Linchao Li | Sustainable Agriculture | Young Innovator Award

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Dr. Linchao Li | Sustainable Agriculture | Young Innovator Award

Inner Mongolia Agricultural University | China

Linchao Li is an agricultural and environmental scientist specializing in climate change impacts on crop systems, extreme weather analysis, and data-driven yield prediction. He is currently an Associate Professor at Inner Mongolia Agricultural University and previously served as a Postdoctoral Research Associate at Iowa State University, with international research experience as a joint PhD student at the NSW Department of Primary Industries, Australia. Dr. Li earned his PhD in Agricultural Resources and Environment from Northwest A&F University, following a master’s degree in Hydraulic Engineering and a bachelor’s degree in Agricultural Water Conservation Engineering. His research integrates machine learning, crop modeling, and multi-source environmental data to improve projections of crop yield, drought risk, precipitation extremes, and greenhouse gas emissions under climate change. He has published extensively in leading journals such as One Earth, Nature Food, Global Change Biology, Agricultural and Forest Meteorology, and Communications Earth & Environment. His work contributes to reducing uncertainty in agricultural climate impact assessments and supporting climate-resilient farming systems. Dr. Li has participated in nationally and internationally funded research projects related to climate adaptation, drought evolution, and sustainable agriculture. With strong expertise in R, MATLAB, GIS, APSIM, AquaCrop, and hydrological models, he continues to advance interdisciplinary research bridging climate science, agronomy, and decision-support systems.

Citation Metrics (Scopus)

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Citations
1117

Documents
40

h-index
20


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Moncef Bouaziz | Soil Fertility | Research Excellence Award

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Dr. Moncef Bouaziz | Soil Fertility | Research Excellence Award

TU Bergakademie Freiberg | Germany

Dr. Moncef Bouaziz is an environmental engineer and remote sensing specialist with extensive academic and applied research experience in environmental monitoring, geospatial analysis, and decision-support systems. He earned his PhD in Environmental Remote Sensing from TU Bergakademie Freiberg, following advanced training in environmental engineering and water and soil management. Since 2022, he has served as scientific staff at TU Bergakademie Freiberg, where he develops spatial decision support systems for post-mining environmental change assessment, leads 3D modeling and platform development, and applies machine learning techniques in Python for remote modeling and forecasting. His academic experience includes prior appointments as an assistant professor at the University of Sfax and as scientific staff at TU Dresden, contributing to projects on land degradation, aeolian dynamics, drought assessment, and ecosystem monitoring. Dr. Bouaziz has coordinated and participated in several nationally and internationally funded research initiatives, including DAAD-supported climate change education platforms and European environmental monitoring programs. His research interests focus on remote sensing–based land degradation analysis, drought prediction, climate change impacts, post-mining hazard assessment, and the integration of machine learning with geospatial data. He has authored numerous peer-reviewed publications in high-impact international journals and actively supervises graduate and undergraduate students. Through interdisciplinary research, teaching, and international collaboration, he aims to advance sustainable environmental management and evidence-based spatial planning.

Citation Metrics (Scopus)

1000
600
400
200
0

Citations
600

Documents
22

h-index
12

Citations

Documents

h-index


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Ayomide Olubaju | Remote sensing | Best Researcher Award

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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.

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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.

Md Ashraful Haque | Precision Agriculture | Best Researcher Award

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Dr. Md Ashraful Haque | Precision Agriculture | Best Researcher Award

ICAR Indian Agricultural Statistics Research Institute | India

Dr. Md. Ashraful Haque is a Scientist in the Division of Computer Applications at ICAR-Indian Agricultural Statistics Research Institute, New Delhi, with over five years of professional experience in artificial intelligence applications in agriculture. He earned his M.Sc. (2016) and Ph.D. (2022) in Computer Applications from ICAR-Indian Agricultural Research Institute under the supervision of Dr. Sudeep Marwaha. His research focuses on artificial intelligence, machine learning, deep learning, computer vision, and digital as well as hyperspectral image processing for solving complex agricultural problems such as crop disease detection, pest identification, stress analysis, and livestock image interpretation. He has published 20 journal articles, 4 book chapters, and 2 computational tools, with his research gaining wide recognition (h-index: 8, 339 citations). Dr. Haque has contributed to international collaborations with IIT Delhi, ICAR institutes, and several universities, and has developed impactful digital tools like AIDISC and WIAYFS. He is the recipient of prestigious fellowships and awards, including the Maulana Azad National Fellowship, ICAR-JRF, IARI Ph.D. Fellowship, and Nehru Memorial Gold Medal. With strong expertise in AI-driven agriculture, he continues to innovate toward sustainable farming solutions. His work demonstrates high relevance, impact, and future research potential in smart agriculture.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

“MuSiC V1.0: A Software Solution for Automated Mustard Silique Count Using YOLOv5”
“An enhanced vision transformer network for efficient and accurate crop disease detection”

“PanicleDet: a deep learning-based model for detection of panicle stages in paddy”

“A comparative analysis of deep learning-based techniques for miRNA prediction associated with mRNA sequences”

“ADNet: An Attention Embedded DenseNet121 Model for Weed Classification”

“Convolution Neural Network (CNN)-Based Live Pig Weight Estimation in Controlled Imaging Platform”
“Rice Disease Identification Using Vision Transformer (ViT) Based Network”