GeoPard Tutorials | Precision Ag Software
  • Introduction to GeoPard
    • GeoPard Platform: Frequently Asked Questions (FAQ)
  • 🖥️Product Tour - Web App
    • Getting Started
      • Create a new farm
      • Draw a new field
      • Upload a field boundary
      • Edit a boundary
      • Edit a field name
      • Add a label
    • 🛰️Satellite Monitoring
      • Order Planet Scope (3m daily) imagery
      • Indices for Crops and Soils
      • 📈Crop Development Index Graph
    • 📊Zones Maps and Analytics
      • Assign Variable Rates in the Zones (Ag inputs Rates Distribution Tool)
      • Creating Zones Map using Satellite Imagery
      • Creating Zones Map using Soil/Yield/As Applied Data
      • Creating Zones Map using Topography
      • Creating Zones Map using a Template
      • Draw Zones Manually, Clone from an existing layer
      • Usage of Data Classification
      • Edit Zones Map: Merge & Split
      • Multi-Year Zones
      • Multi-Layer Analytics
      • Compare layers
      • Heterogeneity factor
      • Relative variation factor
    • 📊Equation-based Analytics
      • Batch Equation analytics
      • Catalog of Predefined Agronomic Equations
      • Catalog of Custom Functions
    • ⛰️Topography
    • ⛰️3D Maps
    • Import
      • Field Boundary
      • Soil Data
      • Yield Data
      • As Applied/As Planted Data
      • Machinery Proprietary Formats
      • Import from MyJohnDeere
    • Export / Download
      • Field Boundary Export
      • Batch Export of Boundaries, Zones and Scouting Pins
      • Zones Map Export as shapefile
      • Satellite Imagery Export as geotiff or geojson
      • Export Scouting Notes as shapefile
      • Export VRA map In ISOXML Format
      • Export to MyJohnDeere
    • 🤖API
    • Operations Log - Track errors/Imports and Analytics
    • Organizations and Roles
    • Farms Sharing between Accounts & Organizations
    • Managing Crop Season information with tags(labels)
      • Case: Managing fields for several clients
      • Case: Season details including crop and year
      • Case: Field operation details
    • ⚙️User Settings
      • Subscriptions, Account limits & Plans
      • Restoring password
      • Order the package
      • Changing preferences
    • 🚜John Deere Operations Center Integration
      • 🚜John Deere Operations Center Integration
        • 1. Create Free Trial Account with you John Deere Account
        • 2. Connect to JohnDeere Operations Center
        • 3. Connect to John Deere Organizations
        • 4. Import into GeoPard from John Deere Operations Center
        • 5. Configure Automated Data Sync
        • 6. Export Rx Maps to John Deere Operations Center as Files
        • 7. Export Field Boundaries into John Deere Operations Center
          • 7a. Organization configuration of export boundary to John Deere Operations Center as File
        • 8. Export Rx Maps to John Deere Operations Center as Work Plans
        • 9. Export Soil, Topography, Satellite or Analytics as Map Layers
      • 🚜John Deere Operations Center Data Sharing
        • 1. "Staff Member" sharing
        • 2. Problem-solving of "Staff Member" sharing
        • 3. "Partner Organization" sharing
        • 4. Problem-solving of "Partner Organization" sharing
        • 5. Problem-solving of import from John Deere to GeoPard
        • 6. Problem-solving of Work Plan creation
        • 7. Sharing Fields/Boundaries Between Partner Organizations in John Deere Ops Center, DataSync config
  • 🚀Changelog & Product Releases
    • Release Notes
      • Release Web April 2025 (Rates Distribution, improved Legends)
      • Release Web March 2025 (Improved Zones, WorkPlans updates, Yield data enhancements)
      • Release Web January 2025 (Free Trial, Usage-based Pricing Plan, USDA Yield Cleaning protocol, Export of calibrated Yield data to John Deere Ops Center, Import of kml)
      • Release Web August 2024 (Data Layer Previews, Spatially localized Zones; Use zones and Equations in new Equations)
      • Release Web July 2024 (Equation Map creation, Spatially localized zones, Seeding and Application Work Plans)
      • Release Web May 2024 (Raw view for Satellite Images, export of Zone Maps as WorkPlan to the John Deere Operations Center, redesign of Batch Analytics)
      • Release Web April 2024 (Batch Equation Maps and enhanced layer transparency)
      • Release Web February 2024 (Per area pricing, units)
      • Release Web January 2024 (many UI improvements)
