@article{article_1116155, title={Determination of Estrus in Cattle with Artificial Neural Networks Using Mobility and Environmental Data}, journal={Journal of Agricultural Faculty of Gaziosmanpaşa University}, volume={39}, pages={40–45}, year={2022}, DOI={10.55507/gopzfd.1116155}, author={Yıldız, Adil Koray and Özgüven, Mehmet Metin}, keywords={Artificial Neural Networks, Dairy Cattle, Estrus Detection, Artificial Neural Networks, Dairy Cattle, Estrus Detection, Artificial Neural Networks}, abstract={Detection of estrus with high accuracy directly affects the possibility of cows becoming pregnant and so also milk production. Most milk is obtained in the early lactation period, after calving. Animals in estrus are more active than others. This mobility can be measured by a testing device called "pedometer." Estrus can be estimated using detected movement changes with artificial neural networks (ANN) models. This study aims to create and assess the effectiveness of a neural network model to estimate estrus in cattle by using movement and environmental data. Movement data of 78 cattle, which showed 184 estruses have been captured along with climatic data during a seven-month period at a private agricultural organization. Data such as cow age, lactation number and number of days elapsed from estrus were also taken into account and evaluated. ANN models were compared with accuracy, precision and F-scores. Two-layer classification networks were tested for feed-forward neural network model. Optimal inputs to the neural network model were found to be motion data, motion data of the previous period, the number of days after the previous estrus, temperature and humidity. Two-layer network with 37 for the first layer and 40 neurons in the second layer has been the most successful model with a 0.1775 F - score. The study has shown that the accuracy of estrus prediction is increased by evaluating movement data along with climate data.}, number={1}, publisher={Tokat Gaziosmanpasa University}