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Precision Agriculture

, Volume 16, Issue 1, pp 15–28 | Cite as

Comparison of crop canopy reflectance sensors used to identify sugarcane biomass and nitrogen status

  • Lucas R. AmaralEmail author
  • José P. Molin
  • Gustavo Portz
  • Felipe B. Finazzi
  • Lucas Cortinove
Article

Abstract

Canopy reflectance sensors are useful tools for guiding nitrogen fertilization in crops. However, studies of sugarcane comparing the efficiency of different devices for determining crop parameters are scarce. The objective of this study was to compare the performance of canopy sensors in detecting sugarcane variability. Four nitrogen (N) rate experiments were conducted (plots), along with biomass sampling, chlorophyll meter readings and leaf N concentration determination in another four fields by canopy sensor readings guided samplings. The examined canopy sensors were GreenSeeker and two Crop Circle models (ACS-210 and ACS-430), which allowed the calculation of different normalized difference vegetation index (NDVI) configurations. Neither of the canopy sensors showed a correlation with the obtained chlorophyll meter readings (SPAD) or leaf N content within the fields, while high correlations with above-ground biomass were found, indicating that the plant population and vigor interfered with the canopy sensor readings. The devices showed similar suitability in terms of N rate differentiation and correlations with crop parameters. However, the NDVI calculated from the Crop Circle ACS-430 sensor using a red-edge waveband (NDRE) showed the best results, displaying the greatest range of measured values and the highest sensitivity as a biomass predictor. Regardless of the canopy sensor and wavebands used, all of the analyzed sensors proved to be good tools for identifying the variability of crop development in sugarcane fields.

Keywords

Precision agriculture Remote sensing Proximal sensing Vegetation indices NDVI 

Notes

Acknowledgments

This work would not have been possible without the collaboration of São Martinho’s Mill team and Máquinas Agrícolas Jacto. We acknowledge the Research and Projects Financing (FINEP) received from the Ministry of Science and Technology, through the PROSENSAP project for financial support and the São Paulo Research Foundation (FAPESP) for providing a doctoral scholarship to the first author. We also thank the Agronomic Institute of Campinas (IAC) and Dr. Heitor Cantarella for making available some of their experimental trials.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Lucas R. Amaral
    • 1
    Email author
  • José P. Molin
    • 1
  • Gustavo Portz
    • 1
  • Felipe B. Finazzi
    • 1
  • Lucas Cortinove
    • 1
  1. 1.Precision Agriculture Lab., Department of Biosystems Engineering, Luiz de Queiroz College of AgricultureUniversity of São PauloPiracicabaBrazil

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