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 99^th 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.

Five essential oil hemp (Cannabis sativaL.) cultivars (Cherry Blossom, Cherry Blossom (Tuan), Berry Blossom, Cherry Wine, and Cherry Blossom × Trump) were treated with six fertigation treatments to quantify the effects of synthetic fertilizer rates and irrigation electrical conductivity on plant growth, biomass accumulation, and cannabinoid profiles. Irrigation water was injected with a commercial 20-20-20 fertilizer at rates of 0, 50, 150, 300, 450, and 600 ppm nitrogen equating to 0.33 (control), 0.54, 0.96, 1.59, 2.22, and 2.85 dS m⁻¹, respectively. Plants were grown under artificial lighting (18 hr) to maintain vegetative growth for eight weeks, followed by an eight-week flowering period. High linear relationship between chlorophyll concentrations and SPAD-502 measurements validated the utilization of SPAD meters to rapidly identify nutrient deficiency in essential oil hemp. Cultivars expressed significant variation in plant height and cannabinoid profiles (% dry mass), in concurrence with limited biomass and cannabinoid (g per plant) yield variation. Cherry Blossom was the best performing cultivar and Cherry Wine was the least productive. Variation in plant growth, biomass, and cannabinoid concentrations were affected to a greater extent by fertilizer rates. Optimal fertilizer rates were observed at 50 ppm N, while increased fertilizer rates significantly reduced plant growth, biomass accumulation, and cannabinoid concentrations. Increased fertilizer rates (> 300 ppm N) resulted in compliant THC levels (< 0.3%), although when coupled with biomass reductions resulted in minimal cannabinoid yields. Additionally, CBD concentration demonstrated higher sensitivity to increased fertilizer rates (> 300 ppm N) compared to THC and CBG (> 450 ppm N). The results of this study can serve as a guide when using fertigation methods on essential oil hemp cultivars; although results may differ with cultivar selection, environmental conditions, and management practices.

A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade, genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red–green–blue (RGB) bands over 15 growth time points and multispectral (RGB, red-edge and near infrared) bands over 12 time points were compared across 280 unique maize hybrids. Through cross-validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP), outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in 3 other cross-validation scenarios. Genome-wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5% of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51% of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, TPP appeared to work successfully on unrelated individuals unlike GP.

High throughput phenotyping (HTP) has expanded the dimensionality of data in plant research, however HTP has resulted in few novel biological discoveries to date. Field based high-throughput phenotyping (FHTP), using small unoccupied aerial vehicles (UAV) equipped with imaging sensors can be deployed routinely to monitor segregating plant population interactions with the environment under biologically meaningful conditions. Here, flowering dates and plant height, important phenological fitness traits, were collected on 520 segregating recombinant maize inbred lines (RILs) in both irrigated and drought stress trials in 2018. Using UAV phenomic, SNP genomic, as well as combined data, flowering times were predicted using several scenarios. Untested genotypes were predicted with 0.58, 0.59 and 0.41 prediction ability for anthesis, silking and terminal plant height using genomic data; but prediction ability increased 0.77, 0.76 and 0.58 when phenomic and genomic data were used together. Using the phenomic data in a genome-wide association study, a heat related candidate gene (GRMZM2G083810; hsp18f) was discovered using temporal reflectance phenotypes belonging to flowering times (both irrigated and drought) trials where heat stress also peaked. Thus, a relation between plants and abiotic stresses belonging to a specific time of growth was revealed only through use of temporal phenomic data. Overall, this study showed that (i) it is possible to predict complex traits using high dimensional phenomic data between different environments, and (ii) temporal phenomic data can reveal time-dependent association between genotypes and abiotic stresses, which can help understand mechanisms to develop resilient plants.

Multiparent advanced generation inter‐cross (MAGIC) populations can provide improved genetic mapping resolution by increasing allelic diversity and effective recombination. The Four Parent Maize (FPM; Zea mays L.) population implemented five different mating designs used in MAGIC and bi‐parental populations to compare empirical effects on genetic resolution and power of quantitative trait locus (QTL) detection; the combined population here comprised of 1149 individuals with 118,509 genetic markers. Measurements were recorded for plant height (PH), ear height (EH), days to anthesis (DTA) and silking (DTS) in seven environments, spanning three years. Linkage disequilibrium (LD) analysis of subpopulations indicated MAGIC population designs should incorporate generations of intermating to overcome initial LD increase caused by population admixture in a non‐intermated four parent population (4way0sib). A 3‐ to 4‐fold increase in genetic resolution ( r ² <0.8) and a 2.5‐fold decrease in the extent of LD decay ( r ² <0.2) compared to the biparental populations was found for the four parent cross at the third generation of intermating (4way3sib). Power of QTL detection was affected to a greater extent by sample size rather than by mating designs. The FPM power simulations indicated that MAGIC populations have the ability to meet or exceed the mapping power of nested association panels with fewer individuals and diversity inputs. Using association mapping software we identified 2, 5, 7, and 6 QTL for PH, EH, DTA, and DTS, respectively. The FPM population is a valuable resource for quantifying empirical improvements of parent number, intermating, and the number of progeny for QTL linkage mapping.

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.