Daniel Essien, Yashan Dhaliwal, Suresh Neethirajan
Livestock systems are increasingly instrumented with heterogeneous sensors, yet the resulting data remain fragmented, short-lived, and rarely documented as integrated infrastructures. This gap limits the development of robust multimodal artificial intelligence under real production conditions. Here we present a longitudinal multimodal data infrastructure for poultry monitoring, spanning 22 consecutive weeks across five commercial-style barns. The dataset combines continuous RGB video (1080 p, 30 fps), continuous audio (48 kHz), periodic radiometric thermal imaging, and twice-daily environmental measurements, yielding 10.2 terabytes of temporally heterogeneous data. Rather than focusing on a specific predictive task, the study addresses the underlying data-engineering challenge: how to acquire, synchronize, store, and preprocess multimodal streams at production scale. We detail a reproducible system architecture for distributed sensing, local buffering, secure transfer, and cloud-based organization, together with standardized preprocessing pipelines for illumination correction, acoustic denoising, and radiometric temperature extraction. Temporal alignment is achieved through timestamp-based normalization across asynchronous modalities, with explicit characterization of alignment granularity and missing data under real-world constraints. This work positions multimodal livestock sensing as a data-systems problem. The resulting dataset supports longitudinal analysis, cross-modal querying, and the development and evaluation of machine learning and multimodal fusion approaches at appropriate temporal scales. By releasing both data and workflows, we provide a transparent and extensible foundation for building and evaluating AI systems in precision agriculture.