Genomics-assisted breeding methods have been rapidly developed with novel technologies such as next-generation sequencing, genomic selection and genome-wide association study. However, phenotyping is still time consuming and is a serious bottleneck in genomics-assisted breeding. In this study, we established a high-throughput phenotyping system for sorghum plant height and its response to nitrogen availability; this system relies on the use of unmanned aerial vehicle (UAV) remote sensing with either an RGB or near-infrared, green and blue (NIR-GB) camera. We evaluated the potential of remote sensing to provide phenotype training data in a genomic prediction model. UAV remote sensing with the NIR-GB camera and the 50th percentile of digital surface model, which is an indicator of height, performed well. The correlation coefficient between plant height measured by UAV remote sensing (PHUAV) and plant height measured with a ruler (PHR) was 0.523. Because PHUAV was overestimated (probably because of the presence of taller plants on adjacent plots), the correlation coefficient between PHUAV and PHR was increased to 0.678 by using one of the two replications (that with the lower PHUAV value). Genomic prediction modeling performed well under the low-fertilization condition, probably because PHUAV overestimation was smaller under this condition due to a lower plant height. The predicted values of PHUAV and PHR were highly correlated with each other (r = 0.842). This result suggests that the genomic prediction models generated with PHUAV were almost identical and that the performance of UAV remote sensing was similar to that of traditional measurements in genomic prediction modeling. UAV remote sensing has a high potential to increase the throughput of phenotyping and decrease its cost. UAV remote sensing will be an important and indispensable tool for high-throughput genomics-assisted plant breeding.

Sorghum (Sorghum bicolor L. Moench) is a C4 tropical grass that plays an essential role in providing nutrition to humans and livestock, particularly in marginal rainfall environments. The timing of head development and the number of heads per unit area are key adaptation traits to consider in agronomy and breeding but are time consuming and labor intensive to measure. We propose a two-step machine-based image processing method to detect and count the number of heads from high-resolution images captured by unmanned aerial vehicles (UAVs) in a breeding trial. To demonstrate the performance of the proposed method, 52 images were manually labeled; the precision and recall of head detection were 0.87 and 0.98, respectively, and the coefficient of determination (R2) between the manual and new methods of counting was 0.84. To verify the utility of the method in breeding programs, a geolocation-based plot segmentation method was applied to pre-processed ortho-mosaic images to extract >1000 plots from original RGB images. Forty of these plots were randomly selected and labeled manually; the precision and recall of detection were 0.82 and 0.98, respectively, and the coefficient of determination between manual and algorithm counting was 0.56, with the major source of error being related to the morphology of plants resulting in heads being displayed both within and outside the plot in which the plants were sown, i.e., being allocated to a neighboring plot. Finally, the potential applications in yield estimation from UAV-based imagery from agronomy experiments and scouting of production fields are also discussed.

At room temperature, the optical, transport and magnetotransport properties of homo-epitaxial MPCVD diamond layers with boron contents in the 2 × 10 20 to 2 × 10 21 cm -3 range are expected to be governed by the characteristics of the boron impurity band. A comparison of room temperature infrared transmittance, reflectance and visible ellipsometry spectra to temperature-dependent Hall effect and conductivity measurements allows a quantitative determination of optical constants and of transport parameters. The results are discussed in reference to the metallic -insulator transition in heavily doped semiconductors.This description enables us to discuss the Raman spectra of p + monocrystalline diamond, focussing on the polarization dependence of the low energy tail, of the unassigned broad peak observed around 500 cm -1 and of the optical phonon frequency range where the Fano interference occurs. On the basis of the observed scattering selection rules, we propose that these features result from electronic scattering in the impurity band and from the electron -phonon coupling on the boron center.

In this study, we investigated the behavior of Sb(V) during the transformation of poorly crystalline Fe(III) oxyhydroxides (two-line ferrihydrite) with various Sb/Fe molar ratios at pH 6.0. Both XRD and Fe EXAFS analyses confirmed that goethite and hematite are the primary transformation products of the ferrihydrite in the presence of Sb(V). The crystallization kinetics showed that the transformation rate with Sb(V) was approximately the same as that of the control (without Sb(V)), which indicates that the presence of Sb(V) does not influence the transformation rate to a noticeable extent. Throughout the transformation, Sb(V) dominantly partitioned in the solid phase and no desorption of Sb(V) was observed. Furthermore, Sb EXAFS analyses suggested that Sb(V) in the solid phase is structurally incorporated into crystalline goethite and/or hematite generated by the ferrihydrite transformation. Hence, Sb(V) transfers into the thermodynamically stable solids from the metastable ferrihydrite with aging, indicating a rigid immobilization of Sb(V). These findings are valuable for making predictions on the long-term fate of Sb associated with ferrihydrite in natural environments.