Target Rx vs As-applied maps difference calculation

Potassium Recommendations based on Two Yield Targets (Productivity Zones)

Fertilizer: Recommendations Guide (South Dakota State University): Potassium / Corn. Review and Revision: Jason Clark | Assistant Professor & SDSU Extension Soil Fertility Specialist

Potassium Use Efficiency in Kg/Ha

Nitrogen Use Efficiency in percentage. Calculation is based on Yield, Protein and Grain Moisture data layers

Nitrogen: Target Rx vs As-Applied

Chlorophyll difference between two satellite images

A user of GeoPard can adjust existing and create their private formulas based on Imagery, Soil, Yield, Topography, or any other data layers GeoPard supports. 

Examples of the template GeoPard Equations

Formula-based analytics helps farmers, agronomists, and data scientists to automate their workflows and make decisions based on multiple data and scientific research to enable easier implementation of sustainable and precision agriculture.

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