Application of a low-cost camera on a UAV to estimate maize nitrogen-related variables
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.
KeywordsCIR camera UAV Colorgrams Vegetation indices Maize
Funding was supported by MIPAAF (Grant No. D.M no. 27335/7303/10).
- 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.
- 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
- 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.
- 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.
- 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
- 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
- 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
- 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
- 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
- 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