数字农科院2.0

A full end-to-end analytical framework for livestock behavior modeling and health assessment using wearable electronic recording system and machine learning

文献类型： 外文期刊

作者： Ni, Guohao;Jia, Yuanzheng;Shi, Zhonghao;Chang, Fangyuan;Miao, Jinfeng;Wang, Jian;Ye, Gengping;Wu, Jie;Yin, Huifang;Jiang, Wei;Han, Xiangan;Tang, Wei

作者机构：

关键词： Machine learning;Wearable inertial sensor;Precision livestock farming;Behavior classification;Health assessment

期刊名称： SMART AGRICULTURAL TECHNOLOGY

ISSN：

年卷期： 2025 年 13 卷

页码：

收录情况： ESCI（2025版）

摘要： Precision Livestock Farming (PLF) aims to enhance animal management through technology, yet its progression is limited by a disconnect between discrete data collection tools and the practical requirement for unified, interpretable decision-support systems. While wearable sensors and machine learning offer potential for behavior monitoring, current solutions are often fragmented, focusing on isolated classification tasks rather than providing a complete, actionable pipeline from raw data to farm management insights. This lack of integration, alongside the technical challenges of model optimization, significantly hinders widespread practical adoption. This work presents a full end-to-end analytical framework that integrates wearable electronic recording system (WERS) hardware with intelligent analytical toolkit (IAT) software to form a fully automated workflow. The IAT incorporates automated model selection and hyperparameter tuning across twelve machine learning algorithms, three feature extraction methods, and six feature selection strategies, enabling flexible and customizable modeling pipelines for sequential data processing, behavior recognition and health evaluation, and visual feedback. The implemented system demonstrates high classification accuracy, strong adaptability, and robust support for cattle behavior sequence analysis and health assessment. The system has been empirically validated on eight dairy cattle over a six-day period, demonstrating its practical applicability in real-world conditions based on the real-time deployment platform built for the system. By providing a systematic and scalable solution for intelligent livestock monitoring, this work bridges the gap between fragmented sensing technologies and operational decision-support systems, ultimately contributing to improved decision-making and operational efficiency in PLF management.

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