arXiv cs.AI· Elisabeth Mayrhuber, Maciej Oczak, Michael Iwersen, David Brunner, Viktoria Dorfer, Stephan Winkler·· 4 小时前AI 评分34
符号回归在精准畜牧业中预测母猪分娩时间的潜力探索
Exploring the Potential of Symbolic Regression in Precision Livestock Farming
AI 导读
一项研究探索用可解释人工智能预测母猪分娩时间，分两阶段：基于活动量变化检测筑窝行为（180 头母猪中 166 头可检测，其中 149 头在分娩前 48 小时内成功），并用符号回归预测距分娩的剩余时间。符号回归平均绝对误差为 9.28 小时，在性能和可解释性上均优于支持向量回归和人工神经网络等黑箱模型。
正文 · 原文
Abstract:The ability to predict farrowing time in pigs is crucial for optimal farm management and ensuring piglet survival. This study investigates the potential of explainable artificial intelligence for precision livestock farming, focusing on acceleration sensor data from sows around the time of farrowing. Accurately predicting farrowing allows farmers to balance both, piglet safety by avoiding piglet crushing events and the mother sow's welfare, by avoiding early crating. Traditional machine learning models often lack interpretability (``closed-box'' problem). Explainable artificial intelligence offers a solution by revealing the decision-making processes within the models. This work is split into two stages. First the detection of nest building behaviour and second the prediction of the remaining time to farrowing. The nest building detection was possible for 166 out of 180 sows and successful within 48 hours before farrowing for 149 sows. The nest building detection is purely based on the change of a sow's activity level and uses no machine learning techniques. Further, this study utilizes symbolic regression, an open-box modelling technique that generates interpretable mathematical formulas, to predict the remaining time until the onset of farrowing. The results are compared with closed-box models, like support vector regression and artificial neural networks, for both explainability and performance. Notably, symbolic regression achieved a mean absolute error of 9.28 hours for the time-to-farrowing prediction, outperforming the closed-box models in terms of performance and interpretability. This research demonstrates that explainable artificial intelligence, specifically symbolic regression, can offer both interpretable results and competitive performance in precision livestock farming.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.39839 [cs.LG] |
| (or arXiv:2610.39839v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.39839 arXiv-issued DOI via DataCite |
Submission history
From: Elisabeth Mayrhuber [view email]
[v1]
Thu, 29 Oct 2026 11:17:21 UTC (268 KB)
来源：arXiv cs.AI · arxiv.org