Development of a recurrent neural networks-based calving prediction model using activity and behavioral data

Keçeli, Ali Seydi; Çatal, Çağatay; Kaya, Aydın; Tekinerdoğan, Bedir

doi:10.1016/j.compag.2020.105285

Cited by 33 publications

(18 citation statements)

References 38 publications

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“…The Adsorbing Markov Chain Model successfully reduced the interval between calving prediction and calving event to 3 h in 25 Holstein and Brown Swiss cows. Another study reported an accurate prediction of the day of calving but not the exact hour [93]. Generally, authors agree that algorithms correctly classify data on eating, ruminating, lying and standing, but they should be refined before extensive application for precision calving prediction.…”

Section: Performance Of Automatic Sensors and Machine-learning For Re...mentioning

confidence: 99%

How to Predict Parturition in Cattle? A Literature Review of Automatic Devices and Technologies for Remote Monitoring and Calving Prediction

Crociati

Sylla

Vincenzi

et al. 2022

Animals

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Cattle farming is facing an increase in number of animals that farmers must care for, together with decreasing time for observation of the single animal. Remote monitoring systems are needed in order to optimize workload and animal welfare. Where the presence of personnel is constant, for example in dairy farms with great number of lactating cows or with three milking/day, calving monitoring systems which send alerts during the prodromal stage of labor (stage I) could be beneficial. On the contrary, where the presence of farm personnel is not guaranteed, for example in smaller farms, systems which alert at the beginning of labor (stage II) could be preferred. In this case, time spent observing periparturient animals is reduced. The reliability of each calving alarm should also be considered: automatic sensors for body temperature and activity are characterized by a time interval of 6–12 h between the alarm and calving. Promising results have been shown by devices which could be placed within the vaginal canal, thus identifying the beginning of fetal expulsion and optimizing the timing of calving assistance. However, some cases of non-optimal local tolerability and cow welfare issues are reported. Future research should be aimed to improve Sensitivity (Se), Specificity (Sp) and Positive Predictive Value (PPV) of calving alert devices in order to decrease the number of false positive alarms and focusing on easy-to-apply, re-usable and well tolerated products.

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Section: Performance Of Automatic Sensors and Machine-learning For Re...mentioning

confidence: 99%

How to Predict Parturition in Cattle? A Literature Review of Automatic Devices and Technologies for Remote Monitoring and Calving Prediction

Crociati

Sylla

Vincenzi

et al. 2022

Animals

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Section: Recurrent Neural Networkmentioning

confidence: 99%

“…Recently, machine learning has been actively used in several fields with the development of computer performance. Machine learning in the livestock field is also actively used to analyze animal behavioral pattern [19,20,[26][27][28], to analyze behavior prior to calving [29][30][31], to analyze the voice of livestock [32], and to predict dependent variables according to various environmental variables [18]. Among several machine learning techniques, ANN has been actively used as a method to accurately predict the dependent variables from independent variables.…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Machine Learning Approach to Predict Air Temperature and Relative Humidity inside Mechanically and Naturally Ventilated Duck Houses: Application of Recurrent Neural Network

Lee

Yeo

et al. 2022

Agriculture

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The duck industry ranks sixth as one of the fastest-growing major industries for livestock production in South Korea. However, there are few studies quantitatively predicting the internal thermal and moisture environment of duck houses. In this study, high-accuracy recurrent neural network (RNN) models were used to predict the internal air temperature and relative humidity of mechanically and naturally ventilated duck houses. The models were developed according to the type of duck houses, seasons, and environmental variables by learning the monitoring data of the internal and external environments. The optimal sequence length of learning data for the development of the RNN model was selected as 120 min. As a result of the validation, both air temperature and relative humidity could be accurately predicted within 1% error. In addition, simplified RNN models were additionally developed by learning only from the data of external air temperature, relative humidity, and duck weight, which are relatively easy to acquire at the farms. The accuracy of the simplified RNN models was similar to the basic model for predicting the internal air temperature and relative humidity of duck houses in real time. In the future, for the convergence of information and communications technologies (ICTs) and application of smart farms in duck houses, the RNN models of duck houses developed in this study can be applied to predict and control the internal environments of duck houses using the model predictive control (MPC) technique.

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“…While RusBoosted Tree classifier allows to predict the remaining 8h before calving. The results they have gotten are respectively for BiLSTM and RusBoosted Tree classifier, with an accuracy of 83.34% and 84.16%, a sensibility of 81.9% and of 80.51%, and finally a specificity of 98.72% and 85.74% [29]. Other researchers like Shahriar et al have used 3D-accelerometer sampled at 10Hz and attached to a collar to detect the heat from high activity index derived from time series by means of kmeans algorithm.…”

Section: Cowsmentioning

confidence: 99%

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

Debauche¹,

Elmoulat²,

Mahmoudi³

et al. 2021

RIA

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Numerous bibliographic reviews related to the use of AI for the behavioral detection of farm animals exist, but they only focus on a particular type of animal. We believe that some techniques were used for some animals that could also be used for other types of animals. The application and comparison of these techniques between animal species are rarely done. In this paper, we propose a review of machine learning approaches used for the detection of farm animals’ behaviors such as lameness, grazing, rumination, and so on. The originality of this paper is matched classification in the midst of sensors and algorithms used for each animal category. First, we highlight the most implemented approaches for different categories of animals (cows, sheep, goats, pigs, horses, and chickens) to inspire researchers interested to conduct investigation and employ the methods we have evaluated and the results we have obtained in this study. Second, we describe the current trends in terms of technological development and new paradigms that will impact the AI research. Finally, we critically analyze what is done and we draw new pathways of research to advance our understanding of animal’s behaviors.

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Development of a recurrent neural networks-based calving prediction model using activity and behavioral data

Cited by 33 publications

References 38 publications

How to Predict Parturition in Cattle? A Literature Review of Automatic Devices and Technologies for Remote Monitoring and Calving Prediction

How to Predict Parturition in Cattle? A Literature Review of Automatic Devices and Technologies for Remote Monitoring and Calving Prediction

Machine Learning Approach to Predict Air Temperature and Relative Humidity inside Mechanically and Naturally Ventilated Duck Houses: Application of Recurrent Neural Network

Farm Animals’ Behaviors and Welfare Analysis with IA Algorithms: A Review

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