Developing a Realistic Simulation Environment for Robotics Harvesting Operations in a Vegetable Greenhouse
Bibliographic record
Abstract
Vegetable greenhouse operations are labour intensive. Automating some of these operations can save growers significant costs in an industry with low-profit margins. One of the most demanding operations is harvesting. Harvesting a tomato is a complex operation due to the significant clutter inherent to a greenhouse and the fragility of the object being grasped. Improving grasp and motion planning requires setting up a realistic testbed or testing on-site, which is expensive and time-limited to the growing season and specific environment. As such, it is important to develop a simulation environment to model this operation to help test various strategies before field testing can be conducted. Using the method presented in this work, 3D images are taken from a commercial greenhouse and used to develop a physics-based realistic simulation environment. The environment is then used to simulate a picking operation using various path planning algorithms to investigate the best algorithm to use in this case. The results show that this environment can be used to explore the best approaches to automate harvesting solutions in a vegetable greenhouse environment.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".