## 3 Results
### AI in Regional Contexts: Business, Industry, and Agriculture
AI is applied across all stages of agricultural production--from soil analysis and water resource management to logistics, ecosystem monitoring, and crop yield forecasting. One of the key areas of application is digital soil mapping and condition monitoring. Studies [25; 35] show how Random Forest models, support vector machines, and decision trees are used to enhance soil resource management, thereby improving the efficiency of agricultural enterprises. AI enables farmers to plan fertilizer and water use with greater precision and facilitates the integration of these processes into digital precision farming platforms--an especially important factor for enhancing the resilience of local agroecosystems to climatic and economic challenges. AI and automation in eco-friendly unmanned farms, as shown in [39], support the transition from traditional agricultural labor to intelligent management systems. These technologies combine ecological practices with digital solutions, enabling the implementation of smart agriculture under conditions of limited resources and increased climate instability. AI-driven solutions for vertical farming also make a significant contribution to the resilience of food systems, as emphasized in [8]. The application of AI in resource management--including water, light, and nutrients--and predictive analytics helps to optimize production processes, reduce losses, and enhance local food security. The study in [3] demonstrates the integration of AI, sensors, drones, and IoT in smart agriculture. These technologies facilitate rapid adaptation to environmental changes, improving crop yields while reducing ecological impacts. In [31], AI models such as Random Forest (RF), support vector machines (SVM), Multiple Discriminant Analysis (MDA), and Classification and Regression Trees (CART) are applied to identify optimal cultivation zones for plants like Moringa peregrina. This contributes to the development of sustainable income sources for local enterprises, reduces risks for farmers, strengthens local value chains, and supports efforts to prevent land degradation. The sustainable food planning model presented in [7] employs AI to analyze urbanization, resource demand, and availability within agri-food subsystems. This approach supports the reduction in food waste, improves land-use efficiency, balances production and consumption, and enhances the resilience of food ecosystems. AI is also applied in studies evaluating the impact of technological innovation and land-use management on crop yields in BRICS countries, as shown in [13]. AI helps identify correlations between productivity, land use, and emissions, which is essential for adapting agriculture to climate challenges and strengthening its sustainability. In [124], AI is proposed for monitoring livestock density and assessing the environmental impacts of cattle farming. Using Random Forest algorithms and Sentinel-2 satellite data, the study provides the first regional-level assessment of cattle stocking intensity--an essential input for sustainable pasture management. A systems-based approach to agroecosystems is demonstrated in [40], where agent-based modeling and life cycle assessment are used to forecast the environmental and economic outcomes of management decisions, based on a case study of agriculture in Luxembourg. These digital models offer valuable tools for designing sustainable agribusiness strategies that account for subsidies, market dynamics, and environmental constraints. Moreover, AI and machine learning are being integrated into digital platforms such as DigiGram, which is designed to support the digital transformation of rural ecosystems in India [125]. This platform facilitates the holistic development of rural regions--spanning agriculture, trade, education, and healthcare--and underscores the importance of digital solutions in advancing smart villages and building resilient and inclusive local ecosystems.