Mastitis and lameness detection in dairy cows by application of fuzzy logic

Kramer, E.; Cavero, D.; Stamer, E.; Krieter, Joachim

doi:10.1016/j.livsci.2009.02.020

Cited by 59 publications

(77 citation statements)

References 11 publications

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“…Fuzzy logic is an easy and powerful method that translates general knowledge into a formal mathematical model suitable for computer processing (Biewer, 1997). Fuzzy logic was used in cows with interesting results (Cavero et al, 2006;de Mol and Woldt, 2001;Kramer et al, 2009), offering better results than univariate methods. Nevertheless, no examples of the application of this method to assess the HS of dairy goats are present in scientific literature.…”

Section: Introductionmentioning

confidence: 99%

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Zaninelli

Rossi

Tangorra

et al. 2014

Italian Journal of Animal Science

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Intramammary infection affects the quality and quantity of dairy goat milk. Health status (HS) and milk quality can be monitored by electrical conductivity (EC). The aim of the study was to determine the detection potential of EC when measured on-line on a daily basis and compared with readings from previous milkings. Milk yields (MYs) were investigated with the same approach. To evaluate these relative traits, a multivariate model based on fuzzy logic technology -which provided interesting results in cows -was used. Two foremilk samples from 8 healthy Saanen goats were measured daily over the course of six months. Bacteriological tests and somatic cells counts were used to define the HS. On-line EC measurements for each gland and MYs were also considered. Predicted deviations of EC and MY were calculated using a moving-average model and entered in the fuzzy logic model. The reported accuracy has a sensitivity of 81% and a specificity of 69%. Conclusions show that fuzzy logic is an interesting approach for dairy goats, since it offered better accuracy than other methods previously published. Nevertheless, specificity was lower than in dairy cows, probably due to the lack of a significant decrease of MY in diseased glands. Still, results show that the detection of the HS characteristics with EC is improved, when measured on-line, daily and compared with the readings from previous milkings.

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Section: Introductionmentioning

confidence: 99%

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Zaninelli

Rossi

Tangorra

et al. 2014

Italian Journal of Animal Science

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“…Ülkemizde oldukça yeni kullanılmaya başla-nan bulanık mantık teorisi, hayvancılık alanında da gerçekleştirilen birçok başarılı çalışmaya konu olmuştur. Örneğin hayvan ıslahı [22,23] , kızgınlık tespiti [24][25][26] , mastitis ve topallık gibi hastalıkların teşhisi [27][28][29][30] hayvan besleme [31,32] çeşitli verim özelliklerinin (süt, yumurta, canlı ağırlık vb.) tahmini ve hayvansal ürünlerin kalite sınıflandırması [33][34][35][36][37] gibi alanlarda kullanılabilmektedir.…”

Section: Makale Kodu (Article Code): Kvfd-2013-9894unclassified

Çiğ Süt Kalite Değerlendirmesinde Bulanık Mantık Yaklaşımı

Akıllı¹,

Atil²,

Kesenkaş³

2014

Kafkas Univ Vet Fak Derg

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ÖzetGelişen toplum yapısı ile birlikte gerçek hayatta yaşanılan sorunlar ve olaylara bakış açıları da değişmektedir. İnsanlar sorunlarını sahip oldukları sözel ve sayısal verileri kullanarak çözmekte ve bunun için çeşitli yöntemlerden yararlanmaktadırlar. Matematiksel yöntemler insanlara kesinlik içeren durumlarda sorunların çözümlenmesinde sayısal verileri analiz ederek yardımcı olurken, belirsizlik içeren durumlarda yetersiz kalabilmektedir. Son yıllarda kalite değerlendirilmesi gibi belirsizlik içeren durumlarda ortaya çıkan problemlerin çözümünde sıklıkla kullanılan bulanık mantık, yapay zeka yöntemlerinden bir tanesidir. Klasik mantık teorisine göre daha esnek bir yapıya sahip olan bulanık mantık teorisi, olayları nesnelere "0" ve "1" arasında atadığı doğruluk dereceleri ile açıklamakta böylece sözel ve sayısal veriler arasında bir bağ oluşturmaktadır. Bu çalışmada, çiğ süt örneklerinin kalite sınıflarına ayrılmasını amaçlayan bulanık mantık tabanlı bir karar destek sistemi geliştirilmiştir. Sistemin girdileri çiğ süt örneklerine ilişkin toplam bakteri sayısı, somatik hücre sayısı ve protein miktarlarının ölçülen değerleridir. Tasarlanan bulanık sistemin çıktısı ise çiğ süt kalite değerlendirmesi şeklindedir. Yapılan analizin başarısını belirlemek amacıyla uzman kararları ile karşılaştırma yapılmış ve sistemin %80 değerinde başarılı olduğu görülmüştür. Sistemin modellenmesi Matlab (sürüm R2010b) programı kullanılarak yapılmıştır. Anahtar sözcükler: Bulanık mantık, Karar destek sistemi, Çiğ süt kalitesi Fuzzy Logic Approach in the Evaluation of Raw Milk Quality SummaryThe problems that faced with in real life and perspective of the events change with developing structure of society. The people in the face of problem use a variety of methods with their verbal and numerical data to find solution. Mathematical methods that including precision are sufficient in the analyses of numerical data while the modeling of verbal data may be insufficient in case of uncertainty. In recent years, fuzzy logic is one of the artificial intelligence methods that used in solution of the problems which are rosed from quality evaluation situations that consists of uncertainty cases. The fuzzy logic theory that has more flexible structure than the theory of classical logic, describe the events with degree of accuracy which is between "0" and "1" appointed to object. Fuzzy logic-based decision support system offers to people a more realistic and objective perspective in decision making. In this study, fuzzy logic base decision support system which aims to classify raw milk samples in quality has been developed. System inputs are; bacteria count for milk samples, somatic cell count and values for measured protein amounts. Designed fuzzy logic output is consist of raw milk quality value measurement; in order to calculate the success of the analysis, results have been compared to specialist's decisions and due to the comparison, it noticed that the system has 80% success rate. Modeling of the system has been made via Matlab (version R2...

