基于加速度计的位置和时间间隔比较,用于预测饲养场系统中小公牛的行为

IF 6.3 Q1 AGRICULTURAL ENGINEERING Smart agricultural technology Pub Date : 2024-08-23 DOI:10.1016/j.atech.2024.100542
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引用次数: 0

摘要

动物行为监测是动物生产的重要工具。这种行为监测策略可以显示动物的福利和健康状况,从而提高动物的生产性能。本研究旨在评估最有效的加速度计固定位置(缰绳或颈圈上)和数据传输时间间隔(6 到 600 秒),以预测饲养场系统中年轻肉牛的行为模式,包括饮水和进食频率以及其他活动。为了达到研究目的,应用了一系列机器学习算法,包括随机森林、支持向量机、多层感知器和天真贝叶斯分类器算法。除了使用天真贝叶斯分类器建立的模型外,所有研究的模型在使用两个附件位置时都产生了较高的性能指标(高于 0.90)。因此,将加速度计与项圈耦合使用在动物身上是一个更可行的选择,因为这样做比将加速度计应用到缰绳上更容易。使用观测数据更多的数据集(即更短的时间间隔)并不能显著提高训练模型的性能指标。因此,使用观测数据较少的数据集更有优势,因为除了能节省本研究中设备的电池外,还能降低模型训练的计算和时间需求。
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Accelerometers-based position and time interval comparisons for predicting the behaviors of young bulls housed in a feedlot system

Animal behavior monitoring is an important tool for animal production. This behavior monitoring strategy can indicate the well-being and health of animals, which can lead to better productive performance. This study aimed to assess the most effective accelerometer attachment position (on either the halter or a neck collar) and data transmission time intervals (ranging from 6 to 600 s) for predicting behavioral patterns, including water and food intake frequencies, as well as other activities in young beef cattle bulls within a feedlot system. A range of machine learning algorithms were applied to satisfy the aims of the study, including the random forest, support vector machine, multilayer perceptron, and naive Bayes classifier algorithms. All studied models produced high performance metrics (above 0.90) when using both attachment positions, except for the models built using the naive Bayes classifier. Therefore, coupling accelerometers with collars is a more viable alternative for use on animals, as doing so is easier than applying accelerometers to halters. Utilizing a dataset with more observations (i.e., shorter time intervals) did not result in considerable improvements in the performance metrics of the trained models. Therefore, using datasets with fewer observations is more advantageous, as it can lead to decreased computational and temporal demands for model training, in addition to saving the battery of the device considered in this study.

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