Prediction of Sow Farrowing Onset Time Using Activity Time Series Extracted by Optical Flow Estimation.

Kejian Liu, Yigui Huang, Junbin Liu, Zujie Tan, Deqin Xiao

Abstract read

In one paragraph

Article in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

5 authors.

Kejian LiuCollege of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China.ORCID 0009-0005-8378-9258

Yigui HuangCollege of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China.

Junbin LiuCollege of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China.

Zujie TanCollege of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China.

Deqin XiaoCollege of Mathematics Informatics, South China Agricultural University, Guangzhou 510642, China.

Funding

the Guangdong Provincial Science and Technology Programme 2022B0202160010the National Key R&D Program of China 2021YFD200802

6 · The paper itself

Abstract

Sow farrowing is a critical stage in pig farming, and predicting its onset can improve sow health and piglet survival. This study proposes a method based on optical flow estimation and time-series forecasting to predict sow farrowing onset. The RAFT optical flow algorithm is applied to visible-light videos of sows in late pregnancy to extract activity levels and generate time-series data. The reliability of the activity extraction algorithm is validated through correlation and trend analysis, showing strong inter-sow correlations with an average Pearson coefficient of 0.819. An in-depth analysis of sow_16 reveals a significant increase in activity 24 h before farrowing. Using these data, the CLA-PTNet model, incorporating CNN, LSTM, and attention mechanisms, is developed for continuous farrowing onset prediction. Experimental results demonstrate high predictive accuracy, with average MAE, RMSE, and R

Indexed as

farrowing predictionoptical flow estimationsowtime series

Identifiers

PMID40218390

DOI10.3390/ani15070998

PMCPMC11987883

What OpenQuestion holds

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LicenceCC BY

Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.