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Integrating big data and artificial intelligence offers a sustainable solution to optimize feed efficiency and reduce the environmental footprint of global poultry farming.
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[Paragraph 1 — The problem]
Global poultry production faces mounting challenges, including feed source shortages, environmental pollution, and the need to maintain animal welfare amid rising consumer demand. Traditional nutrition models often fail to accurately predict dynamic nutrient requirements, leading to wasted resources and excess greenhouse gas emissions. As the industry seeks sustainable expansion, there is an urgent need for more precise, data-driven nutritional strategies.
[Paragraph 2 — What was done]
Researchers from Jilin University, China Agricultural University and Jilin Academy of Agricultural Sciences conducted a comprehensive review of current precision nutrition technologies. They explored how the Internet of Things (IoT), computer vision, and sensor-based monitoring can be combined with advanced AI algorithms and machine learning. The team also summarized how biotechnologies, including omics techniques and biotechnology-derived proteins, can be integrated into modern poultry farming.
[Paragraph 3 — Key findings]
The review highlights that traditional linear regression models are insufficient for capturing the dynamic physiological states and environmental variations in poultry. In contrast, machine learning algorithms demonstrate superior performance in predicting feed energy values and nutrient requirements. Furthermore, the integration of big data allows for precise diet formulation and real-time adjustments, significantly enhancing the accuracy of nutritional supply. The authors emphasize that combining AI with omics technologies provides a deeper biological understanding of nutrient metabolism.
[Paragraph 4 — Significance and implications]
This integration promises to transform current poultry practices by improving feed efficiency and reducing production costs. By precisely matching nutrient supply to animal requirements, producers can significantly lower nitrogen and carbon emissions, mitigating environmental impacts. Ultimately, this data-driven approach supports sustainable agricultural development, ensuring food security while promoting better animal welfare.
DOI: 10.63823/ARP0260010