Skip to content

Conferences

2024

1.Ahmed, M. T., & Kamruzzaman, M. (2024). Hyperspectral imaging and optimized convolutional neural network for quality assessment of sweetpotato. 2024 ASABE Annual International Meeting, 1. Link to DOI

2. Ahmed, M. W., & Kamruzzaman, M. (2024). Portable hyperspectral imaging device for assessing agricultural crops: A design and optimization approach. 2024 ASABE Annual International Meeting, 1. Link to DOI

3. Ahmed, M. W., & Kamruzzaman, M. (2024). Real-time analysis of chemical composition in food products using portable hyperspectral imaging and deep learning. 2024 ASABE Annual International Meeting, 1. Link to DOI

4. Ferreira, M. V. S., Kamruzzaman, M., & Ahmed, M. W. (2024). Portable and field- deployable sensor technologies for rapid food analysis: Applications and future directions. 2024 ASABE Annual International Meeting, 1. Link to DOI

2022

1. Kamruzzaman, M., & Villordon, A. (2022). Quality assessment and grading of sweet potato using VNIR hyperspectral imaging. 2022 ASABE Annual International Meeting, 1, 1039-1048. Link to DOI  

Khaliduzzaman

His groundbreaking work focuses on the transformation of the egg industry through the integration of advanced technologies. By utilizing non-destructive optical sensing, IoT, AI, big data, and cloud computing, he aims to develop smart systems for egg production, quality inspection, and grading. His research highlights the potential of these innovations to enhance automation, biosecurity, and animal welfare. A significant part of his work involves applying various CNN and transfer learning models for nondestructive chicken egg fertility detection, demonstrating their high accuracy and potential for industry-wide implementation

1.  Ahmed, M. W., Hossainy, S. J., Khaliduzzaman, A., Emmert, J. L., & Kamruzzaman, M. (2023). Non-destructive optical sensing technologies for advancing the egg industry toward industry 4.0: A review. Comprehensive Reviews in Food Science and Food Safety, 22, 4378–4403. Link to DOI