Crop Disease Detection
Crop Disease Detection
AI-Based Plant Disease Identification Using Computer Vision
Problem Statement
Farmers often struggle to identify crop diseases at an early stage due to lack of expertise and delayed diagnosis. This results in reduced yield, increased pesticide usage, and financial loss.
Solution Overview
The Crop Disease Detection system uses computer vision and deep learning models to analyze images of crop leaves and accurately detect diseases. The system provides disease name, confidence score, and preventive recommendations to support timely intervention.
Key Features
Farmer Features
- • Upload crop leaf images
- • Instant disease detection
- • Confidence-based predictions
- • Basic treatment suggestions
Admin Features
- • Dataset management
- • Model performance monitoring
- • Disease category updates
- • Analytics dashboard
System Architecture
Leaf Image Upload
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Image Preprocessing
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CNN Disease Classification Model
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Prediction & Confidence Score
Technology Stack
Computer Vision
OpenCV, Image Augmentation Techniques
Deep Learning
TensorFlow / PyTorch, CNN Models
Backend
Python, FastAPI, REST APIs
Frontend
Web Dashboard, Tailwind CSS
Data Storage
Image Storage, Model Metadata Databases
Use Cases
- • Early crop disease identification
- • Precision agriculture solutions
- • Mobile apps for farmers
- • Agricultural advisory platforms
- • Crop yield protection initiatives
- • Sustainable farming practices
Business Impact
The Crop Disease Detection system helps farmers identify diseases early, reduce crop losses, and minimize excessive pesticide use. By enabling faster diagnosis and informed treatment decisions, it improves yield, lowers operational costs, and supports sustainable agriculture.