In the past decades, the Erasmus+ program has been a foundation of the European educational exchanges, promoting academic and professional mobility across the EU. This paper analyzes the regional trends in Erasmus+ project funding across Europe, using heatmap visualizations to represent project density and grant distribution. The study highlights key regions with high project participation and significant grant awards, suggesting a strong concentration in Central and Western Europe, particularly in France, Germany, Benelux, UK, Spain. The integration of thematic clusters provides insights into the types of projects funded and their regional prevalence and suggests that Erasmus-funded projects are more frequent in wealthier, central European countries where there is a stronger institutional network for research and education. These findings emphasize the need for more inclusive funding strategies to support project development in underrepresented regions.

Vineyards are affected by pathogens globally. Some of the most damaging pathogens are Uncinula necator, Plasmopara viticola, and thrips, which affect the plant entirely and threaten the health and productivity of vineyards. To control the emergence and spread of pathogens, early detection is essential. Studies to date focus on visual or molecular detection of pathogens but are limited in terms of scalability, labor intensity, need for equipment and expertise. To tackle these limitations, we propose the early detection of grapevine virus infections using Convolutional Neural Networks on both RGB and thermal infrared imagery captured via a smartphone and an FLIR sensor. To do so, we employ a four-step workflow where we first acquire nearly 500 images detecting symptoms of pathogens, which we then crop in smaller tiles. Then, we use the ArcGIS Train Deep Learning Model tool trained with Single Shot Detector and RetinaNet frameworks to detect image areas showing pathogen presence. Finally, we calculate the IoU score to compare precisions between different tile sizes and frameworks. The results demonstrate that pathogen detection using these models is highly effective, with most images having a IoU score above 0.7. Moreover, 30% of images score a precision of 1.0. The consequences of these findings highlight the importance of early detection of pathogens to better understand their spread and effects on vineyards, which finally contribute to proposing effective management measures.

Considering the current prediction from the Food and Agriculture Organization, food production needs an increase of over 70 percent by 2050, and the agriculture sector requires a boost that is also obtained by integrating novelty technologies and methods of the artificial intelligence (AI) field. This study investigates the innovation lifecycle of AI-driven agriculture through a causal inference analysis within the university–industry–research (UIR) collaboration framework. Using time series data from 1985 to 2023, collected from Web of Science (academic articles), CORDIS (research projects), WIPO (patents), and Crunchbase (start-ups), the study explores the causal dynamics among four key innovation pillars. Results from Granger causality and impulse-response analyses reveal a sequential innovation pathway in which start-up activity precedes research projects, followed by academic publications and patent filings. These findings highlight the interdependence of innovation stages and the evolving role of UIR collaboration in driving agricultural transformation. The study provides quantitative insights for policymakers, investors, and researchers to strategically foster innovation ecosystems in AI-driven agriculture.

With agriculture moving to a more performant and autonomous-driven sustainability, advanced digital monitoring is becoming the key to resilient land management. This study investigates hydro-climatic conditions and urban air pollution in 2024 in the Bărăgan Plain, Romania’s primary grain-producing region. Our approach uses the Google Earth Engine (GEE) platform, high-resolution multispectral imagery (Sentinel-2), and atmospheric trace gas data from Sentinel-5P TROPOMI to map spatiotemporal interactions between agricultural health and urban air pollution. To find the most robust approach we compared three machine learning algorithms: Multiple Linear Regression, Random Forest (RF), and Extreme Gradient Boosting (XGBoost), the Enhanced Vegetation Index (EVI) was used as a predictor of the Normalized Difference Vegetation Index (NDVI). Although the models that included the EVI achieved higher accuracy (R2 of 93%), the non-EVI Random Forest model performed better (R ² of 86%) and revealed moisture availability (NDWI) as the primary regulator of crop vigor, with an importance of 81.8%. To isolate the effect of atmospheric chemistry alone, spatial residuals from the optimized RF model were extracted and plotted. Negative residuals that produced anomalies pointed to a unique type of crop stress in which the plants were underperforming even when water was adequate. These anomalies spatially align the urban pollution plume trajectories. Even if the NO2 has a rapid distance-decay effect, the secondary pollutants (O3) reach over the agricultural land through the photochemical titration effect. Our results show that while water determines regional agricultural baselines, atmospheric chemistry from urban sources can independently cause significant crop stress even in the absence of drought. This study provides a robust proof of concept on the separation of climatic and anthropogenic stressors and lays a basic pathway for a multi-sensor diagnostic framework in the domain of remote sensing.

Considering the current prediction from the Food and Agriculture Organization, food production needs an increase of over 70 percent by 2050, agriculture sector requires a boost that is also obtained by integrating novelty technologies and methods of artificial intelligence (AI) field. The current research explores the innovation lifecycle of AI-driven agriculture field through a causal inference analysis within University-Industry-Research ecosystem, extracting four key pillars: scientific research papers, research projects, patents and startups. Using an extensive time-series database, Granger causality analysis is applied for uncovering prospective causal relationships that guide the interest in innovation within AI-driven agriculture. Our results indicate an overall increase in AI applied agriculture domain within all four pillars starting from the 2010 year for start-ups pillar and impacting in Granger point of view all the way to 2020 for patents perspective. The findings suggest a potential sequential innovation pathway where progress in one pillar propels advancements in the next, according this flow: startups, projects, scientific research and patents. The implications of this study are significant, providing insights that could guide strategic planning and investment in AI applications in agriculture. By understanding the potential causality and sequentially in innovation, policymakers, investors, and entrepreneurs can better align their efforts with the most impactful areas. This research not only advances in academic knowledge but also provides practical insights that influence real-world applications and practices in agricultural technology.