Artificial intelligence for animal science: from applications to integrated knowledge systems
Year: 2025
Type: article
Abstract: AI is shifting from discrete tools to a system-level integrator, requiring a holistic approach to manage farm ecosystems rather than isolated disciplines. Advanced AI transforms farms into real-time living laboratories, accelerating knowledge creation and positioning AI as a co-producer of scientific discovery. The next frontier is a multiscale vision for AI, integrating data across molecular, animal, herd, and environmental levels to sustainably manage agricultural ecosystems. Responsible AI deployment requires co-developed frameworks for ethics, data governance, and equity to protect animal welfare, ensure farmer agency, and build trust. Practitioners should view integrated AI as a long-term investment. Emerging evidence shows measurable productivity and environmental gains over 3–5 year periods, but return of investment timelines will vary by farm. Animal science is at a critical juncture, faced with the challenge of providing sustainable nutrition for a growing global population. Against this backdrop, Artificial Intelligence (AI) has emerged as a powerful transformative force, offering the potential to enhance efficiency, improve animal welfare, and reduce environmental footprints (Distante et al., 2025). To date, the application of AI has largely taken the form of discrete, stand-alone solutions, with specific tools successfully addressing challenges in areas such as health monitoring and feed optimization. This toolbox paradigm has yielded significant gains and demonstrated the clear value of data-driven management (Cabrera, 2024; Menezes et al., 2024). However, a fundamental leap in the technology itself now invites a more ambitious vision. Whereas early AI was primarily analytical, tasked with identifying patterns in existing data, contemporary generative approaches, driven by large language models, vision models, and AI agents (systems that can perceive their environment and take autonomous actions), have begun to demonstrate synthetic reasoning and proactive planning capabilities (Li et al., 2025). Such a qualitative progression, from analytical to generative AI, has the potential to transform the concept of an integrated knowledge system from a distant aspiration into an attainable reality. It is this transition and its profound implications that this review seeks to explore. We articulate how AI is evolving beyond a passive farm management tool to become an active co-producer of scientific knowledge and a cross-disciplinary system integrator. This article argues the revolutionary impact of AI in animal science lies not in isolated tools but in its emerging role as a systemic driver, with the potential to advancing the entire field from disparate applications toward an integrated knowledge and decision-making ecosystem. However, realizing this vision requires addressing fundamental challenges in validation, reproducibility, and responsible deployment. To understand the paradigm shift currently underway, it is necessary to review the evolution of AI in animal science from both a technological and a conceptual standpoint (Ghavi Hossein-Zadeh, 2025). The application of AI has progressed through four identifiable stages, each representing a significant increase in analytical sophistication and operational autonomy (Figure 1). The initial phase was characterized by sensor-driven analytics, where technology was used to monitor discrete variables, such as an animal’s temperature or activity level. These systems were largely reactive, designed to trigger passive alerts when a predefined threshold was crossed, signaling a potential issue for human intervention. Evolution of AI in animal science from data points to digital ecosystems. A significant advancement came with the application of machine learning (ML). As sensor data accumulated, ML algorithms enabled a shift from simple reactive alerts to predictive tasks (García et al., 2020). These models could analyze historical data to forecast outcomes, such as the likelihood of disease, predict milk production, or optimize feed formulations to improve management efficiency. The next leap in capability was driven by the advent of deep learning, particularly through computer vision. This technology unlocked the potential to analyze complex, unstructured data, such as images and video, enabling more sophisticated and noninvasive phenotyping (Okinda et al., 2020). Through image analysis, it became possible to monitor intricate animal behaviors, estimate body weight, and even identify individual animals within a herd automatically. The current frontier marks another fundamental shift, defined by the rise of generative AI and agent-based systems. This fourth stage moves beyond analysis and prediction into the realm of synthesis and autonomous action. Powered by large language and vision models, these systems possess advanced capabilities for reasoning, simulation, and proactive decision-making (Ferreira and Dórea, 2025). More importantly, this technological progression has enabled an equally profound conceptual evolution. Early AI applications were constrained by both a scarcity of on-farm data and the computational power needed to process it. The proliferation of Internet of Things sensors solved the data generation problem but often resulted in information being stored in isolated data silos. Concurrently, the rise of cloud computing and deep learning provided the immense power required to analyze these vast and disparate datasets. The convergence of these two trends, which are widespread data generation and massive computational capacity, has made a holistic systems approach practically achievable for the first time (Kaur et al., 2023). This represents a fundamental change in mindset, moving from reactive, problem-specific solutions (e.g., identifying a sick animal) to proactive, systems-level optimization (e.g., adjusting the herd’s environment to minimize future disease risk). This shift from viewing the farm as a collection of data points to understanding it as an interconnected digital