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RR, Siva Kiran, P, Dhamodhar. (1404). The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review. سامانه مدیریت نشریات علمی, 80(4), 819-832. doi: 10.32592/ARI.2025.80.4.819
Siva Kiran RR; Dhamodhar P. "The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review". سامانه مدیریت نشریات علمی, 80, 4, 1404, 819-832. doi: 10.32592/ARI.2025.80.4.819
RR, Siva Kiran, P, Dhamodhar. (1404). 'The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review', سامانه مدیریت نشریات علمی, 80(4), pp. 819-832. doi: 10.32592/ARI.2025.80.4.819
RR, Siva Kiran, P, Dhamodhar. The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review. سامانه مدیریت نشریات علمی, 1404; 80(4): 819-832. doi: 10.32592/ARI.2025.80.4.819

The Role of Artificial Intelligence and Machine Learning in Advancing Animal Biotechnology: A Review

Archives of Razi Institute
مقاله 3، دوره 80، شماره 4، مهر و آبان 2025، صفحه 819-832    اصل مقاله (678.24 K)
نوع مقاله: Review Article
شناسه دیجیتال (DOI): 10.32592/ARI.2025.80.4.819
نویسندگان
Siva Kiran RR* 1؛ Dhamodhar P2
1Department of Chemical Engineering, MS Ramaiah Institute of Technology, MSR Nagar, Mathikere, Bengaluru, India - 560054
2Department of Biotechnology, MS Ramaiah Institute of Technology, Bengaluru, India – 560054
چکیده
The integration of Machine Learning (ML) and Artificial Intelligence (AI) in animal biotechnology is revolutionizing the field, particularly in developing countries where agriculture and livestock play a significant role in the economy. AI and ML enable more efficient data analysis in areas such as genetic optimization, disease prediction, and livestock management, improving both productivity and sustainability. With the growing availability of data, AI-driven models can process large volumes of information from diverse sources like environmental conditions, genetic markers, and health records, offering more precise insights than traditional methods. Recent advancements include AI-powered diagnostic systems for detecting and managing disease outbreaks, which allow for faster response times and more targeted interventions, ultimately reducing economic losses. Enhanced breeding techniques now leverage machine learning algorithms to predict desirable genetic traits, enabling farmers to make data-informed breeding choices. Feed efficiency improvements, another critical area, benefit from AI's ability to analyze nutrient requirements and optimize feeding schedules based on individual animal needs, reducing waste and costs. Additionally, AI is increasingly applied in animal health monitoring, using tools such as sound-based systems and piezoelectric sensors embedded in smart collars that track behaviors indicative of health issues. In the dairy sector, AI models assess health risks like nitrate contamination in milk, contributing to safer food production and improving public health. In genetic studies, AI enhances selective breeding, improving traits like growth and disease resistance. This manuscript reviews the transformative role of AI and ML in animal biotechnology, focusing on developing regions, where resource optimization is crucial. By simplifying complex techniques and providing step-by-step tutorials, this work aims to equip researchers and practitioners with practical tools for harnessing AI in animal biotechnology.
کلیدواژه‌ها
Machine Learning؛ Animal Biotechnology؛ Forest Animals؛ Aquaculture؛ Simple AI Tutorials
عنوان مقاله [English]
نقش هوش مصنوعی و یادگیری ماشین در پیشبرد بیوتکنولوژی حیوانات: یک مرور
چکیده [English]
ادغام یادگیری ماشین (ML) و هوش مصنوعی (AI) در بیوتکنولوژی حیوانات در حال ایجاد تحولی اساسی در این حوزه است، به ویژه در کشورهای در حال توسعه که کشاورزی و دامداری نقش مهمی در اقتصاد دارند. هوش مصنوعی و یادگیری ماشین امکان تحلیل داده‌ها را در حوزه‌هایی مانند بهینه‌سازی ژنتیکی، پیش‌بینی بیماری و مدیریت دام‌ها به‌طور کارآمدتر فراهم می‌کنند و به بهبود بهره‌وری و پایداری منجر می‌شوند. با افزایش دسترسی به داده‌ها، مدل‌های مبتنی بر هوش مصنوعی می‌توانند حجم زیادی از اطلاعات را از منابع متنوعی مانند شرایط محیطی، نشانگرهای ژنتیکی و سوابق بهداشتی پردازش کرده و بینش‌های دقیق‌تری نسبت به روش‌های سنتی ارائه دهند. پیشرفت‌های اخیر شامل سیستم‌های تشخیصی مبتنی بر هوش مصنوعی برای شناسایی و مدیریت شیوع بیماری‌ها است که امکان پاسخ‌دهی سریع‌تر و مداخلات هدفمندتری را فراهم می‌کند و در نهایت زیان‌های اقتصادی را کاهش می‌دهد. تکنیک‌های پیشرفته پرورش اکنون از الگوریتم‌های یادگیری ماشین برای پیش‌بینی ویژگی‌های ژنتیکی مطلوب استفاده می‌کنند و به کشاورزان این امکان را می‌دهند تا تصمیمات پرورشی مبتنی بر داده بگیرند. بهبود کارایی تغذیه، که یک حوزه حیاتی دیگر است، از توانایی هوش مصنوعی در تحلیل نیازهای تغذیه‌ای و بهینه‌سازی زمانبندی تغذیه براساس نیازهای فردی حیوانات بهره می‌برد و ضایعات و هزینه‌ها را کاهش می‌دهد. علاوه بر این، هوش مصنوعی به‌طور فزاینده‌ای در پایش سلامت حیوانات با استفاده از ابزارهایی مانند سیستم‌های مبتنی بر صدا و حسگرهای پیزوالکتریک در قلاده‌های هوشمند که رفتارهای مرتبط با مشکلات سلامت را ردیابی می‌کنند، به کار گرفته می‌شود. در بخش لبنیات، مدل‌های هوش مصنوعی ریسک‌های بهداشتی مانند آلودگی نیترات در شیر را ارزیابی کرده و به تولید غذای ایمن‌تر و بهبود سلامت عمومی کمک می‌کنند. این مقاله نقش تحول‌آفرین هوش مصنوعی و یادگیری ماشین در بیوتکنولوژی حیوانات را بررسی می‌کند و بر مناطق در حال توسعه که بهینه‌سازی منابع در آن‌ها ضروری است، تمرکز دارد. با ساده‌سازی تکنیک‌های پیچیده و ارائه آموزش‌های گام به گام، این کار تلاش دارد تا محققان و متخصصان را به ابزارهای عملی برای استفاده از هوش مصنوعی در بیوتکنولوژی حیوانات مجهز کند.
کلیدواژه‌ها [English]
یادگیری ماشین, بیوتکنولوژی حیوانات, حیوانات جنگلی, آبزی‌پروری, آموزش‌های ساده هوش مصنوعی
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