README.md

metadata

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.

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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

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How to Use

💡 This model requires preprocessing consistent with training (image resizing, normalization). For ready-to-use prediction.

Inference Example (PyTorch)

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