Applicability of the StatFaRmer time series analysis tool in soybean (Glycine max) digital phenotyping

Contemporary agrobiotechnology research increasingly relies on automated methods for capturing and interpreting morphophysiological and spectral plant characteristics – a field known as digital phenotyping. This approach aims to identify stable differences between genotypes cultivated under non-identical environmental conditions. We previously introduced StatFaRmer, an open-source tool that we further develop here for comprehensive analysis of temporal phenotypic datasets, with a primary focus on crops such as soybean (Glycine max). The tool implements automated data preprocessing procedures, including synchronization of timestamps across samples and removal of noise artifacts and outliers. These features are particularly relevant for multi-month experiments involving assessments of growth parameters, fluctuations in photosynthetic apparatus area, or other biometric indicators. Support for standardized data formats (XLSX, CSV) ensures compatibility with common phenotyping systems, simplifying cross-platform integration. Thus, the tool can integrate with widely used HTPP platforms (e.g., Traitmill, HyperAIxpert, Plant Accelerator), enabling data from diverse sources to be analyzed within a single pipeline. For soybean experiments, StatFaRmer provides customizable analysis of variance (ANOVA) with visualization of diagnostic parameters (normality of distribution, homogeneity of variances) and evaluation of effect significance between user-defined groups. An example application compares growth parameters across 20 soybean cultivars under controlled stress: the tool automatically aggregated data with uneven measurement frequencies (from 1 hour to 3 days), identified anomalies in hypocotyl elongation dynamics, and computed statistical significance between groups (p < 0.01).The tool has been tested on large-scale datasets (over 2,000 measurements per experiment). StatFaRmer is implemented as a Shiny-based web application, with step-by-step deployment guides for Windows and Linux. All processing stages – from raw data to final plots – are documented to ensure transparency and compliance with research reproducibility standards. Thus, StatFaRmer offers a specialized solution for statistical hypothesis testing in soybean digital phenotyping, reducing data preparation time and minimizing risks of error when handling non-stationary time series.

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