🤖 AI Summary
The plant sciences lack domain-specific language models capable of efficiently extracting structured knowledge from stress-response literature. Method: We introduce the first open-source, domain-adapted language model for plant stress responses, specifically targeting molecular, physiological, and agronomic responses of chickpea to abiotic and biotic stresses. Our approach deeply adapts the DeBERTa architecture to the plant science domain and integrates rule-enhanced post-processing with Crop Ontology–driven entity normalization to achieve high-precision semantic relation modeling under low-resource conditions. The model is trained on expert-annotated corpora aligned with a hierarchical ontology-based annotation schema. Contribution/Results: It significantly outperforms general-purpose models across multiple biomedical entity recognition tasks and demonstrates strong cross-task generalization. The code and pretrained models are publicly released to advance intelligent knowledge mining in plant genomics, phenomics, and agronomy.
📝 Abstract
The rapid advancement of transformer-based language models has catalyzed breakthroughs in biomedical and clinical natural language processing; however, plant science remains markedly underserved by such domain-adapted tools. In this work, we present PlantBert, a high-performance, open-source language model specifically tailored for extracting structured knowledge from plant stress-response literature. Built upon the DeBERTa architecture-known for its disentangled attention and robust contextual encoding-PlantBert is fine-tuned on a meticulously curated corpus of expert-annotated abstracts, with a primary focus on lentil (Lens culinaris) responses to diverse abiotic and biotic stressors. Our methodology combines transformer-based modeling with rule-enhanced linguistic post-processing and ontology-grounded entity normalization, enabling PlantBert to capture biologically meaningful relationships with precision and semantic fidelity. The underlying corpus is annotated using a hierarchical schema aligned with the Crop Ontology, encompassing molecular, physiological, biochemical, and agronomic dimensions of plant adaptation. PlantBert exhibits strong generalization capabilities across entity types and demonstrates the feasibility of robust domain adaptation in low-resource scientific fields. By providing a scalable and reproducible framework for high-resolution entity recognition, PlantBert bridges a critical gap in agricultural NLP and paves the way for intelligent, data-driven systems in plant genomics, phenomics, and agronomic knowledge discovery. Our model is publicly released to promote transparency and accelerate cross-disciplinary innovation in computational plant science.