NAPPN Annual Conference Abstract: Automated root phenotyping via deep learning- based landmark detection using SLEAP
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.022 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".