---
license: mit
library_name: ultralytics
pipeline_tag: image-classification
tags:
  - agriculture
  - bangladesh
  - crop-disease
  - tflite
  - yolo26
  - on-device
  - rice
  - chashibhai
language:
  - bn
  - en
base_model_relation: quantized
---

# ChashiBhAI Rice Disease Classifier

| | |
|---|---|
| **Author** | [Shaq2](https://huggingface.co/Shaq2) (Shakil Ahmed) |
| **Crop** | rice (ধান) |
| **Task** | Image classification (leaf disease) |
| **Architecture** | YOLO26-cls → TFLite FP16 |
| **Input** | `[1, 640, 640, 3]` NHWC RGB `/255.0` |
| **Output** | `[1, 8]` softmax probabilities (`nms: false`) |
| **Status** | `production-demo` |
| **App** | ChashiBhAI (Expo / React Native, on-device diagnosis) |
| **Code** | [GitHub](https://github.com/MRSHAKILS/AI-Powered-Smart-Agriculture-Advisory-Platform-for-Bangladesh) |
| **Collection** | [ChashiBhAI on-device classifiers](https://huggingface.co/collections/Shaq2/chashibhai-on-device-disease-classifiers) |

Disease ID in ChashiBhAI **always** runs on-device. Gemini / KrishokChat generate advisory **text only** and never see the photo.

## Files

| File | Role |
|---|---|
| `model.tflite` | On-device graph used by the Android app (~3.12 MB) |
| `labels.json` | Canonical class names **and** preprocess contract |
| `best.pt` | Ultralytics source weights (export parent) |

## Classes (8)

| Label | English | Bangla |
|---|---|---|
| `Rice__Bacterial_Leaf_Blight` | Bacterial Leaf Blight | ব্যাকটেরিয়াজনিত পাতা পোড়া |
| `Rice__Brown_Spot` | Brown Spot | বাদামী দাগ |
| `Rice__Healthy_Leaf` | Healthy Leaf | সুস্থ পাতা |
| `Rice__Leaf_Blast` | Leaf Blast | ব্লাস্ট |
| `Rice__Leaf_Scald` | Leaf Scald | পাতা পোড়া |
| `Rice__Narrow_Brown_Leaf_Spot` | Narrow Brown Leaf Spot | সরু বাদামী দাগ |
| `Rice__Rice_Hispa` | Rice Hispa | হিসপা |
| `Rice__Sheath_Blight` | Sheath Blight | শেথ ব্লাইট |

## Preprocessing (variant C) — required

Do **not** letterbox. Letterbox disagreed with the `.pt` on non-square photos.

1. Resize the **shortest side** to `imgsz` = **640**, keep aspect ratio.
2. **Centre-crop** to `640×640`.
3. RGB, NHWC, `float32 / 255.0`.

`labels.json` is the source of truth (`preprocess: centercrop`).

## Measured export checks

| Check | Result |
|---|---|
| Preprocess verified vs `.pt` | yes (variant C, 100% top-1 vs `.pt` on the rice hold-out used for export) |
| FP16 vs FP32 top-1 agreement | 1.0 |
| FP16 vs FP32 max softmax diff | 0.000372171 |
| Independent machine test | pass (August 2026) |
| Bundled in APK | yes (rice) |

## Validation

Independent test on a **separate machine** (August 2026) confirmed this TFLite export loads and classifies correctly with the variant-C contract in `labels.json`. Rice, brassica, and corn all passed the same check.

## Intended use

- On-device diagnosis in ChashiBhAI for Bangladeshi farmers (Bangla-first UI).
- Research reproduction of the mobile export.

**Out of scope:** detection / bounding boxes, crop auto-routing, chemical dosage (handled by a separate advisory stack with a refuse gate).

## Limitations

Demo-grade on-device classifier. Not a plant-pathologist substitute. Retake if confidence is low or the leaf is not centred.

## Load (Python)

```python
import json
from pathlib import Path
import numpy as np
from PIL import Image
import tensorflow as tf

def preprocess_centercrop(path: str, imgsz: int = 640) -> np.ndarray:
    im = Image.open(path).convert("RGB")
    w, h = im.size
    scale = imgsz / min(w, h)
    nw, nh = int(round(w * scale)), int(round(h * scale))
    im = im.resize((nw, nh), Image.BILINEAR)
    left, top = (nw - imgsz) // 2, (nh - imgsz) // 2
    im = im.crop((left, top, left + imgsz, top + imgsz))
    return (np.asarray(im, dtype=np.float32) / 255.0)[None, ...]

labels = json.loads(Path("labels.json").read_text(encoding="utf-8"))
it = tf.lite.Interpreter(model_path="model.tflite")
it.allocate_tensors()
inp, out = it.get_input_details()[0], it.get_output_details()[0]
it.set_tensor(inp["index"], preprocess_centercrop("leaf.jpg", labels["imgsz"]))
it.invoke()
p = it.get_tensor(out["index"])[0]
i = int(p.argmax())
print(labels["names"][i], float(p[i]))
```

## Related models (same author)

- [Rice](https://huggingface.co/Shaq2/chashibhai-rice-disease-cls)
- [Brassica](https://huggingface.co/Shaq2/chashibhai-brassica-disease-cls)
- [Corn](https://huggingface.co/Shaq2/chashibhai-corn-disease-cls)
- Suite index: [chashibhai-disease-classifiers](https://huggingface.co/Shaq2/chashibhai-disease-classifiers)

## Credit (not this model)

KrishokChat Bengali advisory LLM / RAG is **not** this classifier. See [RaiyanKhaan/KrishokChat-Advisory-System](https://huggingface.co/RaiyanKhaan/KrishokChat-Advisory-System) and arXiv:2606.29243 (Reza & Shahid).

## Citation

```bibtex
@software{ahmed2026chashibhai_rice_cls,
  author = {Ahmed, Shakil},
  title  = {ChashiBhAI Rice Disease Classifier},
  year   = {2026},
  url    = {https://huggingface.co/Shaq2/chashibhai-rice-disease-cls}
}
```

## License

MIT (see `LICENSE`). Ultralytics remains under its own license. This pack redistributes **weights**, not training images.