ABSTRACT High-throughput phenotyping using spectral reflectance measurements offers the potential to provide more information for making better-informed management decisions at the crop canopy level in real time. The aim of this study was to investigate the suitability of hyperspectral reflectance measurements of the crop canopy for the assessment of biomass, nitrogen concentration, nitrogen uptake, relative chlorophyll contents, and yield in 2 peanut cultivars, Giza 5 and Giza 6. Peanuts were grown under field conditions and subjected to 3 doses of nitrogen fertilizer with or without the application of 2 bio-fertilizers, Bradyrhizobium spp. or plant growth-promoting rhizobacteria. Simple linear regression of normalized difference spectral indices and partial least square regression (PLSR) were employed to develop predictive models to estimate the measured parameters. The tested spectral reflectance indices were significantly related to all measured parameters with R2 of up to 0.89. The spectral reflectance index values differed at the same level of nitrogen fertilizer, as well as among the 3 levels of nitrogen fertilizer application for inoculation with Bradyrhizobium and co-inoculation with Bradyrhizobium and plant growth-promoting rhizobacteria. The calibration models of PLSR data analysis further improved the results, with R2 values reaching 0.95. The overall results of this study indicate that hyperspectral reflectance measurements monitoring peanut plants enable rapid and non-destructive assessment of biomass, nitrogen status, and yield parameters of peanut cultivars subjected to various agronomic treatments.

ABSTRACT Spectral remote sensing offers the potential to provide more information for making better-informed management decisions at the crop canopy level in real time. In contrast, the traditional methods for irrigation management are generally time-consuming, and numerous observations are required to characterize them. The aim of this study was to investigate the suitability of hyperspectral reflectance measurements of remote sensing technique for salinity and water stress condition. For this, the spectral indices of 5 maize cultivars were tested to assess canopy water content (CWC), canopy water mass (CWM), biomass fresh weight (BFW), biomass dry weight (BDW), cob yield (CY), and grain yield (GY) under full irrigation, full irrigation with salinity levels, and the interaction between full irrigation with salinity levels and water stress treatments. The results showed that the 3 water spectral indices (R970 − R900)/(R970 + R900), (R970 − R880)/(R970 + R880), and (R970 − R920)/(R970 + R920) showed close and highly significant associations with the mentioned measured parameters, and coefficients of determination reached up to R2 = 0.73*** in 2013. The model of spectral reflectance index (R970 − R900)/(R970 + R900) of the hyperspectral passive reflectance sensor presented good performance to predict the CY, GY, and CWC compared to CWM, BFW, and BDW under full irrigation with salinity levels and the interaction between full irrigation with salinity levels and water stress treatments. In conclusion, the use of spectral remote sensing may open an avenue in irrigation management for fast, high-throughput assessments of water status, biomass, and yield of maize cultivars under salinity and water stress conditions.