PlantCV is a Python-based image analysis tool that lowers the barrier to entry for complex image analysis workflows in plant phenotyping. To provide support for subsequent analysis steps of measured trait data we have developed pcvr, an R package to assist in common plant phenotyping analyses. The goal of pcvr is to make common statistical analyses both easier and more consistent and to lower the barrier to entry for useful Bayesian methods. Here we demonstrate three pieces of a possible analysis covering single value trait analysis, longitudinal modeling, and multi-value trait analysis.

This study aimed to investigate the physiological and morphological responses of maize seedlings to environmental stressors. High-throughput imaging analysis was used to characterize the stress response phenotypes of 47 distinct maize genotypes exposed to water limitation (40% field capacity), heat (38 °C), and the combination of the two stresses. RGB and NIR images were collected daily, and analyzed with open-source, open-development software PlantCV. Our investigation focused on quantitative measurements of daily area, daily water loss, evaluation of estimated water use efficiency (WUE), and near-infrared (NIR) reflectance as an estimation of water content in tissues. Quantitatively comparing two non-normal distributions, like the NIR histogram data, can be challenging but important, since metrics like median mode values often do not capture variation across a sample. One of the primary obstacles lies in defining an appropriate metric to accurately quantify the variation between two distributions. To analyze NIR reflectance, we evaluated the dissimilarity between pairwise NIR histograms by utilizing the earth mover’s distance (EMD) analysis. The EMD quantifies the difference between two NIR reflectance distributions, and therefore indirectly evaluates the difference in leaf water content for stress vs control conditions. Overall, most genotypes displayed growth reduction under drought and heat. Interestingly, specific lines exhibited heightened WUE under water limitation, suggesting response to water scarcity. EMD results showing dissimilarities in pairwise NIR reflectance of control vs drought, can be used to describe variation in dynamic changes in water content levels among these groups.

High-throughput plant phenotyping is increasingly implemented in a wide array of experimentation and presents challenges both logistically and analytically. Phenotype data are often longitudinal and proper modeling of plant growth requires sophisticated modeling techniques to account for the intra-plant correlations and changing variation over time (heteroskedasticity). For this reason, plant growth is often analyzed by comparing only single time points or the start and end points for inference with no regard for the trends themselves. Single time point analysis can be sufficient for simple biological comparisons, but modeling has the potential to unlock additional insights by utilizing all the information at hand. Current plant growth modeling strategies do account for intra-plant correlations but are still limited to constant variance assumptions and therefore perform sub-optimally. Here we propose a Bayesian hierarchical approach as an alternative method for plant growth modeling by demonstrating the utility of heteroskedastic sub-model parameterizations. We show that accounting for heteroskedasticity greatly improves model accuracy and subsequent inference. Additionally, Bayesian methodologies inherently lend themselves to near real-time model updating and we propose integration with Clowder to facilitate adaptive experimental designs. We show by example the utility of Bayesian updating and how it relates to experimental decision making.