Divya Nimma | Applications of Computer Vision | Women Researcher Award

Assist. Prof. Dr. Divya Nimma | Applications of Computer Vision | Women Researcher Award

Assistant Professor | Arkansas Tech University | United States

Dr. Divya Nimma is an accomplished researcher and Assistant Professor at Arkansas Tech University, specializing in Computer Vision, Artificial Intelligence, Image Processing, and Machine Learning. With a strong interdisciplinary footprint, she has contributed extensively to domains spanning environmental monitoring, healthcare analytics, intelligent transportation cybersecurity and immersive technologies. She has published 46 scholarly works and accumulated over 326 citations, with an h-index of 10 and i10-index of 10, underscoring her growing global research influence.Dr. Nimma’s research portfolio reflects a commitment to developing intelligent systems for real-world impact. Her notable contributions include climate-responsive modeling of freshwater ecosystems remote sensing–based marine life assessment for food security transformer-driven object detection , and advanced deep learning frameworks for image forensics and semantic segmentation. She has led and co-authored high-impact studies published in Scientific Reports IEEE Transactions Alexandria Engineering Journal Desalination and Water Treatment Remote Sensing in Earth Systems Sciences and other reputed journals.Her collaborative research spans international teams across the United States  Europe the Middle East  and Asia. Significant works include attention-based models for real-time surveillance explainable AI pipelines for fingerprint recognition IoT-enabled energy management for EV charging predictive maintenance in Industry 4.0 and multisource wearable data analytics for human activity recognition.Dr. Nimma has also made influential contributions to biomedical informatics including cancer detection using optimized deep learning osteoporosis classification and non-invasive brain stimulation–based sleep stage modeling. Additionally her research extends to precision agriculture integrating drone imagery AI and consumer electronics to enhance crop optimization and sustainability.Committed to societal and technological advancement Dr. Nimma’s work demonstrates a unique synthesis of deep learning innovation domain-driven applications and cross-disciplinary collaboration positioning her as a rising scholar and impactful global contributor in modern AI-driven intelligent systems.

Profiles:  Scopus | ORCID | Googlescholar

Featured Publications

1. Nimma, D., Devi, O. R., Laishram, B., Ramesh, J. V. N., Boddupalli, S., Ayyasamy, R., et al. (2025). Implications of climate change on freshwater ecosystems and their biodiversity. Desalination and Water Treatment, 321, 100889. Cited By : 42

2. Srikanth, G., Nimma, D., Lalitha, R. V. S., Jangir, P., Kumari, N. V. S., & Arpita. (2025). Food security-based marine life ecosystem for polar region conditioning: Remote sensing analysis with machine learning model. Remote Sensing in Earth Systems Sciences, 8(1), 65–73. Cited By : 36

3. Nimma, D., Nimma, R., Rajendar, & Uddagiri. (2024). Image processing in augmented reality (AR) and virtual reality (VR). International Journal on Recent and Innovation Trends in Computing and Communication. Cited By : 27

4. Nimma, D., & Zhou, Z. (2024). IntelPVT: Intelligent patch-based pyramid vision transformers for object detection and classification. International Journal of Machine Learning and Cybernetics, 15(5), 1767–1778. Cited By : 25

5. Nimma, D., Nimma, R., & Uddagiri, A. (2024). Advanced image forensics: Detecting and reconstructing manipulated images with deep learning. International Journal of Intelligent Systems and Applications in Engineering.
Cited By : 24

Dr. Divya Nimma’s research advances intelligent vision systems that enhance environmental sustainability, healthcare diagnostics, and smart transportation. Her work integrates AI with real-world applications, driving scientific innovation that strengthens societal resilience and global technological progress.

Prof. Hoonsoo Lee | Applications of Computer Vision | Best Researcher Award

Hoonsoo Lee | Applications of Computer Vision | Best Researcher Award

Hoonsoo Lee at Chungbuk National University, South Korea

Profiles

Scopus

Orcid

 Academic Background:

He is an Associate Professor in the Dept. of Biosystems Engineering at Chungbuk National University, located in Cheongju, Korea. The university is situated at 1 Chungdae-ro, BLDG# S21-24, RM# 202, Seowon-gu, Cheongju-si, Chungcheongbuk-do, 28644, Republic of Korea.

Education:

Prof. Lee earned his Ph.D. in Agricultural Machinery Engineering from Chungnam National University in August 2015, with a dissertation on the rapid detection of pathogenic infections in watermelon seeds using spectral image analysis. He completed his M.S. in the same field in August 2009, focusing on the development of an electronic nose system for evaluating meat freshness. He holds a B.S. in Bioindustrial Machinery Engineering, which he completed in August 2007.

 Employment History:

Prof. Lee has been an Associate Professor at Chungbuk National University since September 2018. Prior to this, he worked as a PostDoc Researcher at the United States Department of Agriculture (USDA) Agricultural Research Service (ARS) in Beltsville, MD, USA, from August 2015 to August 2018. His experience also includes serving as a Research Assistant at Chungnam National University from June 2008 to August 2015 and an internship at USDA, ARS from July 2010 to June 2011.

 Research Interests:

Prof. Lee’s research focuses on developing nondestructive sensing technology for agricultural and food products. He is also interested in data analysis using hyperspectral imaging in conjunction with machine learning and artificial intelligence techniques.

 Research Experience:

Prof. Lee specializes in non-destructive quality measurement of food and agricultural products using vibrational spectroscopic techniques. His work includes developing and commercializing a high-throughput online detection system utilizing optical techniques. He has created hyperspectral and multispectral imaging systems for pathogen-infected seeds and fecal contamination on leafy greens. Additionally, he has developed hyperspectral imaging systems to evaluate food quality, focusing on applications such as detecting physical damages in pears, identifying cracks in tomatoes, assessing color levels in pepper powder, and measuring moisture distribution in cooked meats, rice, and soybeans. Furthermore, he has created a multipurpose floating platform for hyperspectral imaging and monitoring E. coli concentrations in irrigation ponds in Maryland. His research also includes developing Vis/NIR hyperspectral models for assessing the effects of water and fertilizer on crops like cabbage, garlic, and soybeans, as well as laser speckle technology for diagnosing crop stress to enhance precision agriculture practices.

 Publications:

Current trends in the use of thermal imagery in assessing plant stresses: A review
  • Authors: Adhitama Putra Hernanda, R., Lee, H., Cho, J.-I., Cho, B.-K., Kim, M.S.
  • Journal: Computers and Electronics in Agriculture
  • Year: 2024
Chlorophyll Fluorescence Imaging for Environmental Stress Diagnosis in Crops
  • Authors: Park, B., Wi, S., Chung, H., Lee, H.
  • Journal: Sensors
  • Year: 2024
Construction of a sustainable model to predict the moisture content of porang powder (Amorphophallus oncophyllus) based on pointed-scan visible near-infrared spectroscopy
  • Authors: Amanah, H.Z., Rahayoe, S., Harmayani, E., Lee, H.
  • Journal: Open Agriculture
  • Year: 2024
Spectroscopy Imaging Techniques as In Vivo Analytical Tools to Detect Plant Traits
  • Authors: Hernanda, R.A.P., Lee, J., Lee, H.
  • Journal: Applied Sciences (Switzerland)
  • Year: 2023
Snapshot-Based Multispectral Imaging for Heat Stress Detection in Southern-Type Garlic
  • Authors: Ryu, J., Wi, S., Lee, H.
  • Journal: Applied Sciences (Switzerland)
  • Year: 2023