Assessment of plant density for barley and wheat using UAV multispectral imagery for high-throughput field phenotyping

Abstract Cereal plant density is a relevant agronomic trait in agriculture and high-throughput phenotyping of plant density is important for the decision-making process in precision farming and breeding. It influences the water as well as the fertilization requirements, the intraspecific competition, and the occurrence of weeds or pathogens. Recent studies have determined plant density using machine-learning approaches and feature extraction. This requires spatially very highly resolved images (0.02 cm) because the accuracy distinctly decreased when images had lower resolution. In this study, we present an approach that uses the linear relationship between plant density manually counted in the field and fractional cover derived from a RGB and a multispectral camera equipped on an unmanned aerial vehicle (UAV). We assumed that at an early seedling stage fractional cover is closely related to the number of plants. Spring barley and spring wheat experiments, each with three genotypes and four different sowing densities, were examined. The practicability and repeatability of the methodology were evaluated with an independent experiment consisting of 42 winter wheat genotypes. This experiment mainly differed for genotypes, sowing density and season. The empirical regression models that make us of multispectral images having a GSD of 0.69 cm were able to determine plant density with a high prediction accuracy for barley and wheat (R2 > 0.91, mean absolute error (MAE)  The method and the use of UAV image data enable high-throughput phenotyping of cereal plant density with uncertainties of less than 10 %.The practicability, repeatability and robustness of the developed approach were demonstrated in this study.