Application of machine learning algorithms to predict body condition score from liveweight records of mature romney ewes

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dc.citation.issue2
dc.citation.volume11
dc.contributor.authorSemakula J
dc.contributor.authorCorner‐thomas RA
dc.contributor.authorMorris ST
dc.contributor.authorBlair HT
dc.contributor.authorKenyon PR
dc.date.available2021-02
dc.date.issued1/02/2021
dc.description.abstractBody condition score (BCS) in sheep (Ovis aries) is a widely used subjective measure of the degree of soft tissue coverage. Body condition score and liveweight are statistically related in ewes; therefore, it was hypothesized that BCS could be accurately predicted from liveweight using machine learning models. Individual ewe liveweight and body condition score data at each stage of the annual cycle (pre‐breeding, pregnancy diagnosis, pre‐lambing and weaning) at 43 to 54 months of age were used. Nine machine learning (ML) algorithms (ordinal logistic regression, multinomial regression, linear discriminant analysis, classification and regression tree, random forest, k‐nearest neighbors, support vector machine, neural networks and gradient boosting decision trees) were applied to predict BCS from a ewe’s current and previous liveweight record. A three class BCS (1.0– 2.0, 2.5–3.5, > 3.5) scale was used due to high‐class imbalance in the five‐scale BCS data. The results showed that using ML to predict ewe BCS at 43 to 54 months of age from current and previous liveweight could be achieved with high accuracy (> 85%) across all stages of the annual cycle. The gradient boosting decision tree algorithm (XGB) was the most efficient for BCS prediction regardless of season. All models had balanced specificity and sensitivity. The findings suggest that there is potential for predicting ewe BCS from liveweight using classification machine learning algorithms.
dc.description.publication-statusPublished
dc.identifierhttp://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000621977400001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=c5bb3b2499afac691c2e3c1a83ef6fef
dc.identifierARTN 162
dc.identifier.citationAGRICULTURE-BASEL, 2021, 11 (2)
dc.identifier.doi10.3390/agriculture11020162
dc.identifier.eissn2077-0472
dc.identifier.elements-id441088
dc.identifier.harvestedMassey_Dark
dc.identifier.urihttps://hdl.handle.net/10179/16512
dc.relation.isPartOfAGRICULTURE-BASEL
dc.subjectaccuracy
dc.subjectpredictor
dc.subjectmodels
dc.subjectclassification
dc.subject.anzsrc0701 Agriculture, Land and Farm Management
dc.titleApplication of machine learning algorithms to predict body condition score from liveweight records of mature romney ewes
dc.typeJournal article
pubs.notesNot known
pubs.organisational-group/Massey University
pubs.organisational-group/Massey University/College of Sciences
pubs.organisational-group/Massey University/College of Sciences/School of Agriculture & Environment
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