Modeling a monitoring system for agricultural ecological systems based on Big Data

Abstract

Abstract. Due to population growth and food demand, the monitoring of agrarian ecological systems is becoming increasingly important. This is due to the expected use of resources, increased yields and the impacts of agricultural systems in the face of climate change and increasing anthropogenic pressure. The use of such technologies makes it possible to obtain more accurate and objective data on the state of agricultural ecosystems, which, in turn, is based on decisions made aimed at improving the management of agricultural ecosystems and optimizing agricultural practices. Purpose. In this scientific paper, the purpose is to present the results of the assessment of agricultural ecological systems, developed on the basis of the use of Big Data. Methods. The authors of the article analyze the methods of monitoring agroecosystems and justify a new observation that will improve the quality and control of monitoring results. The main emphasis is placed on the use of big data analysis and machine learning methods to obtain more accurate and objective information about the state of agricultural ecosystems. Scientific novelty. The authors have carried out modeling of monitoring systems for agrarian ecological systems based on big data methodology. This represents a transition from classical approaches to more efficient and accurate ones, which is a significant step forward in this field of research. Results. The new model for monitoring agrarian ecological systems provides opportunities for a more accurate and objective study and assessment of the state of agroecosystems. It also allows you to make informed decisions based on the information received, which is an important guarantee for the sustainable development of the agricultural sector. In conclusion, the authors consider the possibilities for improving efficiency and its application models in various areas of agricultural activity.