SpaTemHTP: A Data Analysis Pipeline for Efficient Processing and Utilization of Temporal High-Throughput Phenotyping Data

Abstract

The rapid development of phenotyping technologies over the last years gave the opportunity to study plant development over time. The treatment of the massive amount of data collected by high-throughput phenotyping (HTP) platforms is however an important challenge for the plant science community. An important issue is to accurately estimate, over time, the genotypic component of plant phenotype. In outdoor and field-based HTP platforms, phenotype measurements can be substantially affected by data-generation inaccuracies or failures, leading to erroneous or missing data. To solve that problem, we developed an analytical pipeline composed of three modules: detection of outliers, imputation of missing values, and mixed-model genotype adjusted means computation with spatial adjustment. The pipeline was tested on three different traits (3D leaf area, projected leaf area, and plant height), in two crops (chickpea, sorghum), measured during two seasons. Using real-data analyses and simulations, we showed that the sequential application of the three pipeline steps was particularly useful to estimate smooth genotype growth curves from raw data containing a large amount of noise, a situation that is potentially frequent in data generated on outdoor HTP platforms. The procedure we propose can handle up to 50% of missing values. It is also robust to data contamination rates between 20 and 30% of the data. The pipeline was further extended to model the genotype time series data. A change-point analysis allowed the determination of growth phases and the optimal timing where genotypic differences were the largest. The estimated genotypic values were used to cluster the genotypes during the optimal growth phase. Through a two-way analysis of variance (ANOVA), clusters were found to be consistently defined throughout the growth duration. Therefore, we could show, on a wide range of scenarios, that the pipeline facilitated efficient extraction of useful information from outdoor HTP platform data. High-quality plant growth time series data is also provided to support breeding decisions. The R code of the pipeline is available at https://github.com/ICRISAT-GEMS/SpaTemHTP.

A two-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

“…References for stage-wise analysis of HTP data include the work by van Eeuwijk et al (2019) 2 , where the authors propose to first estimate time-series of spatially adjusted genotypic means from low-level phenotypic traits, that are then subject to temporal dynamic modelling. A similar approach is followed by Kar et al (2020) 12 . In the above-cited references, the information that is carried to the temporal analysis is not at the level of the experimental unit but at the genotype level.…”

“…(2019) 2 and Kar et al (2020) 12 , and it is routinely applied for data derived from the field phenotyping platform of ETH Zurich [15][16][17] . Since analyses are performed separately for each measurement time, our modelling strategy implicitly permits the spatial variation to differ among measurement times, i.e., it allows correcting for both the spatial and temporal evolution of environmental variables and experimental design factors.…”

High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field

“…Web-based image analysis tools, such as Field Phenomics (Guzman et al, 2015), are considered by us to be a hotspot for phenotypic solutions. Kar et al (2020) developed an analysis pipeline with outlier detection, missing value imputation, and spatial adjustment for solving the problem of inaccurate and missing phenotypic data. Toolkits tend to be relatively specific, such as Plant 3D (P3D), which specializes in analyzing 3D point cloud data of plant structures (Ziamtsov and Navlakha, 2020).…”

A one-stage approach for the spatio-temporal analysis of high-throughput phenotyping data

“…Research in this area is increasing, but the data complexity asks for novel methods. One possibility is to use stagewise approaches (Kar et al, 2020; Pérez-Valencia et al, 2022; Roth et al, 2021; van Eeuwijk et al, 2019). Stage-wise proposals have the advantage of being computationally manageable, but the problem relies on the loss of information between and within stages.…”