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Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables

  • Martina Corti
  • Daniele Cavalli
  • Giovanni Cabassi
  • Antonio Vigoni
  • Luigi Degano
  • Pietro Marino Gallina
Article
  • 19 Downloads

Abstract

The development of small unmanned aerial vehicles and advances in sensor technology have made consumer digital cameras suitable for the remote sensing of vegetation. In this context, monitoring the in-field variability of maize (Zea mays L.), characterized by high nitrogen fertilization rates, with a low-cost color-infrared airborne system could be the basis for a site-specific nitrogen (N) fertilization support system. An experimental field with different N treatments applied to silage maize was monitored during the years 2014 and 2015. Images of the field and reference destructive measurements of above ground biomass, its N concentration and N uptake were taken at V6 and V9 development stages. Classical normalized difference vegetation indices (NDVI) and the indices adjusted by crop ground cover were calculated and regressed against the measured variables. Finally, image colorgrams were used to explore the potential of band-related information in variable estimation. A colorgram is a linear signal that summarizes the color content of each digital image. It is composed of a sequence of the frequency distribution curves of the camera bands, of their related parameters and of results of the principal components analysis applied to each image. The best predictors were found to be the ground cover and the adjusted green-based NDVI: regression equation at V9 resulted in R2 of 0.7 and RRMSE < 25% in external validation. Colorgrams did not improve prediction performance due to the spectral limitations of the camera. Therefore, the feasibility of the method should be tested in future research. In spite of limitations of sensor setup, the modified camera was able to estimate maize biomass due to the very high spatial resolution. Since the above ground biomass is a robust proxy of N status, the modified camera could be a promising tool for a low-cost N fertilization support system.

Keywords

CIR camera UAV Colorgrams Vegetation indices Maize 

Notes

Funding

Funding was supported by MIPAAF (Grant No. D.M no. 27335/7303/10).

