Welfare and production efficiency of livestock, especially dairy cattle, in a barn are closely related with environmental factors such as temperature, humidity, etc. Therefore, the aim of this study is to design a low-cost automation device that is based on Temperature Humidity Index (THI). An Arduino microprocessor and associated sensors/electronics were used to design a prototype. The device collects, process and stores temperature, humidity and THI data in a minute interval for automation and long term management purposes. It is capable of estimating and storing theoretical daily reduction in milk production. Average actual daily milk production can also be entered to the system. The cost of the prototype was $ 238 that makes it affordable for low-income operations. Data was collected for a 6month-period to test the performance of the prototype. Totally 1.4 megabyte of capacity is required for data storage. That makes the system affordable and easy to manage the data. The device was installed on a post in the middle of barn. It is found that below the lower limits of mild heat stress category (THI<83) total of 80 Simmental milking cows were not influenced from heat stress as confirmed by literature.
Storing and using trained artificial neural network (ANN) models face technical difficulties. These models are usually stored as files and cannot be run directly. An artificial neural network can be structurally expressed as a graph. Therefore, it would be much more useful to store ANN models in a database and use the graph database as this database system. In this study, training and testing stages of ANN models are provided with software that will allow multiple researchers to conduct joint research on ANN models. The developed software platform is aimed to increase the representation power of the currently used methods by transferring the models developed in the popular ANN frameworks used today. With the study conducted, even someone who has started learning artificial neural network models from scratch will see the process and can visually develop their own model. When models are stored in the graph database, it will be easier to making versions and observing how the model grows. In addition, data to be input and output to the model can be stored in this database, also. In order to feed ANN models with input data and produce outputs, the graph database's own query language was used. This eliminates the dependency on another software library.
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