Tomato ripeness detection method based on FasterNet block and attention mechanism
Why this work is in the frame
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Bibliographic record
Abstract
In modern agriculture, accurate detection of tomato maturity is crucial for efficient harvesting and grading. Traditional detection methods rely on manual experience, which is time-consuming, inefficient, and prone to subjective interference, making them unsuitable for large-scale production. To address this, this study proposes a tomato maturity detection model based on an improved YOLOv11n, incorporating the C3k2-Faster-EMA module to enhance the model's feature extraction capability and detection efficiency. In addition, the SimAM attention mechanism is introduced, enabling the model to intelligently focus on key features of the tomatoes, thereby improving its ability to recognize tomatoes at different maturity stages and enhancing detection accuracy. Furthermore, the generalized intersection over union loss function is employed to introduce a target box overlap metric, optimizing the object localization process and improving the precision of fruit positioning. Experimental results on the tomato maturity dataset show that the proposed method performs excellently in tomato maturity detection, achieving an mAP of 86.0% and an accuracy of 85.4%. Compared to the baseline model, the number of parameters is reduced by 11.2%, while the frames-per-second detection speed is increased by 23.1%, with significant improvements in stability. This provides reliable technical support for intelligent harvesting and grading, with broad application prospects.
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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