Abstract
Hyperspectral imaging spectrometers mounted on unmanned aerial vehicle (UAV) can capture high spatial and spectral resolution to provide cotton crop nitrogen status for precision agriculture. The aim of this research was to explore machine learning use with hyperspectral datacubes over agricultural fields. Hyperspectral imagery was collected over a mature cotton crop, which had high spatial (~5.2 cm) and spectral (5 nm) resolution over the spectral range 475–925 nm that allowed discrimination of individual crop rows and field features as well as a continuous spectral range for calculating derivative spectra. The nominal reflectance and its derivatives clearly highlighted the different treatment blocks and were strongly related to N concentration in leaf and petiole samples, both in traditional vegetation indices (e.g., Vogelman 1, R2 = 0.8) and novel combinations of spectra (R2 = 0.85). The key hyperspectral bands identified were at the red-edge inflection point (695–715 nm). Satellite multispectral was compared against the UAV hyperspectral remote sensing’s performance by testing the ability of Sentinel MSI to predict N concentration using the bands in VIS-NIR spectral region. The Sentinel 2A Green band (B3; mid-point 559.8 nm) explained the same amount of variation in N as the hyperspectral data and more than the Sentinel Red Edge Point 1 (B5; mid-point 704.9 nm) with the lower 10 m resolution Green band reporting an R2 = 0.85, compared with the R2 = 0.78 of downscaled Sentinel Red Edge Point 1 at 5 m. The remaining Sentinel bands explained much lower variation (maximum was NIR at R2 = 0.48). Investigation of the red edge peak region in the first derivative showed strong promise with RIDAmid (R2 = 0.81) being the best index. The machine learning approach narrowed the range of bands required to investigate plant condition over this trial site, greatly improved processing time and reduced processing complexity. While Sentinel performed well in this comparison and would be useful in a broadacre crop production context, the impact of pixel boundaries relative to a region of interest and coarse spatial and temporal resolution impacts its utility in a research capacity.
Highlights
When reflectance overover the red edgeedge waswas separated intointo samples withwith
When reflectance over the red edge was separated into samples with N concentration lower or higher than 3%, there was a clear shift in the mean reflectance curve towards the Near Infra-Red band (NIR) region for higher N levels (Figure 3a)
The ability to well as intra-plot variability provides insight for the implementation of precision agriculidentify inter-plot as well as intra-plot variability provides insight for the implementation ture approaches. These results suggest that high resolution hyperspectral remote sensing of precision agriculture approaches
Summary
Being able to remotely and rapidly identify the nitrogen level in plants and tailor farm management to either boost higher yield potential zones or reduce inputs to lower potential areas promises a way to reduce environmental impact while increasing profits. It’s been widely reported for decades that nitrate leaching into the groundwater [5] and emission as oxides into the atmosphere [6] cause extensive local and global environmental damage, with, for example, intensive agriculture in the USA contributing to ~400 hypoxic zones in coastal marine ecosystems from farms upstream [7]. Lint yields have increased through improved plant genetics and management, there is a need to investigate ways to improve nitrogen use efficiency as yield quantities are not uniformly responsive to the high levels of N applied [8]. By identifying N concentration spatially across a cotton crop the factors influencing N use efficiency can be studied, leading to more tailored N management strategies
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