---
language:
  - en
license: mit
tags:
  - image-classification
  - agriculture
  - rice
  - paddy
  - plant-disease
  - plant-pathology
  - crop-disease
  - dinov2
  - pytorch
  - precision-agriculture
  - smart-farming
  - india
  - bangladesh
  - southeast-asia
  - food-security
pipeline_tag: image-classification
library_name: pytorch
datasets:
  - paddy-doctor
  - brri-kaggle
  - mendeley-rice-disease
metrics:
  - accuracy
  - f1
  - precision
  - recall
model-index:
  - name: rice-disease-net
    results:
      - task:
          type: image-classification
          name: Image Classification
        dataset:
          name: Paddy Doctor in-domain test set (n=938, 15 classes)
          type: paddy-doctor-test
        metrics:
          - type: accuracy
            value: 0.9296
            name: Top-1 Accuracy
          - type: f1
            value: 0.9288
            name: Weighted F1
          - type: f1
            value: 0.9192
            name: Macro F1
          - type: precision
            value: 0.9326
            name: Weighted Precision
          - type: recall
            value: 0.9296
            name: Weighted Recall
          - type: f1
            value: 1.0000
            name: Best Class F1 (yellow_stem_borer)
          - type: f1
            value: 0.6923
            name: Worst Class F1 (bacterial_leaf_streak)
---

# rice-disease-net

DINOv2-large fine-tuned to classify 15 paddy disease and stress conditions from
field photographs. Trained on a deduplicated multi-source dataset of 9,376 images
spanning Tamil Nadu, Bangladesh, and laboratory conditions.

**Test accuracy: 92.96% · Weighted F1: 0.9288 · 15 classes · 304M parameters**

## Model Details

| Property | Value |
|---|---|
| Backbone | facebook/dinov2-large (304M params) |
| Head | Linear(1024→512, GELU) → Dropout(0.3) → Linear(512→15) |
| Training | Linear probe (5 epochs) → Full fine-tune (24 epochs, early stopping) |
| Optimizer | AdamW, head LR 1e-4, backbone LR 1e-5 |
| Schedule | Cosine decay with 5-epoch linear warmup |
| Hardware | 2× RTX 3090, BF16 mixed precision, accelerate |
| Input size | 224×224 RGB |
| Normalization | DINOv2 standard (mean=[0.485,0.456,0.406], std=[0.229,0.224,0.225]) |

## Input / Output

### Input

A single RGB paddy leaf or plant photograph. The model works best on:
- Clear images of individual leaves or panicles
- Field or laboratory lighting (not heavily shadowed)
- Paddy/rice plants (*Oryza sativa*) only — not validated on other crops

```python
from PIL import Image
from transformers import AutoImageProcessor

processor = AutoImageProcessor.from_pretrained("harinpurumandla/rice-disease-net")
image = Image.open("paddy_leaf.jpg").convert("RGB")

# Returns dict with "pixel_values" tensor of shape (1, 3, 224, 224)
inputs = processor(images=image, return_tensors="pt")
```

### Output

Raw logits tensor of shape `(batch_size, 15)`. Higher logit = higher confidence
for that class. Apply softmax for probabilities.

```python
import torch, json

config = json.load(open("config.json"))
idx_to_class = config["idx_to_class"]

with torch.no_grad():
    logits = model(inputs["pixel_values"])   # (1, 15)
    probs = torch.softmax(logits, dim=1)     # (1, 15)
    pred_idx = probs.argmax(dim=1).item()
    confidence = probs[0, pred_idx].item()

print(f"Predicted: {idx_to_class[str(pred_idx)]}  ({confidence:.1%} confidence)")
# Example: "Predicted: blast  (87.3% confidence)"
```

### Full inference example

```python
import json, torch
from PIL import Image
from transformers import AutoImageProcessor

# --- load once at startup ---
import sys
sys.path.insert(0, "path/to/rice-disease-net")
sys.path.insert(0, "path/to/rice-disease-net/train")
from train.model import PaddyClassifier

config = json.load(open("config.json"))
processor = AutoImageProcessor.from_pretrained("harinpurumandla/rice-disease-net")

model = PaddyClassifier(num_classes=15, hidden_dim=512, dropout=0.3)
from safetensors.torch import load_file
model.load_state_dict(load_file("model.safetensors"))
model.eval()

# --- per-image inference ---
def predict(image_path: str) -> dict:
    image = Image.open(image_path).convert("RGB")
    pixel_values = processor(images=image, return_tensors="pt")["pixel_values"]
    with torch.no_grad():
        probs = torch.softmax(model(pixel_values), dim=1)[0]
    idx = probs.argmax().item()
    return {
        "class": config["idx_to_class"][str(idx)],
        "confidence": round(probs[idx].item(), 4),
        "all_probs": {config["idx_to_class"][str(i)]: round(p.item(), 4)
                      for i, p in enumerate(probs)},
    }

result = predict("paddy_leaf.jpg")
print(result)
# {'class': 'blast', 'confidence': 0.8731, 'all_probs': {...}}
```

