## 1 Introduction
\[\text{MAPE}=\frac{1}{n}\sum_{i=1}^{n}\left|\frac{{{\boldsymbol{v}}_{i}-{ \boldsymbol{y}}_{i}}}{{{\boldsymbol{y}}_{i}}}\right|\times 100 \tag{1}\]  
\[\text{RMSE}=\sqrt{\sum_{i=1}^{n}{{{\left({{y_{i}}-{\boldsymbol{y}}_{i}} \right)}^{2}}}}, \tag{2}\]  
where \({\boldsymbol{y}}_{i}\) represents the actual yield, \({\boldsymbol{y}}^{\prime}_{i}\) is the predicted yield, and \({\boldsymbol{n}}\) is the sample size. ANNs function similar to biological neural systems in terms of the ability to learn and acquire resistance to errors [36]. In artificial neural networks, each neuron is assigned an appropriate value of the coefficient (weight), which determines its properties and role in the process of solving a given problem by the network. The weight values are changed during the learning process to reduce the error between the result given by the teacher and that obtained by the network. The set of weight values established in all neurons determines the knowledge of the neural network [37]. Machine learning methods, including artificial neural networks, are characterized by a high self-adaptation ability [38], which enables their application to address many scientific issues [39]. ANNs are mainly used to solve regression and classification problems [40]. ANNs have been used, among others, for the prediction of long-term climatic data [41], in the construction industry [42], and in medicine to identify neoplastic lesions based on mammogram images [43]. In addition, ANNs have been used in the power industry for the estimation of demand for electricity [44] and predicting the power of photovoltaic panels [45], as well as water quality forecasting in agricultural drainage river basins [46] or predicting bioethanol production process from lignocellulosic biomass [47]. The application of artificial neural networks (ANNs) in agriculture solved the problem of the lack of linearity between the crop yield and independent variables. An essential feature of ANNs is the ability to learn by means of two variants of learning: supervised and unsupervised learning. The supervised learning process is based on the training set, which includes the learning cases along with the model answers provided to the model. This allows the network answers to be matched with the pattern answers. Training a neural network makes it capable of solving a task similar to the one on which it was trained. Unsupervised learning is based only on providing a series of sample inputs, without considering any information about the expected outputs. An accurately designed neural network can only use the observations of input signals and, on this basis, build an algorithm for its operation [40; 48]. The ability to transfer the trained knowledge to new cases is known as generalization. Overfitting in generalization is a risk, which causes excessive fitting to irrelevant learning cases. Overfitting of the network results in poor generalization [49]. According to [PERSON] et al. [50], artificial neural networks are one of the best tools for obtaining information from imprecise and non-linear data. An additional advantage of artificial neural networks is the possibility of using qualitative (linguistic) variables without the need to code them first, as is the case with conventional statistical techniques [51]. One of the widely used comparative methods for ANN-based analyses is multiple linear regression (MLR) [52; 53; 54; 55; 56; 57]. Many studies have demonstrated the advantage of ANNs over MLR in forecasting crop yields. [PERSON] et al. [58] analyzed the possibility of using ANN and MLR to forecast the yield of barley grown in Ardabil, Iran. In the study, a multilayer perceptron (MLP) with three input neurons, 15 neurons in the hidden layer, and one output neuron was applied. Basing on the mean absolute error, the authors discovered that ANN was more accurate than MLR. The obtained error values were 0.21 and 0.22 t-ha\({}^{-1}\), respectively. Similar results were also reported by [PERSON] et al. [59] in forecasting the yield size of adjuvant seeds grown in Iran.