Formation of a digital agriculture management system based on monitoring and long-term field experiments

The article is devoted to the development of a digital agriculture management system (DAMS) that combines monitoring of agricultural objects, analysis of long-term field experiment data and modern information technologies. The purpose of the study is to create a theoretical basis for optimizing agricultural production management by integrating artificial intelligence, machine learning, remote sensing, geographic information systems (GIS) and big data. The novelty of the work lies in the use of machine learning algorithms for yield forecasting, scaling of agricultural system archetypes based on spatio-temporal analysis, as well as the development of a spatial decision support system (DSS) that combines geographic information systems, modeling and knowledge bases. The technical implementation of monitoring includes the use of unmanned aerial vehicles, weather stations, soil sensors and satellite images, the data of which are processed in cloud environments. The research results demonstrate the possibility of reducing uncertainty in the management of agricultural technologies through predictive analytics, simulation modeling and standardization of multimodal data. The findings emphasize the practical importance of the digital agriculture management system for increasing sustainability, minimizing risks and profitability of crop production. The proposed system allows moving from generalized recommendations to individualized management that takes into account the spatio-temporal variability of agricultural landscapes. The key components of the digital agriculture management system - modules for yield forecasting, analysis of vegetation indices, differentiated fertilization and integration of field experiment data - are focused on solving the problems of digitalization of agriculture. The results of the work are of practical importance for the introduction of intelligent technologies in the agro-industrial complex.