Doctoral Researcher at Transilvania University of Brasov, Romania
Dr. Gabriel Osei Forkuo is a Ghanaian forestry professional and researcher currently pursuing a Ph.D. in Forest Operations Engineering at Transilvania University of Brașov, Romania. With a strong background in forest management, education, and research, he combines over two decades of practical fieldwork and academic experience. His work primarily focuses on integrating smart technologies like machine learning, computer vision, and mobile LiDAR for postural and environmental assessment in forest operations.
🔹Professional Profile:
Scopus Profile
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🎓Education Background
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Ph.D. in Forest Operations Engineering (2022–Present)
Transilvania University of Brașov, Romania
Focus: Machine Learning & Computer Vision Applications in Ergonomics Assessment
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M.Sc. in Multiple Purpose Forestry (2020–2022)
Transilvania University of Brașov, Romania
Graduated with Excellent Rating (Cumulative Average: 9.76/10)
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B.Sc. in Natural Resources Management (First Class Honours) (1994–1999)
Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, Ghana
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GCE A-Level in Science (1991–1993)
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GCE O-Level in Science (1986–1991)
💼 Professional Development
Dr. Forkuo began his career as a Teaching Assistant at KNUST (1999–2001), supporting research, lab work, and student engagement. He then taught Science and Mathematics at Maria Montessori School in Kumasi (2002–2011). For nearly a decade, he worked as a Forest Range Manager/Supervisor with the Forestry Commission Ghana, leading reforestation programs, nursery planning, and field team supervision. His current doctoral work integrates forest science with emerging technologies for operational enhancement and ergonomic safety in forestry practices.
🔬Research Focus
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Smart solutions for ergonomics and postural assessment in forestry
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Mobile LiDAR technology for soil disturbance mapping
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Forest biometrics and machine learning-based monitoring
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Sustainable forest operations and forest inventory
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GIS, remote sensing, and data visualization in forest sciences
📈Author Metrics:
🏆Awards and Honors:
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First Place, “My Diploma Project” Competition (2022 & 2023 editions)
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Premiul AFCO 2022 – Special Prize for Foreign Students
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Transilvania Academica Scholarship (2020–2022)
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UNITBV Ph.D. Scholarship (2022–2025)
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Government of Ghana Performance Scholarship (1986–1993)
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Poku Transport Ghana Scholarship for Best Forestry Student at KNUST
📝Publication Top Notes
1. Human and Machine Reliability in Postural Assessment of Forest Operations by OWAS Method: Level of Agreement and Time Resources
Authors: GO Forkuo, MV Marcu, N Kaakkurivaara, T Kaakkurivaara, SA Borz
Journal: Forests, 2025
Summary:
This study investigates the reliability of human observers versus automated systems in applying the OWAS (Ovako Working Posture Analysis System) method for postural assessment during forest operations. It evaluates inter-rater agreement levels and the time efficiency of manual versus machine-based methods. Findings highlight potential for automation to reduce labor-intensive assessment work, ensuring consistent evaluations and saving resources.
2. Postural Classification by Image Embedding and Transfer Learning: An Example of Using the OWAS Method in Motor-Manual Work to Automate the Process and Save Resources
Authors: GO Forkuo, SA Borz, T Kaakkurivaara, N Kaakkurivaara
Journal: Forests, Vol. 16(3), Article 492, 2025
Summary:
This paper presents a novel framework that applies image embedding and transfer learning to automate the OWAS-based postural classification in motor-manual forestry work. By leveraging convolutional neural networks (CNNs), the authors demonstrate the effectiveness of computer vision in reducing the need for manual assessments, thus improving efficiency and reproducibility in ergonomic studies.
3. Timber Extraction by Farm Tractors in Low-Removal-Intensity Continuous Cover Forestry: A Simulation of Operational Performance and Fuel Consumption
Authors: GO Forkuo, MV Marcu, E Iordache, SA Borz
Journal: Forests, Vol. 15(8), 2024
Summary:
This study models and simulates the use of farm tractors in low-intensity, continuous cover forestry for timber extraction. The authors analyze operational performance metrics and fuel consumption, emphasizing environmentally friendly practices. The results help guide best practices in small-scale, sustainable timber harvesting.
4. Soil Compaction Induced by Three Timber Extraction Options: A Controlled Experiment on Penetration Resistance on Silty-Loamy Soils
Authors: MF Presecan, GO Forkuo, SA Borz
Journal: Applied Sciences, Vol. 14(12), Article 5117, 2024
Summary:
This controlled experiment assesses soil compaction caused by three different timber extraction methods, using penetration resistance as the key indicator on silty-loamy soils. The findings provide insight into the ecological impact of logging practices and suggest ways to mitigate soil degradation during extraction processes.
5. Development and Evaluation of Automated Postural Classification Models in Forest Operations Using Deep Learning-Based Computer Vision
Authors: GO Forkuo, SA Borz
Platform: SSRN (Preprint) — DOI: 10.2139/ssrn.4875562
Year: 2024
Summary:
This preprint introduces and evaluates deep learning models for automated postural classification in forestry operations. The study explores the use of CNN architectures to detect and classify body postures, showcasing advancements in AI for ergonomic monitoring. The approach promises enhanced efficiency and objectivity in occupational health evaluations.
Conclusion:
Dr. Gabriel Osei Forkuo exemplifies what it means to be a forward-thinking, impact-driven, and technologically skilled researcher in the field of forest operations. His work merges ecological stewardship with smart technologies, creating efficient, safe, and sustainable forestry practices. With a rich academic background, global collaborations, and a focus on AI-driven ergonomics, he is a deserving candidate for the Best Researcher Award.
✅ Final Recommendation: Strongly Recommended for the Best Researcher Award in Forest Operations