Machine Learning to Detect Posture and Behavior in Dairy Cows: Information from an Accelerometer on the Animal’s Left Flank

Balasso, Paolo; Marchesini, Giorgio; Ughelini, Nicola; Serva, Lorenzo; Andrighetto, Igino

doi:10.3390/ani11102972

Cited by 27 publications

(33 citation statements)

References 31 publications

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“…Extreme gradient boosting was the second-best method for the classification of AD-infected mink, which might be because this method could perform implicit variable selections and could capture the non-linear relationships [ 51 , 52 ]. Both the random forest and the extreme gradient boosting showed great potential for the classification of CIEP in the current study with high accuracies, F1 values, and AUCs.…”

Section: Resultsmentioning

confidence: 99%

Applying Machine Learning Algorithms for the Classification of Mink Infected with Aleutian Disease Using Different Data Sources

Ngoc

Davoudi

et al. 2022

Animals

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American mink (Neogale vison) is one of the major sources of fur for the fur industries worldwide, whereas Aleutian disease (AD) is causing severe financial losses to the mink industry. A counterimmunoelectrophoresis (CIEP) method is commonly employed in a test-and-remove strategy and has been considered a gold standard for AD tests. Although machine learning is widely used in livestock species, little has been implemented in the mink industry. Therefore, predicting AD without using CIEP records will be important for controlling AD in mink farms. This research presented the assessments of the CIEP classification using machine learning algorithms. The Aleutian disease was tested on 1157 individuals using CIEP in an AD-positive mink farm (Nova Scotia, Canada). The comprehensive data collection of 33 different features was used for the classification of AD-infected mink. The specificity, sensitivity, accuracy, and F1 measure of nine machine learning algorithms were evaluated for the classification of AD-infected mink. The nine models were artificial neural networks, decision tree, extreme gradient boosting, gradient boosting method, K-nearest neighbors, linear discriminant analysis, support vector machines, naive bayes, and random forest. Among the 33 tested features, the Aleutian mink disease virus capsid protein-based enzyme-linked immunosorbent assay was found to be the most important feature for classifying AD-infected mink. Overall, random forest was the best-performing algorithm for the current dataset with a mean sensitivity of 0.938 ± 0.003, specificity of 0.986 ± 0.005, accuracy of 0.962 ± 0.002, and F1 value of 0.961 ± 0.088, and across tenfold of the cross-validation. Our work demonstrated that it is possible to use the random forest algorithm to classify AD-infected mink accurately. It is recommended that further model tests in other farms need to be performed and the genomic information needs to be used to optimize the model for implementing machine learning methods for AD detection.

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Section: Resultsmentioning

confidence: 99%

Applying Machine Learning Algorithms for the Classification of Mink Infected with Aleutian Disease Using Different Data Sources

Ngoc

Davoudi

et al. 2022

Animals

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“…Moreover, it is possible to collect social interaction data using passive integrated transponder tags (PIT) and proximity loggers [169]. ML applied to accelerometer readings have been successfully used in the study of Cattle behavior [170], [171]. In [132], SVM and KNN were used with accelerometer readings to study five different behaviours in lactating cows to monitor their well-being.…”

Section: Uav Height Of Operationmentioning

confidence: 99%

Livestock Management With Unmanned Aerial Vehicles: A Review

et al. 2022

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The ease of use and advancements in drone technology is resulting in the widespread application of Unmanned Aerial Vehicles (UAVs) to diverse fields, making it a booming technology. Among UAVs' several applications, livestock agriculture is one of the most promising, where UAVs facilitate various operations for efficient animal management. But the field is characterized by multiple environmental, technical, economic, and strategic challenges. However, the use of advanced technological techniques like Artificial Intelligence (AI), Internet of Things (IoT), Machine Learning (ML), Deep Learning (DL), advanced sensors, etc. along with the assurance of animal welfare while operating the UAVs, can lead to widespread adoption of drone technology amongst livestock farmers. This paper discusses livestock management research where UAVs monitor farm animals via detection, counting, tracking animals, etc. In this article, an attempt has been made to elucidate different aspects and broader issues around livestock management while highlighting the associated challenges, opportunities, and prospects. This work is the first review paper on the subject matter with all the necessary information and analysis, to the best of our knowledge. Therefore, the article promises to provide interested researchers with detailed information about the field, guiding future research.

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“…Feeding behavior is one of the key indicators in cattle to measure growth and diseases. Accurate analysis of feeding behavior can help evaluate the feed intake and growth rate, which could be used to provide a reference for cattle breeding ( 3 ). In particular, rumination and eating are the most direct and effective feeding behavior characteristics to confirm the cattle's health status ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

“…Riaboff et al ( 36 ) developed a prediction method for feeding behavior and posture using accelerometer data based on the XGB algorithm and also presented a variety of machine learning algorithms for comparisons. Balasso et al ( 3 ) developed a model to identify posture and behavior from the data collected from a triaxial accelerometer located on the left flank of dairy cows, and four algorithms (RF, KNN, SVM, and XGB) were tested and the XGB model showed the best accuracy. Dutta et al ( 37 ) developed and deployed a neck-mounted intelligent IoT device for cattle monitoring using the XGBoost classifier, which achieved an overall classification accuracy of 97%.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Cattle's Feeding Behaviors Using Noseband Pressure Sensor With Machine Learning

et al. 2022

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Automatic monitoring of feeding behavior especially rumination and eating in cattle is important to keep track of animal health and growth condition and disease warnings. The noseband pressure sensor is not only able to accurately sense the pressure change of the cattle's jaw movements, which can directly reflect the cattle's chewing behavior, but also has strong resistance to interference. However, it is difficult to keep the same initial pressure while wearing the pressure sensor, and this will pose a challenge to process the feeding behavior data. This article proposed a machine learning approach aiming at eliminating the influence of initial pressure on the identification of rumination and eating behaviors. The method mainly used the local slope to obtain the local data variation and combined Fast Fourier Transform (FFT) to extract the frequency-domain features. Extreme Gradient Boosting Algorithm (XGB) was performed to classify the features of rumination and eating behaviors. Experimental results showed that the local slope in combination with frequency-domain features achieved an F1 score of 0.96, and recognition accuracy of 0.966 in both rumination and eating behaviors. Combined with the commonly used data processing algorithms and time-domain feature extraction method, the proposed approach improved the behavior recognition accuracy. This work will contribute to the standardized application and promotion of the noseband pressure sensors.

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Machine Learning to Detect Posture and Behavior in Dairy Cows: Information from an Accelerometer on the Animal’s Left Flank

Cited by 27 publications

References 31 publications

Applying Machine Learning Algorithms for the Classification of Mink Infected with Aleutian Disease Using Different Data Sources

Applying Machine Learning Algorithms for the Classification of Mink Infected with Aleutian Disease Using Different Data Sources

Livestock Management With Unmanned Aerial Vehicles: A Review

Recognition of Cattle's Feeding Behaviors Using Noseband Pressure Sensor With Machine Learning

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