Deep Learning Yolo Based Weed Detection

Project Code :TCMAPY503

Objective

The main objective of this to detect the weed from the crop using the Yolo algorithm of deep learning.

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 v3 model was used to train the dataset of the crop images along with the weed. The dataset consists of the images along with the Labeled data of the weed. Once after the completion of training, we can test the weed that was shown by the bounding boxes around the weed part.

Keywords: Weed detection, deep learning, and YOLO v3.

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

Block Diagram

Specifications

H/W Configuration:
Processor:  I3/Intel Processor

Hard Disk: 160GB 

S/W Configuration:

Operating System:  Windows 7/8/10  

IDE:  Google Colab

Libraries Used:   Numpy, IO, OS

Technology:  Python 3.6+

Learning Outcomes

  •          Testing techniques
  •          Error correction mechanisms
  •          What type of technology versions is used?
  •          Working of Tensor Flow
  •          Implementation of Deep Learning techniques
  •          Implementation of Machine Learning techniques
  •          Working of CNN algorithm
  •          Working of SVM algorithm
  •          Building of model creations
  •          Scope of project
  •          Applications of the project
  •          About Python language
  •          About Deep Learning Frameworks
  •          Use of Data Science
  •          Practical exposure to

o   Hardware and software tools

o   Solution providing for real time problems

o   Working with team/individual

o   Work on creative ideas

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