Model of statistical data analysis on nitrogen content in soybeans (Glicine Max Merrill) in Clavera variety

Abstract

Climate change, drought and high temperatures lately have led to the need to increase the adaptation capacities of plants to these changes. The purpose of the research is to gain knowledge regarding the influence of the biologically active substances Reglalg (compound of algal nature) and Biovit (compound of humic nature) on plant development, productivity and adaptation of plants to new climatic conditions. The research was based on decision support systems, machine learning and graph databases. These systems allow in-depth processing of unstructured data and making the necessary decisions. In this sense, an intelligent model was developed for the processing of biological data as part of a Decision Support System. For data analysis and knowledge generation, a graph database was developed to determine relationships and connections between entities, phenomena or events. Graphs allow the representation of complex information in a simple and intuitive way, which makes it easier to perform and analyze them. The use of Machine Learning methods allows the highlighting of laws and interactions between different elements, which can lead to the discovery of patterns and trends that can be easily identified. The paper presents the structure and functions of some components of the decision support system for the study of nitrogen content in soybean.