Neural Modelling from the Perspective of Selected Statistical Methods on Examples of Agricultural Applications

Modelling plays an important role in identifying and solving problems that arise in a number of scientific issues including agriculture. Research in the natural environment is often costly, labour demanding, and, in some cases, impossible to carry out. Hence, there is a need to create and use specific substitutes for originals, known in a broad sense as models. Owing to the dynamic development of computer techniques, simulation models, in the form of information technology (IT) systems that support cognitive processes (of various types), are acquiring significant importance. Models primarily serve to provide a better understanding of studied empirical systems, and for efficient design of new systems as well as their rapid (and also inexpensive) improvement. Empirical mathematical models that are based on artificial neural networks and mathematical statistical methods have many similarities. In practice, scientific methodologies all use different terminology, which is mainly due to historical factors. Unfortunately, this distorts an overview of their mutual correlations, and therefore, fundamentally hinders an adequate comparative analysis of the methods. Using neural modelling terminology, statisticians are primarily concerned with the process of generalisation that involves analysing previously acquired noisy empirical data. Indeed, the objects of analyses, whether statistical or neural, are generally the results of experiments that, by their nature, are subject to various types of errors, including measurement errors. In this overview, we identify and highlight areas of correlation and interfacing between several selected neural network models and relevant, commonly used statistical methods that are frequently applied in agriculture. Examples are provided on the assessment of the quality of plant and animal production, pest risks, and the quality of agricultural environments.

Automated summary

Modelling is crucial in addressing scientific issues, including agriculture, as it provides a better understanding of empirical systems and aids in the design and improvement of new systems. Artificial neural networks and mathematical statistical methods are empirical mathematical models commonly used in these analyses. In agriculture, these models are employed to assess the quality of plant and animal production, pest risks, and agricultural environments.