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Field-Scale Sensor Evaluation: Reflectance Differences and Variability Analysis

Investigate row-to-row variability in field-scale sensor data using Holland Scientific Crop Circle and NTech GreenSeeker. Analyze differences, scaling, and normalization effects for precision farming applications.

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Field-Scale Sensor Evaluation: Reflectance Differences and Variability Analysis

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  1. Field-Scale Sensor Evaluation Ken Sudduth, Newell Kitchen, Scott Drummond USDA-ARS Columbia MO

  2. Objectives • Investigate row-to-row variability in field-scale reflectance sensor data • Document differences between data collected with Holland Scientific Crop Circle (amber) and NTech GreenSeeker (green)

  3. N Application System • 6-row system with sensors mounted over rows 2 and 5 • System tested on 7 producer sites in 2004

  4. Control Hardware GPS Green GreenSeeker 1 Green GreenSeeker 2 Crop Circle 3 Crop Circle 4 Laptop Computer Application Control System Stored Data: All sensor data GPS data Processed data Valve commands 1x, 2x, and 4x Solenoid Valves

  5. Analysis of Response Plot Sensor Data • Each field site included two strips of N-rate response plots • Reflectance data were collected at the time of sidedress N application • Mean reflectance ratio and NDVI were calculated for each of the four sensors for each 50-foot plot

  6. Response Plot Reflectance Ratio Data • N application at the Diederich (D) field was done near dusk, with only diffuse lighting. Work at all other field sites was completed before 6 pm.

  7. Response Plot Reflectance Ratio Data

  8. Response Plot Reflectance Ratio Data

  9. Response Plot Reflectance Ratio Data • Row-to-row differences are apparent • Is there an ambient light effect? • Within a row, relative differences in sensor output are generally consistent between sensor types • Scaling differences are apparent between sensor types • Amber reflectance vs. green reflectance? • Normalize data - divide by mean of each sensor reading within each field

  10. Normalized Reflectance Ratio Data Row 5 Row 2

  11. Normalized Reflectance Ratio Data

  12. Normalized Reflectance Ratio Data

  13. Normalized Reflectance Ratio Data • Within-site, by-sensor normalization removed much of the sensor-type variability in many (but not all) cases • In practice, a similar normalization is accomplished using reference strip data • Well-fertilized as opposed to unfertilized • How well does it work?

  14. Comparing Sources of Variation Row 2 Row 5 Sensor Variation SE = 0.13 SE = 0.11 GreenSeeker Crop Circle Row-to-row Variation SE = 0.13 SE = 0.10

  15. Comparing Sources of Variation • Considerable variability in ratio (or NDVI) readings between sensor types • Mean normalization removed much of the variation • The remaining variation was of similar magnitude as the variation between corn rows 90 inches apart • How many sensors are needed to “adequately” describe variability? • More in MO where we can’t seem to get uniform corn stands?

  16. Does Between-Sensor Variability Affect N Rate?

  17. Does Between-Sensor Variability Affect N Rate? • In this case, there was not much effect when looking at large-scale patterns of N rate changes

  18. Does Between-Sensor Variability Affect N Rate? • Strong relationship between rates from the two sensors, but somewhat offset from 1:1 line

  19. Does Between-Sensor Variability Affect N Rate? • In some fields, GreenSeeker N rate range was considerably reduced compared to Crop Circle • Diederich field was a worst-case example, perhaps because of a different relationship between the two sensor outputs in low light

  20. Summary • Sensor “types” are different • So are individual crop rows, at a similar magnitude • Application rates with the different sensors are similar in some field conditions, but not in others • Are sensors interchangeable within algorithms, or do we need to consider them as a “package”?

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