      • Release Web November 2023 (Clone Polygons, Subscription management)
      • Release Web October 2023 (Yield Calibration, Equation Maps as ISOXML, PDF Export and John Deere Integration)
      • Release Web September 2023 (Cleaning & Calibrating Yield Datasets, more languages support)
      • Release Mobile August 2023 (Mobile app impovements)
      • Release Web July 2023 (Operations Log page, Sum in datasets)
      • Release Web June 2023 (Improved Equations, Operations log v1)
      • Release Mobile May 2023 (Social Login)
      • Release Web May 2023 (John Deere integration improvements)
      • Release Web January 2023 (huge amount of small improvements)
      • Release Web October 2022 (Integration with AgGateway protocols, Isoxml support and more)
      • Release Web April 2022 (3D maps and Zoning Tools)
  • 👨‍🌾Agronomy
    • Precision Agronomy Use Cases & Best Practices Overview
    • Field Management Zones (Productivity Zones) Creation Process
    • Variable Rate Seeding (Planting) Maps
    • Yield Calibration & Cleaning
    • Synthetic Yield Map
    • Create Soil Sampling Zones, Points, Route, export as KML, and execute
    • Evaluate Accuracy of Seeding Application
    • Evaluate Accuracy of Fertilizer Application
    • Field Trial Analytics
    • Nitrogen Use Efficiency & Uptake
    • Comparing Yield Datasets
    • Compare Soil Scanner Data between Years
    • Flood Detection / Insurance report
    • Profit Maps (COMING)
    • VRA/Rx/Prescription Fertilizer Maps (COMING)
    • VRA/Rx/Prescription Nitrogen Maps (COMING)
    • VRA/Rx/Prescription Spraying Maps (COMING)
    • Multi-Layer Field Potential Maps (COMING)
    • VR Lime Application Based on Soil Scanner pH Data (COMING)
    • Merging Yield Datasets Belonging to the Same Field (COMING)
  • 📱Product Tour - Mobile App
    • Installation
    • Logging in
    • Viewing satellite images
    • Viewing zones maps
    • Viewing soil data
    • Viewing yield data
    • Viewing topography maps
    • Viewing as applied datasets
    • Working in the field/Scouting zones maps
    • Working offline
    • Filters
    • Options
    • Settings
  • 🤖API Docs
    • GeoPard API Overview
    • Getting Started
    • Authorization: ApiKey, Credentials or OAuth 2.0
    • Diagrams with Basic Flows
      • 1. Field Registration
      • 2. GraphQL Subscription
      • 3. Grep Satellite Imagery
      • 4. Upload Soil | AsApplied | Yield Datasets
      • 5. Execute Equations
      • 6. Generate ZonesMap
      • 7. Download Gridded Data
      • 8. Download Original Data
    • Data Schema
    • Requests Overview
      • 1. Subscription: Get Events
      • 2. Query: Get "Fields"
      • 3. Query: Get "SatelliteImages"
      • 4. Query: Get defined "SatelliteImage"
      • 5. Query: Get "RasterMaps"
      • 6. Query: Get "ZonesMaps"
      • 7. Mutation: Generate "ZonesMap"
      • 8. Mutation: Generate "RasterMap"
      • 9. Mutation: Generate "ZonesMap" asynchronously
      • 10. Mutation: Generate "RasterMap" asynchronously
      • 11. Mutation: Generate Yield based "ZonesMap" asynchronously
      • 12. Mutation: Generate Soil based "ZonesMap" asynchronously
      • 13. Mutation: Create "Farm"
      • 14. Mutation: Create a "Field" or edit the boundary of the existing field (with optional labels)
      • 15. Query: Get "TopographyMap"
      • 16. Query: Get "YieldDatasets"
      • 17. Query: Get "SoilDatasets"
      • 18. Mutation: Generate zip archive with "ZonesMap" and "Field"
      • 19. Mutation: Delete "Field"
      • 20. Mutation: Delete "Farm"
      • 21. Mutation: Delete "ZonesMap"
      • 22. Mutation: Delete "RasterAnalytisMap"
      • 23. Mutation: Delete "SoilDataset"
      • 24. Mutation: Delete "YieldDataset"
      • Notes (Pins)
        • 25. Mutation: Save "Note" attached to "Field"
        • 26. Mutation: Save "Note" attached to "ZonesMap"
        • 27. Mutation: Save "Note" attached to "SoilDataset"
        • 28. Mutation: Save multiple "Notes", Batch operation
        • 29. Mutation: Delete "Note"
        • 30. Mutation: Delete multiple "Notes"
        • 31. Query: Get all "Notes" related to "Field"
        • 32. Query: Get "Notes" related to "ZonesMap" and type
        • 33. Query: Get "Notes" related to "SoilDataset" and type
        • 34. Query: Get a selected "Note" with all "Comments"
        • 35. Mutation: Add "Comment" to the selected "Note"
        • 36. Mutation: Add multiple "Comments" to the selected "Notes"
