Guiding principles of AI: application in animal husbandry and other considerations

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a framework for studying AI's sustainability trade-offs in livestock systems.

🏢実務担当者:Farmers and agribusiness can use the principles to plan AI investments for efficiency and environmental compliance.

🏛政策担当者:Offers considerations for regulating AI in agriculture and addressing adoption disparities.

Increased efficiency and productivity: AI can significantly enhance decision-making in various aspects of farm management, from feeding to animal health monitoring. Real-time data analysis enables resource optimization, leading to reduced waste and improved efficiency. Animal welfare improvement: AI tools can monitor animals’ health and welfare in real-time, predicting potential issues before they arise, allowing for earlier interventions to ensure better animal care. Sustainability: AI technologies can help reduce environmental impact by optimizing the use of resources like water, feed, and energy, contributing to more sustainable farming practices. Shift in competencies: AI requires specialized knowledge to operate, leading to a shift in required skill sets. Farmers and agricultural workers will need to develop new technical skills to manage AI technologies effectively. Challenges of data availability: AI’s potential is often hindered by the lack of high-quality, diverse datasets. The success of AI models depends on the availability of comprehensive data covering different species, environments, and farm conditions. Ethical and privacy concerns: The integration of AI into farming raises issues regarding data security and privacy. Sharing detailed farm data with AI providers may expose sensitive business information, creating potential risks. Impact on decision-making: AI may surpass human intuition in making long-term predictions, leading to a shift where AI, rather than farmers, could drive most operational decisions. This raises concerns about the loss of human control in farm management. Disparities in global adoption: AI technology will develop unevenly across different regions due to varying economic and technological capabilities, potentially widening the gap between advanced and less developed agricultural sectors. Governance and control of AI: The control of AI development and deployment is concentrated in a few multinational corporations, raising concerns about dependency on these entities and their geopolitical influence over critical sectors like livestock farming. Trust in AI: Relying on AI for critical decisions, especially when outcomes may seem counterintuitive initially, challenges the trust farmers place in these technologies. This issue will become increasingly critical as AI continues to evolve and influence farm management. Some argue that the rise of Artificial Intelligence (AI) is charting a disquieting course for the future, while others are convinced that it will lead to a much better tomorrow. One thing, however, remains undeniable: although no one can accurately predict where this technological revolution will take us, it is clear that it will profoundly transform our lives, both professionally and personally. We might consider it a continuation, with an immense evolutionary leap, of the scientific discoveries that shaped the industrial era. Yet at the same time, it represents something entirely novel, a form of intelligence capable of peering far beyond the limits of our vision. In livestock farming, the use of AI has so far mainly focused on advanced data analysis rather than the implementation of deep learning. At present, most applications use data from multiple sensors, which are integrated into statistical and analytical models to provide valuable insights through automated processes. However, it is evident that deep learning will soon take on a more prominent role as the technology evolves, finding its way into livestock farming operations (De Oliveira et al., 2023). It is essential to clarify a common misconception: neural networks and deep learning, while related, are not the same. Neural networks are computational models inspired by the workings of the human brain. They consist of nodes, called neurons, arranged in successive layers. Each neuron receives inputs, applies a mathematical transformation, and produces an output that can be passed to subsequent layers. Traditional neural networks usually have few layers: an input layer, one or two hidden layers, and an output layer. These networks have been used effectively to tackle classification, regression, and other machine learning problems. Deep learning, on the other hand, is a branch of neural networks, specifically of the deep kind (hence the term “deep”). What sets it apart is the presence of many hidden layers, which can reach tens or even hundreds of levels. As the number of layers increases, models become more complex and able to learn more detailed and sophisticated representations. The evolution of AI is extraordinarily rapid and ever-accelerating. What seems cutting-edge today may be outdated within months. Therefore, it is crucial to stay constantly updated. This process will be anything but simple, and it will require highly