Search

• Cows and their abdomen areas were located using YOLOv5. • Individual cows were measured remotely, non-invasively, and simultaneously. • Heat stress was detected when the respiration rate reached 60 breaths per minute. In cattle, respiration rate (RR) provides researchers and practitioners with valuable physiological information. However, traditional visual observation requires massive labour and is impractical for large-scale commercial farms. Recently developed vision-based RR measurement methods are highly limited to measuring a small number of animals in a controlled environment. Therefore, this paper aimed to propose a vision-based multi-object RR measurement method for dairy cows lying in free stalls. An RGB camera was aimed at the lying zone and was able to cover about 16 stalls. The proposed framework first utilised two YOLOv5-based networks to segment cow instances and detect cow flank objects. Next, an object tracker was used to link the predictions of each cow throughout the video clip. The Lucas-Kanade optical flow was then calculated specifically on the overlapped area of the cow mask and the flank bounding box. Finally, RR was extracted using Fast Fourier Transform. The results show that the proposed method had a precise RR measurement with a correlation coefficient of 0.944, a root mean square error of 5.35 breaths per minute, and an intraclass correlation coefficient of 0.974 when compared to visual observation. The piecewise regression models identified a change in RR when the ambient temperature reached 23.6 °C or the temperature-humidity index reached 72. The corresponding RR thresholds were 60.9 and 60.2 breaths per minute, respectively. Collectively, these results can be used to inform an automated local cooling system, e.g., fans in the lying area. However, more experiments and calibration with data collected using more cost-effective video recording systems are required before this technology can be applied on farms. [ABSTRACT FROM AUTHOR]

• A novel occlusion-resistant instance segmentation method was developed for lactating piglets. • Predicted object centers were clustered and traced back to achieve piglet instance segmentation. • Mean average precision of 84.1 was achieved on the test set under heavy occlusion. • The predicted center is the occlusion-resistant representation of the location of an occluded object. Computer vision enables the development of new approaches to monitor the behavior, health, and welfare of animals. Instance segmentation is a high-precision method in computer vision for detecting individual animals of interest. This method can be used for in-depth analysis of animals, such as examining their subtle interactive behaviors, from videos and images. However, existing deep-learning-based instance segmentation methods have been mostly developed based on public datasets, which largely omit heavy occlusion problems; therefore, these methods have limitations in real-world applications involving object occlusions, such as farrowing pen systems used on pig farms in which the farrowing crates often impede the sow and piglets. In this paper, we adapt a Center Clustering Network originally designed for counting to achieve instance segmentation, dubbed as CClusnet-Inseg. Specifically, CClusnet-Inseg uses each pixel to predict object centers and trace these centers to form masks based on clustering results, which consists of a network for segmentation and center offset vector map, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm, Centers-to-Mask (C2M), and Remain-Centers-to-Mask (RC2M) algorithms. In all, 4,600 images were extracted from six videos collected from three closed and three half-open farrowing crates to train and validate our method. CClusnet-Inseg achieves a mean average precision (mAP) of 84.1 and outperforms all other methods compared in this study. We conduct comprehensive ablation studies to demonstrate the advantages and effectiveness of core modules of our method. In addition, we apply CClusnet-Inseg to multi-object tracking for animal monitoring, and the predicted object center that is a conjunct output could serve as an occlusion-resistant representation of the location of an object. [ABSTRACT FROM AUTHOR]

• Computer vision methods for livestock behaviour recognition were evaluated. • Journey from computer vision to deep learning was elaborated. • This paper involves with segmentation, identification and behaviour recognition. • Research trend of livestock behaviour recognition was illustrated from four aspects. • This review could provide researchers and producers with technical references. The increasing demand for sustainable livestock products also demands new considerations in animal breeding. Breeding programs are now seeking to integrate animal behavioural phenotypes, as these relate to the productivity, health and welfare of the animals and thereby can influence yield and economic benefits in the industry. Traditional manual observation of pig behaviour is time-consuming, laborious, subjective, and difficult to achieve in continuous and large-scale operations. It is not surprising that computer vision technology with the advantages of being objective, non-invasive and continuous has been widely researched for its use in the recognition of livestock behaviours over recent years. Nevertheless, in studies of livestock behaviour recognition, computer vision technology faces some challenges, e.g., complex scenes, variable illumination, occlusion, touching and overlapping between livestock, which has limited the fast translation of technology to industry. On the other hand, deep learning technology has proven to solve these difficulties to a certain extent and is being adopted to recognise livestock behaviours. This paper mainly evaluates the recent developments in computer vision methods for recognition of these behaviours in pigs and cattle. The focus on these species is made possible by the number of studies exist quantifying behaviours that are of importance for their health, welfare and productivity such as aggression, drinking, feeding, lameness, mounting, posture, tail-biting and nursing. This review paper especially analyses the development of image segmentation, identification and behaviour recognition using tradition computer vision and more recent deep learning methods, and evaluates the evolution of key research in the field. We elaborate the research trend of livestock behaviour recognition from four aspects, i.e., development of robust livestock identification algorithms, recognition of livestock behaviours for different growth stages, further quantification of the results of behaviour recognition, and building evaluation system of growth status, health and welfare. [ABSTRACT FROM AUTHOR]

• Proposed interdisciplinary terms and concepts in precision management of animals. • Sources of uncertainty in animal behavior labeling for computer vision applications. • Directions of advanced computer vision algorithms in pig production facilities. • Image dataset construction workflows for computer vision algorithm development. Surveillance and analysis of behavior can be used to detect and characterize health disruption and welfare status in animals. The accurate identification of changes in behavior is a time-consuming task for caretakers in large, commercial pig production systems and requires strong observational skills and a working knowledge of animal husbandry and livestock systems operations. In recent years, many studies have explored the use of various technologies and sensors to assist animal caretakers in monitoring animal activity and behavior. Of these technologies, computer vision offers the most consistent promise as an effective aid in animal care, and yet, a systematic review of the state of application of this technology indicates that there are many significant barriers to its widespread adoption and successful utilization in commercial production system settings. One of the most important of these barriers is the recognition of the sources of errors from objective behavior labeling that are not measurable by current algorithm performance evaluations. Additionally, there is a significant disconnect between the remarkable advances in computer vision research interests and the integration of advances and practical needs being instituted by scientific experts working in commercial animal production partnerships. This lack of synergy between experts in the computer vision and animal health and production sectors means that existing and emerging datasets tend to have a very particular focus that cannot be easily pivoted or extended for use in other contexts, resulting in a generality versus particularity conundrum. This goal of this paper is to help catalogue and consider the major obstacles and impediments to the effective use of computer vision associated technologies in the swine industry by offering a systematic analysis of computer vision applications specific to commercial pig management by reviewing and summarizing the following: (i) the purpose and associated challenges of computer vision applications in pig behavior analysis; (ii) the use of computer vision algorithms and datasets for pig husbandry and management tasks; (iii) the process of dataset construction for computer vision algorithm development. In this appraisal, we outline common difficulties and challenges associated with each of these themes and suggest possible solutions. Finally, we highlight the opportunities for future research in computer vision applications that can build upon existing knowledge of pig management by extending our capability to interpret pig behaviors and thereby overcome the current barriers to applying computer vision technologies to pig production systems. In conclusion, we believe productive collaboration between animal-based scientists and computer-based scientists may accelerate animal behavior studies and lead the computer vision technologies to commercial applications in pig production facilities. [ABSTRACT FROM AUTHOR]