Development of a High-Level Discrete Event Simulation Model for the Harvesting of White Button Mushrooms with Human-Robot Collaboration (HRC)
Bibliographic record
Abstract
Canada is a leading global mushroom producer, with the white button mushroom (Agaricus bisporus) being the most popular type produced. Harvesting activities are labour-intensive, and farms are currently experiencing labour shortages, which are impacting productivity, growth, and overall revenue. Robotic technologies are valid mechanisms for mushroom harvesting and demonstrate a potential to reduce this gap. Examining the performance of a harvesting system with different robotics scenarios, such as manual, robotics automation, and human robot collaboration (HRC) is crucial to realizing this potential. Simulation models are useful tools for testing and assessing systems without making any physical change to the system. In agriculture, simulation models of harvesting activities are scarce and most consider only manual operations of crops grown in greenhouses or fields. This research focuses on the development of a discrete-event simulation model in AnyLogic for different workforce scenarios (fully manual, fully automated, and HRC) in white button mushroom harvesting at the systems level to investigate the benefits and economic feasibility of each. Data for the model inputs were collected from a commercial mushroom farm and available robotic equipment. A preliminary cost analysis was performed to compare the payback periods for each scenario simulated. Results demonstrated a significantly larger overall equipment effectiveness (OEE) for automation systems, but lower production rates. The most optimal scenario for the existing commercial farm was determined to be a collaboration between automation and manual labour that featured a hybrid schedule. The agri-automation domain is a rapidly growing field, and this research will help to facilitate growth in the Canadian mushroom sector and serve as a framework and a roadmap for future harvesting simulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".