Rapid Agrichemical Inventory via Video Documentation and Large Language Model Identification
Michael Anastario, Cynthia Armendáriz-Arnez, Lillian Shakespeare Largo, Talia Gordon, Elizabeth F. S. Roberts

TL;DR
This paper introduces a method using video and AI to quickly identify agrichemicals in limited time, improving exposure assessments in field research.
Contribution
A novel approach using LLMs to identify agrichemicals from video footage in time-limited field settings.
Findings
LLM correctly identified 75% of agrichemicals from video screenshots.
The method supports rapid data collection when researcher access is limited.
Human validation improved accuracy and corrected LLM errors.
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…
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Taxonomy
TopicsData-Driven Disease Surveillance · Animal Disease Management and Epidemiology · Zoonotic diseases and public health
