Exploring the Potential of Symbolic Regression in Precision Livestock Farming

Elisabeth Mayrhuber; Maciej Oczak; Michael Iwersen; David Brunner; Viktoria Dorfer; Stephan Winkler

cs.LGOct 29, 2026

Exploring the Potential of Symbolic Regression in Precision Livestock Farming

Authors: Elisabeth Mayrhuber, Maciej Oczak, Michael Iwersen, David Brunner, Viktoria Dorfer, Stephan Winkler

Organizations: Bioinformatics Research Group, PLFDoc, University of Applied Sciences Upper Austria, Hagenberg, Austria · Computer Vision Lab, TU Wien, Vienna, Austria · Precision Livestock Farming Hub, The University of Veterinary Medicine Vienna, Vienna, Austria · Center for Animal Nutrition and Welfare, The University of Veterinary Medicine Vienna, Vienna, Austria ·

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.

Figures & tables

Pen type	Training	Test
Wing	48	12
SWAP	44	12
Trapezoid	52	12
Total	144	36

Table 1: Number of sows used training and test split based on pen types.

Model	MAE [h]				PCC
	Training		Validation		Training		Validation
	Mean	Std	Mean	Std	Mean	Std	Mean	Std
SR	17.70	1.21	22.55	2.25	0.60	0.05	0.42	0.06
LR	26.80	0.85	27.22	2.42	0.45	0.01	0.44	0.04
ANN	23.17	1.03	24.56	2.22	0.56	0.02	0.50	0.04
RF	8.83	0.43	24.35	2.73	0.96	0.004	0.49	0.06

Table 2: Training and validation performance for 5-fold cross-validation.

Figure 1: Detection of nest building behaviour for one sow. The dashed line (red) shows the detected start of nest building behaviour, and the highlighted area (green) shows the true time of farrowing. This detection is done online using a sliding window approach.

Method	MAE [h]				PCC
	Training		Test		Training		Test
	Mean	Std	Mean	Std	Mean	Std	Mean	Std
SR	19.57	26.61	9.28	13.09	0.73	0.31	0.76	0.21
LR	22.05	20.59	18.04	10.51	0.89	0.22	0.90	0.29
ANN	21.46	25.33	17.28	14.36	0.81	0.25	0.86	0.22
RF	20.51	22.40	19.10	16.88	0.74	0.33	0.79	0.23

Table 3: Performance of different models in a real time setting for time to farrowing predictions.

Figure 2: The points show the predicted compared to the actual time to farrowing for one sow, using symbolic regression. The line shows how a perfect prediction would look like.

Figure 3: Both images show the performance of the time to farrowing prediction for the same sow as in Figure 2 . The left figure (a) shows the prediction made by the linear regression model and the right figure (b) shows the prediction of the artificial neural network.

Precision Livestock Farming

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Mar 30, 2026

Neural Federated Learning for Livestock Growth Prediction

Livestock growth prediction is essential for optimising farm management and improving the efficiency and sustainability of livestock production, yet it remains underexplored due to limited large-scale datasets and privacy concerns surrounding farm-level data. Existing biophysical models rely on fixed formulations, while most machine learning approaches are trained on small, isolated datasets, limiting their robustness and generalisability. To address these challenges, we propose LivestockFL, the first federated learning framework specifically designed for livestock growth prediction. LivestockFL enables collaborative model training across distributed farms without sharing raw data, thereby preserving data privacy while alleviating data sparsity, particularly for farms with limited historical records. The framework employs a neural architecture based on a Gated Recurrent Unit combined with a multilayer perceptron to model temporal growth patterns from historical weight records and auxiliary features. We further introduce LivestockPFL, a novel personalised federated learning framework that extends the above federated learning framework with a personalized prediction head trained on each farm's local data, producing farm-specific predictors. Experiments on a real-world dataset demonstrate the effectiveness and practicality of the proposed approaches.

