Design of Visual Navigation System of Farmland Tracked Robot Based on Raspberry Pi

Design of Visual Navigation System of Farmland Tracked Robot Based on Raspberry Pi

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Product Code: Embedded - RoS
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Aim:

Aim of this project is to build a visual navigation system for farmland tracked robot based on raspberry pi and to detect the plant leaf diseases using convolutional neural network .


Introduction:

  Usage of robots in agriculture increases rapidly with the development of IoT technology. In agriculture field robots are used for various purposes like crop monitoring, ploughing, weeding and spraying fertilizer etc. It reduces the processing time, cost of execution and human effort when compared to traditional methods. At present, wheeled type robots are used in agriculture field which are convenient to carry load and stabled at farm track. These robots are controlled manually or by using RF remotes for short distance. This system proposes to control the robot using local socket server. Here, Raspberry Pi used as minicomputer and USB camera is attached with raspberry pi. USB camera streams the live video to local server; with the help of live video operator can easily control robot using his computer.  Here, we are using the robot to classify the plant leaf diseases using convolution neural networks. Plant diseases  are a major threat to plant growth and visual examination by experts has been carried out to diagnose plant diseases and biological examination is the second option, if necessary. In the proposed system deep-learning based approach used which can automatically identify the discriminative features of the diseased plant images and detect the types of plant leaf diseases with high accuracy.  Image of affected leaf image captured by camera and fed to CNN model to diagnose the disease


Proposed system:


            Existing system processed based on the difference between colors of the path. When the farm land has multiple paths at the junction or difference between farm land and path reduces, the bot will make wrong decision. We can't use existing for various type of land. This proposed system is fully controlled by user from local server. We can use it in various types of fields and various environments. It can be also used as surveillance robot. We also added plant leaf disease diagnosing feature in this system which can automatically detect the type of disease by deep-learning approach.


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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.