My cart

0 item(s) - CALL FOR PRICE

  • Your shopping cart is empty!

ET-based Irrigation System with Automated Bird Deterrent System
ET-based Irrigation System with Automated Bird Deterrent System
ET-based Irrigation System with Automated Bird Deterrent System

ET-based Irrigation System with Automated Bird Deterrent System

CALL FOR PRICE
Product Code: Embedded - Artificial Intelligence
Availability: In Stock
Viewed 11983 times

Product Description

Aim:

            Aim of the project is to build a small and portable bird classification device to monitor and survey migration birds in sanctuaries with the help of AI and IoT.

Synopsis:

             The monitoring of birds has a widespread potential in numerous applications in ecology, climatology, and avian related zoonosis /infections such as avian influenza. Migratory birds are known to be carriers of the birds’ flu, caused by type A of the influenza virus H5N1 and they can infect domesticated birds. This virus can cause severe disease in humans, but at present it cannot transmit easily from person to person, although fatal human cases were reported. By monitoring wild bird migration a better understanding of the flyways used by the various avian species can be gained.


             There are lot of conventional methods are exist to monitor migration birds such as manual monitoring, RADAR systems and webcams. Manual Motoring requires lot of man power and time consuming process. RADAR system faces a huge problem with ground echo signals which make difficult to identify source of receiving signals whether it is from birds or ground. Installation of web camera to capture video of bird needs a computer, relative power source and networking system.


              In our proposed system, we are using a portable, small ARM based computer (Raspberry Pi) to control the camera and network. It requires minimal amount power when compared other computers. With this raspberry pi, we can add raspberry pi camera as well as USB camera to capture video. This board has the ability to run machine learning models to recognize the bird. This device can be used in bird sanctuaries to identify the different bird species. It will help to gather the large amount data in limited time period with minimal man power. It can be placed any bird habitats like trees, hills tops and any other remote places.


Proposed system:

               In proposed system, we are using trained CNN model to detect the birds and recognize them. Raspberry pi camera is used to capture the video and it fed the image frame to machine learning model. If any values matches with exiting model values system labels the recognized bird with name and store the picture in local storage. Using portable Wifi module we can establish the network connection with cloud database. Collected data are uploading to cloud with the particular interval of time.

When you order from finalyearprojects.in, you will receive a confirmation email. Once your order is shipped, you will be emailed the tracking information for your order's shipment. You can choose your preferred shipping method on the Order Information page during the checkout process.

The total time it takes to receive your order is shown below:

The total delivery time is calculated from the time your order is placed until the time it is delivered to you. Total delivery time is broken down into processing time and shipping time.

Processing time: The time it takes to prepare your item(s) to ship from our warehouse. This includes preparing your items, performing quality checks, and packing for shipment.

Shipping time: The time for your item(s) to tarvel from our warehouse to your destination.

Shipping from your local warehouse is significantly faster. Some charges may apply.

In addition, the transit time depends on where you're located and where your package comes from. If you want to know more information, please contact the customer service. We will settle your problem as soon as possible. Enjoy shopping!

Download Abstract

Click the below button to download the abstract.

Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.