Consideration of NON-factors in complex models of agricultural production

Abstract

Using a specific example of solving the problem of creating and using an integrated planning model for the functioning of a difficult-to-formalize agricultural process for harvesting grass feed, an analysis of the causes of the manifestation of a range of NON-factors that significantly affect the sustainability of the feed production process under consideration has been carried out. The combination of an operational-calendar logical-dynamic model represented by a system of differential equations with fuzzy-probability models describing volume-resource planning and synthesized on the basis of expert knowledge for predicting yield and quality of grasses allows solving a large-scale multiparametric problem of the theory of schedules for individual stages of harvesting grasses for silage. The following NON-factors are recorded in the modeling: uncertainty, fuzziness, underdeterminacy, inaccuracy, etc. At the level of the general description of the complex model, the following properties were additionally highlighted and formally described: incorrectness, inaccuracy, inadequacy of the model and ambiguity of interpretation of modeling results. In the field of artificial intelligence, the modeling of NON-factors is of paramount importance. This is due to the fact that intelligent technologies are aimed at solving creative problems in conditions of significant uncertainty, incompleteness, inaccuracy, and fuzziness of the source data and the relationships between them when modeling complex objects in various subject areas. Such objects are fairly classified as difficult to formalize and poorly structured. It is shown and justified that the use of fuzzy-probabilistic and logical-dynamic approaches makes it possible to successfully identify, recognize the causes of manifestation and overcome the negative impact of most NON-factors, which significantly improves the quality of modeling difficult-to-formalize agricultural production in general and operational and calendar planning of grass harvesting processes for silage in particular.