Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train), and error-prone (derived geometric features are sensitive to instance mask integrity). Here we present a segmentation-free approach which leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using high-throughput phenotyping method Root Architecture 3-D Imaging Cylinder (RADICYL) across multiple species, we show that our approach can reliably and efficiently recover root system topology at greater accuracy, faster speed, and with fewer annotated samples than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots ) for trait extraction directly comparable to existing segmentation-based analysis software. We show that landmark-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots , all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots.
A high-throughput image analysis pipeline was developed to facilitate root phenotyping by reducing time-consuming labeling while maintaining phenotyping accuracy. This pipeline leverages a deep learning-based tool named SLEAP (SLEAP Estimates Animal Poses) which is designed to automate the detection of distinct morphological landmarks. By training SLEAP to detect the root branch points, tips, and midline of each root imaged in a gel cylinder, we were able to robustly and efficiently recover the root system geometry. We trained models to identify these landmarks on primary, lateral, and seminal roots across a range of crop plants, including soybean, rice, canola, and pennycress. We find that our SLEAP models are robust across genotypes and experiments, enabling automated root system quantification at the rate of hundreds of plants per hour. Using predictions of root landmark locations, we developed Python-based pipelines to extract phenotypic traits, including tip depths, root lengths, convex hulls, root angles, measures of curviness, and lateral root distribution (available at https://github.com/talmolab/sleap-roots). In order to extract meaningful patterns from this high-dimensional description of plant phenotypes, we use machine learning-based methods for dimensionality reduction and manifold embedding, allowing us to capture the statistical structure of root phenotypes present in our screens. In future work, we will use these quantitative phenotypic traits as a predictor for root system traits that enhance carbon sequestration capabilities in genome-wide association studies.