faCRSA: An automated pipeline for high-throughput analysis of crop root system architecture

Optimizing root system architecture (RSA) is essential for plants because of its critical role in acquiring water and nutrients from the soil. However, the subterranean nature of roots complicates the measurement of RSA traits. Recently developed rhizobox methods allow for the rapid acquisition of root images. Nevertheless, effective and precise approaches for extracting RSA features from these images remain underdeveloped. Deep learning (DL) technology can enhance image segmentation and facilitate RSA trait extraction. However, comprehensive pipelines that integrate DL technologies into image-based root phenotyping techniques are still scarce, hampering their implementation. To address this challenge, we present a reproducible pipeline (faCRSA) for automated RSA traits analysis, consisting of three modules: (1) the RSA traits extraction module functions to segment soil-root images and calculate RSA traits. A lightweight convolutional neural network (CNN) named RootSeg was proposed for efficient and accurate segmentation; (2) the data storage module, which stores image and text data from other modules; and (3) the web application module, which allows researchers to analyze data online in a user-friendly manner. The correlation coefficients (R²) of total root length, root surface area, and root volume calculated from faCRSA and manually measured results were 0.96^**, 0.97^**, and 0.93^**, respectively, with root mean square errors (RMSE) of 8.13 cm, 1.68 cm², and 0.05 cm³, processed at a rate of 9.74 s per image, indicating satisfying accuracy. faCRSA has also demonstrated satisfactory performance in dynamically monitoring root system changes under various stress conditions, such as drought or waterlogging. The detailed code and deployable package of faCRSA are provided for researchers with the potential to replace manual and semi-automated methods.