      • 37. Query: Get "SatelliteImages" in the defined interval
      • 38. Query: Get "UserData"
      • 39. Mutation: Set custom color schemas to selected "GeoMaps"
      • 40. Query: Get "Labels" on the account level
      • 41. Mutation: Save "Labels" on the account level
      • 42. Mutation: Delete "Label" on the account level
      • 43. Query Get "Fields"
      • 44. Mutation: Set Field Labels
      • 45. Mutation: Save User Data
      • 46. Mutation: Generate multi-layer "ZonesMap" asynchronously
      • 47. Query: Get "ZonesMaps"
      • 48. Query: Get Gridded Data from "TopographyMap"
      • 49. Query: Get Gridded Data from "FieldSatelliteImage"
      • 50. Query: Get Gridded Data from "VectorAnalysisMap"
      • 51. Query: Get Gridded Data from "YieldDataset"
      • 52. Query: Get Gridded Data from "SoilDataset"
      • 53. Query: Get Gridded Data from "AsAppliedDataset"
      • 54. Query: Get Vector Data from "SoilDataset"
      • 55. Upload zip files (over 6 MB)
      • 56. Upload photos
      • 57. Query: Get "Photos" attached to the selected "Note"
      • 58. Query: Get "Photos" attached to "Comments"
      • 59. Query: Get "AsAppliedDatasets"
      • 60. Mutation: Generate As-Applied-based "ZonesMap" asynchronously
      • 61. Mutation: Delete "AsAppliedDataset"
      • 62. Mutation: Share Farms
      • 63. Mutation: Save Organization
      • 64. Mutation: Add Users to Organization
      • 65. Mutation: Delete Users from Organization
      • 66. Mutation: Save Field
      • 67. Mutation: Save Farm
      • 68. Mutation: Refresh "VectorAnalysisMap" Statistics
      • 69. Mutation: Delete "Photo"
      • 70. Mutation: Delete multiple "Photos"
      • 71. Mutation: Generate a zip archive with "Notes"
      • 72. Query: Get Gridded Data as GeoJSON or GeoTIFF
      • 73. Query: Get Gridded Data with the Selected Buffer
      • 74. Mutation: Verify "Equation"
      • 75. Mutation: Generate "EquationMap" asynchronously
      • 76. Query: Get "EquationMap"
      • 77. Mutation: Delete "EquationMap"
      • 78. Query: Find "Fields" by "externalKey"
      • 79. Query: Find "Farms" by "externalKey"
      • 80. Query: Get Original Data
      • 81. Query: Get GeoJSON of "EquationMap"
      • 82. Query: Restore Subscription Events
      • 83. Query: Collect Platform Context
      • 84. Mutation: Calibrate and Clean YieldDataset
      • 85. Mutation: Assign Rates to VectorAnalysisMap (ZonesMap)
      • 86. Query: Get "Farms"
      • 87. Mutation: Save Custom VectorAnalysisMap (ZonesMap)
      • 88. Mutation: Export ZonesMap as Zipped Shapefile
      • 89. Mutation: Export ZonesMap as Zipped ISOXML
    • Geo Endpoints
      • WMS - Get Raster Pictures of Spatial Data Layers
        • 1. LAI
        • 2. RGB
        • 3. Field: boundary
        • 4. Field: thumbnail
        • 5. ZonesMap
        • 6. ZonesMap: custom color schema
        • 7. ZonesMap: thumbnail
        • 8. RasterMap
        • 9. RasterMap: custom color schema
        • 10. RasterMap: thumbnail
        • 11. TopographyMap: elevation in absolute numbers
        • 12. YieldDatasetsMap
        • 13. SoilDatasetsMap
        • 14. SoilDatasetsMap: custom color schema
        • 15. AsAppliedDatasetsMap
        • 16. Satellite Image: cropped by Field boundary
        • 17. Satellite Image: cropped by Field boundary and custom color schema
        • 18. YieldDatasetsMap: custom color schema
        • 19. Satellite Image: 10 colors visualization
      • WFS - Get Spatial Data Layers in Vector format (shp, geojson)
        • 1. Get the Field Boundary as Geojson
        • 2. Get the Zones map as Geojson
        • 3. Get Zones Attributes as JSON
        • 4. Get Soil data as Geojson
        • 5. Get Yield data as Geojson
    • Uploading Files
    • API FAQ
  • 🛣️Platform Roadmap
    • Roadmap
  • GIS quick Hints
    • QGIS: Change String to Number values in the shapefile
    • QGIS: Yield Data Manipulations
    • QGIS: Split Boundaries Into Subfields
    • QGIS: Merge Vector Layers
    • QGIS: Merge Selected Features from Vector Files
    • QGIS: Calculate NDVI for the Drone Geotiff File
    • QGIS: Split Multi-field Shapefiles
    • QGIS: Convert CSV to SHP
    • QGIS: Reproject Shapefile
  • Policies
    • Your Data Stays Yours, Securely Managed By GeoPard
    • Terms & Conditions
    • Privacy Policy
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Start Working with GeoPard

