I am through with my PhD coursework. In just the two-month window before commencing my dissertation, I completed 14 real-world AI and data science projects through WorldQuant University’s labs. Every late night, every bug fixed, every model trained—it has all led me here: the official start of my PhD dissertation journey.
And I’m writing this blog post to do two things:
- Keep myself disciplined and on track
- Hopefully, inspire someone else walking a similar path
My Dissertation: AI + Agriculture + Process Optimization
Here’s what I’m working on:
Title: An AI-Driven Lean Six Sigma Framework for Agricultural Defect Detection: A Computer Vision Approach to Crop Disease Diagnosis and Process Optimization.
Sounds technical? Let me simplify it.
I’m building an AI system that can automatically detect crop diseases using images of leaves. Then, I’ll integrate that into a structured improvement framework called Lean Six Sigma—a system already used globally to eliminate waste and improve quality.
Put simply:
I’m using AI to help farmers catch crop diseases early, improve food production, and reduce waste.
Why This Matters to Nigeria?
I’m Nigerian. And I care deeply about our future.
In Nigeria, agriculture contributes around 25% of the GDP and employs millions. Yet, millions of tons of crops are lost yearly, often due to undetected diseases that spread quickly.
Most farmers lack access to diagnostic tools or agronomic support. Some can’t even recognize crop diseases until it’s too late. My dissertation aims to change that.
Imagine this:
- A farmer takes a photo of a diseased leaf.
- An AI model detects the type of disease instantly.
- The system advises on treatment, helps monitor crop health, and feeds insights back to improve future farming strategies.
That’s the future I’m working toward—a smarter, more productive agricultural system powered by AI.
How My 14 Projects Shaped This Vision
These weren’t just coursework—they were stepping stones.
Here’s a quick look at what I built (and how it all ties into my research):
Eight Data Science Projects
From housing price prediction in Mexico to time-series forecasting of air quality in Nairobi, these projects sharpened my skills in:
- Data cleaning, analysis, and visualization
- Model building and optimization
- Communicating results in simple, actionable ways
All of which will help me:
- Process agricultural data more effectively
- Choose the right models
- Build systems real people can use
Six Computer Vision Projects
These were a game-changer. I built:
- A crop disease classifier model (yes, this became the foundation of my dissertation!)
- A wildlife tracker, traffic detector, face recognition system, GAN for medical images, and even a meme generator
Each taught me how to:
- Train deep learning models
- Handle real-world image data
- Build user-friendly apps with tools like Streamlit and Flask
What’s Next?
This is entry #1 in my dissertation blog. Here’s what you can expect in the posts ahead:
- Behind-the-scenes look at building my prototype
- Challenges and solutions (e.g., how do you label leaf diseases accurately?)
- Reflections on staying motivated
- Insights for others doing AI research in Africa
Why Am I Sharing This?
Because I know what it feels like to doubt yourself. To wonder if your ideas matter.
And because if even one person reads this and decides to start their journey—whether in AI, agriculture, or research—I’ll know this blog is doing its job.
This journey isn’t just about a PhD. It’s about building something meaningful that can help people, right here in Nigeria.
If you’re working on something similar, or you just want to follow along—let’s connect.
With Purpose,
Dipo Tepede
PhD Candidate | AI Researcher | Process Optimization Specialist | Project Management Coach
