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Assessing Agrochemical Risk to Mated Honey Bee Queens

Assessing Agrochemical Risk to Mated Honey Bee Queens

Published on: March 3, 2021

Demeter - a Risk Mitigation Tool for Agriculture Workers.

Joshua Martin¹, Tyler Seward¹, Dino Mintas¹

¹VISIMO - Data Science and Software Design, Carnegie, PA, USA.

Journal of Agromedicine

|March 8, 2024

Summary

This study introduces Demeter, an application using graph neural networks (GNNs) to analyze agricultural worker risks. Demeter provides daily risk assessments and mitigation strategies to enhance farm safety.

Keywords:

Deep Learning Graph Neural Network agriculture worker safety risk analysis

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Area of Science:

Agricultural Safety
Data Science
Machine Learning

Background:

The agriculture industry faces significant worker injury risks.
Current safety tools lack daily hazard analysis and actionable mitigation advice.
Existing datasets are insufficient for comprehensive risk assessment.

Purpose of the Study:

To develop Demeter, an application for collecting farm safety data.
To generate risk analysis and mitigation reports for agricultural workers.
To address the limitations of current agricultural safety assessment tools.

Main Methods:

Utilizing formula-based risk calculations.
Implementing state-of-the-art graph neural networks (GNNs) for risk analysis.
Collecting and processing daily fluctuating hazard data.

Main Results:

Formula-based risk calculations achieved a Mean Absolute Error (MAE) of 0.2110.
The GNN model demonstrated 94.9% accuracy, 0.3521 precision, and 0.8333 recall.
The system is designed to provide immediate and long-term risk mitigation insights.

Conclusions:

Demeter offers a novel approach to agricultural risk analysis.
The application has the potential to significantly reduce injuries and fatalities in the farming sector.
By providing daily alerts and safety precautions, Demeter enhances worker safety protocols.