Real-Time Weed Detection using Machine Learning and Stereo-Vision

Project Code :TMPGAI101

Objective

In this work weed detection system is implemented using machine learning and image processing techniques to detect weeds from plant image

Abstract

In modern days, weed identification / plant detection in plants is more difficult. There has been little work so far to identify weeds while planting vegetables. Traditional approaches for the identification of agricultural weed were primarily directed at directly identifying weed; nevertheless, the variations in weed species are significant.

This study presents a novel technique, which merges deep learning with imaging technology, as opposed to this method. First, the YOLO v2 model was trained to identify and draw boundary boxes for plants surrounding it. Then, the remaining green items fell from the border boxes like weeds. In this approach, just the crops are identified and other weed species are thereby prevented from being handled.

These experiment results demonstrate the feasibility of using the proposed method for the ground-based plant identification in vegetable plantation.

Keywords: Plant identification, deep learning, image processing, deep learning, YOLO v2.

NOTE: Without the concern of our team, please don't submit to the college. This Abstract varies based on student requirements.

Block Diagram

Specifications

Software: Matlab 2020a or above

Hardware:

Operating Systems:

  • Windows 10
  • Windows 7 Service Pack 1
  • Windows Server 2019
  • Windows Server 2016

Processors:

Minimum: Any Intel or AMD x86-64 processor

Recommended: Any Intel or AMD x86-64 processor with four logical cores and AVX2 instruction set support

Disk:

Minimum: 2.9 GB of HDD space for MATLAB only, 5-8 GB for a typical installation

Recommended: An SSD is recommended A full installation of all MathWorks products may take up to 29 GB of disk space

RAM:

Minimum: 4 GB

Recommended: 8 GB

Learning Outcomes

  • Introduction to Matlab
  • What is EISPACK & LINPACK
  • How to start with MATLAB
  • About Matlab language
  • Matlab coding skills
  • About tools & libraries
  • Application Program Interface in Matlab
  • About Matlab desktop
  • How to use Matlab editor to create M-Files
  • Features of Matlab
  • Basics on Matlab
  • What is an Image/pixel?
  • About image formats
  • Introduction to Image Processing
  • How digital image is formed
  • Importing the image via image acquisition tools
  • Analyzing and manipulation of image.
  • Phases of image processing:

    • Acquisition
    • Image enhancement
    • Image restoration
    • Color image processing
    • Image compression
    •  Morphological processing
    • Segmentation etc.,

  • About Artificial Intelligence (AI)
  • About Machine Learning
  • About Deep Learning
  • About layers in AI (input, hidden and output layers)
  • Building AI (ANN/CNN) architecture using Matlab
  • We will able to know, what’s the term “Training” means in Artificial Intelligence
  • About requirements that can influence the AI training process:

    • Data
    • Training data
    • Validation data 
    • Testing data 
    • Hardware requirements to train network

  • How to detect an object using AI
  • How to extend our work to another real time applications
  • Project development Skills

    • Problem analyzing skills
    • Problem solving skills
    • Creativity and imaginary skills
    • Programming skills
    • Deployment
    • Testing skills
    • Debugging skills
    • Project presentation skills
    • Thesis writing skills

Demo Video

mail-banner
call-banner
contact-banner
Request Video

Related Projects

Final year projects