Evidence map›Paper›PMID 42511096›Full record

ArticleAnimals : an open access journal from MDPI2026

Video-Based Feeding Demand Sensing and Offline Feeding Strategy Evaluation in Group-Housed Pigs: A Single-Pen Proof-of-Concept Study Using Oriented Object Detection.

Xinyuan He, Weijia Lin, Guoxing Chen, Yanhua Liu, Enli Lyu, Ziwei Li, Yizhi Luo, Zhixiong Zeng

Abstract read
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Article in Animals : an open access journal from MDPI, 2026. 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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5 · Who and what money

Authors and funding

8 authors.

Xinyuan HeCollege of Engineering, South China Agricultural University, Guangzhou 510642, China.
Weijia LinCollege of Engineering, South China Agricultural University, Guangzhou 510642, China.
Guoxing ChenCollege of Engineering, South China Agricultural University, Guangzhou 510642, China.ORCID 0000-0002-7355-8430
Yanhua LiuCollege of Engineering, South China Agricultural University, Guangzhou 510642, China.
Enli LyuCollege of Engineering, South China Agricultural University, Guangzhou 510642, China.
Ziwei LiCollege of Engineering, South China Agricultural University, Guangzhou 510642, China.
Yizhi LuoInstitute of Facility Agriculture, Guangdong Academy of Agricultural Sciences, Guangzhou 510640, China.ORCID 0000-0002-8362-5481
Zhixiong ZengCollege of Engineering, South China Agricultural University, Guangzhou 510642, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Continuous sensing of group feeding demand is needed to support feeding decisions on large-scale pig farms. In dense group-housing environments, crowding, occlusion, and variation in posture around the trough can reduce the stability of visual feeding-status recognition. Feeding schedules also commonly rely on fixed periods or historical patterns, limiting their ability to assess whether daily feeding-behavior signals are aligned with group demand. Here, we propose a video-based workflow for sensing group feeding activity and conducting an offline replay evaluation of feeding-schedule allocation rules in group-housed pigs. Using synchronized feeding videos and feeder records, we developed a workflow that integrates rotated object detection, visual feeding intensity calculation, time-period-level construction of video-derived feeding demand, and feeding strategy evaluation. Rotated bounding-box detection achieved an mAP50-95 of 0.908 for feeding recognition, supporting its feasibility for feeding/non-feeding recognition in this dense-pen scene. Visual feeding intensity derived from model outputs was most strongly correlated with actual feed intake increment under a 20 min aggregation window with no time lag (r = 0.80). In the evaluation based on video-derived feeding demand, the video-perceived hybrid strategy reduced the peak time difference to 14.51 min, achieved a synchronization correlation of 0.611, and reduced the mismatch rate to 5.13%. These findings suggest that video-based behavioral signals can serve as a group-level proxy for feeding demand and can support offline evaluation of behavior-aware feeding schedules. However, the demand and strategy analyses were based on a single pen observed for 72 h, and validation across additional pens, batches, age stages, seasons, and closed-loop feeding conditions is required before practical deployment.

Indexed as

feeding behavior recognitiongroup-housed pigsintelligent feedingprecision livestock farming

Identifiers

PMID42511096
PMCPMC13405578

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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.