chashibhai-rice-disease-cls

ChashiBhAI Rice Disease Classifier

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

Files

Classes (8)

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

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)

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
• Brassica
• Corn
• Suite index: chashibhai-disease-classifiers

Credit (not this model)

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

Citation

@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.