Cascading Feature Filtering and Boosting Algorithm for Plant Type Classification Based on Image Features

Project Code :TMMAAI205


Plant classification is performed with four classifiers, namely Multi-Layer Perceptron (MLP), k-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Machine (SVM).


Crop and weeds identification is of important steps towards the development of efficient automotive weed control systems. The higher the accuracy of plant detection and classification, the higher the performance of the weeding machine. In this study, the capability of two popular boosting methods including Adaboost.M2 and Logit Boost algorithms was evaluated to enhance the plant classification performance of Random Forest (RF) feature filtering techniques including Information Gain (IG were applied to the image-extracted features and fed into single and boosted classifiers. The RF model trained by IG selected features (IG-RF) was the most appropriate classifier among the evaluated models whether in single or boosted modes. It was also found that boosting of IG-RF by using Adaboost.M2 and LogitBoost algorithms improved the classification accuracy. The accuracy, k, and RMSE were calculated to analyze the performance of algorithms. It was concluded that combination of boosting algorithms and feature selection methods can promote plant type discrimination accuracy, which is a crucial factor in the development of precision weed control systems.

Keywords: Random Forest, Adaboost.M2, Logit Boost, RMSE, Kappa.

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

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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

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 


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 


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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