References

  1. Acutis, M., Alfieri, L., Giussani, A., Provolo, G., Di Guardo, A., Colombini, S., et al. (2014). ValorE: An integrated and GIS-based decision support system for livestock manure management in the Lombardy region (northern Italy). Land Use Policy, 41, 149–162.CrossRefGoogle Scholar
  2. Antonelli, A., Cocchi, M., Fava, P., Foca, G., Franchini, G. C., Manzini, D., et al. (2004). Automated evaluation of food colour by means of multivariate image analysis coupled to a wavelet-based classification algorithm. Analytica Chimica Acta, 515(1), 3–13.CrossRefGoogle Scholar
  3. Bastiaanssen, W. G., Molden, D. J., & Makin, I. W. (2000). Remote sensing for irrigated agriculture: Examples from research and possible applications. Agricultural Water Management, 46(2), 137–155.CrossRefGoogle Scholar
  4. Berni, J. A. J., Zarco-Tejada, P. J., Suárez, L., González-Dugo, V., Fereres, E. (2009). Remote sensing of vegetation from UAV platforms using lightweight multispectral and thermal imaging sensors. In The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol. 38(6)). Retrieved from http://www.isprs.org/proceedings/XXXVIII/1_4_7-W5/paper/Jimenez_Berni-155.pdf.
  5. Cavalli, D., Cabassi, G., Borrelli, L., Fuccella, R., Degano, L., Bechini, L., et al. (2014). Nitrogen fertiliser value of digested dairy cow slurry, its liquid and solid fractions, and of dairy cow slurry. Italian Journal of Agronomy, 9(2), 71–78.CrossRefGoogle Scholar
  6. Cavalli, D., Cabassi, G., Borrelli, L., Geromel, G., Bechini, L., Degano, L., et al. (2016). Nitrogen fertilizer replacement value of undigested liquid cattle manure and digestates. European Journal of Agronomy, 73, 34–41.CrossRefGoogle Scholar
  7. Cilia, C., Panigada, C., Rossini, M., Meroni, M., Busetto, L., Amaducci, S., et al. (2014). Nitrogen status assessment for variable rate fertilization in maize through hyperspectral imagery. Remote Sensing, 6, 6549–6565.CrossRefGoogle Scholar
  8. Eitel, J. U. H., Long, D. S., Gessler, P. E., & Hunt, E. R. (2008). Combined spectral index to improve ground-based estimates of nitrogen status in dryland wheat. Agronomy Journal, 100(6), 1694–1702.  https://doi.org/10.2134/agronj2007.0362.CrossRefGoogle Scholar
  9. Geipel, J., Link, J., Wirwahn, J. A., & Claupein, W. (2016). A programmable aerial multispectral camera system for in-season crop biomass and nitrogen content estimation. Agriculture, 6(1), 4.  https://doi.org/10.3390/agriculture6010004.CrossRefGoogle Scholar
  10. Huang, Y., Thomson, S. J., Lan, Y., & Maas, S. J. (2010). Multispectral imaging systems for airborne remote sensing to support agricultural production management. International Journal of Agricultural & Biological Engineering, 3(1), 50–62.Google Scholar
  11. Hunt, E. R., Hively, W. D., Fujikawa, S. J., Linden, D. S., Daughtry, C. S., & McCarty, G. W. (2010). Acquisition of NIR-green-blue digital photographs from unmanned aircraft for crop monitoring. Remote Sensing, 2(1), 290–305.CrossRefGoogle Scholar
  12. Kim, Y., Reid, J. F., & Zhang, Q. (2008). Fuzzy logic control of a multispectral imaging sensor for in-field plant sensing. Computers and Electronics in Agriculture, 60(2), 279–288.CrossRefGoogle Scholar
  13. Lebourgeois, V., Bégué, A., Labbé, S., Houlès, M., & Martiné, J. F. (2012). A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring. Precision Agriculture, 13(5), 525–541.CrossRefGoogle Scholar
  14. Lebourgeois, V., Bégué, A., Labbé, S., Mallavan, B., Prévot, L., & Roux, B. (2008). Can commercial digital cameras be used as multispectral sensors? A crop monitoring test. Sensors, 8(11), 7300–7322.CrossRefPubMedCentralGoogle Scholar
  15. Li, Y., Chen, D., Walker, C. N., & Angus, J. F. (2010). Estimating the nitrogen status of crops using a digital camera. Field Crops Research, 118(3), 221–227.CrossRefGoogle Scholar
  16. Miao, Y., Mulla, D. J., Randall, G. W., Vetsch, J. A., & Vintila, R. (2009). Combining chlorophyll meter readings and high spatial resolution remote sensing images for in-season site-specific nitrogen management of corn. Precision Agriculture, 10(1), 45–62.CrossRefGoogle Scholar
  17. Noh, H., & Zhang, Q. (2012). Shadow effect on multi-spectral image for detection of nitrogen deficiency in corn. Computers and Electronics in Agriculture, 83, 52–57.CrossRefGoogle Scholar
  18. Noh, H., Zhang, Q., Han, S., Shin, B., & Reum, D. (2005). Dynamic calibration and image segmentation methods for multispectral imaging crop nitrogen deficiency sensors. Transactions-American Society Of Agricultural Engineers, 48(1), 393–401.CrossRefGoogle Scholar
  19. Olfs, H.-W., Blankenau, K., Brentrup, F., Jasper, J., Link, A., & Lammel, J. (2005). Soil- and plant-based nitrogen-fertilizer recommendations in arable farming. Journal of Plant Nutrition and Soil Science, 168(4), 414–431.  https://doi.org/10.1002/jpln.200520526.CrossRefGoogle Scholar
  20. Osborne, S. L., Schepers, J. S., & Schlemmer, M. R. (2004). Using multi-spectral imagery to evaluate corn grown under nitrogen and drought stressed conditions. Journal of Plant Nutrition, 27(11), 1917–1929.  https://doi.org/10.1081/LPLA-200030042.CrossRefGoogle Scholar
  21. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66.CrossRefGoogle Scholar