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“…Oestrus detection based on animal behaviour is well described in literature and used in practice (Brehme et al, 2008). There are also studies on automatic mastitis detection (Kramer et al, 2009;Miekley et al, 2013) and lameness detection (Kamphuis et al, 2013;Miekley et al, 2013;Van Hertem et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…The use of a single predictor variable was very often not strong enough as a classifier for lameness, partly due to high within-and between-cow variability (Kramer et al, 2009). In addition, the large pool of sensor data is not extensively used.…”

Section: Introductionmentioning

confidence: 99%

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

Hertem

Bahr

Tello

et al. 2016

animal

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The objective of this study was to evaluate if a multi-sensor system (milk, activity, body posture) was a better classifier for lameness than the single-sensor-based detection models. Between September 2013 and August 2014, 3629 cow observations were collected on a commercial dairy farm in Belgium. Human locomotion scoring was used as reference for the model development and evaluation. Cow behaviour and performance was measured with existing sensors that were already present at the farm. A prototype of three-dimensional-based video recording system was used to quantify automatically the back posture of a cow. For the single predictor comparisons, a receiver operating characteristics curve was made. For the multivariate detection models, logistic regression and generalized linear mixed models (GLMM) were developed. The best lameness classification model was obtained by the multi-sensor analysis (area under the receiver operating characteristics curve (AUC) = 0.757 ± 0.029), containing a combination of milk and milking variables, activity and gait and posture variables from videos. Second, the multivariate videobased system (AUC = 0.732 ± 0.011) performed better than the multivariate milk sensors (AUC = 0.604 ± 0.026) and the multivariate behaviour sensors (AUC = 0.633 ± 0.018). The video-based system performed better than the combined behaviour and performance-based detection model (AUC = 0.669 ± 0.028), indicating that it is worthwhile to consider a video-based lameness detection system, regardless the presence of other existing sensors in the farm. The results suggest that Θ 2 , the feature variable for the back curvature around the hip joints, with an AUC of 0.719 is the best single predictor variable for lameness detection based on locomotion scoring. In general, this study showed that the video-based back posture monitoring system is outperforming the behaviour and performance sensing techniques for locomotion scoring-based lameness detection. A GLMM with seven specific variables (walking speed, back posture measurement, daytime activity, milk yield, lactation stage, milk peak flow rate and milk peak conductivity) is the best combination of variables for lameness classification. The accuracy on four-level lameness classification was 60.3%. The accuracy improved to 79.8% for binary lameness classification. The binary GLMM obtained a sensitivity of 68.5% and a specificity of 87.6%, which both exceed the sensitivity (52.1% ± 4.7%) and specificity (83.2% ± 2.3%) of the multi-sensor logistic regression model. This shows that the repeated measures analysis in the GLMM, taking into account the individual history of the animal, outperforms the classification when thresholds based on herd level (a statistical population) are used.

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Mastitis and lameness detection in dairy cows by application of fuzzy logic

Cited by 59 publications

References 11 publications

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

On-Line Monitoring of Milk Electrical Conductivity by Fuzzy Logic Technology to Characterise Health Status in Dairy Goats

Çiğ Süt Kalite Değerlendirmesinde Bulanık Mantık Yaklaşımı

Lameness detection in dairy cattle: single predictor v. multivariate analysis of image-based posture processing and behaviour and performance sensing

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