Incident categories and sub-categories.

Introduction
Despite tremendous efforts, including research, teaching, and extension, toward improving the safety of agricultural tractor drivers, the number of incidents related to agricultural tractor drivers has not declined. This evidence points out an urgent need to explore artificial intelligence (AI) solutions to improve the safety of tractor drivers.
Methods
This paper uses 171 Fatality Assessment and Control Evaluation (FACE) reports related to tractor drivers and a new framework called Risk Evolution, Detection, Evaluation, and Control of Accidents (REDECA) to identify existing AI solutions, such as machine learning models for predictive maintenance, sensor-based monitoring, computer vision, and automated safety interventions, and specific areas where AI solutions are missed and can be developed to reduce incidents and recovery time. Fatality reports of tractor drivers were categorized into six main categories, including run over, pinned by/ Crushed and entanglement, fall, fire, roll over, and overturn. Each category was then subcategorized based on similarities of incident causes in the reports.
Results
The application of the REDECA framework, which categorizes risk states into R1 (safe), R2 (hazard exposure), and R3 (incident), revealed potential AI solutions that could improve the safety of tractor drivers. In all categories, the REDECA framework lacks AI solutions for three elements, including the probability of reducing recovery time in R3, detecting changes between R2 and R3, and intervention to send workers to R2. Most of the categories were missing AI solutions for interventions to prevent entry to the R3 element of the REDECA. In addition, the fall, roll over, and overturn categories lacked AI intervention that minimized damage and recovery in R3.
Conclusions
The outcome of this study shows an urgent need to develop AI solutions to improve tractor driver safety.