## 1 Introduction  
Smart poultry progresses firmly following the industry 3.5 and 4.0 frameworks [1; 2; 3]. The current trends in smart poultry are focused on monitoring parameters (by using various sensors) that affect egg/meat production, animal welfare, behaviour, and growth [4; 5; 6; 7]. The objective of these observations is to increase the overall production's quantity and quality. In addition, according to various poultry-related regulations and directives of the European Union [8; 9; 10], the maintenance of greenhouse gases should be one of the primary tasks associated with achieving the said objective. Any successful activity with observation analysis requires systematic data acquisition and processing. This can be solved by implementing a smart poultry management system. There are various commercially available solutions (such as [11; 12]); however, they are designed for medium and large farms, are typically expensive, and require assistance from the management of the solution providers to be successfully used. Our previous work includes the analysis and proposal of the smart poultry farm management system's design [13], the development of the cyber-physical model for data acquisition and processing [14], and the evaluation of egg production process in regard to production efficiency and greenhouse gas emissions [15]. The cyber-physical model in this context is a three-component system: a set of sensors to obtain measurements, data exchange controllers that fulfil the role of an intermediary between sensors and data centre, and a data centre as an analytical hub with decision making capabilities. The term cyber-physical model can be used for almost any digitally and automatically driven production system [3; 16]. Our smart poultry management system's design focuses on decision making regarding the most appropriate feeding process while simultaneously optimising the levels of CO\({}_{2}\) and NH\({}_{3}\). The cyber-physical model was built for the proposed management system and includes three data groups--data to satisfy specific regulations, monitoring data and business-related data. In this context, data were grouped according to their sources and purposes. For example, regulations provide requirements and recommendations in the form of minimal values for environmental parameters, such as CO\({}_{2}\) and NH\({}_{3}\), whereas monitoring data encapsulates all data obtained using sensory equipment. The data of two last groups underwent complementary data fusion processing. Lastly, the implementation of the said cyber-physical model provided enough data to analyse the impact factors on the hen eggs' production. During the phases of the research and development of the egg production forecasting solution, the focus shifted more on evaluating and predicting egg production. Hen egg production by itself requires an in-depth understanding of the processes involved in such aspects as food formula management [17; 18] and animal welfare condition management [19; 20]. The egg production dynamics are typically expressed in a graphical form as a curve. According to [21; 22; 23], the curve applied in egg production is nonlinear and represents the number of eggs laid during a specific time frame. In general, it is considered appropriate to apply nonlinear models in order to both analyse the historical egg production data and to predict future trends. However, based on literature analysis [4; 24; 25; 26] and our previous work, it was concluded that the development of a smart poultry management system must incorporate multiple functional interpreters based on different types of models. In essence, these studies lead to the necessity to assess the usefulness of generally accepted nonlinear and novel machine learning (ML) models in order to (1) determine the potential level of knowledge acquired in result of their application and (2) to incorporate the appropriate models into the framework of a developed smart poultry management system.