A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes.

It is possible to make inferences regarding the feasibility and applicability of plant high-throughput phenotyping via computer simulations. Protocol validation has been a key challenge to the establishment of high-throughput phenotyping (HTP) in breeding programs. We add to this matter by proposing an innovative way for designing and validating aerial imagery-based HTP approaches with in silico 3D experiments for plant breeding purposes. The algorithm is constructed following a pipeline composed of the simulation of phenotypic values, three-dimensional modeling of trials, and image rendering. Our tool is exemplified by testing a set of experimental setups that are of interest in the context of maize breeding using a comprehensive case study. We report on how the choice of (percentile of) points in dense clouds, the experimental repeatability (heritability), the treatment variance (genetic variability), and the flight altitude affect the accuracy of high-throughput plant height estimation based on conventional structure-from-motion (SfM) and multi-view stereo (MVS) pipelines. The evaluation of both the algorithm and the case study was driven by comparisons of the computer-simulated (ground truth) and the HTP-estimated values using correlations, regressions, and similarity indices. Our results showed that the 3D experiments can be adequately reconstructed, enabling inference-making. Moreover, it suggests that treatment variance, repeatability, and the choice of the percentile of points are highly influential over the accuracy of HTP. Conversely, flight altitude influenced the quality of reconstruction but not the accuracy of plant height estimation. Therefore, we believe that our tool can be of high value, enabling the promotion of new insights and further understanding of the events underlying the practice of high-throughput phenotyping.