AgriWheels

1. System Overview 🌐

This guide outlines the implementation of a real-time monitoring solution for a sugar mill, designed to track vehicle movements and manage inventory with 99% accuracy. The system leverages video analytics, deep learning, and cloud technologies to provide comprehensive supply chain visibility.

Key components:

• Video cameras for data capture
• Python-based video processing pipeline
• Deep learning models using Keras and TensorFlow
• AWS cloud services for model deployment and scaling
• React web applications and Power BI dashboards for user interface

2. Component Setup Instructions 🛠️

2.1 Video Cameras 📹

1. Install high-resolution IP cameras at key points around the sugar mill.
2. Ensure cameras have clear views of entry and exit points.
3. Connect cameras to the local network with sufficient bandwidth for streaming.

2.2 Video Processing Pipeline 🖥️

1. Set up a dedicated server or workstation for video processing.
2. Install Python 3.8+ and required libraries:

   pip install opencv-python numpy tensorflow keras

3. Develop a Python script to capture and process video streams:

   import cv2
   import numpy as np

   def process_video_stream(stream_url):
       cap = cv2.VideoCapture(stream_url)
       while True:
           ret, frame = cap.read()
           if not ret:
               break
           # Add your processing logic here
           processed_frame = your_processing_function(frame)
           yield processed_frame

2.3 Deep Learning Models 🤖

1. Use Keras to build and train your vehicle detection model:

   from tensorflow.keras.models import Sequential
   from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

   model = Sequential([
       Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
       MaxPooling2D((2, 2)),
       # Add more layers as needed
       Flatten(),
       Dense(1, activation='sigmoid')
   ])
   model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

2. Train the model on a dataset of vehicle images from your sugar mill.

2.4 AWS SageMaker Setup 🚀

1. Create an AWS account and set up SageMaker.
2. Package your trained model for SageMaker deployment.
3. Deploy the model as a SageMaker endpoint:

   import sagemaker
   from sagemaker.tensorflow import TensorFlowModel

   sagemaker_session = sagemaker.Session()
   model = TensorFlowModel(model_data='s3://your-bucket/model.tar.gz',
                           role='SageMakerRole',
                           framework_version='2.4.1')
   predictor = model.deploy(initial_instance_count=1,
                            instance_type='ml.m5.large')

2.5 React Web Application ⚛️

1. Set up a new React project:

   npx create-react-app sugar-mill-dashboard
   cd sugar-mill-dashboard

2. Install necessary dependencies:

   npm install axios react-chartjs-2 @material-ui/core

3. Create components for vehicle tracking and inventory management.

2.6 Power BI Dashboard 📈

1. Install Power BI Desktop.
2. Connect to your data sources (e.g., AWS RDS, CSV exports).
3. Design dashboards for inventory levels, vehicle movements, and key metrics.

3. Data Flow Configuration 🔄

1. Configure video streams to send data to the processing server.
2. Set up the processing server to send extracted features to AWS SageMaker.
3. Configure SageMaker to process data and store results in a database.
4. Set up the React application to fetch data from the database and SageMaker endpoints.
5. Configure Power BI to connect to the same data sources for analytics.

4. Security Considerations 🔒

1. Implement network segmentation to isolate camera streams.
2. Use AWS IAM roles for fine-grained access control.
3. Enable encryption for data at rest and in transit.
4. Regularly update and patch all system components.
5. Implement multi-factor authentication for admin access.

5. Scaling Considerations 📈

1. Use AWS Auto Scaling for SageMaker endpoints to handle varying loads.
2. Implement a load balancer for the video processing servers.
3. Use AWS ElastiCache for caching frequent queries.
4. Consider using AWS Kinesis for real-time data streaming at scale.

6. Monitoring and Maintenance 🔍

1. Set up AWS CloudWatch for monitoring SageMaker and other AWS services.
2. Implement logging in the Python processing pipeline and React application.
3. Create alerts for anomalies in vehicle movement patterns or inventory levels.
4. Schedule regular model retraining to maintain accuracy.
5. Perform weekly system health checks and updates.

7. Troubleshooting Guide 🔧

1. Camera Issues: Check network connectivity and camera power supply.
2. Processing Pipeline Errors: Review Python logs and ensure all dependencies are up to date.
3. Model Accuracy Drops: Analyze recent data for distribution shifts and consider retraining.
4. Dashboard Latency: Check database query performance and consider caching strategies.

By following this guide, you should be able to set up and maintain a robust real-time monitoring solution for your sugar mill, significantly improving supply chain visibility and operational efficiency.