Image Classification of Rice Leaf Diseases Using Random Forest Algorithm

Project Code :TMMAAI218

Objective

In this work, image classification is used to classify the data set of rice leaf diseases, such as; Brown Spot Rice disease (BSR), and Bacterial Leaf Blight disease (BLB) using Random Forest Algorithm.

Abstract

The problem of rice diseases around the world make to damage and fall into a large number of rices. Caused by many of types, such as; fungi, Bakteri and Viruses. which are the main causes of rice disease affected to farmers. The classification of rice can be classified into several methods. In this research, image classification is used to classify the data set of rice leaf diseases, such as; Brown Spot Rice disease (BSR), Brown Spot Rice disease (BSR), Bacterial Leaf Blight disease (BLB), which is the rice leaf disease with severe outbreaks around Thailand. Moreover, image processing technology in the classification types of rice leaf disease, such as; Random Forest classification algorithm, Decision tree classification algorithm, Gradient Boosting classification algorithm and Nai've-Baye classification algorithm, which is measured by the accuracy, precision and recall of each algorithm.

Keywords: Rice Leaf Diseases, Algorithm, Image processing, Classification, Random Forest.

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 and hardware requirements: 

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 Math Works products may take up to 29 GB of disk space 

RAM:

Minimum: 4 GB 

Recommended: 8 GB

Learning Outcomes

  • Introduction to MATLAB
  • What are EISPACK and 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.,
  • 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

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