Detection of Estrus in Dairy Cows: A Proof-of-Concept Near-Infrared Milk Sensing and Machine-Learning Study
DOI:
https://doi.org/10.59796/jcst.V16N2.2026.181Keywords:
dairy cows, estrus detection, near infrared spectroscopy (NIR), reproductive managementAbstract
Accurate estrus detection is critical for reproductive management in dairy herds, yet current methods are either labor-intensive or require dedicated hardware. This proof-of-concept study investigated whether near-infrared (NIR) milk spectra, combined with routine milk-composition data, can be used to detect estrus in dairy cows. Five clinically healthy Thai milking cows were monitored for 21 days each (total 593 milk samples), with estrus labels assigned based on experienced stockperson observations confirmed by behavioral signs and tail-paint rubbing. For each sample, inline NIR transmission spectra (860–1754 nm) and milk composition (fat, protein, lactose, solids-not-fat, density, pH, daily yield) were acquired. During estrus, milk composition showed modest but consistent shifts: protein increased by approximately 0.29 percentage points and lactose by 0.41 percentage points, while daily milk yield decreased by about 7.4 ± 2.7 kg/day relative to non-estrus days. A leakage-aware, leave-one-cow-out cross-validation framework was used to compare six classifiers. Logistic regression, gradient boosting, and decision-tree-based ensembles achieved internal accuracies of between 0.99 and 1.00, with Extra Trees and random forest yielding F1-scores of 0.99 and 0.92, respectively. Feature-importance analysis indicated that specific NIR bands in the water and protein-related regions, together with milk yield and protein percentage, contributed most strongly to estrus discrimination, whereas density and pH had minimal influence. These proof-of-concept results (N = 5, single site) demonstrate technical feasibility but require multi-site validation in substantially larger cohorts (N ≥ 50) before any clinical or on-farm adoption can be recommended.
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