The Dawn of Intelligent Poultry Science: A Brazilian Vision for a Global FutureKeywords:
Precision poultry farming; generative AI; precision nutrition
Keywords:
Precision poultry farming; generative AI; precision nutrition
Beyond Hype: From Prediction to Proactive Poultry Management
Artificial Intelligence (AI) tools, particularly when integrated with Precision Livestock Farming (PLF) systems, are unlocking new dimensions of real-time environmental control, disease forecasting, and resource optimization. These systems no longer merely react to change; they actively respond to it. They anticipate it. Whether it is an AI-driven adjustment in ventilation patterns based on thermal imaging or early alerts of respiratory diseases via acoustic analysis, we are witnessing a fundamental shift from labor-intensive observation to proactive, intelligent management. Brazilian poultry operations, often densely populated, stand to benefit profoundly from such capabilities. Real-time data analytics can personalize nutritional strategies, minimize metabolic stress, and optimize thermal comfort, elevating not just productivity but also animal welfare as a key performance indicator (Leite et al., 2025; Menezes et al., 2024).
Rethinking Animal Health and Ethics in a Digital Era
We must recognize that AI offers more than economic gains; it reframes the ethics of animal care. By replacing manual, stressful assessments with non-contact sensors, cameras, and AI models trained to recognize distress, gait abnormalities, or subclinical signs of illness from vocalizations, we pave the way for a future in which welfare is not only audited but engineered into every hour of the production cycle (Rosati, 2024). Importantly, such technologies democratize access to welfare assurance, allowing small-scale producers in Brazil’s diverse biomes to utilize the same advanced monitoring capabilities as large industrial operations. Although it may not be a reality now, stakeholders need to be prepared for it (Menezes et al., 2024).
Breaking Academic Silos: Knowledge for Every Farm
One of AI’s most transformative promises lies in its ability to bridge the knowledge divide. Large language models (LLMs) equipped with retrieval-augmented generation (RAG) can serve as intelligent extension agents, processing and translating vast scientific datasets into practical, localized recommendations (Leite et al., 2025). For regions with limited access to veterinary or agronomic consultants, such tools could radically improve the accessibility and quality of decision-making. AI can ensure that farmers in Northern Brazil have access to the same research-backed insights as scientists in the Southern region or even policymakers in Brasília.
Bridging the Divide: From Academic Algorithms to Barn Reality
Nevertheless, as optimism soars, grounded realism must guide our path forward. Most AI innovations remain locked in academic laboratories, detached from the dust, ammonia, and noise of commercial barns. The challenge is no longer solely to prove AI’s value but to engineer scalable, adaptable, and robust systems that work reliably in diverse, often rural environments with intermittent connectivity and legacy infrastructure (Cabrera, 2024). Moreover, critical ethical questions loom prominently: data sovereignty, privacy, algorithmic bias, and the risk of eroding the human-animal bond must be addressed with the same scientific rigor as any production metric.
Charting Brazil’s Leadership Path
Brazil has the opportunity and the responsibility to be more than a user of imported AI solutions. It must be a creator. By investing in locally tailored AI models trained on our unique production systems, training a new generation of AI-literate veterinarians and zootechnicians, and building a national digital infrastructure with inclusivity in mind, Brazil can ensure that this technological wave benefits all, not just those in elite academic or industrial settings (Cabrera, 2024; Rosati, 2024; Leite et al., 2025).
International collaboration, clear regulatory frameworks, and ongoing ethical discourse must underpin this transition (Baumhover & Hansen, 2024). AI is not just a tool; it is a new language of agriculture, and Brazil must achieve fluency in it. Figure 1 illustrates the interconnectivity AI may offer. The circular diagram illustrates the transition from reactive poultry management toward proactive, welfare-engineered systems through the implementation of AI. Key enablers include scalable AI systems, large language models (LLMs) for knowledge translation, non-contact sensors for animal welfare monitoring, ethical frameworks addressing privacy and bias, and locally developed AI tailored to Brazil’s production context. These elements converge to support real-time data analytics that optimize on-farm decisions and animal welfare.
Final Thoughts: A Call to Action
Artificial intelligence will not replace poultry scientists, veterinarians, or farmers, but those who leverage AI will undoubtedly shape the future of poultry science. The time for siloed discussions is over. The convergence of data science, animal welfare, climate resilience, and global food security is upon us, and Brazil, with its scale, talent, and ambition, is poised to lead this AI-driven renaissance in poultry production.
REFERENCES
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Baumhover A, Hansen SL. Preparing the AI-assisted animal scientist: faculty and student perspectives on enhancing animal science education with artificial intelligence. Anim Front. 2024;14(6):54-9. https://doi.org/10.1093/af/vfae038
» https://doi.org/10.1093/af/vfae038 -
Cabrera VE. Artificial intelligence applied to dairy science: insights from the Dairy Brain Initiative. Anim Front. 2024;14(6):60-5. https://doi.org/10.1093/af/vfae040
» https://doi.org/10.1093/af/vfae040 -
Leite MV, Abe JM, Souza MLH, et al. Enhancing Environmental Control in Broiler Production: Retrieval-Augmented Generation for Improved Decision-Making with Large Language Models. AgriEngineering. 2025;7(1):12. https://doi.org/10.3390/agriengineering7010012
» https://doi.org/10.3390/agriengineering7010012 -
Menezes GL, Mazon G, Ferreira REP, et al. Artificial intelligence for livestock: a narrative review of the applications of computer vision systems and large language models for animal farming. Anim Front. 2024;14(6):42-53. https://doi.org/10.1093/af/vfae048
» https://doi.org/10.1093/af/vfae048 -
Rosati A. Guiding principles of AI: application in animal husbandry and other considerations. Anim Front. 2024;14(6):3-8. https://doi.org/10.1093/af/vfae045
» https://doi.org/10.1093/af/vfae045
