Potential use of hyperspectral data to monitor sugarcane nitrogen status

Juliano Araujo Martins, Peterson Ricardo Fiorio, Pedro Paulo Da Silva Barros, José Alexandre Melo Demattê, José Paulo Molin, Heitor Cantarella, Christopher Michael Usher Neale

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Nitrogen management in crops is a key activity for agricultural production. Methods that can determine the levels of this element in plants in a quick and non-invasive way are extremely important for improving production systems. Within several fronts of study on this subject, proximal and remote sensing methods are promising techniques. In this regard, this research sought to demonstrate the relationships between variations in leaf nitrogen content (LNC) and sugarcane spectral behaviour. The work was carried out in three experimental areas in São Paulo State, Brazil, with different soils, varieties and nitrogen rates during the 2012/13 and 2013/14 seasons. A significant correlation was observed between the LNC and variations in the sugarcane spectra. The green and red-edge spectral bands were the most consistent and stable predictors of LNC among the evaluated harvests. Stepwise multiple linear regression analysis (MSLR) generated better models for LNC estimation when calibrated with experimental area, independent of the variety. The present research demonstrates that specific wavelengths are associated with the variation in LNC in sugarcane, and these are reported in the green region (near 550 nm) and in the red-edge wavelengths (680 to 720 nm). These results may help in future research on the direct in situ application of nitrogen fertilizers.

Original languageEnglish (US)
Article numbere47632
Pages (from-to)1-13
Number of pages13
JournalActa Scientiarum - Agronomy
Volume43
DOIs
StatePublished - 2021

Keywords

  • Crop
  • Management
  • Models
  • Regression
  • Sensors

ASJC Scopus subject areas

  • Agronomy and Crop Science

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