pxGPT: A schema-constrained multimodal LLM framework for plant phenotyping

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ID: 333368
2026
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Abstract
Abstract Summary Deep learning can scale image-based plant phenotyping but typically targets predefined traits, whereas manual phenotypic description is more flexible but difficult to scale. We present Phenotype eXplorer Generative Plant Traits (pxGPT), a Python framework that converts multi-view plant images into structured phenotype records. The workflow combines free-text description, dataset-specific schema generation with human review, and schema-constrained information extraction. We evaluated the extraction stage on three Brassica datasets using 78,973 annotations from ten annotators who had no access to pxGPT predictions. For 38-45 qualitative traits per dataset, pxGPT-human agreement ranged from 0.572 to 0.712, compared with 0.620 to 0.739 agreement among human annotators. All 32,736 required trait fields were present; 93 values were empty, and no qualitative descriptor violations were recorded. For selected morphological structures, pooled false-positive rates were 0.5-8.0%, while sensitivity varied across structure types in one mature-plant dataset (37.5-100%). The curated schemas were applied unchanged with two commercial models and one locally hosted open-weight model, with differences in mean accuracy of 0.03 or less. In the evaluated Brassica datasets, these results support schema-guided extraction of visible plant traits without separate model training for each trait. Human-guided schema development offers a path to extend pxGPT to other crops and imaging conditions, with accuracy requiring validation in each new setting. Availability and Implementation The software is available at https://github.com/YaoChengLab/pxgpt and an archival version of the source code is available on Zenodo (https://doi.org/10.5281/zenodo.22931745). Curated schemas used in this study, annotation guides, phenotype records and human annotations are available at https://doi.org/10.5281/zenodo.22907554. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7223127960 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Po‐Xing Zheng, Jheng-Yan Ou, Yu-Lin Wu, Ruei-Shiuan Wang, WU Ting-li, Te-Chang Hsu, Yao-Cheng Lin
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag781
URL
https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag781/72201337/btag781.pdf
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