Multivariate analysis for data mining to characterize poultry house environment in winter.

ABSTRACT: The processing and analysis of massive high-dimensional datasets are important issues in precision livestock farming (PLF). This study explored the use of multivariate analysis tools to analyze environmental data from multiple sensors located throughout a broiler house. An experiment was conducted to collect a comprehensive set of environmental data including particulate matter (TSP, PM₁₀, and PM_2.5), ammonia, carbon dioxide, air temperature, relative humidity, and in-cage and aisle wind speeds from 60 locations in a typical commercial broiler house. The dataset was divided into 3 growth phases (wk 1-3, 4-6, and 7-9). Spearman's correlation analysis and principal component analysis (PCA) were used to investigate the latent associations between environmental variables

SUBMITTER: Li M 

PROVIDER: S-EPMC11000107 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature