Work

Crop phenology data extraction from research papers using a large language model

Year: 2025

Type: article

Abstract: To extract high-quality crop phenology observations reported in field experiments and research papers, we propose a semi-automated extraction method using the LLM-based online tool ChatPDF combined with a Python program. From 164 studies, phenological data for maize, soybean, wheat, and rice were extracted and evaluated against manual extraction. The method substantially reduces extraction workload, while issues remain such as errors in estimating latitude/longitude and harvest years, and omissions caused by ambiguous phenology terminology. We discuss these limitations and potential improvements toward large-scale implementation. Author contribution note :Toshichika Iizumi and Yohei Ono contributed equally as co-first authors.Takahiro Takimoto and CHAOGEJILATU contributed equally as co-second authors.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.17082309

Authors: Iizumi, Toshichika, Ono, Yohei, Takimoto, Takahiro, CHAOGEJILATU

Institutions: National Agriculture and Food Research Organization, National Institute for Agro-Environmental Sciences, St. Luke's International University

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

SDG: Zero hunger

Open Access status: green

OpenAlex ID: W7080502961

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