Integration of LLMs and VLMs in plant stress phenotyping: From trait recognition to decision support
Abstract
The integration of Large Language Models (LLMs) with Vision-Language Models (VLMs) holds transformative potential for plant stress phenotyping, enhancing high-throughput crop monitoring, trait identification, and decision support. Traditional phenotyping methods, often reliant on manual assessments and task-specific Machine Learning (ML) models, face persistent limitations in scalability, adaptability, and contextual interpretation, especially under complex and overlapping stress conditions. VLMs address these challenges by combining deep visual recognition with contextual reasoning, enabling real-time analysis of multimodal inputs such as high-resolution imagery, agronomic text data, and environmental sensor readings. Complementarily, LLMs contribute to text mining, semantic annotation of trait descriptors, and the integration of external knowledge via Retrieval-Augmented Generation (RAG), thereby enhancing the interpretability and adaptability of phenotyping workflows. This review critically evaluates the emerging role of integrating LLMs with VLMs in plant stress phenotyping, highlighting their applications in visual trait recognition, knowledge extraction, and autonomous decision-making. We synthesize current advances and identify key challenges, including data quality, domain-specific generalization, model transparency, and equitable access to AI technologies. As one of the first comprehensive reviews on this topic, we propose a forward-looking framework that integrates…
Read the paper
Related studies
- Knowledge Management Perspective of Generative Artificial Intelligence
- Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work
- Characterizing the evolving research on enterprise content management
- Large language models and multimodal foundation models for precision oncology
- From Generative AI to Extended Reality: Multidisciplinary Perspectives on the Challenges, Opportunities, and Future of Educational Computing
- Generative Artificial Intelligence: A New Engine for Advancing Environmental Science and Engineering
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.