## 3 Results
### AI for Sustainable Agriculture and Agroecosystem Management  
Regional coverage. This sub-section includes 13 studies. The majority of cases pertain to Asia (\(n=6\)), with additional case studies identified in Europe (\(n=2\); e.g., Western Greece and Luxembourg). Two studies adopt a global perspective (\(n=2\)). The integration of artificial intelligence into agriculture is unlocking new possibilities for the sustainable management of agroecosystems in the face of climate change and resource constraints. Machine learning (ML) and deep learning (DL) techniques are being increasingly employed to monitor vegetation and soil conditions, predict land suitability, optimize water and energy use, and minimize the environmental footprint of agricultural practices. AI technologies play a crucial role in the spatiotemporal analysis of vegetation health. For instance, Ref. [29] applied multiple ML models--including Random Forest, AdaBoost, Back Propagation Neural Network (BPNN), and Stacking--to estimate grass height in the Eurasian steppe using MODIS data from 2001 to 2021. By incorporating meteorological and topographic variables, the study provided insights into pasture ecosystem health, biomass estimation, and carbon storage, thereby contributing to a scientific foundation for sustainable steppe management. Additionally, a cubist regression model was employed to investigate global trends in vegetation greening and browning in response to climate change. The study by [30] demonstrated how AI can accurately attribute the effects of temperature, precipitation, nitrogen deposition, and atmospheric CO\({}_{2}\) concentration on vegetation dynamics and forecast ecosystem productivity under various climate scenarios. One of the key research areas involves predicting zones suitable for sustainable agricultural use. In [31], machine learning algorithms--including support vector machine (SVM), Mixture Discriminant Analysis (MDA), Random Forest (RF), and Classification and Regression Trees (CART)--were employed to identify potential areas for cultivating Moringa peregrina, a tree species critical for land restoration and food security. By integrating climatic, geological, and morphometric data, the study identified the primary growth determinants and produced a suitability map for the species. AI algorithms are also widely used for spatial analyses of land suitability across different land-use categories. For example, the study by [32] optimized an Agricultural Land Suitability Analysis (ALSA) by incorporating climatic, soil, and hydrological parameters. The model significantly improved the precision of agricultural potential assessments. However, the authors emphasize the need to account for socio-economic variables to ensure a more comprehensive approach to sustainability. Artificial intelligence is increasingly applied in the management of agricultural resources under conditions of climatic instability. The study by [33] provides an overview of regional CROP-AP models developed for forecasting yield, water demand, greenhouse gas emissions, and climate change impacts. It highlights that AI integration substantially enhances model accuracy and adaptability, and it underscores the importance of open-source codes and shared datasets as priorities for the scientific community. To assess the throughput capacity of interconnected water-land-energy systems in Heilongjiang Province, China, the RBMO-RF algorithm was developed, combining Random Forest with the Red-Billed Blue Magpie Optimization method. The model [34] revealed spatiotemporal patterns and key drivers--such as irrigation efficiency and balanced resource allocation--offering scientific support for sustainable agricultural planning. AI also enhances soil monitoring and management. In [35], machine learning was applied to predict soil quality based on microbiome data and physico-chemical properties, enabling early detection of degradation and more effective agronomic decision making.