Build a Crop-Disease Classifier for a Smallholder Agritech Startup
Overview
What this challenge is about.
Train a CNN on 22,000 cassava images to 0.85 macro-F1 and serve it via FastAPI to earn your verifiable certificate.
The scenario
The startup (around 18 staff, around 90,000 farmer users across Kenya and Uganda) lives on grant funding and revenue-per-call must stay below KES 5 to remain accessible.
The Brief
What you'll do, and what you'll demonstrate.
Beat 0.85 macro-F1 on cassava disease classification with a CNN that costs under EUR 200 per month at 50,000 inference calls.
Earning criteria — what you'll demonstrate
- Fine-tune ImageNet-pretrained CNNs on domain-specific classification
- Handle moderately imbalanced multi-class data
- Ship a model behind a real HTTP inference endpoint
- Model inference cost at realistic traffic levels
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Deep Learning for Computer Vision
Master · Computer Vision
Strong alignment
This challenge maps to Deep Learning for Computer Vision at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Image Classification
Apply image classification to solve real industry problems and demonstrate production-level capability.
- Cnn Architectures
Apply cnn architectures to solve real industry problems and demonstrate production-level capability.
- Transfer Learning
Apply transfer learning to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Fastapi
Apply fastapi to solve real industry problems and demonstrate production-level capability.
- Cost Modeling
Apply cost modeling to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Computer Vision Engineer
Owning a CNN classifier from data through deployed endpoint is exactly the first quarter of work for a junior CV engineer at any product-AI company.
This challenge sharpens
- image-classification
- cnn-architectures
- transfer-learning
Machine Learning Engineer
Shipping a FastAPI endpoint with cost modeling is core MLE work on small product teams.
This challenge sharpens
- pytorch
- fastapi
- cost-modeling
MLOps Engineer
Quantization and inference-cost modeling at production traffic is bread and butter for MLOps engineers on cost-conscious product orgs.
This challenge sharpens
- fastapi
- cost-modeling
- pytorch