  • Go to GeoPard Website
  • Demo Request
  • LinkedIn

This Portal Sections

  • Introduction
  • Product Tour - Web App (incl. video)
  • Product Tour - Mobile App (incl. video)
  • Precision Agronomy Use Cases

Powered by GeoPard Agriculture - Automated precisionAg platform

On this page
  • Quick Overview
  • Real-World Examples
  • Multiple Harvesters Working Together
  • J-turns, Stops, Half Equipment Width Used
  • Abnormally Large Logged Values
  • Data Beyond Field Boundary
  • Calibration Using Provided Average Yield Value
  • Clean Yield Attributes Ignoring Attributes with Anomalies
  • First Step
  • Auto-Processing
  • Compete Manual Configuration
  • Algorithm Versions

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  1. Agronomy

Yield Calibration & Cleaning

Tutorial to run Harvesting data cleaning and calibration. Or just cleaning, or just calibration. It works great even for complex cases: multiple harvesters, no harvester id, etc.

PreviousVariable Rate Seeding (Planting) MapsNextSynthetic Yield Map

Last updated 1 year ago

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Yield data holds immense potential for farmers, but its true power is unlocked only when its precision is perfect. The "Yield Calibration" module is designed to refine the raw Yield Dataset, aligning it with core mathematical tenets to uplift its quality. The end result is a dataset that's not only more robust but also primes it for in-depth, insightful analyses.

This calibration process is instrumental in:

  1. Ensuring Data Consistency: It's not uncommon for multiple harvesters to work in tandem or across different days. This feature ensures that their data sings in harmony.

  2. Homogenizing Data: Yield data can be varied; the calibration ensures it is smooth and consistent, without unwanted spikes or drops.

  3. Filtering Out Noise: Like any data, yield data can have its share of 'noise' or irrelevant info. We make sure it doesn't muddy your insights.

  4. Streamlining Geometries: Any turnarounds or odd geometric data patterns can skew real insights. The calibration is designed to iron these out, ensuring the data truly mirrors field realities.

