Towards combining data prediction and internet of things to manage milk production on dairy cows Abstract   Dairy farms play a fundamental role in both social and economic areas, due to the high quantity of milk production they have. In this area, the nutritional management and the dairy control practices must be carefully analyzed, since they are directly related to the cowsxe2x80x99 productivity in terms of quantity and quality of the milk produced by the animals. To the best of our knowledge, there is a research gap on proposing and evaluating computational models to automate and individualize the feeding of the animals and its impact on milk production. In this way, this work proposes the MooCare model, designed to assist producers in the management of dairy cattle, in order to obtain better productivity indexes. Using the Internet of Things devices, MooCare performs the automation and individualization of the animals feeding. Data gathered using such devices enable the MooCare scientific contribution, which consists of providing the milk production forecasting of each cow, using the ARIMA prediction engine. Thus, farmers can be notified in advance, so enabling them to react in terms of designing a better nutritional plan, which can be individualized for each cow. The evaluations comprise a simulation based on data of cow lactation obtained from a real farm. The experiments presented encouraging results since the assertiveness of the prediction indicated small differences if compared to the actual milk production. Besides, the notification system was able to forecast nutritional issues, allowing the farmers to design individualized plans for specific cows.

A machine learning based decision aid for lameness in dairy herds using farmbased records Abstract   Lameness is a common welfare issue in dairy farms and can negatively influence productivity and farm profitability. Onfarm measurements are time consuming and continuous monitoring of lameness can be thus challenging. A predictive model that is suitable for routine field applications can be thus an efficient strategy to improve lameness status in dairy herds. We explored the use of a machine learning approach based on decision tree induction to detect and monitor the risk level of dairy herds for lameness based on 20 routinely precollected farmbased records related to herd management and housing, milk production performance, reproduction performance, longevity, and genetic merit. The risk of lameness was determined for 229 dairy herds based on herd prevalence. The results based on 10fold repeated crossvalidation suggest that there was little difference between a simple decision tree and more advanced machine learning algorithms such as random forests or boosting algorithms with an area under the receiver operating characteristic curve (AUC) of 0.73xe2x80x930.75, but machine learning performed better than classical multivariate logistic regression (AUC of 0.28). Model sensitivity based on machine learning was highest at 0.58, whereas model specificity was highest at 0.89 among all models tested. Stacking the individual machine learning algorithms to a higherorder model slightly lifted model performance (AUC of 0.76) at a model sensitivity of 0.54 and a model specificity of 0.94 but at a loss in model interpretability. Additional information more closely related to lameness are required to further improve model performance. Nevertheless, decision tree induction was found to be useful in analysing small data sets that frequently occur in various livestock farming disciplines. Overall, the machine learning approach described in our study may be an appropriate and powerful decision support and monitoring tool, and can be implemented in a computerized information system to detect herds with potential deficiencies in lameness.