A Prototype Pick and Place Solution for Harvesting White Button Mushrooms Using a Collaborative Robot
Bibliographic record
Abstract
Mushroom harvesting is a labor-intensive process. Implementing intelligent automation can improve the work environment for harvesters, reducing work-related musculoskeletal disorders caused by repetitive movements and awkward postures. This research prototypes a robotic harvesting solution for white button mushrooms using a systematic approach. The fungi growth cycle, bruising characteristics, and picking motion dynamics were all considered. To establish a bruising threshold, compression loads were incrementally applied to mushrooms until visible damage occurred. A force measurement glove was used to collect data from harvesters at a mushroom farm to determine the average force exerted on a mushroom during picking. Computer vision-based motion analysis was performed to define picking dynamics. Several compliant grippers were designed, simulated in Autodesk Inventor Nastran, and realized via 3D printing. Prototypes underwent durability testing using iterative cycle counts of 100 as well as moisture absorption testing to study performance in high humidity. A collaborative robot with specialty end of arm tooling was explored to harvest mature mushrooms from a selected area, validating the automation strategy. This system repeatedly located, picked, and placed mushrooms without damage. Recommendations for future work include a decision-making algorithm for mushroom grading preharvest and refinement of gripper tips.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".