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13 resultados, página 2 de 2

Systematic Literature Review on Smart Specialization: Future Prospects and Opportunities

Beatriz Rosas Michael Demmler (2023, [Artículo])

"Smart specialisation (SS) has been the new cohesion policy in the European Union during the last two periods. The present study aims to analyse the most relevant existing state-of-the-art literature on smart specialisation through a systematic and bibliometric review. Using the Web of Science bibliographic database, we analysed the content of 207 articles under the TCCM methodology and constructed a network of citations in order to summarize theories, characteristics, context and methods presented in existing studies on the topic. Our results show the theoretical and methodological gaps of the past, such as Entrepreneurial Discovery Process and SS indicators. These remain to the present day. The context analysis showed that the scope of smart specialisation extended beyond the frontiers of the European Union, given how it has been adopted by other countries as well. These results suggest the importance of developing a more robust theoretical, conceptual and methodological framework. Consequently, the guides need to be more accurate and should be continuously updated. Our results are valuable for the EDP actors and have policymaking implications".

Especialización inteligente Estrategias de innovación regional Revisión de literatura sistemática Métodos de especialización inteligente Smart specialization Smart specialization methods CIENCIAS SOCIALES CIENCIAS SOCIALES

The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia

Gerald Blasch David Hodson Francelino Rodrigues (2023, [Artículo])

Very high (spatial and temporal) resolution satellite (VHRS) and high-resolution unmanned aerial vehicle (UAV) imagery provides the opportunity to develop new crop disease detection methods at early growth stages with utility for early warning systems. The capability of multispectral UAV, SkySat and Pleiades imagery as a high throughput phenotyping (HTP) and rapid disease detection tool for wheat rusts is assessed. In a randomized trial with and without fungicide control, six bread wheat varieties with differing rust resistance were monitored using UAV and VHRS. In total, 18 spectral features served as predictors for stem and yellow rust disease progression and associated yield loss. Several spectral features demonstrated strong predictive power for the detection of combined wheat rust diseases and the estimation of varieties’ response to disease stress and grain yield. Visible spectral (VIS) bands (Green, Red) were more useful at booting, shifting to VIS–NIR (near-infrared) vegetation indices (e.g., NDVI, RVI) at heading. The top-performing spectral features for disease progression and grain yield were the Red band and UAV-derived RVI and NDVI. Our findings provide valuable insight into the upscaling capability of multispectral sensors for disease detection, demonstrating the possibility of upscaling disease detection from plot to regional scales at early growth stages.

Very High Resolution Imagery Disease Detection Methods Early Growth Stages CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA UNMANNED AERIAL VEHICLES STEM RUST PHENOTYPING HIGH-THROUGHPUT PHENOTYPING WHEAT