UAS captured increased genetic variation compared with manual terminal height. There were small significant differences in ground filtering methods to extract plant structure. Higher resolution did not improve imagery informativeness with regard to plant height. Logistic function provides informative phenotypes for temporal maize growth. Correlation and prediction accuracy of grain yield increased by ∼20% with UAS heights. Weekly unmanned aerial system (UAS) imagery was collected over the College Station, TX, 2017 Genomes to Fields (G2F) hybrid trial, across three environmental stress treatments, using two UAS platforms. The high‐altitude (120‐m) fixed‐wing platform increased the fraction of variation attributed to genetics and had highly repeatable (R > 60%) height estimates, increasing the genetic variance explained (10–40%) over traditional terminal plant height measurement (PHTTRML ∼30%), as well as over the low‐altitude rotary‐wing UAS platform (10–20%). A logistic function reduced the dimensionality (>20 flights) of each UAS dataset to three parameters (inflection point, growth rate, and asymptote) and produced a more robust predictive model than independent flight dates, effectively summarizing (R2 > 0.98) the UAS flight dates. The logistic model overcame the need to use specific flight dates when comparing different environments. The UAS height estimates (r = 0.36–0.48) doubled the correlations to grain yield in this G2F experiment compared with PHTTRML (r = 0.23–0.28). Parameters of the logistical function achieved equivalent correlations (r = 0.30–0.46) to individual flight dates (r = 0.36–0.48), improving grain yield prediction by ∼400% (R2 = 0.25–0.34) over PHTTRML (R2 = 0.06–0.08). Incorporating other UAS‐derived parameters beyond plant height may allow yield to be accurately predicted before maturity, speeding breeding programs. A new public R function to generate ESRI shapefiles for plot research is also described.
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.
Cultivation of hemp (Cannabis sativa L.) in tropical and subtropical regions can be challenging if the flowering behavior of a given cultivar is unknown, poorly understood, or not accurately selected for the photoperiod. Identifying cultivars adapted to local environmental conditions is key to optimizing hemp vegetative and flowering performance. We investigated the effects of varying light cycles in regulating extension growth and flowering response of 15 essential oil and 12 fiber/grain hemp cultivars both indoors and outdoors. Plants were subjected to 11 photoperiods in the controlled rooms ranging from 12 to 18 h, and natural day length in the field. The critical photoperiod threshold was identified for seven essential oil cultivars and two fiber/grain cultivars. “Cherry Wine-CC,” “PUMA-3,” and “PUMA-4” had the shortest critical day length between 13 h 45 min and 14 h. The flowering of essential oil cultivars was generally delayed by 1–2 days when the photoperiod exceeded 13 h compared with 12 h, and flowering was further delayed by 7–8 days when the photoperiod exceeded 14 h. In fiber/grain cultivars, flowering was generally delayed by 1–3 days when the day length exceeded 14 h. Flowering for most essential oil cultivars was delayed by 5–13 days under a 14-h photoperiod compared with 13 h 45 min, suggesting a photoperiod difference as little as 15 min can significantly influence the floral initiation of some essential oil cultivars. Cultivars represented by the same name but acquired from different sources can perform differently under the same environmental conditions, suggesting genetic variation among cultivars with the same name. Average days to flower of fiber/grain cultivars was correlated with reported cultivar origin, with faster flowering occurring among northern cultivars when compared with southern cultivars. Plant height generally increased as the day length increased in essential oil cultivars but was not affected in fiber/grain cultivars. In addition, civil twilight of ~2 μmol·m−2·s−1 was discovered to be biologically effective in regulating hemp flowering. Collectively, we conclude that most of the essential oil cultivars and some southern fiber/grain cultivars tested express suitable photoperiods for tropical and sub-tropical region cultivation.
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