## 1 Introduction  
For many years, scientists from all over the world have been creating and are still improving plant yield models [1]. Yield simulation models based on growth processes have become more and more evident in the last decades mainly in studies on the impact of climate on agricultural production. Their usefulness is significant in understanding the relationship between genotype, environment and management to aid decision-making in farms, including cultivars selection, sustainable farm management, and economic planning [2]. Yield models are imperfect approximations to the real interactions between biotic and abiotic factors, designed mainly as tools that provide useful information to farmers, researchers, and policy-makers [3]. Predictive models allow users to simulate a potential yield depending on agrotechnical and weather conditions. This is an important element of precision farming, which allows simulation of the expected yield before harvest, and consequently to optimize the entire production process [4]. Therefore, plant yield models are applied in order to create prognostic tools that can be an important element of decision support systems [5]. Projecting the quantity and quality of yields of various cultivars of plants is very important as it affects the planning of the entire production process, including: Application of proper production means, decision-making, transport, storage, and risk management [6; 7]. The ability of the simulation model to capture the historical variability of cereal yields has shown that it can serve as a basis for indicating the usefulness of yield models among interested parties [2]. Plant production is strongly determined by the course of weather conditions during vegetation, which can often be intense and lead to strong fluctuations, caused by continuous climate change in the world. Climate change is a threat to increasing food production in some regions [8]. Therefore, the integration of meteorological data and cultivation data is an important element in the produced models [9; 10]. There are many methods of analyzing yield components that can be used to implement the task. Techniques such as analysis of variance, Pearson correlation coefficient, multiple regression, and path analysis are usually used to analyze yield components [11]. The main problem of regression models is that they are unable to explain the non-linear and complex relationship between the yield and their components [12]. To overcome this problem, in recent years, agricultural scientists have used artificial intelligence (AI) tools, such as artificial neural networks (ANN), genetic expression programming (GEP), and the adaptive neuro-fuzzy interference system (ANFIS) [10; 13; 14; 15; 16; 17]. One of the most common types of ANNs that are used in biological research is the multi-layer perceptron (MLP) [12; 18; 19; 20]. MLP is an ANN model of the feed-forward type, which consists of the input layer, the hidden layer and the output layer. The MLP has many layers connected to each other, and each node is a neuron with a non-linear activation function. Perceptron calculates a single result from multiple inputs, creating a linear combination according to its inputs, and then determines the result using the non-linear transfer function [21]. Plant yield prediction methods based on combining images using remote sensing methods are also gaining popularity. The normalized differential vegetation index (NDVI) is the most commonly used in this type of analysis [22; 23; 24]. However, many researchers use a unifying theory, the Golden Rule of forecasting--a conservative theory. A cautious prediction is consistent with the combined knowledge of both present and the past. To be conservative, the forecasters must search and use all knowledge relevant to the problem, including knowledge about the methods used for the given situation [25]. Models created for the prediction of crop yields are most often based on numerical data.