Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks

Abstract

. 2000. Investigation into the production and conformation traits associated with clinical mastitis using artificial neural networks. Can. J. Anim. Sci. 80: [415][416][417][418][419][420][421][422][423][424][425][426]. A data set comprising milk-recording and conformation data was used to investigate the usefulness of artificial neural networks in detecting influential variables in the prediction of incidences of clinical mastitis. Specifically, these data contained test-day records from dairy herd analysis, phenotypic cow scores for conformation and genetic conformation proofs for cows and their sires. The data were analysed using the milk-recording data only, the conformation data only, and a combination of the two. Results from sensitivity analyses, performed with trained neural nets, indicated that stage of lactation, milk yield on test day, cumulative milk yield and somatic cell count were the major production factors influencing the ability to detect the occurrence of clinical mastitis. Among the conformation traits, such variables as phenotypic scores for rear-teat placement, dairy character and size, cow proof for dairy character, sire reliability for final score and sire proofs for pin-setting (desirability) and loin strength were found to have some influence on the network's predictive ability, although they were all very minor in relation to the production variables mentioned. As a group, cow genetic proofs seemed more important than either sire genetic proofs or cow phenotypic scores. Given the neural network's general abilities to determine the major factors related to the presence or absence of mastitis on a given test day, it may be appropriate to investigate the possibility of using this technology for actual prediction purposes.

Detection of difficult calvings in dairy cows using neural classifier

“…Specificity obtained in this research for ANN was lower than that in the studies by Yang et al (1999) and Yang et al (2000), where it amounted to 0.83-1.00 and 0.67-0.85, respectively. In other studies on mastitis detection these values ranged from 0.38 (Cavero et al 2008) to 1.00 (Nielen et al 1995, Wang & Samarasinghe 2005.…”

“…Accuracy given in the literature was in a similar range as that obtained in the present work and was not lower than 50 %. For example, in the study by Yang et al (1999) these values amounted to 0.62-0.99 and in the work by Yang et al (2000) they ranged from 0.55 to 0.78. Pastell & Kujala (2007) and Morrison et al (1985b) obtained accuracy of 0.96 and 0.84 respectively.…”

Machine Learning as an aid to management decisions on high somatic cell counts in dairy farms

“…However, the performance of our system is consistent with others obtained from literature. Yang et al (2000) using test-day records and conformation traits information to predict clinical mastitis obtained an overall efficiency of the predicting ability of their Artificial Neural Network (ANN) based system of 76.2%; in other words, it has an average error of 23.8% which is similar to the errors reported in Table 3. The probabilities of diagnosing the mastitis bacteriological status of dairy herds by means of ANNs and using test-day records and information of management practices ranged from 57 to 71% in a study by HEALD et al (2000).…”

Application of neural network and adaptive neuro-fuzzy inference system to predict subclinical mastitis in dairy cattle

“…By applying such artificial intelligence concepts as fuzzy logic (FL), artificial neural networks (ANNs) and adaptive neuro-fuzzy interface systems (ANFIS), researchers have sought to achieve early detection of mastitis (Yang et al, 2000;Cavero et al, 2006;Krieter et al, 2007).…”