Muhammad Bilal zia | Agriculture | Best Researcher Award

Published on by Plant Scientist

Mr. Muhammad Bilal zia | Agriculture | Best Researcher Award

University of Southern Queensland | Australia

Muhammad Bilal Zia is a researcher and educator in Information Technology, currently pursuing a PhD at the University of Southern Queensland, Australia. He holds a Master’s degree in Computer Science from Taiyuan University of Technology, China, and a Bachelor’s degree from The Islamia University of Bahawalpur, Pakistan. He has teaching experience across Australian and international institutions, delivering courses in data science, artificial intelligence, and analytics. His research focuses on deep learning, computer vision, and medical image analysis, particularly in disease detection. He has published in reputed Q1 journals and received a Chinese Provincial Scholarship, contributing significantly to AI-driven healthcare solutions.

                          Citation Metrics (Scopus)
8
6
4
2
0

Citations
3

Documents
4

h-index
1

Citations

Documents

h-index

View Scopus Profile

Featured Publications

Angela Hecimovic | Climate Change | Research Excellence Award

Published on by Plant Scientist

Dr. Angela Hecimovic | Climate Change | Research Excellence Award

The University of Sydney Business School | Australia

Dr Angela Hecimovic is Senior Lecturer at the University of Sydney Business School (Accounting, Governance & Regulation discipline). She earned her PhD in 2017 from Macquarie University (thesis: “Assurance of Natural Resource Management”), and holds a Graduate Certificate in Higher Education (2017). With teaching experience spanning two decades, she has served in roles including Lecturer (2010–2023), Industry Placement Coordinator and Unit Convenor, and now Dalyell Program Director. She has been honoured with numerous teaching awards, such as the inaugural Students’ Choice Award, the Wayne Lonergan Excellence in Teaching Award, multiple Dean’s citations, and the 2019 Dean’s Award for Excellence in Teaching. Her research investigates how contemporary developments — especially digital auditing, adoption of artificial intelligence (AI) in auditing, sustainability reporting, non-financial reporting, and global supply-chain assurance — are transforming the auditing profession. Using qualitative case-studies and interview-based research methods (sometimes complemented by surveys), she explores audit quality, assurance in public/non-financial contexts and the impact of technology on audit practices. She has contributed around 13 documented works (articles, conference papers, reports), accumulating about 355 citations and an assumed h-index of 8. Her work contributes to both academic discourse and the practical employability of graduates, bridging theoretical developments and real-world auditing and assurance challenges.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Hecimovic, A., & Canestrari-Soh, D. (2025). Strategies for internal auditors to expand their role in ESG assurance. The British Accounting Review.

Hecimovic, A., & Martinov-Bennie, N. (2023). Audit report construction: Public sector organisation perspectives within a non-financial information context. Journal of Public Budgeting, Accounting & Financial Management,

Ayomide Olubaju | Remote sensing | Best Researcher Award

Published on by Plant Scientist

Mr. Ayomide Olubaju | Remote sensing | Best Researcher Award

Abiola Ajimobi Technical University | Nigeria

Olubaju Ayomide Emmanuel is a dedicated and innovative researcher specializing in Geographic Information Systems (GIS) and Remote Sensing, with growing recognition for his contributions to geospatial science. He holds an M.Tech. in Surveying and Geoinformatics (Remote Sensing) and a B.Tech. in the same discipline from the Federal University of Technology, Akure, Nigeria. Currently serving as an Assistant Lecturer at Abiola Ajimobi Technical University, Ibadan, he combines teaching and research to advance environmental monitoring and sustainable urban planning. His research interests encompass environmental degradation, climate change impact assessment, urban informatics, multi-sensor remote sensing, forest species monitoring, and machine learning applications in geospatial analysis. Olubaju has authored and co-authored several peer-reviewed publications focusing on urbanization, forest ecology, and mining-induced land degradation, accumulating 3 documents, 4 citations, and an h-index of 2. He has participated in national and international conferences, workshops, and collaborative projects addressing climate resilience and spatial data science. A member of professional societies including ISPRS, IAENG, and the Nigeria Institution of Surveyors, Olubaju’s academic and professional journey reflects a commitment to interdisciplinary research and data-driven solutions for sustainable environmental management. His goal is to pursue a Ph.D. to deepen his expertise and contribute to innovative geospatial applications in global environmental research.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Akinbiola, S., Akinsola, J. E. T., Ajagbe, S. A., Salami, A., Olubaju, A., Awotoye, O., & Awoleye, O. M. (2025). Artificial intelligence technique for prediction of carbon stocks and uncertainty estimates in tropical forests. SN Computer Science.

Akinbiola, S., Salami, A. T., Olubaju, A. E., & Awotoye, O. O. (2025). Assessing the impact of environmental variables on the distribution of keystone tree species in Omo-Shasha-Oluwa forest complex using MaxEnt modelling techniques. SSRN Electronic Journal.

Ibukun, J. A., Olubaju, A. E., Thomas, S. F., Sodipo, E. O., Akinbiola, S. A., Oyetunji, S. O., Shitu, K., Kucher, D. E., & Tariq, A. (2025). Modeling mining-induced land degradation in Itagunmodi: A multi-temporal machine learning approach with random forest and gradient boosting. Trees, Forests and People, 21, 100926.

Ibukun, J. A., Olubaju, A. E., Thomas, S. F., Sodipo, E. O., Akinbiola, S. A., Rebouh, N. Y., Said, Y., & Tariq, A. (2025). Assessing vegetation degradation and thermal effects of artisanal small-scale mining using remote sensing time series data. Land Degradation & Development.

Akinbiola, S., Salami, A. T., Olubaju, A. E., & Awotoye, O. O. (2024). Assessing the impact of environmental variables on the distribution of keystone tree species in Omo-Shasha-Oluwa forest complex using MaxEnt modelling techniques. SSRN Electronic Journal.

Yang Hu | Sustainable Agriculture | Best Researcher Award

Published on by Plant Scientist

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.