Use of pattern recognition techniques for early detection of morbidity in receiving feedlot cattle.

Moya, D., Silasi, R., McAllister, T.A., Genswein, B.M.A., Crowe, T.G., Marti, S., and Schwartzkopf-Genswein, K.S.G. (2015). "Use of pattern recognition techniques for early detection of morbidity in receiving feedlot cattle.", Journal of Animal Science, 93(7), pp. 3623-3638. doi : 10.2527/jas.2015-8907  Access to full text

Abstract

Two groups of cattle were used to develop (model data set: 384 heifers, 228 ± 22.7 kg BW, monitored over a 225-d feeding period) and to validate (naïve data set: 384 heifers, 322 ± 34.7 kg BW, monitored over a 142-d feeding period) the use of feeding behavior pattern recognition techniques to predict morbidity in newly arrived feedlot cattle. In the model data set, cattle were defined as morbid (MO) if they were removed from their pen to be treated due to visual observation of clinical signs of bovine respiratory disease and healthy (HL) if they remained within their pen and lacked lung lesions at slaughter. Individual feeding behavior parameters collected with a GrowSafe automated feeding behavior monitoring system were reduced via principal component analysis to 5 components that captured 99% of the variability in the data set. Combinations of clustering and cluster classification strategies applied to those components, along with pattern recognition techniques over different time windows, produced a total of 105 models from which precision, negative predictive value, sensitivity, specificity, and accuracy were calculated by comparing its predictions with the actual health status of individual cattle as determined by visual assessment. When the models with the best specificity (models 79 and 87), sensitivity (models 33 and 66), and accuracy (models 3 and 14) in the model data set were used in a naïve data set, models 79 and 87 were not able to predict any MO heifers (0%), with all animals (100%) being predicted as HL. Model 33 predicted 58.3% of the HL and 66.7% of the MO heifers, with MO heifers identified 3.1 ± 1.64 d earlier than by visual observation. Model 66 predicted 50.0% of the HL and 75.0% of the MO heifers, with MO heifers predicted 3.1 ± 1.76 d earlier than by visual observation. Model 3 predicted 100% of the HL and 50.0% of the MO cattle, with MO cattle predicted 1 d earlier than by visual observation. Model 14 predicted 83.3% of the HL and 58.3% of the MO cattle, with MO cattle detected 2.4 ± 1.99 d earlier than visual observation. The application of pattern recognition algorithms to feeding behavior has potential value in identifying MO cattle in advance of overt physical signs of morbidity. Work on an integrated system that would automatically process data collected from automated feed bunk monitoring systems is still required, however, for this method to have value to the commercial feedlot industry as a practical means of identifying MO cattle in real time.

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