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VisionaryQuant/
5_Crop_Disease_Detection

crop-disease-detection

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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
For the NaijaFarmConsultAI 3MTT Knowledge Showcase project