Evaluating machine learning classifiers and explainability for monitoring cow behaviour with wearable nose rings
Bibliographic record
Abstract
Wearable technologies are revolutionizing precision livestock monitoring by allowing continuous real-time monitoring of animal behaviour. This study investigates and evaluates the use of machine learning techniques to classify dairy cow behaviours using tri-axial accelerometer data collected from novel wearable nose ring sensor. The raw dataset initially included five distinct behaviours: Feeding, Ruminating, Standing, Lying and Walking. However due to data imbalance and data limitations we refined the classification to three core categories: Feeding, Rumination and Walking. While previous studies on this dataset focused solely on Long Short-Term Memory(LSTM) network, the comparative potential of other models remained unexplored. To address this gap, we performed a comparative study on multiple classifiers, including Random Forest (RF), Artificial Neural Network (ANN), Gated Recurrent Unit (GRU) and a hybrid Convolutional Neural Network with LSTM (CNN-LSTM). The obtained results showed that GRU model performed well with an accuracy of 97.78 %, followed by CNN-LSTM, ANN and RF which scored 97.78 %, 68.27 % and 67.6 % respectively. To enhance model transparency, Explainable AI techniques were utilized. SHAP and LIME were utilized to showcase feature importance and interpretability of these models. These findings showcase the effectiveness of deep learning models (GRU, CNN-LSTM) and emphasizes the importance of model explainability in precision livestock management.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".