## 2 Materials and Methods
### Literature Review
To evaluate the egg production, authors applied three groups of models--traditional nonlinear models (Adams-Bell, McNally, Logistic, Compartmental, Modified Compartmental, Modified Gompertz, Modified Logistic), multivariate polynomial models (second and third order), and ANN models (with feed-forward and cascade-forward architectures). Based on the evaluation criteria, overall, the ANN outperformed all models. Relating to the group of nonlinear regression models, the authors stated that these models all fit the production curve well, but the Modified Logistic model had the highest goodness-of-fit scores. * [PERSON], [PERSON], [PERSON], and [PERSON] [37] compared 8 models (Logistic, Morgan Mercer Flodin, Polynomial Fit, Rational Function, Sinusoidal Fit, Quadratic Fit, Gompertz function, Modified Compartmental) in respect to how well these models fit the egg production and egg weight curves of laying hens. The evaluation showed that Rational Function, Modified Compartmental, Sinusoidal Fit, and Polynomial Fit models performed the best in fitting the egg production. It should be emphasised that these nonlinear models were meant to process only one factor, i.e., the age of birds (or the week of laying) as input, and to base the egg productivity completely on it. While this meant that models were applicable in cases where only one factor was available, it also revealed the limitations these models had--there was no adaptability to environmental changes. The environment was determined by such factors as outside and inside temperature, humidity, CO\({}_{2}\) and NH\({}_{3}\) concentration, air ventilation, etc. These factors could also influence egg production, either individually or in combination [38]. However, often, due to the inability to monitor all of these parameters as a complex system, only some of them are available. Therefore, the models that can make use of different parameter combinations should be assessed, similar to the models within the machine learning (ML) field. Advances in the artificial intelligence (AI) field have led to the development of several ML models that possess nonlinearity and can also be used to forecast egg production and identify production-related problems in a timely manner. Some of the applications of the ML models in the poultry industry are described in the following studies:  
* [PERSON], [PERSON], [PERSON], and [PERSON] [39] in their study tested an SVM model with the aim to timely detect problems in egg production. The model's accuracy was high (\(\approx\)0.985) when predicting problems a day in advance. * An ANN approach to detect early problems in egg production was presented in [40] research. In comparison with the SVM model, the ANN technique proved to be very accurate as well when predicting egg production problems a day in advance. In addition, it showed improvement in several performance evaluation metrics in comparison with the LSSVM model. * [PERSON] [4] in his research compared three different ANN architectures and traditional models for egg production forecasting. From the results of the experiment, the author concluded that the ANN models outperformed the traditional nonlinear models. In retrospect, the results of these studies, in addition to the semi-related works [18; 21; 23], concluded that AI techniques were viable for application in egg production forecasting and could also lead to performance improvements (measured by several goodness-of-fit criteria) when compared to traditional, nonlinear models. Based on the literature review on the application of nonlinear models in the poultry industry, the Modified Compartmental model's performance proved to be consistently adequate for egg production evaluation, and it used a relatively small number of parameters that could be biologically and mathematically interpreted [32]. Therefore, the Modified Compartmental model was selected for practical experiments. From the ML group, several models were chosen since their performance stood out, as reported by several articles (described in previous chapter).