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Current methods in measuring maize ( Zea mays L.) southern rust ( Puccinia polyspora Underw.) and subsequent crop senescence require expert observation and are resource-intensive and prone to subjectivity. In this study, unoccupied aerial system (UAS) field-based high-throughput phenotyping (HTP) was employed to collect high-resolution aerial imagery of elite maize hybrids planted in the 2020 and 2021 growing seasons, with 13 UAS flights obtained from 2020 and 17 from 2021. In total, 36 vegetation indices (VIs) were extracted from mosaicked aerial images that served as temporal phenomic predictors for southern rust scored in the field and senescence as scored using UAS-acquired mosaic images. Temporal best linear unbiased predictors (TBLUPs) were calculated using a nested model that treated hybrid performance as nested within flights in terms of rust and senescence. All eight machine learning regressions tested (ridge, lasso, elastic net, random forest, support vector machine with radial and linear kernels, partial least squares, and k-nearest neighbors) outperformed a general linear model with both higher prediction accuracies (92–98%) and lower root mean squared error (RMSE) for rust and senescence scores (linear model RMSE ranged from 65.8 to 2396.5 across all traits, machine learning regressions RMSE ranged from 0.3 to 17.0). UAS-acquired VIs enabled the discovery of novel early quantitative phenotypic indicators of maize senescence and southern rust before being detectable by expert annotation and revealed positive correlations between grain filling time and yield (0.22 and 0.44 in 2020 and 2021), with practical implications for precision agricultural practices.

Unoccupied aerial systems (UAS) based high throughput phenotyping studies require further investigation to combine different environments and planting times into one model. Here 100 elite breeding hybrids of maize ( Zea mays L.) were evaluated in two environment trials–one with optimal planting and irrigation (IHOT), and one dryland with delayed planting (DHOT). RGB (Red-Green-Blue) based canopy height measurement (CHM) and vegetation indices (VIs) were estimated from a UAS platform. Time series and cumulative VIs, by both summation (ΣVI-SUMs) and area under the curve (ΣVI-AUCs), were fit via machine learning regression modeling (random forest, linear, ridge, lasso, elastic net regressions) to estimate grain yield. VIs were more valuable predictors of yield to combine different environments than CHM. Time series VIs and CHM produced high accuracies (~68–72%), but inconsistent models. A little sacrifice in accuracy (~60–65%) produced consistent models using ΣVI-SUMs and CHM during pre-reproductive vegetative growth. Absence of VIs produced poorer accuracies (by about ~5–10%). Normalized difference type VIs produced maximum accuracies, and flowering times were the best times for UAS data acquisition. This study suggests that the best yielding varieties can be accurately predicted in new environments at or before flowering when combining multiple temporal flights and predictors.

Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.

Plant height (PHT) in maize (Zea mays L.) has been scrutinized genetically and phenotypically due to relationship with other agronomically valuable traits (e.g., yield). Heritable variation of PHT is determined by many discovered quantitative trait loci; however, phenotypic effects of such loci often lack validation across environments and genetic backgrounds, especially in the hybrid state grown by farmers rather than the inbred state more often used by geneticists. A previous genome-wide association study using a topcrossed hybrid diversity panel identified two novel quantitative trait variants controlling both PHT and grain yield. Here, heterogeneous inbred families demonstrated that these two loci, characterized by two single nucleotide polymorphisms (SNPs), cause phenotypic variation in inbred lines, but that size of these effects were variable across four different genetic backgrounds, ranging from 1 to 10 cm. Weekly unoccupied aerial system flights demonstrated the two SNPs had larger effects, varying from 10 to 25 cm, in early growth while effects decreased toward the end of the season. These results show that allelic effect sizes of economically valuable loci are both dynamic in temporal growth and dynamic across genetic backgrounds, resulting in informative phenotypic variability overlooked following traditional phenotyping methods. Public genotyping data show recent favorable allele selection in elite temperate germplasm with little change across tropical backgrounds. As these loci remain rarer in tropical germplasm, with effects most visible early in growth, they are useful for breeding and selection to expand the genetic basis of maize.

Unoccupied aerial systems (UAS) based high throughput phenotyping studies require further investigation to combine different environments and planting times into one model. Here 100 elite breeding hybrids of maize (Zea mays L.) were evaluated in two environment trials-one with optimal planting and irrigation (IHOT), and one dryland with delayed planting (DHOT). RGB (Red-Green-Blue) based canopy height measurement (CHM) and vegetation indices (VIs) were estimated from a UAS platform. Time series and cumulative VIs, by both summation ([SIGMA]VI-SUMs) and area under the curve ([SIGMA]VI-AUCs), were fit via machine learning regression modeling (random forest, linear, ridge, lasso, elastic net regressions) to estimate grain yield. VIs were more valuable predictors of yield to combine different environments than CHM. Time series VIs and CHM produced high accuracies (~68-72%), but inconsistent models. A little sacrifice in accuracy (~60-65%) produced consistent models using [SIGMA]VI-SUMs and CHM during pre-reproductive vegetative growth. Absence of VIs produced poorer accuracies (by about ~5-10%). Normalized difference type VIs produced maximum accuracies, and flowering times were the best times for UAS data acquisition. This study suggests that the best yielding varieties can be accurately predicted in new environments at or before flowering when combining multiple temporal flights and predictors.

