In recent years, the use of field-based high-throughput phenotyping (FHTP) has surged across diverse disciplines. Particularly, it has gained significant traction in agricultural research, enabling scientists to efficiently gather extensive data for a deeper understanding of plant biology in the context of plant growth dynamics. This abstract aims to demonstrate potential applications of data obtained through high-throughput phenotyping in the fields of plant biology and predictive plant breeding.The study utilized temporal phenotype data derived from repetitive drone flights equipped with various sensors. These data were incorporated into a novel mixed model, providing insights into the temporal genetic effects on different genotypes/plants. Gaussian or Lorentzian peak models, as well as Functional Principal Component analysis, were employed to characterize the growth patterns of various genotypes in diverse environments. The research revealed that Temporal Effect Sizes of Quantitative Trait Loci (QTLs) influence growth differently across time points, highlighting the dynamic nature of plant development. Furthermore, the study uncovered time-dependent associations between genotypes and their environments based on temporal phenotype values.The predictive capability of temporal phenomic data was found to surpass that of genomic data in predicting complex traits in maize. However, the combination of phenomic and genomic data consistently yielded the most accurate predictions for complex traits. By analyzing drone flights at specific growth stages, the study quantified physiological traits such as senescence progression across multiple time points. This analysis led to the calculation of new traits, including days to senescence and grain filing period, providing valuable insights into plant development and growth dynamics.

Field Based High Throughput Phenotyping Enables the Discovery of Loci Linked to Senescence and Grain Filling Period ORCiD: [Alper Adak; 0000-0002-2737-8041] Keywords: Grain filling period, field-based high throughput phenotyping, days to senescence, temporal phenotype. Senescence occurs progressively over time and is variable among different genotypes. To examine the temporal and genetic variation of senescence, 280 maize hybrids and 520 maize recombinant inbred lines (RILs) grown in 2017 and 2018 were investigated. Hybrids were grown in late and optimal planting trials; RILs were grown in irrigated and non-irrigated trials, both based on range-row design with two replications. Two types of Unmanned aerial systems (UAS, also known as UAV or drones) were flown over the germplasm between 14 and 20 times respectively. Temporal senescence of each row-plot in hybrids and RILs was scored visually according to percentile scoring using four to five rectified drone images between ~90 and ~130 days after planting. A mechanistic growth model was fit to each genotype using the temporal senescence scores, resulting in 0.94 and 0.97 R 2 for hybrids and RILs. Days to senescence (DTSE) and grain filling period (GFP) were calculated for each row plot using the developed mechanistic growth model. To predict the genotypic value for each RIL and hybrid, a mixed model with three-way interaction model (Genotype*Flight*Environment) was then run. Correlation was calculated ~0.84 and ~0.88 between grain yield and GFP and DTSE in hybrids. A major quantitative trait locus was also discovered on chromosome 1 (295.5 to 296.8 kb; 15% explained) linked to GFP in RILs. GFP is known to be physiologically important, UAS provided an easily scalable measure which can greatly increase the evaluation of variation in the field.