104 Non-invasive pregnancy diagnosis in Holstein dairy cows using computer vision analysis of thermal images
Bibliographic record
Abstract
Abstract Thermal visualization of Holstein dairy cattle skin can be indicative of the pregnancy state (cyclic or pregnant) of the cow by observing the size and shape of skin thermal patterns. However, visual assessment of skin thermal patterns can be subjective and laborious. Further, infrared thermography implementation on-farm requires automated analysis to be feasible for dairy producers. The objectives of this study were to implement a machine learning convolutional neural network (CNN) to detect features in thermal images associated with pregnant (Pregnant) and non-pregnant (Cyclic) and to evaluate the accuracy of pregnancy diagnosis in dairy cows. A total of 938 images from 18 Pregnant cows and 982 images from 18 Cyclic were used to build the CNN model, and 120 images from Pregnant (60 images) and Cyclic (60 images) dairy cows were used to validate the model. Pregnant cows were confirmed to be pregnant 90 d after insemination via transrectal ultrasonography. Cyclic cows were confirmed to be non-pregnant using follicular dynamics and corpora lutea presence via transrectal ultrasonography. Images were recorded using a T620s infrared camera at 640 × 480 (307,200) to train the CNN model and images from an E40 FLIR camera at 320 x 240 (76,800 pixels) were used for the validation test. The CNN model was created using TensorFlow in Google Colab to binary classify images into Pregnant or Cyclic using a single output neuron (1: Pregnant and 0: Cyclic) with different Epochs (10, 25, and 100). The diagnosis accuracy (acc) was 0.72 with a validation accuracy (val_acc) of 0.41 for 10 Epochs, acc 0.76 with val_acc 0.37 for 25 Epochs, and acc 0.80 with val_acc 0.63. The acc and val_acc of the training model and validation test were optimized when using images from the T620s exclusively (Epochs 10: acc = 0.70 and val_acc = 0.64; Epoch 25: acc = 0.74 and val_acc = 0.70; Epoch 100: acc = 0.81 and val_acc = 0.80). Greater resolution of thermal images was proven to increase the accuracy of pregnancy diagnosis (307,200 compared with 76,800 pixels) and the greater number of Epoch used to train the model. Machine learning algorithms can diagnose pregnancy using thermal images with an acceptable evaluation accuracy (> 0.70).
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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".