High‐throughput measurement of plant fitness traits with an object detection method using Faster R‐CNN

Abstract

Summary Revealing the contributions of genes to plant phenotype is frequently challenging because loss‐of‐function effects may be subtle or masked by varying degrees of genetic redundancy. Such effects can potentially be detected by measuring plant fitness, which reflects the cumulative effects of genetic changes over the lifetime of a plant. However, fitness is challenging to measure accurately, particularly in species with high fecundity and relatively small propagule sizes such as Arabidopsis thaliana . An image segmentation‐based method using the software I mage J and an object detection‐based method using the Faster Region‐based Convolutional Neural Network (R‐CNN) algorithm were used for measuring two Arabidopsis fitness traits: seed and fruit counts. The segmentation‐based method was error‐prone (correlation between true and predicted seed counts, r 2 = 0.849) because seeds touching each other were undercounted. By contrast, the object detection‐based algorithm yielded near perfect seed counts ( r 2 = 0.9996) and highly accurate fruit counts ( r 2 = 0.980). Comparing seed counts for wild‐type and 12 mutant lines revealed fitness effects for three genes; fruit counts revealed the same effects for two genes. Our study provides analysis pipelines and models to facilitate the investigation of Arabidopsis fitness traits and demonstrates the importance of examining fitness traits when studying gene functions.

Code and data availability

The paper's Data availability statement explicitly deposits all analysis scripts and the final seed and fruit counting models (trained Faster R-CNN phenotyping models) in a public GitHub repository under the authors' ShiuLab account, making it a paper-specific, publicly actionable asset.