Immediate detection and prediction tools for interactive stresses are essential to avoid yield losses. This study investigated the capability of hyperspectral (HSI) and chlorophyll fluorescence imaging (CFI) to characterize individual and interactive frost (−4 ◦C) and drought (40% soil moisture content) stress responses at the booting stage of wheat under controlled environmental conditions. Spectral indices and enzyme activities showed a strong correlation in determining yield losses. Hence, HSI and CFI techniques successfully detected combined frost and drought stresses for rapid quantification.
One of the problems of optical remote sensing of crop above-ground biomass (AGB) is that vegetation indices (VIs) often saturate from the middle to late growth stages. This study focuses on combining VIs acquired by a consumer-grade multiple-spectral UAV and machine learning regression techniques to (i) determine the optimal time window for AGB estimation of winter wheat and to (ii) determine the optimal combination of multi-spectral VIs and regression algorithms. Monitoring AGB prior to flowering was found to be more effective than post-flowering. Moreover, this study demonstrates that it is feasible to estimate AGB for multiple growth stages of winter wheat by combining the optimal VIs and PLSR and RF models, which overcomes the saturation problem of using individual VI-based linear regression models.
Predicting micronutrients in wheat kernel and flour using hyperspectral imaging. The prediction of micronutrients was superior based on the kernel spectra compared to the use of flour spectra. The Ca, Mg, Mo and Zn nutrients in wheat kernel/flour were predicted with a high credibility.