Advancing Livestock Genetics Through IoT and Support Vector Regression for Sustainable Farming
Keywords:
Livestock genetics, support vector regression, breeding, milk production analysis, reproduction analysisAbstract
Improving livestock genetics is essential for increasing agricultural output and sustainability. This research examines the combination of Internet of Things (IoT) technology with Support Vector Regression (SVR) to enhance genetic selection and breeding methods in livestock agriculture. IoT technologies, such as sensors and wearables, gather real-time data on diverse phenotypic characteristics, health indicators, and environmental factors impacting livestock. The data is sent to a centralized cloud platform for analysis. SVR is used to model complex correlations between phenotypic features and genetic performance, allowing farmers to make informed breeding choices using predictive analytics. The proposed method enables personalized breeding techniques to enhance herd quality, decrease disease vulnerability, and improve overall production while mitigating environmental effects. The integration of IoT and SVR establishes a framework for the continuous tracking of genetic advancement, hence promoting sustainable agricultural practices. This novel method is prepared to transform livestock management, hence enhancing food security and resource efficiency in farming.
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