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In the dairy industry, maintaining the welfare of every cow and curbing financial losses requires individual-level surveillance, which is achieved through cow Re-Identification (Re-ID). Computer vision-based Re-ID hinges on visually distinguishing characteristics, such as the specific coat patterns seen in breeds like Holstein. However, manually labelling every cow across all farms is financially unfeasible. We aim to formulate Re-ID strategies that work on both annotated and non-annotated farms, accommodating new individuals and varied settings. Unsupervised Domain Adaptation (UDA) approaches aim to close this divide by transferring knowledge from labelled source domains to unlabelled target domains. However, they have largely been tailored to person and vehicle Re-ID scenarios. Our research proposes Cumulative Unsupervised Multi-Domain Adaptation (CUMDA) to address the challenges of constrained identity variation and dissimilar farm appearances. CUMDA gathers knowledge across all domains, reinforcing specialization in familiar domains while strengthening generalization to unknown ones. Our offerings include a CUMDA technique that adapts to multiple unlabelled target domains while safeguarding source-domain efficacy, along with thorough cross-dataset testing across three cattle Re-ID datasets. These tests reveal substantial gains in source conservation, target-domain specialization, and adaptation to unseen domains.

Contemporary swine husbandry falls short in operational effectiveness, as current systems depend largely on electromechanical controls and can only classify pigs by body mass. Such a method is not only inefficient but also increases labor overheads and the risk of zoonotic infections. Additionally, confining pigs in large herds can worsen disease transmission and make the observation and treatment of unwell animals more difficult. This investigation conducted a trial to develop a deep-learning categorization mechanism, drawing on a dataset enriched with essential metrics and farm imagery amassed over 24 months. The study utilized a Kalman filter-based algorithm to boost the precision of the dynamic categorization process. The experiment introduced a first-of-its-kind machine vision classification system, powered by deep learning, capable of processing live images for multifaceted recognition. The individual recognition model, built on a Residual Neural Network (ResNet), predicts livestock mass using ongoing data. In contrast, the Wasserstein Generative Adversarial Nets (WGAN) image enhancement algorithm improves recognition across diverse conditions, thereby strengthening the model’s resilience. Crucially, the system can detect livestock exhibiting signs of likely illness via atypical body language and sequester them for protection. Experimental results confirm the advantage of the introduced system over standard alternatives. It not only reduces manual intervention and data maintenance costs but also improves the accuracy of livestock recognition and better uses data. The outcomes demonstrate an 89% success rate in livestock ID recognition, a 32% increase in blocked-image recognition, a 95% surge in livestock categorization precision, and a striking 98% success rate in distinguishing pictures of sick pigs. In essence, this research enhances identification efficiency, reduces operating costs, and provides superior tools for disease surveillance.