One of the most common and reliable ways of monitoring udder health and milk quality in dairy herds is by monthly cow composite somatic cell counts (CMSCC). However, such sampling can be time consuming, and more automated sampling tools entail extra costs. Machine learning methods for prediction have been widely investigated in mastitis detection research, and CMSCC is normally used as a predictor or gold standard in such models. Predicted CMSCC between samplings could supply important information and be used as an input for udder health decision-support tools. To our knowledge, methods to predict CMSCC are lacking. Our aim was to find a method to predict CMSCC by using regularly recorded quarter milk data such as milk flow or conductivity. The milk data were collected at the quarter level for 8 wk when milking 372 Holstein-Friesian cows, resulting in a data set of 30,734 records with information on 87 variables. The cows were milked in an automatic milking rotary and sampled once weekly to obtain CMSCC values. The machine learning methods chosen for evaluation were the generalized additive model (GAM), random forest, and multilayer perceptron (MLP). For each method, 4 models with different predictor variable setups were evaluated: models based on 7-d lagged or 3-d lagged records before the CMSCC sampling and additionally for each setup but removing cow number as a predictor variable (which captures indirect information regarding cows' overall level of CMSCC based on previous samplings). The methods were evaluated by a 5-fold cross validation and predictions on future data using models with the 4 different variable setups. The results indicated that GAM was the superior model, although MLP was equally good when fewer data were used. Information regarding the cows' level of previous CMSCC was shown to be important for prediction, lowering prediction error in both GAM and MLP. We conclude that the use of GAM or MLP for CMSCC prediction is promising.

Milk quality and clinical mastitis in dairy cows are monitored by detecting visually abnormal milk. A standardized method to evaluate clots in milk and studies of the incidence and dynamics of clots in milk at the quarter level are lacking. We validated a method to score clot density in quarter milk samples and describe the prevalence and dynamics of the density scores between consecutive samplings and periods in 4 farms with automatic milking systems. Using in-line filters, we collected quarter milk samples at each milking during 3 periods of 30 h each in each farm. Clot density was scored based on coverage of the filter area as 0 (negative), 1 (trace), 2 (mild), 3 (moderate), 4 (heavy), and 5 (very heavy). The score for a specific quarter and milking is referred to as the quarter milking score (QMS). Three assessors independently scored 902 images of filter samples with a Fleiss kappa value of 0.72. In total, 21,202 quarter milk samples from 5,398 milkings of 621 cows were collected. Of the quarter filter samples, 2.4% had visible clots, distributed as mild (1.4%), moderate (0.6%), heavy (0.3%), and very heavy (<0.1%, n = 8). Cases with a cow period sum of QMS ≥ 4, corresponding to 9.4% of all periods, harbored 86% and 94% of all QMS of 2 to 5 and 3 to 5, respectively. Of these cases, cows sampled in all 3 periods and clots in only 1 period had a quarter period sum score ≥ 1 in 1.8 different quarters in average. Corresponding numbers for the cows with clots or traces in 2 or 3 periods were 2.2 and 2.5 different quarters, respectively. A QMS of 2 to 5 in the preceding milking increased the chance of a QMS >1 in the following milking, with an average chance of 38%. The probability of a QMS > 1 increased with increasing previous QMS, a higher sum of QMS during the milking period, longer milking interval, and higher lactation number, but decreased with increasing days in milk. Our study showed that the method of clot-density scoring is feasible to perform and reproducible for investigating the occurrence and dynamics of clots in milk. Elevated clot-density scores clustered within certain cows and cow periods and appeared in new quarters of the cows over time. The low recurrence of QMS of 1 and 2 within quarters indicated that QMS 3 could be a reasonable threshold for detecting quarters with abnormal milk that require further attention.

To ensure milk quality and detect cows with signs of mastitis, visual inspection of milk by prestripping quarters before milking is recommended in many countries. An objective method to find milk changed in homogeneity (i.e., with clots) is to use commercially available inline filters to inspect the milk. Due to the required manual labor, this method is not applicable in automatic milking systems (AMS). We investigated the possibility of detecting and predicting changes in milk homogeneity using data generated by AMS. In total, 21,335 quarter-level milk inspections were performed on 5,424 milkings of 624 unique cows on 4 farms by applying visual inspection of inline filters that assembled clots from the separate quarters during milking. Images of the filters with clots were scored for density, resulting in 892 observations with signs of clots for analysis (77% traces or mild cases, 15% moderate cases, and 8% heavy cases). The quarter density scores were combined into 1 score indicating the presence of clots during a single cow milking and into 2 scores summarizing the density scores in cow milkings during a 30-h sampling period. Data generated from the AMS, such as milk yield, milk flow, conductivity, and online somatic cell counts, were used as input to 4 multilayer perceptron models to detect or predict single milkings with clots and to detect milking periods with clots. All models resulted in high specificity (98-100%), showing that the models correctly classified cow milkings or cow milking periods with no clots observed. The ability to successfully classify cow milkings or cow periods with observed clots had a low sensitivity. The highest sensitivity (26%) was obtained by the model that detected clots in a single milking. The prevalence of clots in the data was low (2.4%), which was reflected in the results. The positive predictive value depends on the prevalence and was relatively high, with the highest positive predictive value (72%) reached in the model that detected clots during the 30-h sampling periods. The misclassification rate for cow milkings that included higher-density scores was lower, indicating that the models that detected or predicted clots in a single milking could better distinguish the heavier cases of clots. Using data from AMS to detect and predict changes in milk homogeneity seems to be possible, although the prediction performance for the definitions of clots used in this study was poor.

This research paper presents a study investigating if sensor data from an automatic milking rotary could be used to model cow somatic cell count (composite milk SCC: CMSCC). CMSCC is valuable for udder health monitoring and individual cow udder health surveillance could be improved by predicting CMSCC between routine samplings. Data regularly recorded in the automatic milking rotary, in one German dairy herd, were collected for analysis. The cows (Holstein-Friesian, n = 372) were milked twice daily and sampled once weekly in afternoon milkings for 8 weeks for CMSCC. From the potential independent variables, including quarter conductivity, milk flow, blood in milk, kick-offs, not milked quarters and incomplete milkings, new variables that combined quarter data were created. Past period records, i.e. lags, of up to seven days before the actual CMSCC sampling event were added in the dataset to investigate if they were of use in modeling the cell count. Univariable generalized additive models (GAM) were used to screen the data to select potential independent variables. Furthermore, several multivariable GAM were fitted in order to compare the importance of the potential independent variables and to explore how the model performance would be affected by using data from various number of days before the CMSCC sampling event. The result of the model selection showed that the best explanation of CMSCC was provided by the model incorporating all significant variables from the variable screening for the seven preceding days, including the day of the CMSCC sampling event. However, using data from only three days before the CMSCC sampling event is suggested to be sufficient to model CMSCC. Variables combining conductivity quarter data, together with quarter conductivity, are suggested to be important in describing CMSCC. We conclude that CMSCC can be modeled with a high degree of explanation using the information routinely recorded by the milking robot.