Visible Near-infrared Camera Research Articles

Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging.

Photosynthesis is currently measured using time-laborious and/or destructive methods which slows research and breeding efforts to identify crop germplasm with higher photosynthetic capacities. We present a plot-level screening tool for quantification of photosynthetic parameters and pigment contents that utilizes hyperspectral reflectance from sunlit leaf pixels collected from a plot (~2 m×2 m) in <1 min. Using field-grown Nicotiana tabacum with genetically altered photosynthetic pathways over two growing seasons (2017 and 2018), we built predictive models for eight photosynthetic parameters and pigment traits. Using partial least squares regression (PLSR) analysis of plot-level sunlit vegetative reflectance pixels from a single visible near infra-red (VNIR) (400-900 nm) hyperspectral camera, we predict maximum carboxylation rate of Rubisco (Vc,max, R2=0.79) maximum electron transport rate in given conditions (J1800, R2=0.59), maximal light-saturated photosynthesis (Pmax, R2=0.54), chlorophyll content (R2=0.87), the Chl a/b ratio (R2=0.63), carbon content (R2=0.47), and nitrogen content (R2=0.49). Model predictions did not improve when using two cameras spanning 400-1800 nm, suggesting a robust, widely applicable and more 'cost-effective' pipeline requiring only a single VNIR camera. The analysis pipeline and methods can be used in any cropping system with modified species-specific PLSR analysis to offer a high-throughput field phenotyping screening for germplasm with improved photosynthetic performance in field trials.

Estimating biophysical and geometrical parameters of grapevine canopies ('Sangiovese') by an unmanned aerial vehicle (UAV) and VIS-NIR cameras

Three zones of different vine vigour were identified in a mature vineyard (Vitis vinifera 'Sangiovese') to test the potential of the Visible-Near Infrared (VIS-NIR) spectral information acquired from an unmanned aerial vehicles (UAV) in estimating the leaf area index (LAI), leaf chlorophyll, pruning weight, canopy height and canopy volume of grapevines. A significant linear correlation between the normalized differential vegetation index (NDVI) and LAI or between NDVI and leaf chlorophyll was found at day of the year (DOY) 162 and 190, whereas in August the relationship between NDVI and leaf chlorophyll was less evident. The canopy volume of low-vigour (LV) vines was 35 and 45 % of the high-vigour (HV) and medium-vigour (MV) ones, respectively. The pruning weight was linearly correlated with NDVI values of each vigour cohort. A good correlation between the measured canopy volume and UAV-estimated one as well as between measured and estimated canopy height was found. Our results indicated that the combined use of VIS-NIR cameras and UAV is a rapid and reliable technique to determine canopy structure and LAI of grapevine.