Detection of Neonatal Calf Diarrhea Using Suckle Pressure and Machine Learning

Beibei Xu, Claira R. Seely, Tapomayukh Bhattacharjee and Taika von Konigslow ()
Additional contact information
Beibei Xu: Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, USA
Claira R. Seely: Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, USA
Tapomayukh Bhattacharjee: Department of Computer Science, Cornell University, Ithaca, NY 14853, USA
Taika von Konigslow: Department of Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14853, USA

Agriculture, 2025, vol. 15, issue 17, 1-18

Abstract: Neonatal calf diarrhea (NCD) remains one of the most prevalent and economically burdensome health challenges in preweaned calves, leading to compromised growth, increased morbidity, and high mortality rates worldwide. While traditional methods such as physical examination and clinical health scoring are widely used, they often require trained personnel, are resource-intensive, and are prone to subjectivity, which limits their scalability in large dairy operations. This observational cohort study investigated the feasibility of using suckle pressure measurement combined with machine learning (ML) techniques for NCD detection. A total of 51 female Holstein calves on a commercial dairy farm were enrolled at birth and health scored daily from 1 to 21 days of age. Suckle pressures were measured at 1, 3, 5, 7, 10, 14, and 21 days, as well as daily following NCD diagnosis until fecal consistency returned to normal. Pressure measurements were captured using impression film-wrapped nipples, producing 349 images, of which 54 were from calves diagnosed with NCD. Image features, including pixel density, color saturation, entropy, and histogram-based features, were extracted for analysis. Multiple ML classifiers—Support Vector Machine, K-Nearest Neighbors, Random Forest, Gradient Boosting, and Easy Ensemble (EE)—were applied to detect NCD status based on image features. The EE classifier achieved the best detection performance, with an accuracy of 0.90, precision of 0.64, and recall of 0.82, effectively handling data imbalance. Notably, the results also demonstrated that NCD onset could be predicted up to one day prior to clinical manifestation by training classifiers on pre-symptomatic suckle pressure data and testing on post-onset data. The EE classifier also outperformed other models in this early prediction window, with an accuracy of 0.74, precision of 0.67, and recall of 0.70. The results of our preliminary study suggest that suckle pressure may offer a novel, non-invasive approach for precision health monitoring in dairy systems, enabling timely intervention to reduce disease severity, improve calf health, and minimize economic losses.

Keywords: neonatal calf diarrhea; suckle pressure; machine learning; early disease detection (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/17/1831/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/17/1831/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:17:p:1831-:d:1736193

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().