A computational framework for modeling and predicting maize senescence: integrating UAV phenotyping, logistic growth, and genomics

Abstract

Advances in sensing technologies have led to the emergence of a new paradigm in plant biology and predictive plant breeding able to improve the precision in quantifying and predicting physiological traits such as plant senescence. Here, 517 recombinant inbred lines (RILs) were previously genotyped and phenotyped in this study using an unoccupied aerial system (UAS also known as UAV or drone) equipped with an RGB sensor, enabling efficient monitoring of senescence at multiple developmental stages across 14 flights from 28 to 128 days after planting. Temporal senescence was scored in the last five flights and uniquely subjected to a logistic growth model (R² = 0.99 ± 0.01). Days to Senescence (DTSE) and a modified Grain Filling Period (GFP) were introduced in this study, derived from the logistic growth model using temporal senescence data.

The study also explored the predictive power of logistic growth model-driven genomic (M1) and combined genomic and phenomic (M2) models. The combined model (M2), incorporating phenomic data from vegetation indices (NGRDI and ExR) collected before senescence, outperformed the genomic model (M1), particularly in challenging scenarios involving untested RILs and time points (CV2: 0.32 vs 0.48; CV00: 0.22 vs 0.33), showcasing potential for predictive breeding of delayed senescence. M1 achieved prediction abilities of 0.45 and 0.38 for DTSE and GFP, respectively, which improved to 0.47 and 0.40 with M2.

Overall, this research advances the prediction of temporal senescence dynamics through computational frameworks integrating phenotyping, modeling, and genomic data. These advancements enable the selection of genotypes with optimized senescence rate.