  5. Cropping by Field Boundary: Harvesters often operate across adjacent areas. For accurate analytical results, it's essential to consider only the data situated within the specified boundary.

Quick Overview

Real-World Examples

In the realm of agriculture, corrupted yield datasets can pose significant challenges. Below, you can find real-world examples where such datasets were encountered. Through GeoPard's advanced calibration and cleaning algorithms, these datasets were effectively refined and optimized.

Multiple Harvesters Working Together

When dealing with complex scenarios, a two-step calibration process is recommended for optimal accuracy. Begin by running the initial calibration using the Machine ID attribute. Following that, proceed with a second round of calibration, this time utilizing the Simulated (Synthetic) Machine Paths tickbox. This layered approach ensures a thorough and precise calibration, essential for managing intricate cases effectively.

J-turns, Stops, Half Equipment Width Used

Abnormally Large Logged Values

Data Beyond Field Boundary

Calibration Using Provided Average Yield Value

Clean Yield Attributes Ignoring Attributes with Anomalies

The Yield Dataset occasionally includes attributes with irregularities in Moisture, Speed, Elevations, or other secondary (non-yield) attributes. During the execution of Clean or Calibrate activities, it is essential to disregard these anomalies. This can be efficiently achieved using the GeoPard Yield Clean-Calibrate interface.

First Step

The "Yield Calibrate and Clean" module is initiated directly from the User Interface. The primary requirement is to have an uploaded Yield Dataset. Adjacent to each Yield Dataset, you'll find a button to commence the dataset adjustments.

From there, several options are available for proceeding:

  1. Auto-Processing: Use the default, GeoPard-recommended settings for a one-click calibration.

  2. Clean Only: Configure and execute only the CLEAN operation.

  3. Calibrate & Clean: Choose the sequence of operations and customize the parameters.

  4. Calibrate Only: Configure and execute just the CALIBRATE operation.

Auto-Processing

The Auto-Processing option is the preferred choice to utilize GeoPard's recommended settings for calibrating and cleaning the Yield Dataset. However, the configuration is always open for review and potential modifications.

Key configuration parameters include:

  1. Choosing an attribute as a basis for calibration, typically represented by machines operating in the field or a timestamp.

  2. Identifying attributes to be calibrated.

  3. Selecting an attribute that represents target yield values for cleaning.

Hint for Abnormal Values Sometimes Inherent to Yield Datasets.

If an attribute selected for calibration or cleaning predominantly contains zero values across the majority of geometries, these geometries will be excluded from the final Yield Dataset.

To ensure integrity, attributes with such anomalies should be excluded from the list of attributes to be calibrated (2).

Once configured, click Run to apply the logic.

The processed results will be displayed alongside the original dataset, marked with Calibrated and/or Cleaned labels, accompanied by the version of the algorithm used.

From version 3.0 of the Clean/Calibrate algorithm onward, GeoPard introduces the Crop by Field Boundary feature. This keeps only geometries located within the Field Boundary and results in more accurate statistical data distribution.

Starting with version 4.0, the Clean/Calibrate algorithm in GeoPard now incorporates a feature for calibration based on Average or Total Values across any attribute. A prevalent application of this enhancement is the calibration of WetMass, which can now be adjusted by the known measured Average Yield for a specific Field.

Compete Manual Configuration

The Calibrate & Clean option offers comprehensive manual configurations for the calibration and cleaning processes. It's ideal for those seeking full control over the algorithm, making the operations transparent as a white-box approach. The Calibrate Only and Clean Only alternatives are essentially individual components of the Calibrate & Clean process.

Specify the sequence you prefer: first "Calibrate", then "Clean" or the reverse.

Hint for Data Anomalies

If a user encounters anomalies in the data, such as values at or near zero, or unusually large values (for instance, an average of 10 with a maximum of 8000), the Clean & Calibration workflow is advised.

Prioritizing data Cleaning before Calibration ensures the removal of errors, missing values, or inconsistencies, thereby enhancing data quality and accuracy.

Hint for Data without Initial Errors

For datasets initially free from errors, missing values, or inconsistencies, and when multiple harvesters are known to be involved, consider the Calibration & Clean workflow.

