A Context-Aware Method-Based Cattle Vocal Classification for Livestock Monitoring in Smart Farm
Bibliographic record
Abstract
This paper focuses on livestock monitoring on a smart farm to improve animal well-being and production. The great potential for increased automation and technological innovation in agriculture could help livestock farmers to monitor the welfare of their animals for precision livestock farming. A new acoustical method exploiting contextual information is introduced for cattle vocal classification. The proposed scheme considers the raw recordings which contain cattle sounds. Then a set of contextual acoustic features is constructed as input to the MSVM classifier to track the types of cattle vocalizations. Categorized noisy cattle calls are finally classified into four types of calls (i.e., cattle food anticipating call, animal estrus call, cough sound, and normal call) with an overall classification accuracy of 84% outperforming the results obtained using conventional MFCC features. We used an open access dataset consists of 270 cattle classification records acquired using multiple sound sensors. Promising results are obtained by the proposed method for livestock monitoring enabling farm owners to determine the status of their cattle.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".