PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912350283497472 |
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| author | Chen, Feng Stogiannidis, Ilias Wood, Andrew Bueno, Danilo Williams, Dominic Macfarlane, Fraser Grieve, Bruce Wells, Darren Atkinson, Jonathan A. Hawkesford, Malcolm J. Rolfe, Stephen A. Lawson, Tracy Pridmore, Tony Giuffrida, Mario Valerio Tsaftaris, Sotirios A. |
| author_facet | Chen, Feng Stogiannidis, Ilias Wood, Andrew Bueno, Danilo Williams, Dominic Macfarlane, Fraser Grieve, Bruce Wells, Darren Atkinson, Jonathan A. Hawkesford, Malcolm J. Rolfe, Stephen A. Lawson, Tracy Pridmore, Tony Giuffrida, Mario Valerio Tsaftaris, Sotirios A. |
| contents | Plant phenotyping increasingly relies on (semi-)automated image-based analysis workflows to improve its accuracy and scalability. However, many existing solutions remain overly complex, difficult to reimplement and maintain, and pose high barriers for users without substantial computational expertise. To address these challenges, we introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction. PhenoAssistant leverages a large language model to orchestrate a curated toolkit supporting tasks including automated phenotype extraction, data visualisation and automated model training. We validate PhenoAssistant through several representative case studies and a set of evaluation tasks. By significantly lowering technical hurdles, PhenoAssistant underscores the promise of AI-driven methodologies to democratising AI adoption in plant biology. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_19818 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping Chen, Feng Stogiannidis, Ilias Wood, Andrew Bueno, Danilo Williams, Dominic Macfarlane, Fraser Grieve, Bruce Wells, Darren Atkinson, Jonathan A. Hawkesford, Malcolm J. Rolfe, Stephen A. Lawson, Tracy Pridmore, Tony Giuffrida, Mario Valerio Tsaftaris, Sotirios A. Multiagent Systems Artificial Intelligence Plant phenotyping increasingly relies on (semi-)automated image-based analysis workflows to improve its accuracy and scalability. However, many existing solutions remain overly complex, difficult to reimplement and maintain, and pose high barriers for users without substantial computational expertise. To address these challenges, we introduce PhenoAssistant: a pioneering AI-driven system that streamlines plant phenotyping via intuitive natural language interaction. PhenoAssistant leverages a large language model to orchestrate a curated toolkit supporting tasks including automated phenotype extraction, data visualisation and automated model training. We validate PhenoAssistant through several representative case studies and a set of evaluation tasks. By significantly lowering technical hurdles, PhenoAssistant underscores the promise of AI-driven methodologies to democratising AI adoption in plant biology. |
| title | PhenoAssistant: A Conversational Multi-Agent AI System for Automated Plant Phenotyping |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2504.19818 |