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.
Fourier-transform (FT) NIR was applied in this study to determine the eight biochemicals in four types of sorghum samples: hulled grain flours, hull-less grain flours, whole grains, and grain flours.
Nitrogen (N) remobilization is a critical process that provides substantial N to winter wheat grains for improving yield productivity. However, our understanding on the N remobilization efficiency (NRE) is still limited due to the lack of efficient, repeatable approaches for evaluating the NRE. In this study, we provided a proof of concept for estimating N concentration and assessing N remobilization using hyperspectral data of individual organs, which offers a non-chemical and low-cost approach to screen germplasms for an optimal NRE in drought-resistance breeding.
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.
A DH population was used to map the quantitative trait locus (QTL) contributing to HMF. Three traits including anther emergence rate (AER), anther emergence score (AES) and pollen production score (PPS) of the haploid population were evaluated at two locations. We also applied genomic selection to HMF traits, and found satisfied results which might be usefull for breeding.
Three new haploid inducers were developed for screening immature haploid embryos during in vivo haploid induction. MAS of both qhir1 and qhir8 was used for inducers breeding, the efficiency improve greatly.
Here, we present the discovery that mutation of a non-Stock6-originating gene in qhir8, namely, ZmDMP, enhances and triggers haploid induction. ZmDMP was identified by map-based cloning and further verified by CRISPR–Cas9-mediated knockout experiments.
Twin embryo seeds was found during in vivo haploid induction, an enhanced HI ability can increase twin-embryo kernel frequency, and the observed high frequency of both haploid twin-embryo kernels and aneuploidy provides new evidence for chromosome elimination during HI in vivo. The results also provide new ideas for studying the effect of phytohormone with twin-embryo kernel.
The QTL *qhir8* affecting in vivo haploid induction in maize was mapped to a 789 kb region, embryo abortion rate and segregation ratios were analyzed, linkage markers for MAS were developed.