Africa's first AI-powered Tilapia detection model — PRISTINE X
# PRISTINE X — Tilapia Detection Model v1
> Africa's first AI-powered underwater Nile Tilapia detection model,
> built for sub-Saharan African farm conditions.
## What This Is
A YOLOv8 model fine-tuned on Nile Tilapia (*Oreochromis niloticus*)
underwater footage. Built as the first proof of concept for PRISTINE X
— a precision aquaculture technology company developing AI monitoring
systems for intensive RAS fish farms in sub-Saharan Africa.
## Model Performance (v1)
| Metric | Score |
|--------|-------|
| mAP50 | 82.1% |
| Precision | 81.8% |
| Recall | 73.4% |
| Training images | 44 |
| Training time | <1 min (T4 GPU) |
| Model size | 6.2MB |
## Generalisation Testing
The model was evaluated on two independent video sources it had never
seen during training. Results confirm the model generalises beyond its
training distribution across different environments and water clarity
conditions.
| Test | Source | Frames | Detection Rate | Avg Confidence |
|------|--------|--------|----------------|----------------|
| Training | youtu.be/oN96Xs-JTnk | 44 | 100% | 0.820 |
| Test Video 1 | youtu.be/Bqsy7enwtsI | 107 | 100% | 0.693 |
| Test Video 2 | youtu.be/1Wy62mLNqWM | 147 | 100% | 0.585 |
**Note:** Test Video 2 was filmed in murky RAS tank water — conditions
representative of Nigerian intensive fish farm environments. The
confidence drop from 0.820 → 0.693 → 0.585 across increasing water
turbidity is expected and scientifically consistent. Detection rate
remained 100% across all tests.
## The Story Behind This
Generic object detection models (COCO-trained YOLOv8) were pointed
at Nigerian Tilapia farm footage. Across 7,831 frames — zero fish
detected. The model saw donuts, hot dogs, and birds instead.
This model was built to fix that. Trained on 44 annotated frames
of real Tilapia underwater footage, it correctly detects and labels
every fish in real time — proof that the technology works and that
the only missing ingredient is a large, properly collected Afri …