scholarly journals Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tania Bobbo ◽  
Stefano Biffani ◽  
Cristian Taccioli ◽  
Mauro Penasa ◽  
Martino Cassandro
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Health Status ◽  
Somatic Cell ◽  
Somatic Cell Count ◽  
Machine Learning Algorithms ◽  
Udder Health ◽  
Somatic Cell Counts ◽  
Machine Learning Methods ◽  
Cell Counts

AbstractBovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms to predict the udder health status of cows. In this study, we compared eight different machine learning methods (Linear Discriminant Analysis, Generalized Linear Model with logit link function, Naïve Bayes, Classification and Regression Trees, k-Nearest Neighbors, Support Vector Machines, Random Forest and Neural Network) to predict udder health status of cows based on somatic cell counts. Prediction accuracies of all methods were above 75%. According to different metrics, Neural Network, Random Forest and linear methods had the best performance in predicting udder health classes at a given test-day (healthy or mastitic according to somatic cell count below or above a predefined threshold of 200,000 cells/mL) based on the cow’s milk traits recorded at previous test-day. Our findings suggest machine learning algorithms as a promising tool to improve decision making for farmers. Machine learning analysis would improve the surveillance methods and help farmers to identify in advance those cows that would possibly have high somatic cell count in the subsequent test-day.

scholarly journals Prediction of total quarter milk somatic cell counts based on foremilk sampling

2009 ◽  
Vol 76 (3) ◽  
pp. 326-330 ◽  
Author(s):  
Olga Wellnitz ◽  
Marcus G Doherr ◽  
Marta Woloszyn ◽  
Rupert M Bruckmaier
Keyword(s):  
Dairy Cows ◽  
Somatic Cell ◽  
Cell Count ◽  
Somatic Cell Count ◽  
Practical Reasons ◽  
Udder Health ◽  
Somatic Cell Counts ◽  
Cell Numbers ◽  
Cell Counts

Determination of somatic cell count (SCC) is used worldwide in dairy practice to describe the hygienic status of the milk and the udder health of cows. When SCC is tested on a quarter level to detect single quarters with high SCC levels of cows for practical reasons, mostly foremilk samples after prestimulation (i.e. cleaning of the udder) are used. However, SCC is usually different in different milk fractions. Therefore, the goal of this study was the investigation of the use of foremilk samples for the estimation of total quarter SCC. A total of 378 milkings in 19 dairy cows were performed with a special milking device to drain quarter milk separately. Foremilk samples were taken after udder stimulation and before cluster attachment. SCC was measured in foremilk samples and in total quarter milk. Total quarter milk SCC could not be predicted precisely from foremilk SCC measurements. At relatively high foremilk SCC levels (>300×103 cells/ml) foremilk SCC were higher than total quarter milk. At around (50–300)×103 cells/ml foremilk and total quarter SCC did not differ considerably. Most interestingly, if foremilk SCC was lower than 50×103 cells/ml the total quarter SCC was higher than foremilk SCC. In addition, individual cows showed dramatic variations in foremilk SCC that were not very well related to total quarter milk SCC. In conclusion, foremilk samples are useful to detect high quarter milk SCC to recognize possibly infected quarters, only if precise cell counts are not required. However, foremilk samples can be deceptive if very low cell numbers are to be detected.

scholarly journals The milkability and somatic cell counts of milk cows in various systems of keeping and milking

2007 ◽  
Vol 23 (5-6-1) ◽  
pp. 209-216 ◽  
Author(s):  
P. Mijic ◽  
I. Knezevic ◽  
M. Matkovic ◽  
M. Baban ◽  
Z. Ivkic
Keyword(s):  
Somatic Cell ◽  
Cell Count ◽  
Milk Production ◽  
Milk Yield ◽  
Somatic Cell Count ◽  
Udder Health ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
Significant Difference ◽  
Milk Cows

