Kharif Knights - Smart Farming Solutions

Introduction

Kharif Knights is a smart farming solution designed to empower farmers with data-driven insights and intelligent tools for optimizing crop health, managing resources, and improving yields. It provides real-time crop monitoring, pest and disease detection, and an AI-powered farm management assistant.

Features

• Real-time Crop Monitoring: Kharif Knights uses advanced image recognition to monitor your crops remotely, providing data-driven insights about crop health, growth stages, and potential risks.
• Pest Detection: Identify and classify common pests affecting your crops using our AI-powered pest detection system. Get early warnings and recommended actions to prevent infestations.
• Disease Prediction: Detect potential diseases early by analyzing leaf patterns and discoloration. Receive timely alerts and guidance to mitigate disease outbreaks.
• Farm Management Assistant (AgriCare): AgriCare is an AI-powered assistant that provides personalized farm management guidance, optimizing resource usage and improving crop yields through intelligent recommendations and monitoring.
• Sensor Dashboard: Monitor key environmental factors such as temperature, humidity, and soil moisture in real-time with our integrated sensor dashboard.

Technologies Used

• React
• TypeScript
• Vite
• Tailwind CSS
• shadcn-ui
• ONNX Runtime Web
• Firebase

Setup Instructions

Follow these steps to set up the project locally:

1. Clone the repository:

   git clone https://github.com/Monish-KS/Kharif-Knights.git

2. Navigate to the project directory:

   cd page-wizard-makes-one

3. Install the necessary dependencies:

   npm install

4. Configure Firebase:

   • Create a Firebase project on the Firebase Console.
   • Enable the Realtime Database.
   • Copy your Firebase configuration object and replace the placeholder in src/lib/firebase.ts.

5. Run the development server:

   npm run dev

   This will start the development server with auto-reloading and an instant preview at http://localhost:5173.

Usage

• Uploading Images for Analysis:

  • Navigate to the AgriVision page.
  • Click the "Upload Image" button.
  • Select an image of your crop.
  • The system will analyze the image and display the results.

• Interacting with the AgriCare Assistant:

  • Navigate to the AgriCare page.
  • Type your question in the input field.
  • Click the "Send" button to submit your question.
  • The AI assistant will provide a response.

• Viewing Sensor Data on the Dashboard:

  • Navigate to the main dashboard page.
  • The sensor data (temperature, humidity, soil moisture, NPK values) will be displayed in real-time.

Model Details

AgriVision uses ONNX models for pest detection and nutrient prediction.

• Pest Detection Model:

  • Input: Image data (224x224 pixels, NCHW format).
  • Output: A tensor of shape [1, 9, 1029] containing bounding box coordinates, confidence scores, and class probabilities for each potential detection.
  • Labels: The public/models/pest_detection_labels.yaml file maps class indices to pest names.

• Nutrient Prediction Models (Nitrogen, Phosphorus, Potassium):

  • Input: Temperature, humidity, and soil moisture values.
  • Output: Predicted nutrient levels (kg/ha).

Contributing

We welcome contributions to AgriVision! Please follow these guidelines:

• Fork the repository.
• Create a new branch for your feature or bug fix.
• Submit a pull request with a clear description of your changes.

License

MIT License