About
Final-year PhD candidate specialising in the application of Artificial Intelligence and Computer Vision to food quality assessment and process optimisation.
My doctoral research develops real-time machine vision systems, integrating deep learning models such as YOLOv8 and YOLO26 for automated rice milling quality control, morphological detection, and adaptive closed-loop process optimisation. With a background spanning electrical engineering, field services, and AI research, I am committed to leveraging emerging technologies to address critical challenges in African food systems, particularly in post-harvest processing, food safety, and value chain efficiency.
Technical Innovation in Rice Milling
We are dedicated to advancing Artificial Intelligence and Computer Vision technologies that improve food quality, optimize agricultural processes, and address real-world challenges through innovative, impactful, and sustainable research.
Our mission is to develop intelligent, AI-driven technologies that enhance food quality, reduce post-harvest losses, strengthen agricultural value chains, and contribute to food security and sustainable development, particularly across Africa. Through research, teaching, and innovation, I aim to bridge the gap between cutting-edge technology and practical solutions that improve lives and industries.
AI-Driven Innovation
One of the core innovations across my research is the development of intelligent computer vision systems capable of performing real-time rice quality inspection during the milling process. Traditional quality assessment relies heavily on manual sampling and post-processing inspection, which is slow, labour-intensive, and unable to prevent quality losses during production.
A second major innovation is the integration of artificial intelligence with adaptive process control to create self-optimising manufacturing systems. Instead of using AI solely for inspection, my research allows AI predictions to directly influence machine behaviour through an intelligent feedback loop.
Traditional Method Analysis
We analyze the historical techniques of rice milling to identify sustainable practices that can be integrated with modern industrial efficiency.
Quality Control Metrics
Our research establishes standardized quality control metrics for rice milling, ensuring consistency and high standards for global market demand.
Awards & Recognition
01
Grant Jamieson Engineering Scholarship.
Won the Grant Jamieson Engineering Scholarship from PPMA BEST, the charity of the UK Process and Packaging Machinery Association.
Academic Engagement
Presenting technical research and sustainable innovation at global conferences and industry forums.
2025
26th International Symposium on Measurement and Control in Robotics (ISMCR 2025)
April 2025
The European Federation of Food Science and Technology (EFFoST)
March 2026
European Federation of Food Science and Technology (EFFoST) Annual Conference 2025.
2023
Quality Assessment and Process Optimisation of Rice Milling Using Artificial Intelligence Technologies. MERI Seminar Series.
2023
Quality Assessment and Process Optimisation of Rice Milling Using Artificial Intelligence Technologies. AFIC Seminar Series.
Sheffield Hallam University.
2024
European Federation of Food Science and Technology (EFFoST) Annual Meeting.
2024
Quality Assessment and Process Optimisation of Rice Milling Using YOLOv8 for Instance Segmentation. Winter Poster Event.
2023
Quality Assessment and Process Optimisation of Rice Milling Using Artificial Intelligence Technologies. Winter Poster Event.
Sheffield Hallam University.
Sambreville, Belgium.
Brugge, Belgium
Sheffield Hallam University.
Sheffield Hallam University.
Valencia, Spain
Porto, Portugal.