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Enhancing Occupational Safety Through AI: A Review of Key AI Technologies 
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: Journal of the ASABE. 69(1): 165-179. (doi: 10.13031/ja.16502) @2026
Authors: Sahar Yousefi, Bhaskar Aryal, John Shutske, Salah F. Issa
Keywords: Agricultural safety, Artificial intelligence, Computer vision, Health, Injury prevention, Large language models, Machine learning, Occupational safety, Predictive modeling, Wearable technology.
Highlights AI can enhance agricultural safety through predictive models, LLMs, computer vision, and wearables. Machine learning is a tool that has the potential to predict hazards using historical data on injury incidents, weather, and worker behavior. Computer vision and remote sensing applications could be used to detect unsafe conditions for real-time risk mitigation. Wearable AI devices present opportunities to monitor worker health and prevent injuries in agricultural environments.
ABSTRACT. Artificial intelligence (AI) has emerged as a transformative tool in various industries, including agriculture, where it has the potential to enhance safety and reduce injury risks. This review explores the application of AI techniques in occupational safety with a special focus on agricultural safety, focusing on predictive modeling, large language models (LLMs), computer vision, and wearable technologies. Due to the limited number of studies that address AI in agricultural safety, research from related fields, such as construction safety, was also considered for its potential applicability. The findings indicate that (1) predictive models that leverage machine learning (ML) algorithms can assess historical data and forecast hazards, enabling proactive safety measures. (2) LLMs can improve injury report analysis by extracting key terms and patterns to identify recurring risks. (3) Computer vision and remote sensing technologies enhance environmental monitoring by detecting real-time unsafe conditions. At the same time, (4) AI-powered wearable devices can track worker health indicators such as heart rate, potentially preventing injuries. A total of 85 studies were analyzed, providing insights into the diverse applications of AI in mitigating occupational hazards, specifically agricultural hazards. This review highlights the current advancements and future research opportunities for AI-driven safety interventions in high-risk occupational environments.
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