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.

Debashree Borthakur | Plant Microbiology | Best Researcher Award

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Ms. Debashree Borthakur | Plant Microbiology | Best Researcher Award

Assam Don Bosco University | India

Dr. Debashree Borthakur is a research scholar in Microbiology at Tripura University, with a strong foundation in microbial biotechnology and environmental microbiology. She holds a Master’s degree in Microbiology from Bangalore University and ranked 5th in her university. With a CSIR-NET qualification (AIR-31), her research work focuses on microbial applications in health, environment, and biotechnology, including screening of aroma-producing microbes, microbial remediation of plastic toxicity, and plant growth-promoting rhizobacteria. She has authored several research articles and has two national patents filed—on an OCR pen and a deep convolutional neural network model for colon cancer detection. With an h-index of 3, 5 Scopus-indexed documents, and over 12 citations, her contributions are growing steadily in the scientific community. She has served as a reviewer for peer-reviewed journals and technical program committee (TPC) member for reputed international conferences like CSNT, IEEE TALE, and CICN. Her work has been recognized with awards such as the National Biotech Youth Award and General Proficiency Award. Actively engaged in academic seminars, FDPs, and national/international conferences, she bridges interdisciplinary knowledge in life sciences and engineering. Her research integrates innovation, microbiology, and AI, aiming for impactful solutions in health, agriculture, and sustainability.

Profiles: Scopus | Google Scholar | Orcid

Featured Publications

Borthakur, D. (2024). Screening and Characterization of Aroma and Flavour Producing Bacteria and Yeast from Traditional Fermented Food and Beverages of Northeast India. Proceedings of the International Conference on Communications, Electronics and Digital Technologies (NICEDT).

Borthakur, D. (2023). Evaluation of Tea Rhizospheric Microbes in Promoting Seed Germination and Plant Growth – An Alternative Strategy Using Biofertilizer. Proceedings of the International Conference on Modern Trendz in Microbiology.

Borthakur, D. (2022). Bioprospectation of Plant-Associated Endophytes in Plastic Degradation. Proceedings of the International Conference at NIT Jalandhar.

Borthakur, D. (2022). Microbial Pathogenesis and Recent Advances in Diagnosis of Omicron. Proceedings of the International Conference by Palamuru University.

Borthakur, D. (2020). Microremediation: A Natural Technology Towards Remediation of Plastic Toxicity. Proceedings of BIOINVENTION’20 – Advances in Biosciences.

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”