Dr. Emmanuel Ekene Okere | Engineering | Best Researcher Award

Cape Peninsula University of Technology, South Africa

Emmanuel Ekene Okere is a motivated and detail-oriented postdoctoral researcher specializing in machine learning applications across a diverse range of technological domains. With a firm grounding in electronic engineering and a keen interest in postharvest technology, hyperspectral imaging, wireless networks, and time series analysis, he has emerged as a dynamic scholar with a growing publication record. Emmanuel has contributed significantly to multidisciplinary research by integrating artificial intelligence and non-destructive testing techniques to improve food quality assessment and prediction systems. He is driven by a vision to harness emerging technologies for solving practical, real-world challenges in agriculture and smart systems.

Profile

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Education

Emmanuel earned his PhD in Electronic Engineering from Stellenbosch University between 2020 and 2023, where his research focused on hyperspectral imaging and machine learning algorithms for detecting and classifying bruise severity in fruits. Prior to his doctoral studies, he obtained a Master of Engineering (cum laude) from the same institution in 2020, where he explored non-destructive quality evaluation of fruits using near-infrared spectroscopy. His foundational training came from Abubakar Tafawa Balewa University, Nigeria, where he completed a Bachelor’s degree in Electrical/Electronic Engineering with Second Class Upper Honours in 2015. His academic path reflects a strong commitment to advanced study and innovation in data-driven agricultural and sensor-based research.

Experience

Emmanuel has gathered rich academic and professional experience, notably through multiple postdoctoral fellowships at Stellenbosch University and Cape Peninsula University of Technology. In these roles, he actively contributed to manuscript preparation, revisions, and research supervision. From 2018 to 2023, he served as a teaching assistant and laboratory facilitator at Stellenbosch University, helping to instruct undergraduate students in electronic engineering modules and overseeing hands-on research activities in postharvest labs. Earlier in his career, he gained practical experience in Nigeria through industrial placements with Holborn Nigeria Ltd and the Power Holding Company of Nigeria, where he handled plant maintenance and electrical fault resolution, respectively.

Research Interest

Emmanuel’s research interests lie at the intersection of machine learning, postharvest technology, blockchain applications, wireless communication systems, and hyperspectral imaging. He is particularly focused on developing non-invasive, sensor-based solutions for quality assessment of agricultural produce. His work also delves into the integration of AI with blockchain for secure data sharing in the Internet of Vehicles and performance optimization in sixth-generation networks. With a deep interest in predictive modeling, he explores time series forecasting in agricultural markets, aiming to enhance decision-making for food producers and vendors.

Awards

While the CV does not specifically list formal awards, Emmanuel’s academic achievements such as graduating cum laude for his Master’s degree, securing multiple postdoctoral fellowships, and being invited as a speaker at international conferences demonstrate recognition of his scholarly contributions. His participation in esteemed symposiums and consistent publication in reputable journals also underscore his excellence and dedication in research and academia.

Publications

Emmanuel has authored over thirteen research articles, conference papers, and book chapters. Selected key publications from the past five years include:

1. “A Deep Learning-Based Prediction and Forecasting of Tomato Prices for the Cape Town Fresh Produce Market” (2025, Forecasting, cited by 4 articles).

2. “Sixth Generation Enabling Technologies and Machine Learning Intersection: A Performance Optimization Perspective” (2025, Future Internet, cited by 2 articles).

3. “Advances in Blockchain-Based Internet of Vehicles Application: Prospect for Machine Learning Integration” (2024, Future Internet, cited by 3 articles).

4. “Early Bruises Detection on Pomegranate Using Hyperspectral Imaging Coupled with Artificial Neural Network” (2023, Technology in Horticulture, cited by 5 articles).

5. “Vis-NIR and SWIR Hyperspectral Imaging Method to Detect Bruises in Pomegranate Fruit” (2023, Frontiers in Plant Science, cited by 6 articles).

6. “Non-destructive Evaluation of the Quality Characteristics of Pomegranate Kernel Oil Using Infrared Spectroscopy” (2022, Frontiers in Plant Science, cited by 8 articles).

7. “Non-invasive Methods for Predicting the Quality of Processed Horticultural Food Products: A Review” (2021, Foods, cited by 12 articles).

Conclusion

Emmanuel Ekene Okere exemplifies a modern researcher who merges technical expertise with interdisciplinary application. With a robust background in machine learning and spectroscopy-based evaluation, he is contributing to a more sustainable and data-driven agricultural sector. His scholarly output, innovative research interests, and commitment to academic growth position him as a valuable contributor to the scientific and engineering communities. As he continues his research journey, his work holds promise for further advancements in smart agriculture, wireless networks, and digital systems optimization.