Artificial Intelligence (AI) is becoming increasingly important in promoting sustainable and efficient practices in poultry and precision livestock farming. By providing farmers and producers with insightful information, AI helps them optimize their operations and improve efficiency, productivity, sustainability, and, ultimately, the health and welfare of their animals. Additionally, AI helps policymakers diagnose and assess mortality and morbidity risks, predict and monitor disease outbreaks, and plan health policies.
Artificial Intelligence is a broad field of computer science aimed at creating systems capable of performing tasks commonly attributed to human intelligence, such as reasoning, problem-solving, understanding natural language, pattern recognition, and decision-making. In this context, Machine Learning (ML), a subdiscipline of AI, plays a crucial role by enabling computers to learn from data and solve tasks without explicit instructions on how to achieve them. These tasks can include classification, forecasting, and segmentation of unknown data.
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Artificial Intelligence in the livestock industry
AI is transforming and supporting the livestock export industry, particularly in dairy farming, and addressing long-standing challenges by automating tasks like feeding, weight tracking, and cattle counting.
“Precision digital livestock farming, underpinned by AI and sensor technology, offers innovative solutions to persisting issues in the dairy livestock export industry. These disruptive technologies facilitate real-time monitoring, proactive intervention, and data-driven decision-making, promising enhanced animal welfare, productivity, and streamlined supply chain operations.” [1]
Artificial Intelligence in poultry farming
One significant use of AI in the poultry industry is identifying patterns among diverse and unstructured data used to monitor animal health. A recent study carried out by the University of Nottingham [2] used big data and ML for the surveillance of antimicrobial resistance (AMR) in the animal production industry. This study showed promising results in the fight against AMR. The researchers focused on Escherichia coli (E. coli) as an indicator of AMR in the chicken gut. Results of this research show a clinical correlation between the chicken gut, resistome (all detected antimicrobial resistance genes), and the farm environment, such as humidity and temperature, and antibiotic resistance genes (ARGs).
Machine learning played a crucial role in analyzing data and making predictions in the study. The researchers developed a machine learning-based method to predict antibiotic resistance in Escherichia coli using the data collected from the microbial community and the resistome in the chicken gut. Researchers trained machine learning models using a large dataset of samples from 10 large-scale chicken farms in China. The models were able to accurately predict the antibiotic resistance of E. coli based on features extracted from the gut microbiome and the resistome data, which can help address antimicrobial resistance challenges in agricultural settings.
Our AI-powered solution
AI has the potential to revolutionize the animal production industry in many ways. At PhageLab®, we have a powerful diagnostic platform that relies on AI-based tools and expert scientific teams to provide us with complex and in-depth epidemiological analysis of microbial communities. Our technology allows us to obtain a comprehensive microbiological profile of bacteria and expedite the development of customized tailored products to the unique needs of our clients. One of the primary use cases of our ML-based tools is to utilize the insights gathered from our bioinformatics pipelines to assist our scientists in selecting the most appropriate phages for each solution, reducing the time required to develop it.
[1] Neethirajan, S. Artificial Intelligence and Sensor Technologies in Dairy Livestock Export: Charting a Digital Transformation. Sensors 2023, 23, 7045. https://doi.org/10.3390/s23167045
[2] Baker, M., Zhang, X., Maciel-Guerra, A. et al. Machine learning and metagenomics reveal shared antimicrobial resistance profiles across multiple chicken farms and abattoirs in China. Nat Food 4, 707–720 (2023). https://doi.org/10.1038/s43016-023-00814-w
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