acepxGPT: A schema-constrained multimodal LLM framework for plant phenotyping
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…
Schema-constrained extraction from multi-view images recorded visible Brassica traits without separate training per trait, with agreement close to that among human annotators.
Sensitivity varied across structure types in one mature-plant dataset, and accuracy must be validated again for each new crop or imaging setting.
Results are limited to the evaluated Brassica datasets, sensitivity varied by structure type in one mature-plant dataset, and accuracy must be validated for other crops and imaging conditions.
Evidence
- Peer-reviewedBioinformatics2026-10-20
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Truvace Impact Record TRV-2026-2199, v1: “pxGPT: A schema-constrained multimodal LLM framework for plant phenotyping.” Truvace, 2026-10-23. /record/TRV-2026-2199 (accessed at citation time). sha256 20a17677ee273fee…
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