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---
license: apache-2.0
tags:
- crop-disease-detection
- computer-vision
- pytorch
- efficientnet
- agriculture
- plant-diseases
datasets:
- plant-village
- dhan-shomadhan
- custom
language: en
---
# Crop Disease Detection Model (EfficientNet-B3)
## Overview
This model detects and classifies crop diseases using computer vision and deep learning. Built on **EfficientNet-B3** and trained on a curated dataset of 13,000+ images, the model can recognize 17 disease classes across **five major crops**:
- **Corn** (Common Rust, Gray Leaf Spot, Northern Leaf Blight, Healthy)
- **Potato** (Early Blight, Late Blight, Healthy)
- **Rice** (Brown Spot, Leaf Blast, Neck Blast, Healthy)
- **Wheat** (Yellow Rust, Brown Rust, Healthy)
- **Sugarcane** (Red Rot, Bacterial Blight, Healthy)
✅ **Accuracy:** 94.8%
✅ **Precision:** 95.4%
✅ **Recall:** 94.5%
The model contributes to **SDG 2 – Zero Hunger**, **SDG 12 – Responsible Consumption**, and **SDG 13 – Climate Action** by enabling early intervention and sustainable agriculture practices.
---
## Model Details
- **Architecture**: EfficientNet-B3 (pretrained on ImageNet)
- **Classifier Head**: Replaced with `Linear(1536 → 17)`
- **Framework**: PyTorch
- **Total Parameters**: ~10.7M
- **Training**:
- 5-fold cross-validation
- Early stopping (best at epoch 29)
- Augmentation & normalization
---
## How to Use
> 💡 This model requires preprocessing consistent with training (image resizing, normalization). For ready-to-use prediction.
### Inference Example (PyTorch)
```python
import torch
from torchvision import transforms
from PIL import Image
import requests
from huggingface_hub import hf_hub_download
# Download the model file from Hugging Face
model_path = hf_hub_download(repo_id="VisionaryQuant/5_Crop_Disease_Detection", filename="best_crop_disease_model.pt")
# Load the model (make sure your architecture matches)
model = torch.load(model_path, map_location=torch.device('cpu'))
model.eval()
# Preprocess input image
image = Image.open("your_crop_image.jpg").convert("RGB")
transform = transforms.Compose([
transforms.Resize((300, 300)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
input_tensor = transform(image).unsqueeze(0)
# Run inference
with torch.no_grad():
logits = model(input_tensor)
probs = torch.nn.functional.softmax(logits, dim=1)
predicted_idx = torch.argmax(probs, dim=1).item()
# Map class index to label
idx2label = {0: "Corn___Common_Rust", 1: "Corn___Gray_Leaf_Spot", ..., 16: "Sugarcane___Healthy"} # Add full mapping
print("Prediction:", idx2label[predicted_class])
```
## Real-World Applications
- Smart Farming: Disease detection via mobile/drones
- Scalable Monitoring: Surveying across large farmlands
- Yield Optimization: Early diagnosis = lower crop loss
## Citation
If you use this model, please cite it as:
**BibTeX:**
```
@misc{5cropdiseasedetection2025,
title = {Crop Disease Detection using EfficientNet-B3},
author = {Abdullahi Olalekan Abdulmumeen},
year = {2025},
url = {https://huggingface.co/VisionaryQuant/5_Crop_Disease_Detection}
}
```
**APA:**
```
Abdulmumeen, A. O. (2025). Crop disease detection using EfficientNet-B3 [Model]. Hugging Face. https://huggingface.co/VisionaryQuant/5_Crop_Disease_Detection
```
## Contact & Credits
Developed by Abdullahi Olalekan Abdulmumeen <br/>
For the NaijaFarmConsultAI 3MTT Knowledge Showcase project |