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In the process of calf rearing, it is inevitable to encounter issues of illness and death among calves. Often, due to the inability to detect sicknesses such as diarrhoea in a timely fashion, these sicknesses lead to the calves’ demise. This research starts from the practical application needs, and proposes the development of a monit...
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Dairy cattle kept in barns with a free-stall system have contact with the structural elements of the stalls in the lying area. As a result of the cattle entering the lying stall, lying down, and standing up, shiny surfaces appear on the neck rails and partitions. These shiny surfaces can be a source of information about the activity of the animals...
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... Vázquez-Diosdado et al (2024) have also used an AdaBoost ensemble learning algorithm to classify calves' play and non-play behaviours and achieved an overall accuracy greater than 94%. Zhang et al (2024) have classified between healthy calves and sick calves (with diarrhoea) using YOLOv8n deep learning model. Features such as standing time, lying time, number of lying bouts, and average bout duration (using video feeds) have been used for the model training, and the model achieved a mean average precision of 0.995. ...
... This approach ensures that each dataset contains data from distinct, nonoverlapping calves). In contrast, some studies in the literature mix the data initially and use a simple split ratio to divide into training and testing sets (Carslake et al, 2020;White et al, 2008;Zhang et al, 2024). This approach will often result in data from the same subject appearing in both sets, compromising the assessment of generalization performance. ...
In recent years, there has been considerable progress in research on human activity recognition using data from wearable sensors. This technology also has potential in the context of animal welfare in livestock science. In this paper, we report on research on animal activity recognition in support of welfare monitoring. The data comes from collar-mounted accelerometer sensors worn by Holstein and Jersey calves, the objective being to detect changes in behaviour indicating sickness or stress. A key requirement in detecting changes in behaviour is to be able to classify activities into classes, such as drinking, running or walking. In Machine Learning terms, this is a time-series classification task, and in recent years, the Rocket family of methods have emerged as the state-of-the-art in this area. We have over 27 hours of labelled time-series data from 30 calves for our analysis. Using this data as a baseline, we present Rocket's performance on a 6-class classification task. Then, we compare this against the performance of 11 Deep Learning (DL) methods that have been proposed as promising methods for time-series classification. Given the success of DL in related areas, it is reasonable to expect that these methods will perform well here as well. Surprisingly, despite taking care to ensure that the DL methods are configured correctly, none of them match Rocket's performance. A possible explanation for the impressive success of Rocket is that it has the data encoding benefits of DL models in a much simpler classification framework.
En los últimos años, las tecnologías de visión por computadora han ganado popularidad en el ámbito agrícola; sin embargo, el enfoque en animales está en desarrollo debido a los desafíos que implica su integración. Esta investigación se realiza con el objetivo de estudiar el uso de tecnologías de visión por computadora para el manejo productivo y sanitario de animales de granja, para ello se utilizó la metodología “Systematic Literature Review” propuesta por Kitchenham & Charters, la cual consiste en realizar una búsqueda exhaustiva a través de bases de datos científicas, para este estudio se utilizó Scopus, IEEE Xplore, ScienceDirect y Google Scholar siguiendo criterios específicos de inclusión y exclusión. El proceso incluye la formulación de preguntas de investigación, la identificación de palabras claves relevantes y la evaluación de la calidad de los estudios seleccionados. A través de este proceso, se garantiza un análisis exhaustivo y riguroso de la literatura existente, logrando obtener resultados precisos y relevantes con la finalidad de estudiar diversos problemas con animales de granja tales como pollos, cerdos y ganado, que producen materia prima para el sustento y negocios de familias en sectores rurales. Tareas como la identificación, y rastreo de animales es posible gracias a herramientas como YOLOv, CNN, técnicas de segmentación, colorimetría, imágenes 2D, 3D, y cámaras que sirven como entrada de datos que se convierten en información procesada para el análisis y toma de decisiones inteligentes. Estas herramientas son útiles en prácticas con distintos propósitos que engloban el bienestar, la sanidad y la productividad de los animales. Además, esta investigación recomienda herramientas que podrían competir o superar las soluciones actuales, aportando a la ganadería de precisión en la mejora continua del uso tecnológico y su relación con el agricultor.