A high milk production, time limited milking and healthy udders are priority tasks at milking farms. The aim of our research was to study how different ways of keeping (free rang and tying) and milking (milking parlour, bucket machine and pipeline milking) influence on the mentioned cattle characteristics. Investigation was conducted at four milking farms and 382 Holstein cows in eastern Croatia. The variance analysis has shown significant difference (P<0,05) among researched farms for the milk yield per milking (MYM), the somatic cell count (LSCC) and the maximum milk flowing rate (MFR). Farms at which cows were kept and milked bound up in stables had more problems with udder health than farms at which milking was conducted at milking places. Also at these farms (at which cows were kept and milked bound up in stables) the maximum milk flowing rate was uneven, what was caused by uneven vacuum and obsoletes milking equipment. Keeping cows free at the stable and milking at a milking place have appeared to be more appropriate for cow?s udder health, what finally influences a higher milking production. Such farms should be the future of modern milking production in Croatia.

Evaluation of the udder health status in subclinical mastitis affected dairy cows through bacteriological culture, somatic cell count and thermographic imaging

2015 ◽  
Vol 18 (4) ◽  
pp. 799-805 ◽  
Author(s):  
A. Bortolami ◽  
E. Fiore ◽  
M. Gianesella ◽  
M. Corrò ◽  
S. Catania ◽  
...  
Keyword(s):  
Health Status ◽  
Dairy Cows ◽  
Somatic Cell ◽  
Cell Count ◽  
Somatic Cell Count ◽  
Subclinical Mastitis ◽  
Bacteriological Culture ◽  
Udder Health ◽  
Streptococcus Uberis ◽  
Thermographic Imaging

Abstract Subclinical mastitis in dairy cows is a big economic loss for farmers. The monitoring of subclinical mastitis is usually performed through Somatic Cell Count (SCC) in farm but there is the need of new diagnostic systems able to quickly identify cows affected by subclinical infections of the udder. The aim of this study was to evaluate the potential application of thermographic imaging compared to SCC and bacteriological culture for infection detection in cow affected by subclinical mastitis and possibly to discriminate between different pathogens. In this study we evaluated the udder health status of 98 Holstein Friesian dairy cows with high SCC in 4 farms. From each cow a sample of milk was collected from all the functional quarters and submitted to bacteriological culture, SCC and Mycoplasma spp. culture. A thermographic image was taken from each functional udder quarter and nipple. Pearson’s correlations and Analysis of Variance were performed in order to evaluate the different diagnostic techniques. The most frequent pathogen isolated was Staphylococcus aureus followed by Coagulase Negative Staphylococci (CNS), Streptococcus uberis, Streptococcus agalactiae and others. The Somatic Cell Score (SCS) was able to discriminate (p<0.05) cows positive for a pathogen from cows negative at the bacteriological culture except for cows with infection caused by CNS. Infrared thermography was correlated to SCS (p<0.05) but was not able to discriminate between positive and negative cows. Thermographic imaging seems to be promising in evaluating the inflammation status of cows affected by subclinical mastitis but seems to have a poor diagnostic value.

Effects of season, milking routine and cow cleanliness on bacterial and somatic cell counts of bulk tank milk

2011 ◽  
Vol 78 (4) ◽  
pp. 436-441 ◽  
Author(s):  
Maddalena Zucali ◽  
Luciana Bava ◽  
Alberto Tamburini ◽  
Milena Brasca ◽  
Laura Vanoni ◽  
...  
Keyword(s):  
Somatic Cell ◽  
Cell Count ◽  
Somatic Cell Count ◽  
Bacterial Count ◽  
Cold Season ◽  
Plate Count ◽  
Bulk Tank Milk ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
Bulk Tank

