A Lightweight Deep Learning and Data Augmentation Framework for Cattle Weight Estimation Under Unstable Camera Conditions and Small Datasets
Bibliographic record
Abstract
Cattle weight measurement is an important task for cattle ranchers and farmers. The weight of the cattle is useful for estimating the growth rate, time to slaughter, current and future need for feed, and the amount of medication for the sick cattle. Traditionally, weights are measured using physical weigh scales or tapes. This process requires physical labor, is time-consuming, and disturbs the daily routine of the livestock. The semi-automated computer-assisted methods will free farmers from this laborious task. However, machine learning and deep learning prediction methods require large amounts of data, and the collection is costly and time-consuming. In addition to that, unstable camera position, changing background, and illumination affect the extracted features used in the prediction. In this paper, we propose an image alignment method combined with semantic-aware data augmentation to address unstable camera conditions and limited datasets. Our lightweight deep learning model achieves significant improvements(about 18%) in cattle weight estimation accuracy compared to traditional machine learning, curve fitting, and MLP regression models.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".