Precision Livestock Farming (PLF) has evolved dramatically from basic monitoring systems to sophisticated artificial intelligence (AI)-driven decision support systems that enhance livestock management efficiency, sustainability, and animal welfare. This review examines the technological evolution of PLF since 2017, highlighting significant advancements in sensing technologies, computer vision, and AI. Non-invasive technologies, including red-green-blue and depth cameras, 3D imaging systems, and Internet of Things-enabled platforms, now capture detailed biometric and behavioral data in real time, while AI algorithms enable early disease detection, optimize feeding strategies, and improve reproductive management. Integrating these technologies with mechanistic models has created hybrid intelligent frameworks that address longstanding challenges in precision nutrition modeling. Future PLF development will likely focus on integrating large language models, adopting federated learning approaches to address data privacy concerns, and democratizing technologies for small-scale producers. Despite technological progress, challenges remain regarding data standardization, connectivity in rural environments, high implementation costs, and ethical considerations around increased animal monitoring. By fostering interdisciplinary collaboration among animal scientists, engineers, computer scientists, and social scientists, PLF can continue to drive sustainable and efficient practices in livestock production while ensuring that technologies complement rather than replace traditional husbandry knowledge.

Sensor systems have increasingly been explored as tools to support precision livestock farming, particularly in monitoring cow health and improving decision-making. This systematic literature review aims to evaluate advancements in sensor systems for detecting health conditions in dairy cows especially on mastitis, fertility, locomotion, and metabolic disorders. Relevant articles published between 2014 and 2024 were identified from Scopus. Each article was categorized by health condition and assigned to one of four development levels: sensor technique (Level I), data interpretation (Level II), integration of information (Level III), and decision making (Level IV). Relevant information from the articles was systematically reviewed and discussed. We identified 132 articles published in the past 10 years, describing a total of 151 sensor systems. Most sensor systems were aimed at mastitis and reproduction, followed by locomotion and metabolic disorders. The far majority of the articles were at level II (data interpretation) presenting research on (novel) algorithms to detect disease. A large number of different statistical, machine-learning or deep-learning models were described and evaluated, among others random forests. Level II systems applied statistical analysis or machine-learning/deep-learning models (e.g., random forests, you only look once, support vector machine, or convolutional neural network). These algorithms used a wide range of sensor data. Only a few articles aimed at level III research, integration of information and decision support. The Level III sensor systems integrated information from the sensor with economic information and other information (i.e., medication dosage, cost per disease, and supplier selection) and simulated various treatment scenarios. This review highlights the need for sensor systems research to be driven by real-world requirements for on-farm decision making. To move from proof-of-concept toward practical, future research must integrate sensor outputs with herd records and financial models, validate systems across multiple farms and at higher data frequencies, and embed economic evaluation alongside sensitivity and specificity metrics. Addressing these technical, integration, and economic challenges is essential before sensor systems can fully support automated, value-driven health management on commercial dairy farms.

This paper describes how animal management was done in the past and how we evolved to continuous animal monitoring by using technology. Based upon peer-reviewed literature, we show several technologies (cameras, microphones, sensors) developed and used for monitoring and managing indoor farm animals applied to different animal species: broilers, pigs, dairy cows, and horses. The main idea in developing precision livestock farming (PLF) technology is continuous 24/7 monitoring to generate objective data for farmers, veterinarians, and other stakeholders (pharma, feed company, equipment, climate control, etc.). Development started in laboratory settings and evolved to data collection in commercial farms. This paper shows that PLF monitoring allows us to measure objective data in each term of the fundamental process equation in producing animal products: the transfer from feed energy into animal product (meat, milk, eggs, fiber, and reproduction). The accuracies obtained are impressive in several examples. Regarding where to go with PLF, we show the great importance of animal welfare in the efficiency of the production process. Improving process efficiency is key in avoiding the need for even more farm animals to be slaughtered every year to fulfill the increasing worldwide demand for animal products. Field implementation of PLF technology for continuous objective measurements on indoor farm animals, to improve the efficiency of the production process, is an important contribution to feeding the worldwide increasing demand for animal products without a high increase of the number of farm animals.

Precision livestock farming in dairy production is advancing through biosensor-based monitoring that converts frequent, longitudinal measurements into actionable information to support animal-level decision-making under commercial conditions. This review summarizes biosensors for precision dairy farming with a systems perspective that connects sensing, data transfer, analytics, and visualization. Biosensors can be categorized by sensing locus as at-animal, near-animal, and from-animal to clarify practical tradeoffs among invasiveness, scalability, maintenance burden, and diagnostic specificity. The review also describes how information should progress from raw signals to interpretable indicators and decision-support outputs, emphasizing that farm value depends on reliable interpretation and timely intervention rather than on measurement alone. Key applications are synthesized across nutrition and feeding behavior, reproduction (including estrus and calving), health monitoring (such as mastitis, lameness, and metabolic disorders), welfare assessment, and environmental sustainability, highlighting where different modalities best support screening, early warning, and confirmatory detection. Finally, the review discusses on-farm barriers, including missing data, sensor drift, attachment stability, communication failures, and alert fatigue, and proposes future directions in standardization, interoperability, and artificial intelligence-enabled decision support to strengthen end-to-end system reliability, scalability, and economic sustainability under commercial conditions.

The growing global demand for animal-derived food products is placing unprecedented pressure on livestock production systems to improve efficiency while also assuring animal welfare, environmental sustainability and economic viability. Precision livestock farming (PLF) has emerged as a transformative paradigm that integrates advanced sensing technologies, computer vision, internet of things infrastructures and artificial intelligence (AI) to enable continuous, automated and individualized animal monitoring. This paper explores the evolution of livestock management from conventional observation-based practices to sophisticated, data-driven architecture. It also synthesizes recent advancements in PLF emphasizing its system architecture, key applications in cattle production, cross-sector expansion and emerging challenges. The core architecture of PLF is structured into three functional layers: (i) data acquisition through multi-modal sensors, with a primary emphasis in this review on visual and environmental monitoring system; (ii) data analytics employing machine learning and deep learning techniques to establish behavioral and physiological baselines; and (iii) decision-support mechanisms that translate analytics into actionable farm management interventions. Major applications, including individual animal identification, body condition score estimation, lameness detection, calving time prediction and AI-powered health monitoring, are critically discussed. The extension of PLF principles to aquaculture and other livestock sectors is also discussed. By shifting from herd-level to individual-animal management, PLF provides a scalable, noninvasive approach for early disease detection, optimized resource utilization, improved welfare standards and long-term economic sustainability. The current limitations, including high capital investment, data interoperability challenges and model generalizability constraints, have been analyzed and future research directions emphasizing explainable AI and welfare-oriented system design have been proposed. Overall, PLF represents a systemic transformation of animal agriculture, allowing for data-driven, sustainable and welfare-centered production systems.