The aim of the study was to investigate the effects of season, cow cleanliness and milking routine on bacterial and somatic cell counts of bulk tank milk. A total of 22 dairy farms in Lombardy (Italy) were visited three times in a year in different seasons. During each visit, samples of bulk tank milk were taken for bacterial and somatic cell counts; swabs from the teat surface of a group of cows were collected after teat cleaning and before milking. Cow cleanliness was assessed by scoring udder, flanks and legs of all milking cows using a 4-point scale system. Season affected cow cleanliness with a significantly higher percentage of non-clean (NC) cows during Cold compared with Mild season. Standard plate count (SPC), laboratory pasteurization count (LPC), coliform count (CC) and somatic cell count, expressed as linear score (LS), in milk significantly increased in Hot compared with Cold season. Coagulase-positive staphylococci on teat swabs showed higher counts in Cold season in comparison with the other ones. The effect of cow cleanliness was significant for SPC, psychrotrophic bacterial count (PBC), CC and Escherichia coli in bulk tank milk. Somatic cell count showed a relationship with udder hygiene score. Milking operation routine strongly affected bacterial counts and LS of bulk tank milk: farms that accomplished a comprehensive milking scheme including two or more operations among forestripping, pre-dipping and post-dipping had lower teat contamination and lower milk SPC, PBC, LPC, CC and LS than farms that did not carry out any operation.

Electronic Somatic Cell Count—Chemical Method, DMSCC, and WMT Tests for Raw Milk

1976 ◽  
Vol 39 (12) ◽  
pp. 854-858 ◽  
Author(s):  
D. R. THOMPSON ◽  
V. S. PACKARD ◽  
R. E. GINN
Keyword(s):  
Somatic Cell ◽  
Cell Count ◽  
Confidence Intervals ◽  
Somatic Cell Count ◽  
Raw Milk ◽  
Field Method ◽  
Regression Equations ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
Coefficients Of Variation

The Direct Miscroscopic Somatic Cell Count — field method (DMSCC), Wisconsin Mastitis Test (WMT), and Electronic Somatic Cell Count (ESCC) were studied to determine variability and relationship to each other. The coefficients of variation computed at a DMSCC count near one million were 15.6% (DMSCC), 6.3% (WMT), and 4.2% (ESCC). Linear regression equations were determined for predicting DMSCC results by WMT and ESCC. The approximate width of the 95% confidence intervals for ESCC predicting DMSCC were ± 275,000 and for WMT predicting DMSCC were ± 600,000. The prediction of square root and log transformations of DMSCC by WMT exhibited narrower confidence intervals for low somatic cell counts, but wider intervals for high counts (greater than 1,000,000).

scholarly journals Ontario Bulk Milk Somatic Cell Count Reduction Program. 2. Dynamics of Bulk Milk Somatic Cell Counts

1992 ◽  
Vol 75 (12) ◽  
pp. 3359-3366 ◽  
Author(s):  
Ynte H. Schukken ◽  
K.E. Leslie ◽  
A.J. Weersink ◽  
S.W. Martin
Keyword(s):  
Somatic Cell ◽  
Cell Count ◽  
Somatic Cell Count ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
Bulk Milk ◽  
Reduction Program

scholarly journals The Effect of Exogenous Melatonin on Milk Somatic Cell Count in Buffalo

2021 ◽  
Vol 41 (01) ◽  
pp. 152-155
Author(s):  
Lin Feng
Keyword(s):  
Superoxide Dismutase ◽  
Somatic Cell ◽  
Somatic Cell Count ◽  
Environmental Temperature ◽  
Antioxidant Activities ◽  
Economic Loss ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
Quality Of Milk

High somatic cell counts (SCCs) in milk significantly influence the quality of milk and give rise to substantial economic loss. The present study was aimed to investigate the effect of extreme heat and cold compared to other season and melatonin (MLT) on milk SCCs in Chinese crossbred (Nili-Ravi×Murrah) buffaloes. We collected the 1948 milk SCCs data records from 2012 to 2017 to explore the effect of different month in China on milk SCCs. Meanwhile, twenty buffaloes with relatively high milk SCCs were employed and randomly divided into two groups (T1 and T2, n=10 each group) to evaluate the effect of MLT treatment on milk SCCs, blood antioxidant activities and immune levels of buffaloes during summer in China. Results showed that the milk SCCs in high temperature seasons (July and August) and low temperature seasons (December, January and February) were significantly higher compared with other months (P<0.05). In summer, MLT treatment significantly reduced milk SCCs and increased the IgM and superoxide dismutase (SOD) levels in plasma on day 1 after MLT treatment, and then both IgM and SOD levels were decreased significantly. In conclusion, our study demonstrated that environmental temperature stress (heat and cold) caused the higher milk SCCs and MLT treatment improved the quality of milk by reducing SCCs suggesting that MLT could improve immune activity in buffaloes

scholarly journals Application of Machine Learning Algorithms for Solving Problems in the Oil and Gas Sector

