Abstract :
[en] Heat stress detection in dairy cows has long been connected with production loss. However, the reduction in milk yield lags behind the exposure to heat stress events for about two days. Other stress responses, such as physiological and behavioural changes, are well documented to be activated by dairy cows in the earlier stage of heat stress compared with production loss. Among all candidate indicators, body surface temperatures (BST), respiration rate (RR), and relevant behaviours have been concluded to be the most appropriate indicators due to their high feasibility of acquisition and early response. Vision-based methods are promising for accurate measurements while adhering to animal welfare principles. Meanwhile, predictive models show a non-invasive alternative to obtain these data and can provide useful insights with their interpretations. Thus, this thesis aimed to provide non-invasive solutions to the detection of heat stress in dairy cows by using artificial intelligence techniques. The detailed research content and relevant conclusions are as follows:
An automated tool based on improved UNet was proposed to collect facial BST from five facial landmarks (i.e., eyes, muzzle, nostrils, ears, and horns) on cattle infrared images. The baseline UNet model was improved by replacing the traditional convolutional layers in the decoder with Ghost modules and adding efficient channel attention modules. The improved UNet outperformed other comparable models with the highest mean Intersection of Union of 80.76% and a slightly slower but still good inference speed of 32.7 frames per second (FPS). Agreement analysis reveals small to negligible differences between the temperatures obtained automatically in the area of eyes and ears and the ground truth.
A vision-based method was proposed to measure RR for multiple dairy cows lying on free stalls. The proposed method involved various computer vision tasks (i.e., instance segmentation, object detection, object tracking, video stabilisation, and optical flow) to obtain respiration-related signals and finally utilised Fast Fourier Transform to extract RR. The results show that the measured RR had a Pearson correlation coefficient of 0.945, a root mean square error (RMSE) of 5.24 breaths per minute (bpm), and an intraclass correlation coefficient of 0.98 compared with visual observation. The average processing time and FPS on 55 test video clips (mean ± standard deviation duration of 16 ± 4 s) was 8.2 s and 64, respectively.
A deep learning-based model was proposed to recognise cow behaviours (i.e., drinking, eating, lying, standing-in, and standing-out) that are known to be influenced by heat stress. The YOLOv5s model was selected due to its ability to compress the weight size while maintaining accuracy. It had a mean average precision of 0.985 and an inference speed of 73 FPS. Further validation demonstrates the excellent capacity of the proposed model in measuring herd-level behavioural indicators, with an intraclass correlation coefficient of 0.97 compared with manual observation.
Critical thresholds were determined by using piecewise regression models with environmental indicators as the predictors and animal-based indicators as the outcomes. An ambient temperature (Ta) threshold was determined at 26.1 °C when the automated measured mean eye temperature reached 35.3 °C. A Ta threshold of 23.6 °C and a temperature-humidity index (THI) threshold of 72 were determined when the automated measured RR reached 61.1 and 60.4 bpm, respectively. In addition, the test dairy herd began to change their standing and lying behaviour at the earliest Ta of 23.8 ℃ or THI of 68.5.
Four machine learning algorithms were used to predict RR, vaginal temperature (VT), and eye temperature (ET) from 13 predictor variables from three dimensions: production, cow-related, and environmental factors. The artificial neural networks yielded the lowest RMSE for predicting RR (13.24 bpm), VT (0.30 ℃), and ET (0.29 ℃). The results interpreted with partial dependence plots and Local Interpretable Model-agnostic Explanations show that P.M. measurements and winter calving contributed most to high RR and VT predictions, whereas lying posture, high Ta, and low wind speed contributed most to high ET predictions.
Based on these results, an integrative application of all the proposed measurement, prediction, and assessment methods has been suggested, wherein RGB and infrared cameras are used to measure animal-based indicators, and critical thresholds, along with model interpretation, are used to assess the heat stress state of dairy cows. This strategy ensures timely and thorough cooling of cows in all areas of the dairy farm, thereby minimising the negative impact of heat stress to the greatest extent.
