Bimodal data analysis for early detection of lameness in dairy cows using artificial intelligence

Abstract

Lameness remains a leading cause of economic loss in Canadian dairy herds while also compromising animal welfare. To address the urgent need for early detection, we introduce a novel bimodal artificial intelligence (AI) framework that leverages both facial biometric data and accelerometer-based movement metrics. Over a 21-day period, six Holstein cows were monitored to capture variations in facial expressions and locomotion, and a multimodal model was built by combining DenseNet-121 for image analysis with Long Short-Term Memory (LSTM) networks for time-series data. Crucially, our model employs a multi-head attention mechanism to fuse visual and movement features, enabling it to overcome confounding factors such as lighting conditions, barn environments, and individual behavioral differences. This approach achieved a 99.55 % accuracy—substantially exceeding single-modality baselines—and Grad-CAM interpretations revealed key facial cues (orbital tightening, ear posture, muzzle tension) linked to lameness. Lame cows also exhibited prolonged resting times, especially during peak activity hours, underscoring their discomfort. These findings illustrate how integrating facial and accelerometer data can promote timely interventions, significantly enhancing cow welfare and reducing medical expenditures and productivity losses. Moreover, our results highlight how tie-stall barn systems can exacerbate lameness by restricting natural movement, further supporting recommendations to transition toward more open, movement-friendly housing. In doing so, producers not only protect cow well-being but also safeguard vital economic returns.