Yidan Tang | Spectroscopy | Research Excellence Award

Published on by sciencefather

Dr. Yidan Tang | Spectroscopy | Research Excellence Award

The University of Melbourne, Australia

Dr. Yidan Tang is an accomplished agricultural scientist whose work bridges plant physiology, remote sensing, and sustainable farming systems. She earned her PhD in Agricultural Science from the University of Melbourne, where she received distinctions including the Albert Shimmins Award, Best Student Presentation Award, and the Crawford Fund Scholarship. Her professional experience spans research, teaching, and science communication, beginning with her contributions to grant research projects and agricultural innovation initiatives. As a Research Fellow at the University of Melbourne, she advances drought resilience strategies for the horticulture industry, integrating survival signatures with cutting-edge sensing technologies for major crops such as almonds, pears, and nectarines. She has previously served as a Tutor and Associate Lecturer, developing interactive learning materials across multiple undergraduate and postgraduate subjects and supporting high-achieving students in sustainability-focused research. Her experience also includes STEMM outreach with Agriculture Victoria and leadership roles in university engagement, workshops, and science events. Her research interests focus on drought adaptation, precision agriculture, plant-environment interactions, and data-driven decision support for sustainable farming. With a strong record of scientific impact and communication excellence, Dr. Tang continues to contribute meaningfully to advancing resilient, technology-informed agricultural systems.

Profile: Orcid

Featured Publications

Tang, Y., Fitzgerald, G. J., Gupta, D., Delahunty, A., Nuttall, J. G., & Walker, C. (2026). Predicting faba bean yield and grain quality pre-harvest using chemometric modelling. Precision Agriculture. https://doi.org/10.1007/s11119-025-10306-5

Fuentes, S., Viejo, C. G., Hall, C., Tang, Y., & Tongson, E. (2021). Berry cell vitality assessment and the effect on wine sensory traits based on chemical fingerprinting, canopy architecture and machine learning modelling. Sensors, 21(21), 7312. https://doi.org/10.3390/s21217312

Dr. Abhijeet Das | Environmental Pollution | Environmental Chemistry Commitment Award

Published on by sciencefather

Dr. Abhijeet Das | Environmental Pollution | Environmental Chemistry Commitment Award

C.V. Raman Global University (CGU), Bhubaneswar | India

Dr. Abhijeet Das is a dedicated researcher and academic in the field of Water Resource Engineering, with a strong background in civil and environmental studies. He holds a Ph.D. in Water Resource Engineering from C.V. Raman Global University, Bhubaneswar (2024), an M.Tech in Water Resource Engineering from Biju Patnaik University of Technology, Rourkela (2017), and a B.Tech in Civil Engineering from the same university (2015). With over ten years of cumulative professional experience, he has contributed as a Project Consultant at Madhu Smita Design & Engineers Studio, Bhubaneswar, and served as a faculty member at IGIT Sarang and CET Bhubaneswar, shaping the academic and professional growth of students. His research interests span watershed hydrology, hydrological modeling, hydrologic extremes such as floods and droughts, climate change impact assessment, food-energy-water nexus, machine learning, GIS and remote sensing, simulation-optimization, water quality, and environmental impact assessment. He has been widely recognized for his scholarly contributions, securing numerous Best Paper Awards at prestigious national and international conferences for his innovative approaches in surface water quality assessment and hydrological studies. Driven by a passion for sustainable water management and advanced geospatial techniques, Dr. Das continues to make impactful contributions to the field of water resources engineering.

Profile: Scopus

Featured Publications

An optimization based framework for water quality assessment and pollution source apportionment employing GIS and machine learning techniques for smart surface water governance

Reimagining biofiltration for sustainable industrial wastewater treatment

A data-driven approach utilizing machine learning (ML) and geographical information system (GIS)-based time series analysis with data augmentation for water quality assessment in Mahanadi River Basin, Odisha, India

Evaluation and prediction of surface water quality status for drinking purposes using an integrated water quality indices, GIS approaches, and machine learning techniques

Bioplastics: a sustainable alternative or a hidden microplastic threat?

Surface water quality assessment for drinking and pollution source characterization: A water quality index, GIS approach, and performance evaluation utilizing machine learning analysis

Geographical Information System–driven intelligent surface water quality assessment for enhanced drinking and irrigation purposes in Brahmani River, Odisha (India)