Predicting Dairy Calf Body Weight from Depth Images Using Deep Learning (YOLOv8) and Threshold Segmentation with Cross-Validation and Longitudinal Analysis

dc.contributor.authorLiao, Mingsien
dc.contributor.authorMorota, Gotaen
dc.contributor.authorBi, Yeen
dc.contributor.authorCockrum, Rebecca R.en
dc.date.accessioned2025-03-27T13:05:46Zen
dc.date.available2025-03-27T13:05:46Zen
dc.date.issued2025-03-18en
dc.date.updated2025-03-26T15:34:27Zen
dc.description.abstractMonitoring calf body weight (BW) before weaning is essential for assessing growth, feed efficiency, health, and weaning readiness. However, labor, time, and facility constraints limit BW collection. Additionally, Holstein calf coat patterns complicate image-based BW estimation, and few studies have explored non-contact measurements taken at early time points for predicting later BW. The objectives of this study were to (1) develop deep learning-based segmentation models for extracting calf body metrics, (2) compare deep learning segmentation with threshold-based methods, and (3) evaluate BW prediction using single-time-point cross-validation with linear regression (LR) and extreme gradient boosting (XGBoost) and multiple-time-point cross-validation with LR, XGBoost, and a linear mixed model (LMM). Depth images from Holstein (n = 63) and Jersey (n = 5) pre-weaning calves were collected, with 20 Holstein calves being weighed manually. Results showed that You Only Look Once version 8 (YOLOv8) deep learning segmentation (intersection over union = 0.98) outperformed threshold-based methods (0.89). In single-time-point cross-validation, XGBoost achieved the best BW prediction (R<sup>2</sup> = 0.91, mean absolute percentage error (MAPE) = 4.37%), while LMM provided the most accurate longitudinal BW prediction (R<sup>2</sup> = 0.99, MAPE = 2.39%). These findings highlight the potential of deep learning for automated BW prediction, enhancing farm management.en
dc.description.versionPublished versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationLiao, M.; Morota, G.; Bi, Y.; Cockrum, R.R. Predicting Dairy Calf Body Weight from Depth Images Using Deep Learning (YOLOv8) and Threshold Segmentation with Cross-Validation and Longitudinal Analysis. Animals 2025, 15, 868.en
dc.identifier.doihttps://doi.org/10.3390/ani15060868en
dc.identifier.urihttps://hdl.handle.net/10919/125091en
dc.language.isoenen
dc.publisherMDPIen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectdairy calfen
dc.subjectbody weight predictionen
dc.subjectdeep learningen
dc.subjectmachine learningen
dc.subjectYOLOv8en
dc.subjectdepth imageen
dc.subjectcross-validationen
dc.subjectlongitudinal analysisen
dc.titlePredicting Dairy Calf Body Weight from Depth Images Using Deep Learning (YOLOv8) and Threshold Segmentation with Cross-Validation and Longitudinal Analysisen
dc.title.serialAnimalsen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten

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