Unmanned Aerial System (UAS)-Based Asymmetric Cotton Growth Model for High Throughput Phenotyping

Mounted with advanced sensors onboard, UAS enables the acquisition of crop data at spatial and temporal scales previously unobtainable via traditional remote sensing methods. A novel framework to monitor cotton growth using asymmetric models from a series of UAS data collected over the growing season is proposed in this study. Canopy Cover (CC), Canopy Height (CH), and Canopy Volume (CV) measurements were extracted from the UAS data and these measurements were fitted with non-linear growth models including 4 parameter Richard model, 5 parameter Richard model, and asymmetric logistic function. Growth rate curves will be generated from the growth curves by calculating first derivatives, and various phenotypic features will be extracted from the growth rate curves including 1) maximum growth rate, 2) days after emergence at maximum growth rate, 3) increasing growth rate slope, 4) decreasing growth rate slope, 5) increasing growth rate duration, 6) decreasing growth rate duration, 7) days above half maximum growth rate. These features will be used to develop models to estimate crop yield from the UAS data. The framework developed in this study is expected to serve as an important tool for cotton breeders.