用机器学习分析多种因素，实现牛只体重增长的远程自动预测。
Learning-based estimation of cattle weight gain and its influencing factors
- 基于多源数据融合的机器学习模型，综合环境、遗传、行为等变量。
- 揭示关键影响因素对牛体重增长的显著作用，提升预测准确性。
- 适合农业智能化、精准养殖研究者参考，推动智慧畜牧发展。
许多牧场仍依赖人工定期测量牛只活重增长，耗时费力且对动物和工作人员造成压力。采用机器学习（ML）或深度学习（DL）的远程自主监测系统可实现更高效、低侵入式的连续监控，并具备未来体重增长预测能力。该系统能综合考虑环境条件、遗传倾向、饲料供应、活动模式与行为等多重因素，实时估算个体牛只的活重增长、生长速率及体重波动。尽管已有研究探索了基于ML/DL方法估算牛体重增长（CWG）的效率，但其应用仍缺乏一致性。不同研究使用的特征各异，且面临数据质量、样本量、标注偏差等挑战。本文综述2004至2024年间相关研究，系统分析当前常用工具、方法与特征，评估其优劣。结果表明，先进机器学习方法在CWG估算中具有显著价值，且对关键影响因素的作用尤为突出。同时识别出数据融合、模型泛化、长期追踪等研究空白，为未来牛只生长预测研究提供方向指引。
原文摘要 · Abstract (English)
Many cattle farmers still depend on manual methods to measure the live weight gain of cattle at set intervals, which is time consuming, labour intensive, and stressful for both the animals and handlers. A remote and autonomous monitoring system using machine learning (ML) or deep learning (DL) can provide a more efficient and less invasive method and also predictive capabilities for future cattle weight gain (CWG). This system allows continuous monitoring and estimation of individual cattle live weight gain, growth rates and weight fluctuations considering various factors like environmental conditions, genetic predispositions, feed availability, movement patterns and behaviour. Several researchers have explored the efficiency of estimating CWG using ML and DL algorithms. However, estimating CWG suffers from a lack of consistency in its application. Moreover, ML or DL can provide weight gain estimations based on several features that vary in existing research. Additionally, previous studies have encountered various data related challenges when estimating CWG. This paper presents a comprehensive investigation in estimating CWG using advanced ML techniques based on research articles (between 2004 and 2024). This study investigates the current tools, methods, and features used in CWG estimation, as well as their strengths and weaknesses. The findings highlight the significance of using advanced ML approaches in CWG estimation and its critical influence on factors. Furthermore, this study identifies potential research gaps and provides research direction on CWG prediction, which serves as a reference for future research in this area.
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