| 2. |
- Guzhva, Oleksiy
(författare)
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Individual detection and tracking of group housed pigs in their home pen using computer vision
- 2021
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Ingår i: Frontiers in animal science. - : Frontiers Media SA. - 2673-6225. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- Modern welfare definitions not only require that the Five Freedoms are met, but animals should also be able to adapt to changes (i. e., resilience) and reach a state that the animals experience as positive. Measuring resilience is challenging since relatively subtle changes in animal behavior need to be observed 24/7. Changes in individual activity showed potential in previous studies to reflect resilience. A computer vision (CV) based tracking algorithm for pigs could potentially measure individual activity, which will be more objective and less time consuming than human observations. The aim of this study was to investigate the potential of state-of-the-art CV algorithms for pig detection and tracking for individual activity monitoring in pigs. This study used a tracking-by-detection method, where pigs were first detected using You Only Look Once v3 (YOLOv3) and in the next step detections were connected using the Simple Online Real-time Tracking (SORT) algorithm. Two videos, of 7 h each, recorded in barren and enriched environments were used to test the tracking. Three detection models were proposed using different annotation datasets: a young model where annotated pigs were younger than in the test video, an older model where annotated pigs were older than the test video, and a combined model where annotations from younger and older pigs were combined. The combined detection model performed best with a mean average precision (mAP) of over 99.9% in the enriched environment and 99.7% in the barren environment. Intersection over Union (IOU) exceeded 85% in both environments, indicating a good accuracy of the detection algorithm. The tracking algorithm performed better in the enriched environment compared to the barren environment. When false positive tracks where removed (i.e., tracks not associated with a pig), individual pigs were tracked on average for 22.3 min in the barren environment and 57.8 min in the enriched environment. Thus, based on proposed tracking-by-detection algorithm, pigs can be tracked automatically in different environments, but manual corrections may be needed to keep track of the individual throughout the video and estimate activity. The individual activity measured with proposed algorithm could be used as an estimate to measure resilience.
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| 3. |
- Guzhva, Oleksiy, et al.
(författare)
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The Hitchhiker's Guide to Integration of Social and Ethical Awareness in Precision Livestock Farming Research
- 2021
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Ingår i: Frontiers in animal science. - : Frontiers Media SA. - 2673-6225. ; 2
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Tidskriftsartikel (refereegranskat)abstract
- While fully automated livestock production may be considered the ultimate goal for optimising productivity at the farm level, the benefits and costs of such a development at the scale at which it needs to be implemented must also be considered from social and ethical perspectives. Automation resulting from Precision Livestock Farming (PLF) could alter fundamental views of human-animal interactions on farm and, even further, potentially compromise human and animal welfare and health if PLF development does not include a flexible, holistic strategy for integration. To investigate topic segregation, inclusion of socio-ethical aspects, and consideration of human-animal interactions within the PLF research field, the abstracts from 644 peer-reviewed publications were analysed using the recent advances in the Natural Language Processing (NLP). Two Latent Dirichlet Allocation (LDA) probabilistic models with varying number of topics (13 and 3 for Model 1 and Model 2, respectively) were implemented to create a generalised research topic overview. The visual representation of topics produced by LDA Model 1 and Model 2 revealed prominent similarities in the terms contributing to each topic, with only weight for each term being different. The majority of terms for both models were process-oriented, obscuring the inclusion of social and ethical angles in PLF publications. A subset of articles (5%, n = 32) was randomly selected for manual examination of the full text to evaluate whether abstract text and focus reflected that of the article as a whole. Few of these articles (12.5%, n = 4) focused specifically on broader ethical or societal considerations of PLF or (9.4%, n = 3) discussed PLF with respect to human-animal interactions. While there was consideration of the impact of PLF on animal welfare and farmers in nearly half of the full texts examined (46.9%, n = 15), this was often limited to a few statements in passing. Further, these statements were typically general rather than specific and presented PLF as beneficial to human users and animal recipients. To develop PLF that is in keeping with the ethical values and societal concerns of the public and consumers, projects, and publications that deliberately combine social context with technological processes and results are needed.
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