Shoujin Wang, Mingze Ni, Wei Liu +6

University of Technology Sydney, Sydney, Australia · AgriWebb, Sydney, Australia · Queensland University of Technology, Brisbane, Australia

Federated Learning Time Series Forecasting Privacy-Preserving ML

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May 25, 2026

SAM3-Assisted Training of Lightweight YOLO Models for Precision Pig Farming

Deep learning-based object detection has revolutionized Precision Livestock Farming (PLF), yet a critical barrier remains: high-performance Foundation Models (such as SAM 3) are too computationally intensive for edge deployment, while lightweight models (like YOLO) require prohibitive manual annotation efforts. This work proposes a fully automated knowledge distillation pipeline that leverages the Segment Anything Model 3 (SAM 3) to generate zero-shot pseudo-labels for training efficient YOLOv8 detectors. By treating SAM 3 as an offline auto-annotator, we eliminate the manual labeling bottleneck, producing models capable of real-time inference on resource-constrained hardware. We systematically evaluate this approach on the PigLife dataset, comparing SAM 3-supervised models against human-annotated baselines. Results demonstrate that a SAM 3-trained YOLOv8m achieves a mean Average Precision (mAP) of 79.4% without human intervention, while reducing inference latency by approximately 200

\times

compared to the teacher model. Furthermore, stratified analysis reveals that in low-occlusion scenarios, the automated pipeline achieves detection rates comparable to human benchmarks (

AP_{50} > 99\%

). These findings indicate that foundation models can serve as effective, zero-annotation-cost supervisors, enabling scalable edge computing solutions for smart agriculture.

Marcos Vinicius Mendes Faria, Thiago Borges Pereira, Isabella C. F. S. Condotta +2

Coordenadoria de Inform´atica, Instituto Federal do Esp´ırito Santo (IFES), Serra, ES · Department of Animal Sciences, University of Illinois at Urbana-Champaign, USA

Edge Inference Pseudo-Labeling Object Detection Real-Time Object Detection Knowledge Distillation Precision Livestock Farming

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Aug 6, 2026

Hybrid Machine Learning Framework for Herd-Level Cattle Growth Pattern and Weight Gain Forecasting in Grazing-Based Production Systems

Commercial grazing systems yield irregular livestock observations, which challenge cattle growth forecasting. This study developed a hybrid machine learning framework for herd level cattle weight forecasting using automated sensing observations collected between 2022 and 2024 in southeastern Australia. Weekly live weight observations, demographic variables, and lagged environmental predictors were integrated into structured forecasting datasets. Herd level forecasting trajectories were generated through temporal aggregation of animal level predictions. Four hybrid architecture families were evaluated, including residual, stacked, cascade, and ensemble assisted frameworks. ARIMA, LSTM, and GRU models were used as comparative baselines. Independent testing demonstrated strong predictive agreement across multiple forecasting horizons. The cascade GB to RF to NN architecture achieved the best performance, with a test R^2 of 0.889, RMSE of 21.319 kg, and MAE of 15.462 kg. Hybrid architectures maintained greater robustness than recurrent sequential models under sparse observation conditions. Forecasting error increased progressively across extended prediction horizons. Feature importance analysis identified animal age, rainfall, and temperature as dominant predictors influencing herd level growth forecasting. The proposed framework may support feed allocation, grazing management, and livestock marketing decisions under heterogeneous sensing environments.

Muhammad Riaz Hasib Hossain, Rafiqul Islam, Shawn R. McGrath +2

a School of Computing, Mathematics and Engineering, Charles Sturt University, Wagga Wagga, NSW 2650, Australia · b School of Computing, Mathematics and Engineering, Charles Sturt University, Albury, NSW 2640, Australia · c Gulbali Institute for Agriculture, Water and Environment, Charles Sturt University, Wagga Wagga, NSW 2678, Australia +4

Irregular Time-Series Modeling Multivariate Time Series Forecasting Ensemble Learning Time Series Forecasting

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Abstract

Precision Livestock Farming

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