---
license: mit
tags:
  - image-classification
  - aquaculture
  - shrimp-disease
  - tabular-classification
library_name: pytorch
---

# AquaSense — Trained Artifacts

Model weights for **AquaSense**, a multimodal early-warning system for shrimp
(udang) disease detection and pond risk scoring. Built for RISTEK Datathon 2026
by Tim 3 Plenger.

Application code: https://github.com/baihaqidawanis/dataton-semifinal

## Contents

| File | Module | Description |
|---|---|---|
| `manifest.json` | all | Single source of truth for metrics, thresholds, and the M2 form schema. The backend serves this via `GET /meta`. |
| `m1_vision.pt` | M1 | Image classifier over `{healthy, black_gill, wssv}`. Contains `state_dict`, `classes`, `backbone`, calibration temperature, and the cost-tuned decision threshold. |
| `m2_risk.pkl` | M2 | Tabular outbreak-risk model. Bundles two variants: `model`/`fitur` (39 survey features) and `model_web`/`fitur_web` (23 features the web form collects). **The application must load the `_web` variant.** |
| `m3_meta.pkl` | M3 | Fusion metadata. |

## Intended use

Decision support for shrimp farmers — **not** clinical confirmation. Outputs
should be combined with direct inspection and local farm SOPs.

## Training data

- **M1**: ShrimpDiseaseBD + BD Fish & Shrimp Disease (public). Split *per shrimp
  individual*, not per photo, to prevent leakage across train/val/test.
- **M2**: WSD Affected Shrimp Farmers (233 ponds, Bangladesh).

## Known limitations

- Black Gill recall is 54.5% — the model misses roughly half of Black Gill cases.
- M2 ROC-AUC is moderate (0.70 on the 23-feature web subset); treat it as a
  screening signal, not a diagnosis.
- Multimodal fusion is a transparent noisy-OR rule, not a trained meta-learner:
  no public dataset pairs photos with pond surveys for the same pond, so a
  learned fusion could not be validated.
- All training data is from Bangladesh; generalisation to Indonesian ponds is
  untested.

Decision thresholds are tuned by **economic cost** (false negative ≈ 223× the
cost of a false positive), not accuracy — the models deliberately over-flag.