Unoccupied aerial systems (UAS) based high throughput phenotyping studies require further investigation to combine different environments and planting times into one model. Here 100 elite breeding hybrids of maize (Zea mays L.) were evaluated in two environment trials–one with optimal planting and irrigation (IHOT), and one dryland with delayed planting (DHOT). RGB (Red-Green-Blue) based canopy height measurement (CHM) and vegetation indices (VIs) were estimated from a UAS platform. Time series and cumulative VIs, by both summation (ΣVI-SUMs) and area under the curve (ΣVI-AUCs), were fit via machine learning regression modeling (random forest, linear, ridge, lasso, elastic net regressions) to estimate grain yield. VIs were more valuable predictors of yield to combine different environments than CHM. Time series VIs and CHM produced high accuracies ( 68–72%), but inconsistent models. A little sacrifice in accuracy ( 60–65%) produced consistent models using ΣVI-SUMs and CHM during pre-reproductive vegetative growth. Absence of VIs produced poorer accuracies (by about 5–10%). Normalized difference type VIs produced maximum accuracies, and flowering times were the best times for UAS data acquisition. This study suggests that the best yielding varieties can be accurately predicted in new environments at or before flowering when combining multiple temporal flights and predictors.

Studies of spin dynamics of highly polarized dilute mixtures of 3He in superfluid 4He have been performed by various researchers over the past three decades. One series of experiments performed at Cornell University in the early 1990as revealed a novel long timescale excitation. We present the numerical solution of the non-linear Leggett spin dynamics equation in one spatial dimension subject to boundary conditions consistent with the Cornell experiments. Experimentally observed phenomena are composed of trains of bursts in the transverse magnetization lasting several seconds. The simulations capture the time evolution of the individual bursts localized in time. Preliminary results of two dimensional simulations are also presented.

Studies of spin dynamics of highly polarized dilute mixtures of 3 He in superfluid 4 He have been performed by various researchers over the past three decades. One series of experiments performed at Cornell University in the early 1990’s revealed a novel long timescale excitation. We present the numerical solution of the non-linear Leggett spin dynamics equation in one spatial dimension subject to boundary conditions consistent with the Cornell experiments. Experimentally observed phenomena are composed of trains of bursts in the transverse magnetization lasting several seconds. The simulations capture the time evolution of the individual bursts localized in time. Preliminary results of two dimensional simulations are also presented.

Among the elements, Helium has the largest steps among its internal energy structure that can keep for long periods of time, hence the metastable helium moniker. It is referred to as a “nano-grenade” in some circles because of how much energy it can deliver to a space roughly the size of an atom. This work demonstrates a method to create metastable helium abundantly and it is used to excite trace amounts of oxygen to the point where the signal received from the oxygen was larger than the signal received from the helium in a cold atomized jet. Further cooling of the jet and turbulence added by a liquid helium surface worked to increase the oxygen signal and decrease the helium signal. This work investigates the possibility of forming a strong metastable helium source from a flowing helium gas jet excited by passing through ring electrodes introduced into a cryogenic environment using evaporated helium as a buffer gas. Prior study of luminescence from trace gases at cold helium temperatures is virtually absent and so it is the motivation for this work to blaze the trail in this subject. The absence of ionic oxygen spectral lines from the transfer of energy that was well over the first ionization potential of oxygen made for a deeper understanding of collision dynamics with multiple collision partners. This opened the possibility of using the high energy states of oxygen after metastable transfer as a lasing transition previously unavailable and a preliminary analysis suggested that the threshold for lasing action should be easily overcome if feedback were introduced by an optical cavity. To better understand the thermodynamics of the jet it was proposed to use diatomic nitrogen as an in situ thermometer, investigating whether the rotational degrees of freedom of the nitrogen molecule were in thermal equilibrium with the surrounding environment. If the gas was truly in thermodynamic equilibrium then the temperature given by the method of using collisions of a buffer gas and the rotational temperature determined spectroscopically should be in agreement. It was found that metastable transfer from helium in the jet provides enough energy to create a population of hot molecules of nitrogen that never reach thermal equilibrium with the buffer gas which is almost entirely made up of ground state helium atoms. This effect can be corrected for by adding some significant changes to the optical setup but are not outside the realm of possibility as they have been designed and built for other experiments. Therefore the rotational spectra of nitrogen could be used as an in situ thermometer at cryogenic temperatures with metastable helium present provided the hot molecule contribution due to collisions of the second kind of nitrogen and helium is measured and removed.

The second hyperpolarizability of a molecule is the microscopic version of the third order susceptibility. Direct measurements of the ratio of the second hyperpolarizability of carbon tetrachloride to diatomic nitrogen are made possible through electric field induced second harmonic generation. Whenever the dispersion of the second hyperpolarizability is not negligible, there should be deviations from Kleinman symmetry. Previous experimental data for second hyperpolarizability of this molecule have only been at two frequencies and theory predicts the zero frequency value. In order to provide for a better extrapolation to zero frequency, additional gas phase measurements of this ratio at optical frequencies are presented and discussed.