A Vector Representation of Lactation Curves for Dairy Cows

• Published in: Agriculture (Basel) Vol. 12; no. 3; p. 395
• Main Authors: Lee, Seonghun, Park, Jaehwa
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2022
• Subjects: Algorithms; Animal lactation; Approximation; Bayesian analysis; Critical point; Dairy cattle; Data analysis; Lactation; lactation curve; Learning algorithms; Machine learning; Mathematical analysis; Mathematical functions; Milk production; Neural networks; Parameter estimation; piecewise linear regression; Regression analysis; Regression models; Representations; Segments; vector representation
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture12030395

Machine learning techniques provide efficient data analysis tools without mathematical derivations. Data-centric LC representations are highly demanded to use these tools for LC-related research. A novel data-oriented LC representation model using piecewise linear regression (PWLR) is presented. This representation is intended to be used directly as data for machine learning along with other associated data at an individual base. An LC is represented in vector form as a series of connected line segments and the location and number of segments are determined by the maximum residual. The critical points are determined at the rapid transit point in the LC. The Bayesian information criterion was used to choose the proper number of line segments to avoid the overfitting problem. To demonstrate the validity of the PWLR model as an LC descriptor, its approximation accuracy and representation generality were tested experimentally. The results revealed that the PWLR model is advantageous for representing the LCs of an individual or a large herd that are directly applicable to data-driven approaches.