Computer Vision-Based Characterization of Temporal Feeding Dynamic in Group-Housed Dairy Goats

Feeding behavior is closely related to nutrient intake, health, and production performance, yet its temporal dynamics are difficult to quantify in group-housed dairy goats. We developed a computer vision-based framework to characterize feeding dynamics under commercial farm conditions. Video data were collected from 550 Guanzhong dairy goats housed in five pens over three consecutive days. YOLO11, RF-DETR, and RT-DETR were compared for feeding-area goat detection, and temporal feeding features were analyzed to characterize high feeding activity (HFA) and low feeding activity (LFA). In a subsequent recording phase, head-down feeding behavior was assessed in 150 goats originating from the classified source pens. A total of 24 of these goats (12 HFA and 12 LFA) were selected for exploratory milk-production and serum comparisons. RF-DETR showed the best overall detection performance, with a precision (P) of 87.39%, recall (R) of 84.00%, F1-score of 85.66%, mean average precision (mAP)@0.5 of 90.38%, and [email protected]:0.95 of 53.30%. Goats exhibited a “rapid entry–peak aggregation–gradual departure” feeding pattern after feed delivery. Among the temporal features, rise time of feeding-area goat count (RTFGC), fall time of feeding-area goat count (FTFGC), and feeding-area goat count increase rate (FGCIR) were the most informative features for distinguishing HFA from LFA. HFA goats reached peak feeding-area occupancy earlier, entered the feeding area at a higher rate, and maintained feeding activity for longer than LFA goats. HFA goats also showed higher head-down feeding proportions, greater average daily dry matter intake (DMI), and higher milk yield, as well as distinct serum immune, antioxidant, and neuroendocrine-related profiles. These findings indicate that feeding dynamics derived from video observations can provide a quantitative digital behavioral phenotype for characterizing individual differences in dairy goat feeding activity and their associations with production and physiological status.

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Publication Details

Journal
Animals
Published
2026-10-27
DOI
https://doi.org/10.3390/ani16213390
Primary Topic
Smart Agriculture and AI
Type
article
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article

Computer Vision-Based Characterization of Temporal Feeding Dynamic in Group-Housed Dairy Goats

Jingwei Qi, Xiaoping An, Yuan Wang, Na Liu et al.
2 links
Animals
Smart Agriculture and AI
article

Computer Vision-Based Characterization of Temporal Feeding Dynamic in Group-Housed Dairy Goats

Jingwei Qi, Xiaoping An, Yuan Wang, Na Liu, Yifeng Song, Qier Mu, Junqing Guo
article en

Abstract

Feeding behavior is closely related to nutrient intake, health, and production performance, yet its temporal dynamics are difficult to quantify in group-housed dairy goats. We developed a computer vision-based framework to characterize feeding dynamics under commercial farm conditions. Video data were collected from 550 Guanzhong dairy goats housed in five pens over three consecutive days. YOLO11, RF-DETR, and RT-DETR were compared for feeding-area goat detection, and temporal feeding features were analyzed to characterize high feeding activity (HFA) and low feeding activity (LFA). In a subsequent recording phase, head-down feeding behavior was assessed in 150 goats originating from the classified source pens. A total of 24 of these goats (12 HFA and 12 LFA) were selected for exploratory milk-production and serum comparisons. RF-DETR showed the best overall detection performance, with a precision (P) of 87.39%, recall (R) of 84.00%, F1-score of 85.66%, mean average precision (mAP)@0.5 of 90.38%, and [email protected]:0.95 of 53.30%. Goats exhibited a “rapid entry–peak aggregation–gradual departure” feeding pattern after feed delivery. Among the temporal features, rise time of feeding-area goat count (RTFGC), fall time of feeding-area goat count (FTFGC), and feeding-area goat count increase rate (FGCIR) were the most informative features for distinguishing HFA from LFA. HFA goats reached peak feeding-area occupancy earlier, entered the feeding area at a higher rate, and maintained feeding activity for longer than LFA goats. HFA goats also showed higher head-down feeding proportions, greater average daily dry matter intake (DMI), and higher milk yield, as well as distinct serum immune, antioxidant, and neuroendocrine-related profiles. These findings indicate that feeding dynamics derived from video observations can provide a quantitative digital behavioral phenotype for characterizing individual differences in dairy goat feeding activity and their associations with production and physiological status.

AnimalsVol. 16(21)
Inner Mongolia Agricultural University (CN)
Zero hunger
Openalex Percentile: Top 15%
Smart Agriculture and AI
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