Skip to main content

Advertisement

SpringerLink
Log in

Use of a virtual-reference concept to interpret active crop canopy sensor data

  • Published:
Precision Agriculture Aims and scope Submit manuscript

Abstract

Active crop canopy sensors make possible in-season fertilizer nitrogen (N) applications by using the crop as a bio-indicator of vigor and N status. However, sensor calibration is difficult early in the growing season when crops are rapidly growing. Studies were conducted in the United States and Mexico to evaluate procedures to determine the vegetation index of adequately fertilized plants in producer fields without establishing a nitrogen-rich reference area. The virtual-reference concept uses a histogram to characterize and display the sensor data from which the vegetation index of adequately fertilized plants can be identified. Corn in Mexico at the five-leaf growth stage was used to evaluate opportunities for variable rate N fertilizer application using conventional tractor-based equipment. A field in Nebraska, USA at the twelve-leaf growth stage was used to compare data interpretation strategies using: (1) the conventional virtual reference concept where the vegetation index of adequately fertilized plants was determined before N application was initiated; and (2) a drive-and-apply approach (no prior canopy sensor information for the field before initiating fertilizer application) where the fertilizer flow-rate control system continuously updates a histogram and automatically calculates the vegetation index of adequately fertilized plants. The 95-percentile value from a vegetation-index histogram was used to determine the vegetation index of adequately fertilized plants. This value was used to calculate a sufficiency index value for other plants in the fields. The vegetation index of reference plants analyzed using an N-rich approach was 3–5 % lower than derived using the virtual-reference concept.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Mention of a company or trade name does not imply endorsement by the USDA-ARS or the University of Nebraska.

References

  • Biggs, G. L., Blackmer, T. M., Demetriades-Shah, T. H., Holland, K. H., Schepers, J. S., & Wurm, J. H. (2002). Method and apparatus for real-time determination and application of nitrogen fertilizer using rapid, non-destructive crop canopy measurements. U.S. Patent #6,393,927. Issued May 28, 2002.

  • Gitelson, A. A., Gritz, U., & Merzlyak, M. N. (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology, 160, 271–282.

    Article  PubMed  CAS  Google Scholar 

  • Gitelson, A., & Merzlyak, M. N. (1996). Signature analysis of leaf reflectance spectra: Algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology, 148, 494–500.

    Article  CAS  Google Scholar 

  • Gitelson, A. A., Viña, A., Rundquist, D. C., Ciganda, V., & Arkebauer, T. J. (2005). Remote estimation of canopy chlorophyll content in crops. Geophysical Research Letters, 32, L08403. doi:10.1029/2005Gl022688.

    Article  Google Scholar 

  • Holland, K. H. (2009). Sensor-based chemical management for agricultural landscapes. U.S. Patent #7,723,660. Issued May 25, 2010.

  • Holland, K. H., & Schepers, J. S. (2010). Derivation of a variable rate nitrogen application model for in-season fertilization of corn. Agronomy Journal, 102, 1415–1424.

    Article  Google Scholar 

  • Holland, K. H., & Schepers, J. S. (2011). Active-crop sensor calibration using the virtual reference concept. In J. V. Stafford (Ed.), Precision Agriculture 2011, Proceedings of the 8th European Conference on Precision Agriculture (pp. 469–479). Prague: Czech Republic, Czech Centre for Science and Society.

    Google Scholar 

  • Peterson, T. A., Blackmer, T. M, Francis, D. D., & Schepers, J. S. (1993). Using a chlorophyll meter to improve N management, NebGuide G93-1171A. Lincoln: University of Nebraska-Lincoln Cooperative Extension Service.

  • Raun, W., Solie, J., May, J., Zhang, H., Kelly, J., Taylor, R., et al. (2010). Nitrogen Rich strips for wheat, corn and other crops. Publication E-1022. Stillwater: Oklahoma State University Extension.

  • Raun, W. R., Solie, J. B., Stone, M. L., Johnson, G. V., Lukina, E. V., Thomason, W. E., et al. (2001). In-season prediction of yield potential using wheat canopy reflectance. Agronomy Journal, 93, 131–138.

    Article  Google Scholar 

  • Raun, W. R., Solie, J. B., Stone, M. L., Martin, K. L., Freeman, K. W., & Zavodny, D. L. (2005). Automated calibration stamp technology for improved in-season nitrogen fertilization. Agronomy Journal, 97, 338–342.

    Google Scholar 

  • Ritchie, S. W., Hanway, J. J., Thompson, H. E., & Benson, G. O. (1997). How a corn plant develops. Special reprint 48 (revised edition). Ames: Iowa State University of Cooperative Extension Service.

  • Roberts, D. F., Ferguson, R. B., Kitchen, N. R., Adamchuk, V. I., & Shanahan, J. F. (2012). Relationships between soil-based management zones and canopy sensing for corn nitrogen management. Agronomy Journal, 104, 119–129.

    Article  Google Scholar 

  • Scharf, P. C., Kitchen, N. R., Sudduth, K. A., Davis, J. G., Hubbard, V. C., & Lory, J. A. (2005). Field-scale variability in optimal N fertilizer rate for corn. Agronomy Journal, 97, 452–461.

    Article  Google Scholar 

  • Schepers, J. S., Francis, D. D., Vigil, M., & Below, F. E. (1992). Comparison of corn leaf nitrogen and chlorophyll meter readings. Communications in Soil Science and Plant Analysis, 23(17–20), 2173–2187.

    Article  CAS  Google Scholar 

  • Shanahan, J. F., Kitchen, N. R., Raun, W. R., & Schepers, J. S. (2007). Responsive in-season nitrogen management for cereals. Computer & Electronics in Agriculture, 61, 51–62.

    Article  Google Scholar 

  • Solari, F., Shanahan, J. F., Ferguson, R. B., & Adamchuk, V. I. (2010). An active sensor algorithm for corn N applications based on a chlorophyll meter sufficiency index framework. Agronomy Journal, 102, 1090–1098.

    Article  Google Scholar 

  • Solari, F., Shanahan, J., Ferguson, R., & Schepers, J. (2008). Active sensor reflectance measurements of corn nitrogen status and yield potential. Agronomy Journal, 100, 571–579.

    Article  CAS  Google Scholar 

  • Varvel, G. E., Wilhelm, W. W., Shanahan, J. F., & Schepers, J. S. (2007). Nitrogen fertilizer applications for corn based on sufficiency index calculations. Agronomy Journal, 99, 701–706.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

Special appreciation is extended to Mr. Adalberto Mustieles, Musol LLC, Culiacan, Mexico for working with producers to establish field studies.

Author information

Authors and Affiliations

  1. Holland Scientific, Lincoln, NE, USA

    Kyle H. Holland

  2. Agricultural Research Service, United States Department of Agriculture, and Agronomy and Horticulture Department, University of Nebraska, Emeriti, Lincoln, NE, USA

    James S. Schepers

Authors
  1. Kyle H. Holland
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. James S. Schepers
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to James S. Schepers.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Holland, K.H., Schepers, J.S. Use of a virtual-reference concept to interpret active crop canopy sensor data. Precision Agric 14, 71–85 (2013). https://doi.org/10.1007/s11119-012-9301-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11119-012-9301-6

Keywords

Search

Navigation