PRODUCT QUALITY MANAGEMENT IN CLIMATE-SMART AGRICULTURE WITH THE HELP OF CORPORATE INFORMATION SYSTEMS BASED ON MACHINE LEARNING

We dwelt on the directions of product quality management in the conditions of climate-smart agriculture with the application of corporate information systems based on machine learning. The revealed aspects are used with different results within different countries and territories, which have individual climate characteristics and approaches to agricultural production. Specifics of agriculture could be unified with innovative climate-smart approaches to certain processes. We showed that such synthesis allows creating the most optimal solutions to reduce the climate footprint and raise productivity. The considered countries have a potential for further creation of intellectual digital solutions to improve quality management in climate-smart agriculture. This would help achieve results to ensure national and global food security. The goal of this paper was to reveal the key features of using the means of machine learning in corporate information systems to ensure product quality management in climate-smart agriculture. The scientific novelty of this research consisted in the improvement of theoretical and practical substantiation of the forms of interaction between parties that are interested in an increase in the efficiency of climate-smart agriculture with the use of machine learning tools. The main research methods were a systemic approach, comparison of advantages and disadvantages, statistical analysis, and ranking method. © 2025 Elsevier B.V., All rights reserved.