Design and Optimization of a Biomimetic Pineapple Harvester Device Based on the Mechanical Properties of the Stem-Fruit Junction
Bibliographic record
Abstract
In major pineapple-producing regions of China, conventional manual harvesting is challenged by high labor intensity and cost. Existing mechanical harvesters, still largely in the research and development stage, often suffer from low efficiency and high susceptibility to fruit damage, failing to meet large-scale production demands. This study focuses on the Tainung 16 pineapple, determining that the tensile force required to separate the fruit stem at the calyx ranges from 100.42 N to 165.38 N. Drawing on the biomimetic principles of manual stem-breaking, we designed a harvesting device featuring a curved fixed baffle and a rotating unit. Using theoretical analysis and ADAMS simulation, a mechanical model of the device–stem interaction was established to simulate the force application, bending, and separation processes. This led to the identification of optimal operational parameters: a forward speed of 1.5 m/s, a harvesting unit rotational speed of 37 r/min, and a motion trajectory parameter of 1.3. Field tests demonstrated an average harvesting success rate of 81.23% with a fruit damage rate as low as 9.35%. The device thus effectively addresses the critical industry challenges of low efficiency and high damage. This work provides a direct technical reference and theoretical foundation for the engineering development, refinement, and standardized field operation of pineapple harvesters, facilitating the transition to mechanized large-scale harvesting.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".