Build an Image Classifier for Plant Species Identification

In this machine learning project, we will use binary leaf images and extracted features, including shape, margin, and texture to accurately identify plant species using different benchmark classification techniques.

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What will you learn

  • Understanding the problem statement and importing the file

  • Initializing the libraries and understand it's use

  • Difference between StratifiedShuffleSplit and Simple Random Sampling

  • Using info and describe the function and extracting information from the results

  • Label encoding necessary columns

  • Importing necessary classifiers; Linear, Non-linear, bagging and boosting

  • Applying models like Random Forest, KNN, SVC, GradientBoosting and naive Bayes for on-spot checking

  • Hyperparameter tuning them by defining suitable parameters

  • Defining evaluation metrics "Log Loss"

  • Performing LDA(Linear Discriminant Analysis)

  • Plotting graphs for accuracy and loss versus classifier

  • Selecting the best model for prediction

  • Understanding Confusion Matrix and it's importance

  • Standardization and Normalization of the Dataset

  • Comparing the final output before and after standardization of the Dataset

  • Making final predictions and saving the result

Project Description

The objective of this machine learning project is to use binary leaf images and extracted features, including shape, margin, and texture, to accurately identify 99 species of plants. Leaves, due to their volume, prevalence, and unique characteristics, are an effective means of differentiating plant species. They also provide a fun introduction to applying techniques that involve image-based features. We are going to apply different classification techniques to benchmark the relevance of classifiers in image classification problem.

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Curriculum For This Mini Project

 
  Problem Statement
14m
  Data Set Overview
02m
  Encoding
06m
  Stratified Random Sampling
06m
  Selection of Classifiers
02m
  Logging for Visual Comparison
05m
  Grid Search Mode
14m
  Grid Search for Hyper Parameter Tuning
03m
  KNeighbour Classifier Tuning
06m
  SVC Classifier Tuning
09m
  NSVC Classifier Tuning
05m
  Decision Tree Classifier Tuning
03m
  Random Forest Classifier Tuning
04m
  Adapting Boosting Classifier
04m
  Gradient Boosting Classifier
08m
  Run all Classifiers
02m
  Linear Descriminant Analysis
02m
  Gaussian Naive Bayes
00m
  Quadratic Discriminant Analysis
00m
  Re Run all Classifiers
05m
  Recap
00m
  Apply Tensor Flow Models
19m
  Accuracy and Loss of the Model
04m
  Conclusion
06m