The digital transformation of agriculture depends not only on the adoption of modern technologies, but also on the capacity of human resources in rural areas to use digital tools effectively in professional and entrepreneurial activities. This study aims to develop a conceptual model for using artificial intelligence to enhance the professional skills of farmers, farm managers, cooperative members, and rural entrepreneurs. The research is exploratory and applied in nature and is based on a documentary analysis of a digital vocational training platform that integrates adaptive artificial intelligence, behavioural assessment, personalized educational content, virtual simulations, and progress-monitoring tools. Based on the identified functionalities, the main training needs in agriculture were correlated with possible digital learning pathways focused on farm management, data use, negotiation, cooperation, communication, decision-making, and risk management. The analysis indicates that the platform contains functionalities that could support the personalization of the educational process, provide learner-adapted feedback, and document skill development. The proposed model may help reduce geographical barriers to training, increase the relevance of educational programs, and strengthen rural human capital. However, validating its effectiveness requires empirical research conducted in farms, cooperatives, and rural enterprises.

Flowers have long held economic, cultural, and ornamental importance, while several species are also valued as raw materials for the cosmetic and pharmaceutical industries. In Romania, tuberose is appreciated particularly for its decorative qualities and intensely fragrant flowers. This study aimed to evaluate the influence of supplemental light treatments applied to tuberose bulbs before planting on plant development and bulbil production. The experiment was conducted using the ‘Avatea’ cultivar in a vertical greenhouse at the Vegetable Research and Development Station Buzău, Romania. Before planting, the bulbs were exposed to supplemental lighting provided by high-power light-emitting diode panels emitting monochromatic blue, red, or white light. An untreated variant was included as the control. During the vegetation period, biometric measurements were performed to assess the plants' response to the different pre-planting light treatments. At the end of the experiment, bulbil production was determined separately for each experimental variant and compared with that of the control. The results indicated that pre-planting exposure to light-emitting diode treatments influenced the reproductive performance of tuberose plants. The red-light treatment, V2, produced the highest bulbil weight and total bulb-plus-bulbil biomass, whereas the white-light treatment, V4, produced the highest mean number of bulbils. Both treatments showed distinct advantages compared with the untreated control. These findings suggest that supplemental lighting of tuberose bulbs before planting may represent a useful technological approach for improving the production of propagation material under protected cultivation conditions.