Cleaning the data post-calibration helps to refine the dataset further by potentially eliminating any artifacts introduced during calibration.

For the Calibrate step, configuration parameters include:

  1. A smoothing level to mitigate sudden fluctuations in values.

  2. Choose a calibration type: Pathwise, Average/Total, or Conditional.

  3. Attributes to calibrate.

  4. The calibration basis attribute often relates to the machinery path in the field or timestamps. In the absence of real machinery paths, simulated paths can be utilized.

  5. Option to manually input Average/Total or Conditional values.

Hint for Abnormal Values Sometimes Inherent to Yield Datasets.

If an attribute selected for calibration or cleaning predominantly contains zero values across the majority of geometries, these geometries will be excluded from the final Yield Dataset.

To ensure integrity, attributes with such anomalies should be excluded from the list of attributes to be calibrated (3).

The Clean step's configuration involves:

  1. Attributes representing target yield values.

  2. Exclusion parameters that determine attributes exempt from the cleaning operation (optional).

  3. Setting conditions to discard attributes based on min/max thresholds (optional).

Hint for Abnormal Values Sometimes Inherent to Yield Datasets.

If an attribute selected for calibration or cleaning predominantly contains zero values across a majority of geometries, these geometries will be excluded from the final Yield Dataset.

To ensure integrity, attributes with such anomalies should be excluded from the list of attributes to be cleaned (2).

Click Run to initiate the process.

Algorithm Versions

Post-processing, the outcomes are showcased adjacent to the original dataset, distinctly marked with Calibrate" and/or "Clean" labels, as well as the algorithm version utilized.

From version 3.0 of the Clean/Calibrate algorithm onward, GeoPard introduces the Crop by Field Boundary feature. This keeps only geometries located within the Field Boundary and results in more accurate statistical data distribution.

Starting with version 4.0, the Clean/Calibrate algorithm in GeoPard now incorporates a feature for calibration based on Average or Total Values across any attribute. A prevalent application of this enhancement is the calibration of WetMass, which can now be adjusted by the known measured Average Yield for a specific Field.

The Yield Calibration interface leverages the associated GeoPard API (), specifically integrating with the CALIBRATE and CLEAN operations. This functionality is accessible through the GeoPard User Interface and can be programmatically invoked via the GeoPard API.

To address areas lacking logged Yield Data and achieve completeness of the Yield Map, consider utilizing the GeoPard Synthetic Yield Map approach. This method effectively restores missing data, ensuring a comprehensive yield analysis. Learn more about this technique .

👨‍🌾
LINK
HERE

Harvesting data Cleaning and Calibration Video Tutorial. The difference between options explained
5MB
Automated_Yield_Data_Cleaning_Calibration_with_GeoPard.pdf
pdf
Download Yield Cleaning PDF Brochure
Example 1: Multiple Harvesters Working Together
Example 2: Multiple Harvesters Working Together
Example 3: Multiple Harvesters Working Together
Example 1: U-turns, Stops, Half Equipment Width Used
Example 2: U-turns, Stops, Half Equipment Width Used
Example 1: Abnormally Large Logged Values
Example 2: Abnormally Large Logged Values
Example 3: Abnormally Large Logged Values
Example 4: Abnormally Large Logged Values
Example 5: Abnormally Large Logged Values
Example: Data Beoynd Field Boundaries
Example: Calibration Using Provided Average Yield Value (28 t/ha)
Example: Anomalies in the Moisture Attribute
Example: Clean Yield Data Ignoring Anomalies in Moisture
Start the flow
Select an option to proceed
Revisit the configuration of Auto-Processing option
Review the configuration of Auto-Processing option
The result of the Auto-Processing execution (version 2.0)
The result of the Auto-Processing execution (version 3.0)
The result of the Calibration execution using Average Yield of 6 t/ha (version 4.0)
Select a "Calibrate & Clean" option
Select the operations order
Configure the Calibrate step
Configure the Clean step
The result of Calibrate & Clean execution (version 2)
The result of the Auto-Processing execution (version 3.0)
The result of the Calibration execution using Average Yield of 6 t/ha (version 4.0)