## Classes

| Index | Class | Disease / Condition | Causal Agent |
|---|---|---|---|
| 0 | bacterial_leaf_blight | Bacterial leaf blight | *Xanthomonas oryzae* pv. *oryzae* |
| 1 | bacterial_leaf_streak | Bacterial leaf streak | *Xanthomonas oryzae* pv. *oryzicola* |
| 2 | bacterial_panicle_blight | Bacterial panicle blight | *Burkholderia glumae* |
| 3 | blast | Blast (leaf + neck) | *Magnaporthe oryzae* |
| 4 | brown_spot | Brown spot | *Bipolaris oryzae* |
| 5 | downy_mildew | Downy mildew | *Sclerophthora macrospora* |
| 6 | hispa | Rice hispa | *Dicladispa armigera* |
| 7 | leaf_roller | Rice leaf roller | *Cnaphalocrocis medinalis* |
| 8 | leaf_scald | Leaf scald | *Monographella albescens* |
| 9 | sheath_blight | Sheath blight | *Rhizoctonia solani* |
| 10 | stem_rot | Stem rot | *Sclerotium oryzae* |
| 11 | tungro | Tungro | Rice tungro spherical + bacilliform virus |
| 12 | yellow_stem_borer | Yellow stem borer | *Scirpophaga incertulas* |
| 13 | normal | Healthy plant | — |
| 14 | potassium_deficiency | Potassium deficiency (abiotic) | Nutrient stress |

## Evaluation — In-Domain Test Set (n=938)

### Summary Metrics

| Metric | Value |
|---|---|
| Top-1 Accuracy | **92.96%** |
| Weighted F1 | **0.9288** |
| Weighted Precision | **0.9326** |
| Weighted Recall | **0.9296** |
| Macro F1 | **0.9192** |
| Macro Precision | **0.9361** |
| Macro Recall | **0.9138** |
| Best class F1 | 1.000 (yellow_stem_borer) |
| Worst class F1 | 0.692 (bacterial_leaf_streak, n=17) |

> Weighted metrics weight each class by its test-set support count.
> Macro metrics treat all 15 classes equally regardless of support.

### Per-Class Results

| Class | Precision | Recall | F1 | Support |
|---|---|---|---|---|
| bacterial_leaf_streak | 1.000 | 0.529 | 0.692 | 17 |
| downy_mildew | 0.727 | 0.828 | 0.774 | 29 |
| blast | 0.943 | 0.839 | 0.888 | 137 |
| tungro | 0.878 | 0.952 | 0.913 | 83 |
| brown_spot | 0.872 | 0.962 | 0.915 | 78 |
| hispa | 0.910 | 0.935 | 0.922 | 108 |
| bacterial_panicle_blight | 0.917 | 0.957 | 0.936 | 23 |
| bacterial_leaf_blight | 0.970 | 0.925 | 0.947 | 106 |
| normal | 0.924 | 0.965 | 0.944 | 113 |
| leaf_scald | 1.000 | 0.909 | 0.952 | 11 |
| potassium_deficiency | 0.952 | 0.976 | 0.964 | 41 |
| leaf_roller | 0.973 | 0.973 | 0.973 | 37 |
| sheath_blight | 1.000 | 0.958 | 0.979 | 24 |
| stem_rot | 0.976 | 1.000 | 0.988 | 41 |
| yellow_stem_borer | 1.000 | 1.000 | 1.000 | 90 |

![Confusion Matrix](confusion_matrix_v2.png)

## Limitations and Known Issues

**Weak classes:**
- `bacterial_leaf_streak` (F1=0.692, support=17): Only 17 test samples; the model is
  precise when confident but misses ~47% of actual cases. More field data needed.
- `downy_mildew` (F1=0.774): Visually similar to early blast and nutrient deficiency;
  low precision suggests over-prediction.
- `blast` (F1=0.888, recall=0.839): Leaf blast and neck blast were merged. Some blast
  images are classified as brown_spot.

**Geographic scope:** Training data covers Tamil Nadu (Paddy Doctor) and Bangladesh
(BRRI). Performance on Telangana, Andhra Pradesh, West Bengal, and Southeast Asian
varieties has not been validated. Expect degraded accuracy on field conditions
significantly different from the training distribution.

**Not a diagnostic tool:** Model output should be reviewed by an agronomist before
treatment decisions. Abiotic stress (potassium_deficiency) shares visual symptoms
with several diseases.

## Training Data

| Source | Classes Used | Images (kept after dedup) |
|---|---|---|
| Paddy Doctor (Kaggle 2022) | All 13 original classes | ~6,600 |
| BRRI Kaggle (valid set) | blast, bacterial_leaf_blight, brown_spot, hispa, leaf_scald, sheath_blight, tungro | ~1,400 |
| Mendeley Rice Disease V1 (2026) | bacterial_leaf_blight, leaf_roller, stem_rot, potassium_deficiency | ~1,400 |

Total after SHA-256 exact dedup + pHash near-dedup (Hamming ≤ 10): **9,376 images**.
Split: 80% train / 10% val / 10% test (stratified, frozen test set).

## Citations

**Backbone:**
```bibtex
@misc{oquab2023dinov2,
    title={DINOv2: Learning Robust Visual Features without Supervision},
    author={Maxime Oquab and others},
    year={2023},
    eprint={2304.07193},
    archivePrefix={arXiv}
}
```

**Paddy Doctor dataset:**
```bibtex
@misc{paddy-disease-classification,
    author={Paddy Doctor and Pandarasamy Arjunan (Samy) and Petchiammal},
    title={Paddy Doctor: Paddy Disease Classification},
    year={2022},
    howpublished={\url{https://kaggle.com/competitions/paddy-disease-classification}},
    note={Kaggle}
}
```

**Mendeley dataset:**
Raki, Nishat Sultana; Bakki, Md. Abdul; Sheikh, Foysal; Pria, Mosa. Nadia Sultana;
Parvin, Shahnaj; Matin, Mafiul Hasan (2026), "Rice Disease Image Dataset",
Mendeley Data, V1, doi: 10.17632/jw7hp6r5gj.1

**BRRI dataset:** Attributed to Bangladesh Rice Research Institute (https://brri.gov.bd/).
No formal citation provided by dataset authors.