Leaf Disease Detection

11000 ~~12000~~

You Save 8% (Inclusive of all taxes)

Product Name: Leaf Disease Detection System
Version: 1.0
Platform: Python 3.x
Hardware Requirements:
- Camera Module or Smartphone (for capturing leaf images)
- Raspberry Pi, PC, or similar computing device
Software Requirements:
- Python 3.x
- OpenCV library for image processing
- TensorFlow/Keras or PyTorch (for disease detection model)
- Numpy library
- Flask (optional, for web interface)
- Matplotlib/Seaborn (for data visualization)
Functional Requirements:
- Real-time image capture and preprocessing
- Detection and classification of leaf diseases using a trained machine learning model
- Display of detection results and recommended actions
- User-friendly interface for managing plant data and viewing results
Non-functional Requirements:
- High accuracy in disease detection
- Low latency in processing and result generation
- Robustness and reliability in various lighting conditions and plant types
- Scalability for different plant species and disease types

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Key Features:

Real-time Image Capture and Preprocessing:
- The system captures images of plant leaves in real-time using a camera module or smartphone.
- Images are preprocessed (e.g., resizing, normalization) for disease detection.
Disease Detection and Classification:
- The system uses a pre-trained machine learning model to detect and classify diseases in leaf images.
- The detection model is trained on a diverse dataset of leaf images with labeled diseases to achieve high accuracy.
Result Display:
- The system displays the detection results, including the identified disease and confidence score.
- Recommended actions and treatments for the detected disease are also provided.
User Management:
- An administrative interface allows for managing plant data, updating the detection model, and viewing historical detections.
- Users can add, modify, or delete plant records and manage their disease detection results.