ecosystem provides the foundation for AI’s emerging role as a true system integrator. The next revolutionary step for AI in animal science is its function as a system integrator (Figure 2) capable of understanding of complex interactions. For example, a change in a feed ration may have cascading effects on an animal’s reproductive health and methane emissions, but these connections can be difficult to quantify when data is not integrated. AI provides a unifying computational layer. This layer should not be envisioned as a single, monolithic model (e.g., a specific GPT), but rather as a sophisticated architecture that allows multiple, specialized AI models (such as computer vision models, forecasting models, and generative agents) to interoperate. This is that architecture ingests diverse data streams from these areas, placing them within a single analytical framework to reveal previously hidden interconnections and enable proactive, cross-domain optimizations. AI as a system integrator connects siloed domains of dairy science into a digital twin framework enabling proactive, cross-domain optimization. The ultimate expression of this integrated approach is the digital twin, a dynamic, virtual replica of a physical entity (such as a single animal or an entire farm) defined by a continuous, bidirectional flow of data between the physical and virtual worlds (Escribà-Gelonch et al., 2024). Real-time information from sensors on the physical entity constantly updates the virtual model, while simulations and optimizations run on the model provide decision support to directly guide actions in the real world. This capability allows managers to conduct complex hypothetical analyses and elevates farm management from descriptive, backward-looking assessments to proactive, predictive optimization. To make this abstract concept concrete, the pioneering effort to connect the DairyBrain project (Cabrera et al., 2020; Ferris et al., 2020; Cabrera, 2024) at the University of Wisconsin–Madison with the Ruminant Farm Systems (RuFaS) model (Kebreab et al., 2019; Hansen et al., 2021; Li et al., 2023) illustrates the practical pathway toward this vision. DairyBrain functions as a data integration hub, using an application programming interface to automatically pull, clean, and standardize data from previously disconnected on-farm software systems, including herd management, feeding, and milking parlor data (Wangen et al., 2021). The RuFaS model, in turn, is a next-generation, open-source simulation tool designed to assess how different management practices affect systemic outcomes like economic profitability, environmental footprint, and animal welfare. The explicit goal of the collaboration between these projects is to create a data pipeline where the unified stream from DairyBrain can feed the RuFaS model, thereby laying the groundwork for a complete loop from raw data collection to sophisticated, cross-disciplinary knowledge generation. This level of deep integration and virtualization gives rise to a powerful new concept: the computational phenotype. This is not merely a collection of metrics but a high-dimensional, data-driven expression of the animal, derived from the unification of its extensive behavioral, physiological, and production data. In practice, this expression manifests as a series of quantifiable novel derived traits (Brito et al., 2025). This computational phenotype can capture subtle, complex patterns, such as minor behavioral changes preceding clinical disease, resilience calculated from the raw data of milk yield fluctuations or fertility traits defined through activity monitoring data, that are difficult or impossible to measure with traditional methods. It provides a new language for animal breeding and management, suggesting a future where selection goals may include not only traditional genetic traits but also the optimization of these comprehensive, data-defined derived traits. While the vision of AI as a system integrator is compelling, translating it from research prototypes to widespread on-farm reality requires navigating a significant gap. The path to adoption is not merely a matter of refining algorithms or scaling infrastructure. It involves overcoming fundamental barriers that determine whether these powerful tools will be accepted, trusted, and used effectively in the real world (Dibbern et al., 2024). Addressing these challenges within agriculture, informed by the valuable lessons learned from other high-stakes industries, provides a constructive path forward. The widespread adoption of integrated AI systems is hindered by an interconnected set of technical, socioeconomic, and human-centric barriers (Baldin et al., 2025). The technical challenges are rooted in the lack of data standards and interoperability between systems from different vendors, which prevents the seamless data fusion necessary for a true digital twin (Cabrera et al., 2025). Furthermore, while low-earth-sorbit satellite providers are solving historic issues of low speed and latency, they introduce new adoption barriers, including high initial hardware costs, reliance on proprietary technology, and a lack of local technical support (McMahon et al., 2025). From a socioeconomic perspective, the high initial investment and switching costs are significant deterrents for farmers, particularly as the return on investment has historically been difficult to quantify. However, concrete, longitudinal evidence is beginning to emerge. For example, a life-cycle assessment of intensive dairy goat farms implementing an integrated smart-farming platform found significant environmental benefits over 4 years, including an 11% reduction in greenhouse gas emissions and a 9–16% reduction in other impacts. These gains were driven by improved resource efficiency, lower replacement rates (−20%), and higher annual milk productivity (+11%; Pardo et al., 2022). 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Source: Animal Frontiers
DOI: 10.1093/af/vfaf054
Authors: Mutian Niu, Chuanyi Guo, Victor E Cabrera
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: gold
Estimated APC paid: $3,500
OpenAlex ID: W4417526430