PREDICTIVE “BIG DATA” ANALYTICS IN THE PROCESS OF GRAIN FIELD MONITORING

Abstract

The article examines the role of predictive “Big Data” analytics in the field of monitoring grain production, which is of strategic importance for ensuring food security and sustainable development of agriculture. The work emphasizes that predictive analytics, based on the analysis of large volumes of data, provides a powerful tool for accurate and prompt monitoring of processes associated with grain production, allowing real-time diagnosis of plant condition, monitoring grain quality and predicting phytosanitary conditions, which gives farmers have the opportunity to quickly respond to any changes and problems that arise in the production cycle. The article also identifies a number of serious obstacles preventing the successful intellectualization of monitoring processes. These include limited access to high-speed Internet in rural areas, a shortage of specialists with the necessary digital skills, and a lack of clear methodology for working with data. Overcoming these barriers requires a comprehensive approach, including investments in infrastructure development, education and training of specialists, as well as the creation of systematic methodologies for working effectively with data. Overall, the work allows us to better understand the importance of predictive Big Data analytics in agriculture and focuses on the challenges and opportunities faced by grain producers in the process of digital transformation.