corn-model-disease-detection

🌽 Maize Disease Detection

A deep learning model that can detect two common maize diseases from leaf images:

• Maize Streak Virus (MSV)
• Maize Lethal Necrosis (MLN)

🚀 Try it out!

1. Upload an image of a maize leaf
2. The model will predict if the leaf is healthy or affected by MSV or MLN
3. Get detailed confidence scores for each class

📊 Model Details

• Architecture: ResNet-style CNN
• Input size: 128x96 pixels
• Classes: Healthy, MSV, MLN
• Accuracy: 98%

💻 API Usage

You can use this model in your applications via the Hugging Face Inference API:

import requests

API_URL = "https://api-inference.huggingface.co/models/YOUR_USERNAME/maize-disease-detection"
headers = {"Authorization": "Bearer YOUR_API_TOKEN"}

def query(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(API_URL, headers=headers, data=data)
    return response.json()

🔧 Next.js Integration

To use this model in a Next.js application:

1. Create an API route in your Next.js app:

// pages/api/predict.ts
import type { NextApiRequest, NextApiResponse } from 'next'

export default async function handler(
  req: NextApiRequest,
  res: NextApiResponse
) {
  if (req.method !== 'POST') {
    return res.status(405).json({ message: 'Method not allowed' })
  }

  try {
    const response = await fetch(
      'https://api-inference.huggingface.co/models/YOUR_USERNAME/maize-disease-detection',
      {
        method: 'POST',
        headers: {
          'Authorization': `Bearer ${process.env.HUGGINGFACE_API_KEY}`,
          'Content-Type': 'application/json',
        },
        body: JSON.stringify(req.body),
      }
    )

    const data = await response.json()
    res.status(200).json(data)
  } catch (error) {
    res.status(500).json({ message: 'Error processing request' })
  }
}

1. Create a component to use the API:

// components/DiseaseDetector.tsx
import { useState } from 'react'

export default function DiseaseDetector() {
  const [file, setFile] = useState<File | null>(null)
  const [prediction, setPrediction] = useState<any>(null)

  const handleSubmit = async (e: React.FormEvent) => {
    e.preventDefault()
    if (!file) return

    const formData = new FormData()
    formData.append('file', file)

    try {
      const response = await fetch('/api/predict', {
        method: 'POST',
        body: formData,
      })
      const data = await response.json()
      setPrediction(data)
    } catch (error) {
      console.error('Error:', error)
    }
  }

  return (
    <div>
      <form onSubmit={handleSubmit}>
        <input
          type="file"
          accept="image/*"
          onChange={(e) => setFile(e.target.files?.[0] || null)}
        />
        <button type="submit">Analyze</button>
      </form>
      
      {prediction && (
        <div>
          <h3>Results:</h3>
          <pre>{JSON.stringify(prediction, null, 2)}</pre>
        </div>
      )}
    </div>
  )
}

📝 License

MIT License

🙏 Acknowledgments

• Model trained on the Nelson Mandela African Institution of Science and Technology Maize dataset
• Special thanks to the researchers who contributed to the dataset