1 November 2007 Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption
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J. of Applied Remote Sensing, 1(1), 013547 (2007). doi:10.1117/1.2824287
Abstract
A low-altitude remote sensing (LARS) system with an unmanned radio-controlled helicopter platform was used to acquire high-quality images of land and crop properties with higher spatial and temporal resolution. It is vital to visualize the relationship of LARS-based images with crop parameters, such as crop nutrient levels, etc. Five N-treatment (0, 33, 66, 99 and 132 kg ha-1) rates with three replications each were arranged in a randomized manner for testing the LARS image acquisition system. Images were taken by the image acquisition unit of the system operated at a height of 20 m over the experimental plots. The coefficient of determination (r2) between N-treatments against NDVIlars, NDVIspectro, GNDVIlars, and chlorophyll content estimated from leaf radiance values were in the range from 0.70 to 0.90, showing a high level of correlation between them. The test to verify the suitability of LARS-based images against spectrophotometer readings showed linear variation for the NDVI index with r2 of 0.70 and 0.80 for 45-day-old and 65-day-old crops, respectively, Linear models were also developed to estimate chlorophyll content from NDVIlars and GNDVIlars index values, from the images, with better correlation for the latter (r2 ≈ 0.82) and subsequently could determine the nitrogen deficiency level. The yield estimation model, with higher r2 values of 0.95 and 0.98 for NDVIlars and GNDVIlars, respectively, further justified the suitability of the LARS system.
Kishore C. Swain, Hemantha P.W. Jayasuriya, Vilas M. Salokhe, "Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption," Journal of Applied Remote Sensing 1(1), 013547 (1 November 2007). http://dx.doi.org/10.1117/1.2824287
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KEYWORDS
Nitrogen

Image acquisition

Agriculture

Remote sensing

Spectrophotometry

Image analysis

Vegetation

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