Applying machine learning techniques on feeding behavior data for early estrus detection in dairy heifers

Cairo, Fernando Correia; Pereira, L. G. R.; Campos, M. M.; Tomich, T. R.; Coelho, Sandra Gesteira; Lage, C.F.A.; Fonseca, Adolfo Perez; Borges, Álan Maia; Alves, Bruno Henrique; Dórea, João Ricardo Rebouças

doi:10.1016/j.compag.2020.105855

Cited by 20 publications

(3 citation statements)

References 17 publications

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“…Automated systems, such as activity monitors employing accelerometers and acoustic sensors, have significantly enhanced reproductive performance by facilitating oestrous detection and optimizing insemination schedules (Giordano et al., 2011; Nebel et al., 2000). However, wearable devices are limited by susceptibility to environmental and physiological disruptions affecting movement and temperature detection (Cairo et al., 2020).…”

Section: Methods Of Oestrous Detectionmentioning

confidence: 99%

Early prediction of oestrus for herd fertility management in cattle and buffaloes – a review

Rajput,

Mishra,

Rajawat

et al. 2024

Reprod Domestic Animals

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Oestrus is defined as a period when a female animal exhibits characteristic sexual behaviour in the presence of a mature male. Oestrous manifestation in dairy animals is due to the oestrogen (E2) effect on the central nervous system (CNS). It is a critical issue to be considered on a priority basis. Inefficient oestrous detection reduces the fertility status of the herd. The primary and most reliable indicator of oestrus is standing to be mounted by a bull or another female herd mate, signalling receptivity and the pre‐ovulatory state in dairy cattle. Oestrous detection is primarily a management challenge requiring skill and vigilance. To improve the efficiency of oestrous detection in dairy cattle, visual observation is one of the best methods if done three times a day; however, heat detection aids, if combined, give better results. However, techniques like using teaser bulls, tail painting, chin ball markers, ultrasound (USG) examination, hormonal analysis and examination of cervicovaginal mucus (CVM) improve oestrous detection efficiency. Moreover, the changes in production systems have reduced the expression of oestrous behaviour among cows, due to higher oestrogen (E2) metabolism. Therefore, automated systems, such as pedometers, accelerometers and acoustic sensors like infrared thermography (IRT) and image processing, have significantly enhanced reproductive performance by facilitating oestrous detection and optimizing insemination schedules. From this review, we would conclude that oestrous detection alone contributes considerably to the reproductive status of the herd; therefore, applying different methods of oestrous detection reduces the incidence of missed oestrus and improves the fertility status of the herd.

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Section: Methods Of Oestrous Detectionmentioning

confidence: 99%

Early prediction of oestrus for herd fertility management in cattle and buffaloes – a review

Rajput,

Mishra,

Rajawat

et al. 2024

Reprod Domestic Animals

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“…The authors of [24] found that water intake fell from an average of four occasions in non-estrus cattle to an average of three in estrus animals. In addition, the duration of water visits also decreased during estrus.…”

Section: A C C E T E Dmentioning

confidence: 99%

Measuring the effects of estrus on rumen temperature and environment, behavior and physiological attributes in Korean Native breeding cattle

Lee¹,

Jo²,

Lee³

2022

J Anim Sci Technol

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“…They are also widely used methods in agriculture [ 17 , 18 , 19 ], such as for high throughput phenotyping [ 20 ] or predicting plant diseases [ 21 ]. In animals, the ML algorithms have been used for monitoring the health status [ 22 , 23 ], product quality [ 24 , 25 ], and prediction of diseases [ 26 , 27 , 28 , 29 , 30 ]. Selection of the ML algorithms for studies on farm animals depends on the traits and data, and their performance also varies among the studies [ 17 , 28 , 31 ].…”

Section: Introductionmentioning

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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Applying machine learning techniques on feeding behavior data for early estrus detection in dairy heifers

Cited by 20 publications

References 17 publications

Early prediction of oestrus for herd fertility management in cattle and buffaloes – a review

Early prediction of oestrus for herd fertility management in cattle and buffaloes – a review

Measuring the effects of estrus on rumen temperature and environment, behavior and physiological attributes in Korean Native breeding cattle

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

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