2021 ◽  
Vol 19 (3) ◽  
pp. 55-64
Author(s):  
K. N. Maiorov ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Reinforcement Learning ◽  
Machine Learning Algorithms ◽  
Design Stage ◽  
Learning Methods ◽  
Field Development ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
The Neural Network

The paper examines the life cycle of field development, analyzes the processes of the field development design stage for the application of machine learning methods. For each process, relevant problems are highlighted, existing solutions based on machine learning methods, ideas and problems are proposed that could be effectively solved by machine learning methods. For the main part of the processes, examples of solutions are briefly described; the advantages and disadvantages of the approaches are identified. The most common solution method is feed-forward neural networks. Subject to preliminary normalization of the input data, this is the most versatile algorithm for regression and classification problems. However, in the problem of selecting wells for hydraulic fracturing, a whole ensemble of machine learning models was used, where, in addition to a neural network, there was a random forest, gradient boosting and linear regression. For the problem of optimizing the placement of a grid of oil wells, the disadvantages of existing solutions based on a neural network and a simple reinforcement learning approach based on Markov decision-making process are identified. A deep reinforcement learning algorithm called Alpha Zero is proposed, which has previously shown significant results in the role of artificial intelligence for games. This algorithm is a decision tree search that directs the neural network: only those branches that have received the best estimates from the neural network are considered more thoroughly. The paper highlights the similarities between the tasks for which Alpha Zero was previously used, and the task of optimizing the placement of a grid of oil producing wells. Conclusions are made about the possibility of using and modifying the algorithm of the optimization problem being solved. Аn approach is proposed to take into account symmetric states in a Monte Carlo tree to reduce the number of required simulations.

scholarly journals Effect of somatic cell counts occurred in milk on quality of Slovak traditional cheese – Parenica

10.5219/1099 ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 675-680 ◽  
Author(s):  
Viera Ducková ◽  
Margita Čanigová ◽  
Peter Zajác ◽  
Zuzana Remeňová ◽  
Miroslav Kročko ◽  
...  
Keyword(s):  
Somatic Cell ◽  
Cell Count ◽  
Somatic Cell Count ◽  
Dry Matter ◽  
Correlation Coefficients ◽  
Mean Value ◽  
Matter Content ◽  
Dry Matter Content ◽  
Somatic Cell Counts ◽  
Cell Counts

The aim of this work was to compare somatic cell count in milk used for making steamed cheese Parenica in Slovak industrial dairies and small farm dairies and to find out whether somatic cell counts in milk affect the dry matter content of Parenica cheese. The samples of raw milk were taken from 3 industrial dairies (A, B, C) and from 3 farm dairies (E, F, G), produced traditional Slovak cheese Parenica in period from January untill December 2018. The somatic cell count in milk was determined by FossomaticTM 5000 (Foss, Denmark) and dry matter of cheese by oven drying method to constant weight. There were no statistically significant differences (p >0.05) for somatic cell counts in milk processed in industrial and farm dairies. Lower somatic cell counts were determined in milk amples from industrial dairies (mean value 326.55 thousand in 1 mL) in comparison to milk samples from farm dairies (mean value 507.67 thousand in 1 mL). Statistically lower dry matter content (p <0.01) in the samples of Parenica cheese was found out in farm dairy E in comparison to other dairies. The relationship between somatic cell count in milk and dry matter in cheese was confirmed by the relatively low correlation coefficients in dairies, A = 0.22; C = 0.15 and F = -0.12 and higher correlation coefficients in dairies, B = -0.32; D = 0.45 and E = -0.48. Obtaining a more accurate effect of somatic cell count on cheese quality requires the continuation of the research on a larger number of samples and consideration of other factors.

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