Search

Estimating plant nitrogen concentrations (PNCs) with remote sensing technology is critical for ensuring precise field nitrogen (N) management. Compared with other remote sensing platforms, low-altitude unmanned aerial vehicles (UAVs) produce images with high spatial resolutions that can be used to clearly identify soil and vegetation. Previously, many spectral indices were designed to remove soil effects to obtain optimal PNC predictions. Herein, we attempt to enhance the PNC prediction accuracy only by removing soil pixels in high-resolution images. Thus, we aimed to collect a dataset containing different crops and image types to investigate whether removing soil pixels to purify crop spectra can improve PNC estimations. For this purpose, N fertilizer experiments were conducted on cotton (Gossypium hirsutum L.), wheat (Triticum aestivum L.) and maize (Zea mays L.), and multispectral and hyperspectral UAV images and PNCs were collected at different growth stages. The multispectral images had actual high spatial resolutions, while the hyperspectral images had virtual high spatial resolutions constructed by fusing high resolution panchromatic images and coarse resolution hyperspectral images. These images represent two typical UAV image types. First, for each crop, the relative changes and driving forces associated with the purified and nonpurified spectra were analyzed under different growth stage, N treatment. Then, three commonly used methods, the spectral index (SI), partial least squares regression (PLSR) and artificial neural network (ANN) methods were used to design PNC prediction model using purified and nonpurified spectra respectively. The results showed the differences between purified and nonpurified spectra were affected by the proportion of crop pixel, sunlit soil pixel and sunshade soil pixel in image. This influence had various trends and magnitudes among different N treatment, growth stages and crop types. It is better to remove soil pixels in imagery, when designing PNC prediction model for plants across growth stages, crop types or even in a single growth stage. The results from actual high spatial resolution images demonstrated this point, with the best PNC prediction model from purified spectra. When considering virtual high spatial resolution image, as the spectrum obtained for each vegetation pixel still represented a mixed vegetation and soil spectrum, removing soil pixels showed no improved performance for PNC estimation. These results provide a reference for others to reasonably choose an optimal data-processing method for constructing PNC prediction models. • Plant nitrogen concentration can be estimated using UAV-based images. • Purified and nonpurified spectra exist significant difference. • Plant nitrogen concentration can be better estimated using purified spectra. [ABSTRACT FROM AUTHOR]

Cadmium (Cd) toxicity in agricultural soils is serious concern these days which needs continuous attention. Little is known about the combined use of berseem and/or maize residues soil applied as a green manure alone or along with foliar dressing of zinc oxide nanoparticles (ZnONPs) on Cd accumulation in wheat (Triticum aestivum L.). A pot experiment under ambient conditions with wheat grown in Cd-contaminated soil was performed after soil applied different green manure amendments and foliar dressing of ZnONPs was done during plant growth and plants were harvested at full maturity. Compared with control, plant growth attributes and biomass of above ground parts substantially increased with applied amendments being maximum with combined use of ZnONPs + B75 (berseem residue, 75 mg/kg) followed by ZnONPs + M75 (maize residue, 75 mg/kg). All the treatments improved the leaf chlorophyll contents and improved the leaf antioxidant enzyme activities thereby reduced the leaf electrolyte leakage. The Cd accumulation in roots and aboveground parts of the wheat was reduced especially in ZnONPs + B75 followed by ZnONPs + M75. The higher rate of soil applied amendments along with NPs minimized the available Cd in soil extracts but soil post-harvest pH was not much affected by the applied amendments. In conclusion, incorporation of berseem and maize residues as a green manure applied in Cd-contaminated soil combined with foliar NPs may decrease Cd phytoavailability and its accumulation in wheat grains. However, substantial field studies are required under various environmental conditions before final recommendations at field levels. [Display omitted] • The highest Cd concentrations were found in plants without any treatments. • ZnONPs and green manure increased growth and biomass of wheat. • ZnONPs and green manure enhanced activities of antioxidants enzymes in leaves. • ZnONPs and green manure decreased Cd contents in grains of wheat. [ABSTRACT FROM AUTHOR]