Technical Note: Ripe Tomato Detection for Robotic Vision Harvesting Systems in Greenhouses
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Effective recognition and localization of ripe tomatoes from a complex background is the key issue for robotic harvesting systems in greenhouses. In this study, a detection approach for ripe tomatoes is proposed based on their color and shape features. Images containing ripe tomatoes are first segmented by K-means clustering using the L*a*b* color space. To recognize a single ripe tomato, mathematical morphology is used to denoise the image and to handle the situations of image overlapping and sheltering. To improve the accuracy of tomato detection, the shape features are combined with the color features. Finally, the center of the detected single ripe tomato is calculated. Experimental results demonstrate the effectiveness of the proposed method. The successful detection rate is approximately 94%. Even with the overlapping and complex cluster background, the proposed algorithm still shows a very strong robustness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it