Abstract
The efficiency of modern agricultural production management was shown to depend largely on the implementation of digitalisation processes, which transformed the paradigm of production process management and created conditions for increasing agricultural productivity through the use of innovative digital technologies. The purpose of the article was to review scientific publications devoted to the digitisation of field experiment databases in order to enhance the profitability of agricultural production through the development of effective management decisions in crop production based on data obtained from numerous sources of digital transformation. It was demonstrated that the diversity of technological processes, as well as input and output data involved in agricultural production, resulted in significant complexity and integrative challenges in assessing production sustainability. It was identified that the majority of farmers were not specialists in information technologies and were therefore unable to fully comprehend the complexity of the algorithms underlying modern digital solutions. Approaches to the use of big data in agriculture were systematised with regard to the evolution of the 4V concept towards the 5V framework, in which data value was emphasised as a critical requirement. A scientific and methodological approach aimed at improving agricultural production efficiency through the application of machine learning methods was proposed, including automatic crop recognition, disease and weed detection, and the forecasting of crop yield and product quality. It was established that, alongside material and technical support, the intellectualisation of production and management activities based on digitalisation constituted a priority factor in the development of the agro-industrial sector, being considered a highly effective means of increasing the return on investment in agriculture by 10-15%