DSpace@Çankaya

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

Basit öğe kaydını göster

dc.contributor.author Keçeli, Ali Seydi
dc.contributor.author Çatal, Çağatay
dc.contributor.author Kaya, Aydın
dc.contributor.author Tekinerdoğan, Bedir
dc.date.accessioned 2021-06-10T11:33:52Z
dc.date.available 2021-06-10T11:33:52Z
dc.date.issued 2020-03
dc.identifier.citation Keçeli, Ali Seydi...et al (2020). "Development of a recurrent neural networks-based calving prediction model using activity and behavioral data", Computers and Electronics in Agriculture, Vol. 170. tr_TR
dc.identifier.issn 0168-1699
dc.identifier.issn 1872-7107
dc.identifier.uri http://hdl.handle.net/20.500.12416/4761
dc.description.abstract Accurate prediction of calving time in dairy cattle is crucial for dairy herd management to reduce risks like dystocia and pain. Prediction of calving using traditional, manual observation such as observing breeding records and visual cues, however, is a complicated and error-prone task whereby even experts can fail to provide a proper prediction. Moreover, manual prediction does not scale for larger farms and becomes very soon time-consuming, inefficient, and costly. In this context, automated solutions are considered to be promising to provide both better and more efficient predictions, thereby supporting the health of the dairy cows and reducing the unnecessary overhead for farmers. Although the first automated solutions appear to have mainly focused on statistical solutions, currently, machine learning approaches are now increasingly being considered as a feasible and promising approach for accurate prediction of calving. In this context, the objective of this study is to develop machine learning-based prediction models that provide higher performance compared to the existing tools, methods, and techniques. This study shows that the calving of the cattle can be predicted by applying several behaviors of cattle, behavioral monitoring sensors, and machine learning models. Bi-directional Long Short-Term Memory (Bi-LSTM) method has been applied for the prediction of the calving day, and the RusBoosted Tree classifier has been used to predict the remaining 8 h before calving. The experimental results demonstrated that Bi-LSTM provides better performance compared to the LSTM algorithm in terms of classification accuracy, while the RusBoosted Tree algorithm predicts the remaining 8 h accurately before calving. Furthermore, Recurrent Neural Networks provide high performance for the prediction of calving day. tr_TR
dc.language.iso eng tr_TR
dc.relation.isversionof 10.1016/j.compag.2020.105285 tr_TR
dc.rights info:eu-repo/semantics/openAccess tr_TR
dc.subject Calving Prediction tr_TR
dc.subject Recurrent Neural Networks tr_TR
dc.subject Machine Learning tr_TR
dc.subject Precision Dairy Farming tr_TR
dc.title Development of a recurrent neural networks-based calving prediction model using activity and behavioral data tr_TR
dc.type article tr_TR
dc.relation.journal Computers and Electronics in Agriculture tr_TR
dc.contributor.authorID 3530 tr_TR
dc.identifier.volume 170 tr_TR
dc.contributor.department Çankaya Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü tr_TR


Bu öğenin dosyaları:

Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.

Basit öğe kaydını göster