Study on Feedback and Correction of Tomato Picking Localization Information
Bibliographic record
Abstract
In the process of picking tomatoes, due to the mechanical error caused by the mechanical arm, the tomato positions cannot be detected accurately, and the information feedback of the positioning is not available, affecting the picking efficiency.Therefore, this article proposed visual feedback information and correction, and designed an improved yolov5s model lightweight detection method, whose backbone network was replaced with lightweight ShuffleNetV2.In addition, the Bidirectional Feature Pyramid Network (BiFPN) was added to obtain richer feature information.Experimental results showed that the improved model achieved 97.4 percent mAP, 97.5 percent accuracy and 1.89 MB model size, with inference time of 4.8 ms per image.This detection method quickly calculated the Euclidean distance between the reference point and the target tomato.The target tomato, with the Euclidean distance less than 58.12 mm, was picked successfully, while the one, with the Euclidean distance greater than 58.12 mm, was not picked.Then the error needs to be calculated and fed back to the robot for picking again.The whole process realized information feedback and correction and improved the picking efficiency with less feedback time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".