Hyperspectral aerial imagery for detecting nitrogen stress in two potato cultivars

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Computers and Electronics in Agriculture, ISSN: 0168-1699, Vol: 112, Page: 36-46

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Tyler J. Nigon; David J. Mulla; Carl J. Rosen; Yafit Cohen; Victor Alchanatis; Joseph Knight; Ronit Rud
Elsevier BV
Agricultural and Biological Sciences; Computer Science
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article description
To use remotely sensed spectral data for determining rates and timing of variable rate nitrogen (N) applications at a commercial scale, the most reliable indicators of crop N status must be determined. This study evaluated the ability of hyperspectral remote sensing to predict N stress in potatoes ( Solanum tuberosum ) during two growing seasons (2010 and 2011). Spectral data were evaluated using ground based measurements of leaf N concentration. Two canopy-scale hyperspectral images were acquired with an AISA-Eagle hyperspectral camera in both years. The experiment included five N treatments with varying rates and timing of N fertilizer and two potato cultivars, Russet Burbank (RB) and Alpine Russet (AR). Partial Least Squares regression (PLS) models resulted in the best prediction of leaf N concentration ( r 2 = 0.79, Root Mean Square Error of Cross Validation (RMSECV) = 14% across dates for RB; r 2 = 0.77, RMSECV = 13% across dates for AR). Applying the Nitrogen Sufficiency Index (NSI) formula to spectral indices/models made them mostly insensitive to the effects of cultivar. The most promising technique for determining N stress in potato based on spectral indices was found to be the MERIS Terrestrial Chlorophyll Index (MTCI) due to a combination of relatively high r 2 values, lower RMSECVs, and high accuracy assessment. Pairwise comparison tests from the means separation showed that spectral indices/models from the imagery resulted in more statistically significant groupings of crop stress levels for the spectra than leaf N concentration because canopy-scale spectral data are affected by both tissue N concentration and biomass. The results of this study suggest that upon proper sensor calibration, canopy-scale spectral data may be the most sensitive tool available to detect N status of a potato crop.