  22. Pauly, K. (2014). Applying conventional vegetation vigor indices to UAS-derived orthomosaics: issues and considerations. In Proceedings of the 12th international conference for precision agriculture. Retrieved from https://www.ispag.org/proceedings/?action=abstract&id=1464.
  23. Pauly, K. (2016). Towards calibrated vegetation indices from UAS-derived orthomosaics. In Proceedings of the 13th international conference for precision agriculture. Retrieved from https://www.ispag.org/proceedings/?action=abstract&id=2073.
  24. Perry, E. M., & Roberts, D. A. (2008). Sensitivity of narrow-band and broad-band indices for assessing nitrogen availability and water stress in annual crop. Agronomy Journal, 100(4), 1211–1219.CrossRefGoogle Scholar
  25. Rasmussen, J., Ntakos, G., Nielsen, J., Svensgaard, J., Poulsen, R. N., & Christensen, S. (2016). Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? European Journal of Agronomy, 74, 75–92.CrossRefGoogle Scholar
  26. Raun, W. R., Solie, J. B., Taylor, R. K., Arnall, D. B., Mack, C. J., & Edmonds, D. E. (2008). Ramp calibration strip technology for determining midseason nitrogen rates in corn and wheat. Agronomy Journal, 100(4), 1088–1093.  https://doi.org/10.2134/agronj2007.0288N.CrossRefGoogle Scholar
  27. Reyniers, M., & Vrindts, E. (2006). Measuring wheat nitrogen status from space and ground-based platform. International Journal of Remote Sensing, 27(3), 549–567.  https://doi.org/10.1080/01431160500117907.CrossRefGoogle Scholar
  28. Ritchie, S. W., Hanway, J. J, & Benson, G. O. (1993). How a corn plant develops. Revised edition Special Report 53. Iowa State University Cooperative Extension Service, Ames, IA, USA.Google Scholar
  29. Rorie, R. L., Purcell, L. C., Karcher, D. E., & King, C. A. (2011a). The assessment of leaf nitrogen in corn from digital images. Crop Science, 51(5), 2174–2180.  https://doi.org/10.2135/cropsci2010.12.0699.CrossRefGoogle Scholar
  30. Rorie, R. L., Purcell, L. C., Mozaffari, M., Karcher, D. E., King, C. A., Marsh, M. C., et al. (2011b). Association of “Greenness” in corn with yield and leaf nitrogen concentration. Agronomy Journal, 103(2), 529–535.  https://doi.org/10.2134/agronj2010.0296.CrossRefGoogle Scholar
  31. Sakamoto, T., Gitelson, A. A., Nguy-Robertson, A. L., Arkebauer, T. J., Wardlow, B. D., Suyker, A. E., et al. (2012a). An alternative method using digital cameras for continuous monitoring of crop status. Agricultural and Forest Meteorology, 154, 113–126.CrossRefGoogle Scholar
  32. Sakamoto, T., Gitelson, A. A., Wardlow, B. D., Arkebauer, T. J., Verma, S. B., Suyker, A. E., et al. (2012b). Application of day and night digital photographs for estimating maize biophysical characteristics. Precision Agriculture, 13(3), 285–301.  https://doi.org/10.1007/s11119-011-9246-1.CrossRefGoogle Scholar
  33. Sripada, R. P., Heiniger, R. W., White, J. G., & Weisz, R. (2005). Aerial color infrared photography for determining late-season nitrogen requirements in corn. Agronomy Journal, 97(5), 1443–1451.CrossRefGoogle Scholar
  34. Swain, K. C., Jayasuriya, H. P. W., & Salokhe, V. M. (2007). Low-altitude remote sensing with unmanned radio-controlled helicopter platforms: A potential substitution to satellite-based systems for precision agriculture adoption under farming conditions in developing countries. International Commission of Agricultural Engineering, 9, 1–16.Google Scholar
  35. Toth, C., & Jóźków, G. (2016). Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 22–36.CrossRefGoogle Scholar
  36. Ulrici, A., Foca, G., Ielo, M. C., Volpelli, L. A., & Fiego, D. P. L. (2012). Automated identification and visualization of food defects using RGB imaging: Application to the detection of red skin defect of raw hams. Innovative Food Science & Emerging Technologies, 16, 417–426.CrossRefGoogle Scholar
  37. Vergara-Díaz, O., Zaman-Allah, M. A., Masuka, B., Hornero, A., Zarco-Tejada, P., Prasanna, B. M., et al. (2016). A novel remote sensing approach for prediction of maize yield under different conditions of nitrogen fertilization. Frontiers in Plant Science, 7(666), 1–13.Google Scholar
  38. Verhoeven, G. J. J. (2010). It’s all about the format–unleashing the power of RAW aerial photography. International Journal of Remote Sensing, 31(8), 2009–2042.CrossRefGoogle Scholar
  39. Wójtowicz, M., Wójtowicz, A., & Piekarczyk, J. (2016). Application of remote sensing methods in agriculture. Communications in Biometry and Crop Science, 11, 31–50.Google Scholar
  40. Zhou, Z., Jabloun, M., Plauborg, F., & Andersen, M. N. (2018). Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato. Computers and Electronics in Agriculture, 144, 154–163.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Martina Corti
    • 1
  • Daniele Cavalli
    • 1
  • Giovanni Cabassi
    • 2
  • Antonio Vigoni
    • 3
  • Luigi Degano
    • 2
  • Pietro Marino Gallina
    • 1
  1. 1.Department of Agricultural and Environmental Sciences – Production LandscapeAgroenergy, Università degli Studi di MilanoMilanItaly
  2. 2.Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, CREA-ZALodiItaly
  3. 3.Sport Turf Consulting-Servizi per l’agricoltura con aeromobili a pilotaggio remotoRescaldinaItaly

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