Vera Pavese | Plant Breeding | Best Researcher Award

Dr. Vera Pavese | Plant Breeding | Best Researcher Award

Università di Torino | Italy

Dr. Vera Pavese is a Postdoctoral Researcher at the University of Turin, Department of Agricultural, Forest and Food Sciences (DISAFA), Italy. She earned her PhD in Agricultural, Forest, and Food Sciences in 2022 under the supervision of Prof. Roberto Botta, following her Master’s in Plant Biotechnology (2017) and Bachelor’s in Natural Sciences (2015) from the same institution. Her research focuses on developing biotechnological tools to enhance the resilience of woody crops, integrating in vitro culture, CRISPR/Cas9 genome editing, LED-based priming, and RNA-based gene silencing (SIGS). She has pioneered transgene-free genome editing systems and demonstrated the biostimulant potential of LEDs in woody plant micropropagation. Dr. Pavese has authored 24 peer-reviewed papers, accumulating 237 citations and an h-index of 10. Her achievements have earned her four national Best Oral Presentation Awards, invitations as a keynote speaker at international symposia, and recognition as Session Chair at major conferences. A recipient of the RILO Young Researchers Grant (2024), she actively promotes Open Science and serves as Guest Editor for Plants and Agronomy. Her work advances sustainable crop improvement and genetic innovation, positioning her as a rising leader in woody plant biotechnology and genome engineering.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Marino, L. A., Ruffa, P., Mozzanini, E., Patono, D. L., Sereno, A., & Pavese, V. (2025). LEDs in plant tissue culture: Boosting micropropagation of Castanea sativa cultivars. Journal of Plant Growth Regulation.

Marino, L. A., Pavese, V., Ruffa, P., Ferrero, M., Acquadro, A., Barchi, L., Botta, R., & Torello Marinoni, D. (2024). Guardians of quality: Advancing Castanea sativa traceability using DNA analysis from seed to processed food. Scientia Horticulturae.

Pavese, V., Moglia, A., Acquadro, A., Barchi, L., Portis, E., Torello Marinoni, D., Valentini, N., Milani, A. M., Abbà, S., Silvestri, C., et al. (2023). Development of biotechnological tools for hazelnut breeding. Acta Horticulturae.

Ferrucci, A., Lupo, M., Turco, S., Pavese, V., Torello Marinoni, D., Botta, R., Cristofori, V., Mazzaglia, A., & Silvestri, C. (2023). A roadmap of tissue culture and biotechnology in European hazelnut (Corylus avellana L.). Plant Physiology and Biochemistry.

Nerva, L., Dalla Costa, L., Ciacciulli, A., Sabbadini, S., Pavese, V., Dondini, L., Vendramin, E., Caboni, E., Perrone, I., Moglia, A., et al. (2023). The role of Italy in the use of advanced plant genomic techniques on fruit trees: State of the art and future perspectives. International Journal of Molecular Sciences.

Yang Hu | Sustainable Agriculture | Best Researcher Award

Mr. Yang Hu | Sustainable Agriculture | Best Researcher Award

Central South University of Forestry and Technology | China

Hu Yang is a researcher specializing in artificial intelligence applications in agriculture and forestry, currently with the Artificial Intelligence Application Research Institute at Central South University of Forestry and Technology. With a focus on multi-modal data fusion, computer vision, and deep learning, Hu has contributed to over 10 peer-reviewed publications in high-impact journals, including Computers and Electronics in Agriculture, The Plant Journal, Information Fusion, and European Journal of Agronomy, with several manuscripts under revision or review. His work has achieved a total of 5documents, over 13 citations, and an h-index of 2, reflecting a strong influence in AI-driven plant disease recognition, image segmentation, and data augmentation techniques. Hu has led multiple projects funded by the National Natural Science Foundation and university-level innovation programs, including multimodal agricultural and forestry disease assessment systems and forest area road crack detection using reinforcement learning. His research interests include multi-modal machine learning, image-based disease detection, precision agriculture, and smart forestry monitoring. Recognized with numerous awards such as the National Scholarship (2025) and Outstanding Graduate (2024), Hu continues to advance AI frameworks that bridge cutting-edge computational methods with practical applications, aiming to improve crop health, sustainable forestry, and environmental protection through intelligent, data-driven solutions.

Profile: Scopus

Featured Publication

Mendes, L. de C., et al. (2025). AMF: A multi-modal framework for crop leaf diseases segmentation. Computers and Electronics in Agriculture.