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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Project Template Outcomes

  • 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

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Project Description

Introduction to Plant Species Identification

Imagine you are planning to start your own tea-leaves business. You’d want to price them depending on their quality. And if you have no idea how to do that, there is no need to worry as we have the leaf classification python code in this image identification project that will help you achieve it. Due to their volume, prevalence, and unique characteristics, leaves are an effective means of differentiating plant species. They also provide a fun introduction to applying techniques that involve image-based features. Thus, one can build an image classifier for plant species identification by implementing image processing techniques and computer vision methods over images of leaves.

Plant Identification Project Objective

The objective of this machine learning project is to use binary leaf images and extracted features, including shape, margin, and texture, to identify 99 species of plants accurately. We will apply different classification techniques to benchmark the relevance of classifiers in image classification problems. This project will help you understand which Python libraries out of sklearn, scipy, and TensorFlow will best suit the specific files in the dataset for building an efficient plant species identification system.

 

Plant Identification Project Objective

Plant Data used in this Machine Learning Project

The dataset for this plant classification project is divided into four files: train.csv, test.csv, sample_submission.csv, and images. The train and test files contain the following:

 

id- an anonymous id unique to an image

margin_1, margin_2,..., margin_64 - each of the 64 attribute vectors for the margin feature

shape_1, shape_2,..., shape_64- each of the 64 attribute vectors for the shape feature

texture_1, texture_2,..., texture_64 - each of the 64 attribute vectors for the texture feature

Species- species of the plant in the image

 

There are about 990 images in the dataset, 10 for each class of species. Thus, the dataset contains information on 99 different plant species.

Concepts to Learn in this Machine Learning Plant Identification Project

Let us discuss the concepts that you will master through this plant identification project.

Data Preprocessing

In this project, the task is to build a plant classifier using the given dataset. The dataset has various features: margins, shapes, and textures of different plants, which have been extracted from the plant images. And before serving this data of 99 other species to machine learning algorithms, the species variable (target variable) will be converted into numerical value using encoding techniques in this project. Additionally, the project solution shows how to split the training dataset into training and validation subsets so that the split is unbiased towards any particular class in the dataset.

Machine Learning Classification Algorithms

As the problem is of classification type, you will learn about different classification algorithms in this project. We will discuss the following algorithms in detail:

  • K-Nearest neighbors

  • Support Vector Classifier

  • Decision Tree

  • Random Forests

  • Gradient Boosting

  • Adaptive Boosting

  • Naive Bayes

  • Linear Discriminant Analysis

  • Quadrant Discriminant Analysis

You will apply all these algorithms to the given dataset and evaluate which model performs the best by default. For hyperparameter tuning, the project solution will use the grid search method and assess the accuracy of each model. Additionally, it will show you how to evaluate the loss for each algorithm and visualize it using a logarithmic plot.

Deep Learning Algorithm: Neural Networks

Besides the train and test data files, the dataset contains images of different leaves. So, why not build a plant classifier app that can search a plant by its image? This project will cover that and teach you how to perform plant identification using TensorFlow. You will learn simple neural network algorithms and a special type of neural network called CNN (convolutional neural networks) that are widely used with image datasets. Using Keras with TensorFlow in the backend, you will learn how to build different layers of a CNN, how to set activation functions, choose pooling methods, etc., in the Python programming language. Furthermore, the project will also teach you how to determine the accuracy and loss of a convolutional neural network.

FAQs for Plant Search by Image Project

What is an Image Classifier?

An image classifier is a generic term used to describe application systems that can correctly identify the image's class by analyzing its pixels.

Is CNN a Classifier?

CNN(convolutional neural network) is a kind of neural network used to solve classification problems in deep learning. It is not only limited to classification problems but can also be used in summarization, machine translation, time series, etc. Because it involves the convolution operation, CNNs are widely used to solve image and text classification problems.

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