Rapid Agrichemical Inventory via Video Documentation and Large Language Model Identification

Michael Anastario (), Cynthia Armendáriz-Arnez, Lillian Shakespeare Largo, Talia Gordon and Elizabeth F. S. Roberts
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Michael Anastario: Department of Health Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA
Cynthia Armendáriz-Arnez: Escuela Nacional de Estudios Superiores Unidad Morelia, Universidad Nacional Autónoma de México, Morelia 58190, Mexico
Lillian Shakespeare Largo: Department of Health Sciences, Northern Arizona University, Flagstaff, AZ 86011, USA
Talia Gordon: Department of Anthropology, University of Michigan, Ann Arbor, MI 48109, USA
Elizabeth F. S. Roberts: Department of Anthropology, University of Michigan, Ann Arbor, MI 48109, USA

IJERPH, 2025, vol. 22, issue 10, 1-9

Abstract: Background: This technical note presents a methodological approach to agrichemical inventory documentation. It complements exposure assessments in field settings with time-restricted observational periods. Conducted in Michoacán, Mexico, this method leverages large language model (LLM) capabilities for categorizing agrichemicals from brief video footage. Method: Given time-limited access to a storage shed housing various agrichemicals, a short video was recorded and processed into 31 screenshots. Using OpenAI’s ChatGPT (model: GPT-4o ® ), agrichemicals in each image were identified and categorized as fertilizers, herbicides, insecticides, fungicides, or other substances. Results: Human validation revealed that the LLM accurately identified 75% of agrichemicals, with human verification correcting entries. Conclusions: This rapid identification method builds upon behavioral methods of exposure assessment, facilitating initial data collection in contexts where researcher access to hazardous materials may be time limited and would benefit from the efficiency and cross-validation offered by this method. Further refinement of this LLM-assisted approach could optimize accuracy in the identification of agrichemical products and expand its application to complement exposure assessments in field-based research, particularly as LLM technologies rapidly evolve. Most importantly, this Technical Note illustrates how field researchers can strategically harness LLMs under real-world time constraints, opening new possibilities for rapid observational approaches to exposure assessment.

Keywords: agrichemical identification; large language models; avocado production; exposure assessment (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2025
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