---
license: mit
library_name: pytorch
pipeline_tag: image-classification
tags:
- computer-vision
- agriculture
- plant-disease
- efficientnetv2
- onnx
- onnxruntime
- pytorch
- grad-cam
- precision-agriculture
datasets:
- plantvillage
metrics:
- accuracy
- f1
model-index:
- name: crop-disease-classifier-efficientnetv2
  results:
  - task:
      type: image-classification
      name: Image Classification
    dataset:
      name: PlantVillage
      type: plantvillage
    metrics:
    - type: accuracy
      value: 0.9989
      name: Validation Accuracy
---

# 🌱 Crop Disease Classifier (EfficientNetV2-S — 38 Classes)

A production-ready **EfficientNetV2-S** model fine-tuned on the **PlantVillage** benchmark (54,306 images across 38 disease & healthy categories spanning 14 crop species). 

Achieves **99.89% validation accuracy** with sub-4ms GPU inference latency and includes **Grad-CAM explainability** and **ONNX** edge deployment weights.

---

## 🎯 Benchmark Results

| Model | Val Accuracy | Macro F1 | Latency (RTX 5080) | Parameters | Format |
|:---|:---:|:---:|:---:|:---:|:---:|
| **EfficientNetV2-S (Ours)** | **99.89%** | **~0.999** | **~3.9 ms** | **21.5M** | PyTorch + ONNX |
| ResNet50 (Baseline) | 97.10% | 0.969 | ~3.8 ms | 25.6M | PyTorch |

*Trained on NVIDIA GeForce RTX 5080 (CUDA 13.2, SM_120) with cosine learning rate schedule, AdamW optimizer, label smoothing 0.1, batch size 64.*

---

## 🌿 Crops & Disease Coverage (38 Classes)

The model detects specific pathologies as well as healthy leaves across **14 agricultural crop species**:

- **Apple:** Apple Scab, Black Rot, Cedar Apple Rust, Healthy
- **Blueberry:** Healthy
- **Cherry:** Powdery Mildew, Healthy
- **Corn (Maize):** Cercospora / Gray Leaf Spot, Common Rust, Northern Leaf Blight, Healthy
- **Grape:** Black Rot, Esca (Black Measles), Leaf Blight (Isariopsis), Healthy
- **Orange:** Huanglongbing (Citrus Greening)
- **Peach:** Bacterial Spot, Healthy
- **Bell Pepper:** Bacterial Spot, Healthy
- **Potato:** Early Blight, Late Blight, Healthy
- **Raspberry:** Healthy
- **Soybean:** Healthy
- **Squash:** Powdery Mildew
- **Strawberry:** Leaf Scorch, Healthy
- **Tomato:** Bacterial Spot, Early Blight, Late Blight, Leaf Mold, Septoria Leaf Spot, Two-Spotted Spider Mite, Target Spot, Yellow Leaf Curl Virus, Mosaic Virus, Healthy

---

## 🔍 Explainable AI: Grad-CAM

In agriculture, black-box predictions are not enough — agronomists and farmers need to know *where* the model detected symptoms. The model's activations align precisely with pathological lesions, rust pustules, and necrosis spots rather than background artifacts.

---

## 🚀 Quick Start (Inference)

### 1. Using ONNX Runtime (No PyTorch required, fast & lightweight)

```bash
pip install onnxruntime pillow numpy huggingface_hub
```

```python
import json
import numpy as np
from PIL import Image
import onnxruntime as ort
from huggingface_hub import hf_hub_download

# Download model & classes
onnx_model = hf_hub_download(repo_id="BiernyVR/crop-disease-classifier", filename="efficientnet_v2_s_best.onnx")
onnx_data = hf_hub_download(repo_id="BiernyVR/crop-disease-classifier", filename="efficientnet_v2_s_best.onnx.data")
classes_file = hf_hub_download(repo_id="BiernyVR/crop-disease-classifier", filename="classes.json")

with open(classes_file, "r") as f:
    classes = json.load(f)["classes"]

# Preprocess image
img = Image.open("leaf.jpg").convert("RGB").resize((224, 224), Image.Resampling.BILINEAR)
arr = (np.array(img, dtype=np.float32) / 255.0 - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
tensor = np.expand_dims(np.transpose(arr, (2, 0, 1)), axis=0).astype(np.float32)

# Run inference
session = ort.InferenceSession(onnx_model, providers=["CPUExecutionProvider"])
logits = session.run(None, {"input": tensor})[0][0]
probs = np.exp(logits - np.max(logits))
probs /= probs.sum()

top_class = classes[np.argmax(probs)]
print(f"Prediction: {top_class} ({np.max(probs)*100:.2f}%)")
```

### 2. Standalone CLI

```bash
python infer.py --image sample_leaf.jpg --topk 3
```

---

## 📦 Files in this Repository

- `efficientnet_v2_s_best.pth`: Full PyTorch model checkpoint.
- `efficientnet_v2_s_best.onnx` + `.onnx.data`: ONNX exported weights for TensorRT / mobile / ONNX Runtime.
- `classes.json`: Complete mapping of 38 disease and healthy classes.
- `sample_leaf.jpg`: Test apple scab sample leaf image.
- `sample_gradcam.png`: Grad-CAM visualization output.
- `infer.py`: Self-contained evaluation script.
- `confusion_matrix.png`, `training_curves.png`, `per_class_accuracy.png`: Evaluation and training metrics plots.