A decision support system for evaluating mastitis information

Prediction of breeding values for dairy cattle using artificial neural networks and neuro-fuzzy systems

Developing machine learning and soft computing techniques has provided many opportunities for researchers to establish new analytical methods in different areas of science. The objective of this study is to investigate the potential of two types of intelligent learning methods, artificial neural networks and neuro-fuzzy systems, in order to estimate breeding values (EBV) of Iranian dairy cattle. Initially, the breeding values of lactating Holstein cows for milk and fat yield were estimated using conventional best linear unbiased prediction (BLUP) with an animal model. Once that was established, a multilayer perceptron was used to build ANN to predict breeding values from the performance data of selection candidates. Subsequently, fuzzy logic was used to form an NFS, a hybrid intelligent system that was implemented via a local linear model tree algorithm. For milk yield the correlations between EBV and EBV predicted by the ANN and NFS were 0.92 and 0.93, respectively. Corresponding correlations for fat yield were 0.93 and 0.93, respectively. Correlations between multitrait predictions of EBVs for milk and fat yield when predicted simultaneously by ANN were 0.93 and 0.93, respectively, whereas corresponding correlations with reference EBV for multitrait NFS were 0.94 and 0.95, respectively, for milk and fat production.

A case-acquisition and decision-support system for the analysis of group-average lactation curves

A case-acquisition and decision-support system was developed to support the analysis of group-average lactation curves and to acquire example cases from domain specialists. This software was developed through several iterations of a three-step approach involving 1) problem analysis and formulation in consultation with two dairy nutrition specialists; 2) development of a case-acquisition and decision-support prototype by the system developer; and 3) use of the prototype by the domain specialists to analyze and classify milk-recording data from example herds. The overall problem was decomposed into three subproblems: removal of outlier tests and lactation curves of individual cows; interpretation of group-average lactation curves; and diagnosis of detected abnormalities at the herd level through the identification of potential management deficiencies. For each subproblem, a software module was developed allowing the user to analyze both graphical and numerical performance representations and classify these representations using predefined linguistic descriptors. The example-based method for the development of the program proved to be very useful, facilitating the communication between system developer and domain specialists, and allowing the specialists to explore the appropriateness of the various prototypes developed. The resulting software represents a formalization of the approach to group-average lactation curve analysis, elicited from the two domain specialists. In future research, the case-acquisition and decision-support system will be complemented with knowledge to automate identified classification tasks, which will be captured through the application of machine-learning techniques to example cases, acquired from domain specialists using the software.

A computerized mastitis decision aid using farm-based records: an artificial neural network approach

A computer module was developed and tested that used field survey and Dairy Herd Improvement Association (DHIA) data to broadly classify bacterial causes of mastitis in dairy herds. Further development of the computer model could aid interpretation of DHIA data by dairy record processing centers and herd consultants. This diagnostic module was developed with an artificial neural network, a technology that processes complex data in a manner similar to human brain function. Information describing herd management practices, quarter milk samples, and monthly DHIA data was collected from Pennsylvania dairy herds with moderate to high somatic cell counts. This information was used to develop or train an artificial neural network model that discriminated among four categories of bacterial organisms (contagious, environmental, no significant growth, and other) associated with clinical and subclinical mastitis. After training the model, new DHIA and management data were presented to the model to assess its ability to classify bacteriological etiology. When the artificial neural network was used, the probabilities of diagnosing the bacteriologic status from three randomly selected cow groups and from new untested herds ranged from 57 to 71%. Performance of the artificial neural network model was best in herds with higher frequency of minor and contagious pathogens. Prediction results for the same test data with linear discriminant analysis were less successful, ranging from 42 to 57%.

An approach to summarize somatic cell score trends for a data-driven, decision support system

A set of analytical routines were developed to determine significant trends in values for herd DHI somatic cell scores. These trends were used as input to a data-driven, decision support system to aid mastitis management of dairy cattle. The trends of interest were those experienced over the last six DHI sample periods for the entire herd, for three parity groups, and for three stages of lactation. First, cows within the herd were split randomly into two equal groups to account for within-herd variation. For each group, linear regression was calculated for somatic cell score over time within each parity by stage of lactation group. The 18 slope estimates were then analyzed using a two-way ANOVA to test for main fixed effects of parity, stage of lactation, and their interaction. The results of the trend analyses were converted to facts that were asserted to an embedded expert system for further evaluation.