specialized professionals to assist companies in managing AI applications. AI functions will undoubtedly be multifaceted, not limited to a single service but capable of orchestrating the entire management of a livestock farm. Data will be collected by an advanced Internet of Things (IoT), with information coming from both inside and outside the farm, gathered through various methods and units of measurement (Neethirajan, 2023). Consequently, AI will make decisions on every aspect of farm management, from identifying replacement animals to feed choices and the management of sales and purchases. The decisions currently made by farmers will increasingly be delegated to or suggested by AI. The quality of the incoming information is crucial: the more accurate the data, the better the results and the efficiency of the farm. AI solutions will need to be highly flexible, adaptable to the different structures and management styles of livestock farms, while ensuring economic efficiency in the production of AI systems. These solutions must be specifically “trained” on the farm before they can be operational. AI opens up new opportunities to optimize operations, improve animal welfare (Papakonstantinou, 2024), and increase productivity along the entire supply chain by leveraging technologies such as precision farming and predictive analytics (Fuentes et al, 2022). However, the widespread adoption of AI models is currently hindered by a lack of high-quality datasets. A significant obstacle is access to a sufficient amount of diverse data, which is essential for effectively training AI algorithms. These datasets must include information across different species and breeds, as well as varied environmental conditions and management practices, to ensure that AI is generalizable and not limited to the contexts in which it was initially trained. AI can improve various aspects of livestock management, including feeding, disease prevention, genetic selection programs, and resource allocation. By utilizing real-time data streams from IoT sensors, satellite images, and remote sensing technologies, AI can optimize resource use, reduce waste, and minimize environmental impact. AI algorithms already provide valuable insights into animal welfare, underscoring their transformative potential in livestock management. This progress represents a true paradigm shift, paving the way for a more sustainable and efficient future for livestock farming. The main goal of AI in livestock farms should be to enhance efficiency through optimal decision-making in every aspect of farm management. In practice, AI acts as a constant consultant, supporting complex decisions, minimizing errors, and suggesting the best solutions for each situation and objective. We should consider AI as a resource from which to continually demand more, so that it can provide increasingly precise and accurate answers. AI offers a revolutionary solution to address the main production and sustainability challenges in animal husbandry. A central issue is management inefficiency, which leads to resource wastage, including feed, water, and energy. Through the analysis of data collected from sensors and IoT devices, AI enables real-time monitoring of animal and environmental conditions, optimizing resource use. For example, advanced algorithms can automatically adjust feeding based on each animal’s specific needs, reducing waste and improving efficiency. From a sustainability perspective, AI helps reduce environmental impact by collecting and analyzing data related to land use, greenhouse gas emissions, and energy consumption. AI supports decisions that reduce the ecological footprint and make the entire production system more resilient, predicting environmental or climatic stress scenarios and enabling farmers to take timely action. Disease prevention is another area where AI can have a significant impact. By monitoring animals’ vital parameters, AI can detect anomalies early, preventing potential outbreaks and reducing the use of preventive drugs. Additionally, AI can optimize genetic selection programs, improving productivity and resilience through precise analysis of genetic traits. In summary, AI is a key ally in increasing efficiency, reducing environmental impact, and improving animal health and welfare, contributing to a more sustainable and competitive livestock industry. The future of AI-assisted livestock farming is difficult to predict, as it requires imagining how these technologies will evolve and adapt to the needs of farms. Beyond improving the services already mentioned, several future scenarios can be envisaged. For instance, farm management automation could become a reality through AI, which would be capable of coordinating activities such as staff management, logistics, and maintenance. Another potential development is advanced predictive analytics, which would allow farmers to anticipate health and behavioral problems in animals, optimizing their feeding and reproduction accordingly. Personalized feed management, based on the specific needs of each animal, could also become one of AI’s concrete applications. AI could also help optimize natur

• openalex https://doi.org/10.1093/af/vfae045first seen 2026-08-02 18:22:22