AI Crop Disease Detection System
Upload a leaf image and let AI detect plant diseases instantly.
About the Project
This project uses Machine Learning and Computer Vision to revolutionize crop disease detection.
Detects diseases from leaf images with precision and speed.
Helps farmers diagnose problems early and take immediate action.
Plant & Leaf Detection
How It Works
Simple 4-step process to detect crop diseases instantly
Upload Leaf Image
Take a photo of any leaf showing symptoms
AI Processes Image Using CNN Model
Advanced neural network analyzes every pixel
Disease Identified with Confidence Score
Get accurate diagnosis with probability scores
Provides Remedies and Suggestions
Actionable recommendations for treatment
Features
Powerful capabilities for accurate crop disease detection
Fast Detection
Get results in seconds, not minutes
High Accuracy
90%+ accuracy with advanced AI models
User-Friendly Interface
Simple, intuitive design for everyone
Remedy Recommendations
Get specific treatment advice instantly
Supports Multiple Crops
Compatible with various plant species
Real-time Updates
Continuous model improvements
Try It
Upload Leaf Image
Drag & drop or click to select
Results
Predicted Disease
Early Blight
Confidence
94.7%
Recommended Solutions:
- • Remove affected leaves immediately
- • Apply copper-based fungicide
- • Improve air circulation
Solutions
Remove Infected Leaves
Quickly remove affected plant parts to prevent disease spread
Apply Fungicide
Use recommended fungicides based on disease type and severity
Maintain Proper Spacing
Ensure adequate air circulation between plants to reduce humidity
Use Drip Irrigation
Avoid overhead watering to keep leaves dry and prevent disease
Monitor Field Regularly
Weekly inspections help catch problems early for better outcomes
Technology
Dataset: PlantVillage
Comprehensive dataset containing 54,306 images across 38 plant disease categories
- • High-resolution leaf images
- • Multiple crop varieties
- • Balanced class distribution
Model Type: CNN / MobileNetV2
Optimized deep learning architecture for mobile deployment
Accuracy: 90%+
Validated performance across diverse environmental conditions
Image Preprocessing
- • Resize to 224x224 pixels
- • Normalization and augmentation
- • Noise reduction and enhancement
- • Color space optimization
Contact
Get in Touch
Have questions about AI crop disease detection? We're here to help!
Email: srishtysinghal99@gmail.com
Created by: Srishty Singhal
Made with AI & Machine Learning