Xanthomonas campestris pv. campestris (Xcc), the causal agent of black rot, has a significant impact on cabbage production worldwide. The goal of this research was to evaluate the effect of preventive foliar treatments with Bacillus velezensis strain RD-FC 88 on the primary and secondary metabolism of Xcc-infected cabbage cv. Futoški plants. Special attention was given to measuring metabolites’ changes, aiming to determine the influence of the applied biocontrol treatment on the development of plant immune response and resistance to pathogen. This study reports the first comprehensive biochemical and physiological analysis of the interaction between host plant, biocontrol strain and pathogen, thus providing novel insight into black rot management. Pathogen inoculation caused a significant decrease in the majority of measured metabolites, including most free amino acids (Gln, Ala, BCAA), phenolics, and glucosinolates. Preventive application of B. velezensis strain in Xcc-infected plants restored the levels of aromatic amino acids, Asp, Glu, Leu, Val, and Ala to control values. A similar pattern was observed in aliphatic glucosinolates sinigrin and glucoiberin, as well as for the indolic glucosinolate 4-methoxy-glucobrassicin. Additionally, increased accumulation of hydroxybenzoic acids, hydroxycinnamic acids, and kaempferol derivatives was also observed in the plants treated with the biocontrol strain and subsequently infected with Xcc, compared to plants solely infected with Xcc. The obtained results imply that the RD-FC 88 strain holds potential as an efficient priming agent, capable of stimulating cabbage cv. Futoški defense responses and enhancing its resistance to Xcc.

The rapid growth of global supply chains has brought major environmental challenges, particularly through rising transport-related emissions. In response, businesses and policymakers increasingly view artificial intelligence (AI) as a tool for achieving both operational efficiency and sustainability. This paper analyses the potential of AI-driven route optimisation within the context of green supply chains. The theoretical framework contrasts AI with classical optimisation methods, highlighting how machine learning, heuristics, and reinforcement learning introduce adaptability, scalability, and multi-objective optimisation that traditional models lack. The empirical part reviews three areas of AI application. Transport and logistics illustrate direct contributions through route optimisation, last mile efficiency, and multimodal planning. Predictive analytics improves demand forecasting and risk anticipation, reducing unnecessary trips and inventories. Digital twins and simulation offer advanced modelling for complex logistics systems, supporting resilience and sustainability under uncertainty. Practical illustrations demonstrate the tangible impact of these technologies. The paper concludes that AI-driven optimisation is not merely a technical innovation but a structural enabler of the green transition, aligning operational efficiency with sustainability objectives and paving the way for more resilient global value chains.