## 1 Introduction  
Most of the Polish businesses operating in the fruit and vegetable industry are small and medium enterprises employing less than 250 employees. Large enterprises account for only about 15% of businesses, but they hold a major share of the quantity of fruit and vegetables processed in Poland. Large enterprises produce several types of products, while small enterprises are predominantly oriented towards one product. Typical industry products include fruit juices and drinks, fruit and vegetable concentrates, and frozen fruit and vegetables. In fruit and vegetable industry plants, water is both a raw material and energy carrier; it is used in hydro-transport, for raw material washing, and for the maintenance of production hygiene [1]. The diversity of the technological operations performed in the production process, as well as the seasonality of production, make the objective assessment of water consumption in fruit and vegetable processing plants difficult [2]. The few available publications on this subject specify the numerical values of unit consumption indices without any in-depth analysis of the cause-and-effect relationships in the use of water in the facilities of this industry. Artificial neural networks are used in the study of biosystems in which it is difficult to formally describe the analyzed parameters. In the literature, there are many examples of this type of use of artificial neural networks, for example, to forecast corn and soybean yields [3], the stage of development of sweet pepper fruits [4], fuel consumption in vehicles [5], or river flow [6]. For predicting corn and soybean yields, the authors of [3] proposed a new convolutional neural network model called YieldNet, which utilizes a novel deep learning framework that uses transfer learning between corn and soybean yield predictions by sharing the weights of the backbone feature extractor. Similarly, in [4], an ensemble model of convolutional neural network (CNN) and multilayer perceptron (MLP) models was developed to detect sweet pepper fruits in images and predict their development stages. In the forecasting of fuel consumption in vehicles, the adopted research methodology was based on the use of MLP artificial neural networks and sensitivity analysis. It is very difficult to predict desired water flow using physically based models and conventional regression-based methods due to the nonlinear and fuzzy nature of hydrological activity and the scarcity of relevant data. These traditional methods are incapable of handling the complex non-linearity and non-stationary process of water flow. Thus, the aim of [6] was to develop an intelligent hybrid artificial intelligence model, namely, a genetic-algorithm-based Artificial Neural Network (GA-ANN) for monthly water flow prediction in the Mahanadi river system. All parameters associated with the artificial neural network (ANN) model were simultaneously optimized automatically using the Genetic Algorithm (GA) for the prediction of the water flow. In this study, it was also decided to use artificial neural networks and genetic algorithms to analyze water consumption in fruit and vegetable processing plants.  
The objective of this paper was to elaborate on existing research and verify the following research hypothesis: processing structure and the relationship between the weights of specific products have an impact on water consumption in fruit and vegetable processing plants. For this purpose, neural water consumption models were developed from the largest agricultural and food processing plants in Poland that employ similar technology. Subsequently, water consumption was optimized, using genetic algorithms, in relation to processing structure. The analysis of the existing research on water consumption in fruit and vegetable companies and the results of optimization may constitute guidelines for rationalization in water management.