20 MSc · 2 PGD · 5 PhD
The completed PhD theses were co-supervised, alongside Master's and PGD research across several crop systems.
Supervision & students
Research supervision should produce independent judgement.
My aim is not only to help a student complete a thesis. It is to help the researcher learn how to frame a question, defend a design, interrogate evidence and communicate a conclusion without hiding behind software output.
Supervision record
Supervision across crop improvement, quantitative genetics and emerging research methods.
Completed supervision includes 20 MSc dissertations, 2 PGD projects and 5 PhD theses as co-supervisor. Current PhD supervision comprises 10 candidates: 5 as main supervisor and 5 as co-supervisor.
10 ongoing candidates
5 as main supervisor and 5 as co-supervisor.
Machine learning and molecular approaches
Recent supervision includes machine-learning-assisted early sex identification in fluted pumpkin and molecular-marker work in crop stress studies.
Areas of interest
Questions I am well placed to supervise.
Crop improvement
Plant breeding & quantitative genetics
Genetic variability, selection, trait relationships, heritability and breeding populations.
Experimentation
Design, analysis & interpretation
Field experimental design, ANOVA, assumptions, multi-environment trials and defensible interpretation.
Emerging methods
Agricultural analytics & responsible AI
Research questions where data science, software or machine learning are genuinely useful to agricultural inference.
How I approach supervision
A good research project begins before the data arrive. I therefore place emphasis on the research question, experimental unit, replication, randomisation, data structure and analysis plan before software choice. Students are expected to understand and defend their decisions, not simply reproduce code or tables.
Start with research fit, then follow the formal university route.
Formal postgraduate admission is through the Federal University of Technology, Akure. Before writing to me, review the research areas on this page and send a concise research-interest statement, your academic background or CV, and the specific problem or question you hope to investigate.
Funding: funding should not be assumed. Treat an opportunity as funded only when a specific funded position or call is explicitly advertised.
Academic contact →Make the proposed collaboration concrete.
For collaboration enquiries, briefly state the research problem, why the collaboration fits my programme, the role you are proposing, and the expected output or decision the work should support.
Review research programmes →Specific enquiries are easier to answer well.
A focused one-page concept, a CV and a clear question are more useful than a generic request for supervision or collaboration.
Contact details →