Pig weight estimation using a movable top-mounted depth camera with improved EfficientNetV2
Bibliographic record
Abstract
Accurate pig weight estimation is crucial for optimizing feed allocation, health management, and slaughter timing. However, most existing methods depend on data collection in constrained environments, limiting their applicability in free-range farming. This study proposes a non-contact estimation system tailored for free-range settings, employing a movable, top-mounted depth camera to capture depth images without restricting pig movement. To enhance estimation accuracy, an improved EfficientNetV2 model is introduced, incorporating the CBAM (Convolutional Block Attention Module) to strengthen spatial feature extraction and adapt to varying pig body shapes. Additionally, multiple pooling strategies in the prediction head are evaluated, and the optimal configuration is selected. The proposed model achieves an MAE of 3.263 kg, with only 5.920 M parameters and 0.752 GFLOPs, outperforming existing approaches. Limitations include potential occlusions in densely populated scenes. Future research will explore multi-view data integration and further improve robustness under complex farm conditions.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".