EweAcT：适配加速度计数据的母羊行为监测系统，用于粗放放牧系统中的活动监测

中文摘要 AI 辅助

在粗放放牧条件下监测牲畜行为，可为评估动物对 agroecological 系统中环境扰动（如热浪、寄生虫感染、捕食者攻击）的适应情况提供有价值的见解。可通过颈圈式加速度计数据结合人工智能模型监测动物行为，但开发准确的行为预测模型需要大量与标注行为对齐的加速度计数据，尤其是粗放放牧系统的可靠模型需涵盖多种代表性条件下采集的数据。本数据集包含 2021 至 2024 年出生的 120 只 Romane 母羊的 79 小时三轴加速度计数据，这些母羊源自 10 年前启动的 3 代和 4 代选育后的两个分化遗传品系：社交吸引力低（S-）和高（S+）品系，以及对人类容忍度低（H-）和高（H+）品系。它们在法国阿韦龙省圣让圣保罗的 La Fage 试验站（INRAE 的 UEF）应用的粗放系统下饲养，250 只绵羊全部在南部 280 公顷牧场上露天饲养。第一批数据于 2024 年 3 月、6 月、7 月在 UEF 的多种粗放条件下采集，包括斜坡牧场和热浪时期，母羊配备专为牧场幼羊设计的加速度计颈圈，被分组在实验围场中 4 至 8 小时，提供新鲜牧草和自由饮水，同时使用高架闭路电视（CCTV）摄像机对动物进行视频记录。行为标注使用 Behavioral Observation Research Interactive Software 完成，聚焦牧场主要行为：放牧、反刍、休息、移动及“其他”（归为其余所有活动），基于时间同步程序用 Python 将标注内容与对应加速度计序列对齐。第二批数据于 2025 年 11 月采集，用于补充移动活动相关数据，为此母羊配备加速度计颈圈，从饲养区沿步道移动至牧场，对应约 10 分钟的步行路程，利用每只母羊移动的起止时间将对应加速度计数据与移动活动对齐，再将这些数据与第一批数据集合并。所得数据集可直接用于应用人工智能模型，从加速度计数据对粗放放牧系统下绵羊的 5 种主要行为进行分类。

英文摘要

Monitoring livestock behaviour under extensive conditions would provide valuable insights to assess animal adaption to environmental perturbations in agroecological systems (e.g., heat waves, parasitism, predator attacks). Animal behaviour can be monitored using accelerometer data collected from neck-collars combined with artificial intelligence models. However, large amounts of accelerometer data aligned with annotated behaviours are necessary to develop accurate models of behaviour prediction. In particular, developing reliable models for extensive systems requires data collected across a wide range of representative conditions. The dataset includes 79 hours of tri-axial accelerometer data aligned with behaviours manually annotated from video recordings for 120 Romane ewes born between 2021 and 2024. The ewes were derived from two divergent genetic lines after three and four generations of selection started 10 years ago: low and high social attractiveness, noted S-and S+, and low and high tolerance towards humans, noted H-and H+. They were reared under the extensive system applied to the Experimental Unit of La Fage (UEF, INRAE, Saint-Jean-et-saint Paul, Aveyron) where 250 sheep were reared exclusively outdoors on 280 hectares of rangeland in southern France. First batch of data was collected on March, June and July 2024 at the UEF under a range of extensive conditions, including sloping pastures and heat-wave periods. Ewes were equipped with accelerometer neck-collars specifically designed for young sheep on pasture. They were grouped on experimental paddocks for 4 to 8 hours and provided with fresh grass and ad libitum access to water. The animals were simultaneously video-recorded using an elevated CCTV camera. Behaviour annotation was carried out using Behavioral Observation Research Interactive Software focusing on the main behaviours on pasture: Grazing, Ruminating, Resting, Moving, and ''Other'', grouping all remaining activities. Annotations and corresponding accelerometer sequences were aligned using Python language, based on a time synchronization procedure. A second batch of data was acquired on November 2025 to supplement the dataset with the moving activity. For that purpose, ewes were equipped with the accelerometer collars and moved on tracks from the housing area to the pastures, corresponding to an approximately 10 minute-walk. The start and end times of the moves for each ewe were used to align the corresponding accelerometer data with the moving activity. These data were then merged with the dataset from the first batch. The resulting dataset is ready to use for applying artificial intelligence models to classify the 5 main behaviours of sheep under extensive grazing systems from accelerometer data.

发表机构

• Université de Toulouse（图卢兹大学）
• INRAE（法国国家农业、食品与环境研究院）
• ENVT（图卢兹国立兽医学校）

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