Campus Units

Agricultural and Biosystems Engineering, Industrial and Manufacturing Systems Engineering

Document Type

Article

Publication Version

Accepted Manuscript

Publication Date

9-2016

Journal or Book Title

Proceedings of the Human Factors and Ergonomics Society Annual Meeting

Volume

60

Issue

1

First Page

1414

Last Page

1418

Research Focus Area(s)

​Operations Research

DOI

10.1177/1541931213601325

Abstract

Harvesting is one of the most important agricultural operations because it captures the value from the entire cropping season. In modern agriculture, grain harvesting has been mechanized through the combine harvester. A combine harvester enables highly productive crop harvesting. Combine harvesting performance depends on the highly variable skill of combine operators and associated operator error. An approach was developed to analyze the risk of the combine harvesting operation as it relates to operator error. Specifically, a risk analysis model was built based on a task analysis from operator interviews and estimates of the probability of operator error. This paper employs a Bayesian approach to assess risks in combine operation. This approach applies a Bayesian Belief Network to agriculture operations, which represents a new application for this risk analysis tool. Sensitivity analysis of different errors and operator skill levels was also performed. The preliminary results indicate that a reduction of human operator action errors can substantially improve the outcomes of the human-machine interaction.

Comments

This is an accepted manuscript of an article from Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60 (2016): 1414, doi: 10.1177/1541931213601325. Posted with permission.

Copyright Owner

The Authors

Language

en

File Format

application/pdf