Researcher Story
Precision Agriculture and Intelligent Sensing
Dr. Md Abdul Momin’s research focuses on precision agriculture and intelligent sensing, integrating machine vision, proximal and remote sensing, UAV imagery, spectroscopy, machine learning, and artificial intelligence to address challenges across agricultural production and postharvest systems. His work involves developing advanced sensing and data-driven technologies to assess crop and soil conditions, characterize plant traits, evaluate agricultural product quality, and improve agricultural decision-making.
Current research includes high-throughput corn root phenotyping, UAV-based weed detection and mapping, sensing-based evaluation of soil properties, AI-assisted analysis of agricultural imagery, and smartphone-based systems for automated assessment of harvested agricultural products. Through this work, his research group seeks to translate advanced sensing and computational methods into practical, accessible tools that improve the efficiency, sustainability, and reliability of agricultural production and quality assessment.
How RCD Supports the Research
Research Computing & Data provides access to high-performance computing resources for processing large datasets and conducting computationally intensive analyses. These shared resources support work involving high-resolution UAV and smartphone imagery, agricultural sensing datasets, machine learning, and artificial intelligence at scales that would be difficult to manage efficiently on an individual workstation.
Research Team
Dr Abdul Momin
Agricultural Engineering and Technology, School of Agriculture
Smartphone-assisted soybean sample screening integrates a three-stage structured sieving platform, standardized imaging, artificial intelligence, and on-device analysis to support rapid assessment of harvested soybean samples.