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Monitoring pig drinking has been a topic of interest to pig researchers and producers for many years. However, challenges still remain due to the fact that pigs like to play with drinkers in nursery environments and that the drinking pig is often touching others. These factors negatively influence the performance of camera-based pig drinking detection algorithms. The aim of this study is to investigate a deep learning method based on convolutional neural network (CNN) and long short-term memory (LSTM) to classify drinking and drinker-playing. In the experiment, two pens of pigs were video recorded for 3 days. In video from the first pen, 5400 2 s drinking episodes and 5400 2 s drinker-playing episodes were generated with 80% of these data being allocated as training set and the remaining 20% as validation set. In video from the second pen, 12,000 2 s drinking and drinker-playing episodes were generated as a test set. Firstly, the CNN architecture ResNet50 was used to extract spatial features. These features were input into LSTM framework to extract spatial–temporal features. Through the fully connected layer, the prediction function Softmax was finally used to classify these drinking and drinker-playing episodes. In the test set, the classification accuracy in the body and head regions of interest was 87.2% and 92.5%, respectively. The results indicate that the proposed method can be used to classify pigs' drinking and drinker-playing. These classification results have potential to improve the accuracy of pig drinking detection and help farmers to estimate pig welfare. • A novel video-based deep learning method was used to study pig drinking behaviour. • Long short-term memory was used to extract the spatial–temporal features. • Classification accuracy in the body and head regions of interest was 87.2% and 92.5%. • Drinking behaviour can be detected under conditions of pigs touching and overlapping. • Shortening the region of interest can be used to improve the classification accuracy. [ABSTRACT FROM AUTHOR]

• It is the first time to use computer vision to recognise pig enrichment engagement. • A HSV colour space-based method was proposed to track the object region of interest. • InceptionV3 and LSTM were used to extract discriminative spatial-temporal features. • This algorithm has reference value for classifying similar behaviours of pigs. • Pigs preference to 3 enrichment objects was also determined in this study. As providing objects that pigs prefer can reduce the occurrence of tail-biting and aggression and consequently improve animal welfare, automatic recognition of pigs' engagement with different objects can have practical value. Therefore, aim of this study was to develop a computer vision based approach that utilised a recurrent neural network-based deep learning algorithm to recognise pig enrichment engagement (EE) behaviours and preliminarily determine the preference to objects. Two pig pens were studied. 1 day of video was recorded in pen 1, which generated 2400 1 s EE and 2400 1 s non-EE episodes. 80% of these data was randomly selected as training set and the remaining 20% as validation set. Moreover, 4 days of video were recorded and used as the test set in pen 2. Firstly, the HSV (Hue, Saturation, Value) colour space-based tracking algorithm was developed to locate object region of interest. Secondly, the convolutional neural network (CNN) architecture InceptionV3 was used to extract spatial features from each frame. These features were input into the long short-term memory (LSTM) framework to extract spatial-temporal features from each episode. Through the fully connected layer, the prediction function Softmax was finally used to classify these episodes as EE or non-EE behaviour. In the validation set, the proposed algorithm could recognise EE with blue ball, golden ball and wooden beam with an accuracy of 95.2%, 95.4% and 97.3%, respectively. By shortening the radius of the region of interest into a half of the average length of pig body, the corresponding accuracy could be improved into 96.9%, 97.1% and 97.9%, respectively. In the test set, the proposed algorithm could recognise EE with each of these 3 objects with an accuracy of 96.5%, 96.8% and 97.6%, respectively. The proportion of EE with each of these 3 objects was 75.8%, 6.0% and 18.2%, respectively. These results indicate that the proposed method can be used to recognise EE behaviours of pigs, and halving the radius of the region of interest can improve the recognition accuracy of EE behaviours. Moreover, the preference of pigs to objects based on EE duration were preliminarily determined as blue ball > wooden beam > golden ball. The obtained duration of EE behaviours can help farmers to evaluate the enrichment used and thereby to increase the health and welfare of the pigs in their care. Furthermore, the proposed algorithm has reference value for the classification of the behaviours with similar motion patterns. [ABSTRACT FROM AUTHOR]

• A deep learning method based on CNN and LSTM was used to recognise pig aggression. • This study directly process video episodes rather than individual frames. • CNN features were input into LSTM framework to extract spatial-temporal features. • The proposed method could recognise aggressive episodes with an accuracy of 97.2%. • A frame skipping approach can improve the accuracy to 98.4% and halve running time. Aggression is considered as a major animal welfare problem in commercial pig farming. The aim of this study is to develop a deep learning method based on convolutional neural network (CNN) and long short-term memory (LSTM) to recognise aggressive episodes of pigs. Compared to previous studies of pig behaviours based on deep learning, this study directly process video episodes rather than individual frames. In the experiment, nursery pigs (8/pen) were mixed for 3 days and then 8 h of video was recorded in each day. From these videos, 600 aggressive 2 s-episodes were manually selected and then augmented into 2400 episodes by using horizontal, vertical and diagonal mirroring. From the videos, 2400 non-aggressive 2 s-episodes were also manually selected. 80% of the data were randomly allocated as training set and the remaining 20% as validation set. Firstly, the CNN architecture VGG-16 was used to extract spatial features. These features were then input into LSTM framework to further extract temporal features. Through fully connected layer, the prediction function Softmax was finally used to determine if the current episode is aggression or non-aggression. Using the proposed method, aggressive episodes could be recognised with an accuracy of 97.2%. This result indicates that this method can be used to recognise aggressive episodes of pigs. Additionally, this paper further investigates the validity of this method under the conditions of skipping frames and reducing the episode length. The results show that a frame skipping approach whereby 30 fps is reduced into 15 fps within each 2 s-episode can improve the accuracy into 98.4% and halve the total running time. [ABSTRACT FROM AUTHOR]