The Early Prediction of Common Disorders in Dairy Cows Monitored by Automatic Systems with Machine Learning Algorithms

Zhou, Xiaojing; Xu, Chuang; Wang, Hao; Xu, Wei; Zhao, Zixuan; Chen, Mengxing; Jia, Bin; Huang, Baoyin

doi:10.3390/ani12101251

Cited by 26 publications

(27 citation statements)

References 30 publications

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“…Accelerometers can also monitor rumination [ 66 ]. It has been widely demonstrated that monitoring rumination can be very useful for accurately predicting and diagnosing multiple disorders in dairy cows [ 67 ]. Although it is not widely used in fattening units, it has been demonstrated that animals suffering from BRD had a lower rumination index compared to healthy ones at three to six days before the onset of clinical signs [ 68 ].…”

Section: Resultsmentioning

confidence: 99%

Technological Tools for the Early Detection of Bovine Respiratory Disease in Farms

Puig

Ruiz²,

Bassols³

et al. 2022

Animals

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Classically, the diagnosis of respiratory disease in cattle has been based on observation of clinical signs and the behavior of the animals, but this technique can be subjective, time-consuming and labor intensive. It also requires proper training of staff and lacks sensitivity (Se) and specificity (Sp). Furthermore, respiratory disease is diagnosed too late, when the animal already has severe lesions. A total of 104 papers were included in this review. The use of new advanced technologies that allow early diagnosis of diseases using real-time data analysis may be the future of cattle farms. These technologies allow continuous, remote, and objective assessment of animal behavior and diagnosis of bovine respiratory disease with improved Se and Sp. The most commonly used behavioral variables are eating behavior and physical activity. Diagnosis of bovine respiratory disease may experience a significant change with the help of big data combined with machine learning, and may even integrate metabolomics as disease markers. Advanced technologies should not be a substitute for practitioners, farmers or technicians, but could help achieve a much more accurate and earlier diagnosis of respiratory disease and, therefore, reduce the use of antibiotics, increase animal welfare and sustainability of livestock farms. This review aims to familiarize practitioners and farmers with the advantages and disadvantages of the advanced technological diagnostic tools for bovine respiratory disease and introduce recent clinical applications.

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Section: Resultsmentioning

confidence: 99%

Technological Tools for the Early Detection of Bovine Respiratory Disease in Farms

Puig

Ruiz²,

Bassols³

et al. 2022

Animals

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“…In cows, individual cows were not more similar than different cows across days, when a clustering was applied on activity and area use in the barn per minute (Stachowicz et al , 2022). However, classification of different features of 24-h patterns may help discriminate between healthy and sick cows prior to clinical symptoms (Stachowicz et al , 2022; Zhou et al , 2022).…”

Section: Discussionmentioning

confidence: 99%

“…The effect of disease on rumination time in cows has been described in many studies, most recently by Zhou et al (2022), who showed that rumination duration in diseased cows decreased compared with healthy cows prior to clinical signs. Moreover, the diseased cows displayed an increase in the ratio of rumination time at daytime compared to nighttime, indicating a deviance in timing from their normal rhythm (Zhou et al, 2022). Both nighttime rumination time and the ratio between daytime and nighttime rumination time, as well as the difference between recorded and expected rumination time per 2-h, were important features for the prediction of healthy vs. diseased cows using classification models.…”

Section: Individual Patternsmentioning

confidence: 99%

“…Cows exhibiting a less robust pattern in eating and lying prior to calving were more prone to disease in the postpartum period (van Dixhoorn et al, 2018). The effect of disease on rumination time in cows has been described in many studies, most recently by Zhou et al (2022), who showed that rumination duration in diseased cows decreased compared with healthy cows prior to clinical signs. Moreover, the diseased cows displayed an increase in the ratio of rumination time at daytime compared to nighttime, indicating a deviance in timing from their normal rhythm (Zhou et al, 2022).…”

Section: Individual Patternsmentioning

confidence: 99%

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Do you see the pattern? Make the most of sensor data in dairy cows

Kok,

Ternman,

Thorup

2023

Journal of Dairy Research

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Sensors are increasingly being used to monitor animal behaviour. Data handling methods have, however, lagged behind the continuous data stream to some extent, often being limited to summarizing data into daily averages at group level. This research reflection presents our opinion of the neglected application of 24-h pattern analysis. Recent studies of dairy cow behaviour have demonstrated that additional ways of analysing data improve our understanding of animal behaviour and add value to data that were already retrieved. The terminology for the described 24-h patterns differs between these studies, making them difficult to compare. Thus, diurnal, circadian, daily, periodicity and 24-h pattern are all terms used to describe dairy cow activities over a 24-h period. Several studies have shown that the 24-h behavioural pattern at herd level is relatively consistent over time, and that with well-established management routines, a specific herd signature will be evident. However, within a herd, individual cows may have individual 24-h patterns with more or less variability. Recent studies suggest that deviations from herd and/or individual 24-h patterns can be used to describe cow robustness, as well as to predict disease. We strongly believe that individual and herd 24-h patterns provide a great deal of information about behaviour and that these patterns offer opportunity for more precise and timely health management and welfare monitoring.

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“…In recent years, the health monitoring of ruminants such as cows and sheep has become increasingly important. (1)(2)(3)(4)(5)(6) In particular, pH monitoring of the cow's rumen has attracted attention because of the strong relationship between the pH and the deadly disease of rumen acidosis. (7)(8)(9) Giving cows rapidly digestible carbohydrates, which is effective for the stable production of rich milk and marbled beef, can cause rumen acidosis.…”

Section: Introductionmentioning

confidence: 99%

Stability Evaluation of Reference and Indicating Electrodes of pH Sensor During Monitoring of Cow’s Rumen

Shimodan¹,

Yamamoto²,

Sawada³

et al. 2023

Sensors and Materials

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Drift has been a problem for rumen pH sensors, but it is not known whether the reference electrode or the indicating electrode of the pH sensor is the main cause of the drift. In this study, the drift of each electrode was evaluated by placing pH sensors in a cow's rumen and temporarily removing them at intervals over a four-month period. Our experiment revealed that the drift of the indicating electrode caused by changes in sensitivity is the critical drift issue in realizing a stable rumen pH sensor, although the drift is caused by both the reference electrode and the indicating electrode. The sensitivity of the indicating electrode was around 57.0 mV/pH unit at the start of the experiment but dropped to 37.1 and 49.0 mV/pH unit for Cow1 and Cow2, respectively, after placing the pH sensors in the rumen for four months. By assuming a change in the rumen's pH of around 2 pH units, the maximum drift caused by the indicating electrode appears to be 0.68 pH unit. From the observation of the indicating electrode, we found that the drift was caused by impurities on the glass surfaces, and the pH sensitivity was recovered by removing these impurities. Because the glass electrode cannot be cleaned when in the cow's rumen, the development of a glass electrode with resistance to impurities is required for rumen pH sensors.

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The Early Prediction of Common Disorders in Dairy Cows Monitored by Automatic Systems with Machine Learning Algorithms

Cited by 26 publications

References 30 publications

Technological Tools for the Early Detection of Bovine Respiratory Disease in Farms

Technological Tools for the Early Detection of Bovine Respiratory Disease in Farms

Do you see the pattern? Make the most of sensor data in dairy cows

Stability Evaluation of Reference and Indicating Electrodes of pH Sensor During Monitoring of Cow’s Rumen

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