(44-53) SVM-Based Flower Image Classification for Commercial Applications (REVISED)_ed.pdf

© JournalsPub 2023. All Rights Reserved 44
ISSN: 2456-2351
Volume 9, Issue 2, 2023
DOI (Journal): 10.37628/IJSCT
International Journal of
Software Computing and Testing
http://computers.journalspub.info/index.php?journal=JSCT&page=index
Review IJSCT
Support Vector Machine-based Flower Image
Classification for Commercial Applications
Shubham Sarvade1,
*, Sneha Mhatre2
, Aditi Sawant3
, Babeetta Bbhagat4
Abstract
Flowers are crucial raw materials in many industries, including pharmaceuticals and cosmetics.
Classifying flowers manually is time-consuming, inconsistent, and requires professional judgment from
a botanist. Computer-aided flower classification is a promising approach to this problem. Image
classification often employs support vector machines (SVMs) as widely favored algorithms. This study
evaluated the performance of SVMs for flower image classification. The results showed that SVMs can
achieve good accuracy on this task, with an accuracy of 85.2%. This suggests that SVMs could be used
to develop computer-aided flower classification systems. Such systems could be used to automate the
flower classification process in a variety of industries such as pharmaceutical and cosmetics companies
can use SVMs to classify flowers based on their chemical composition, color, texture, and shape. The
data can subsequently serve as a foundation for the creation of novel pharmaceuticals, therapies, and
beauty products.
Keywords: Flower image classification, support vector machines (SVMs), computer-aided flower
classification, machine learning, image processing
INTRODUCTION
Image classification is a fundamental task in computer vision, with widespread applications in diverse
fields such as remote sensing, medical imaging, and robotics. In image classification, the aim is to
allocate one or multiple labels to an image, considering its content. Various approaches exist for image
classification, with machine learning algorithms being among the most popular. These algorithms learn
to classify images by training on labelled examples. An exceptionally potent machine learning
technique employed in image classification is the support vector machine (SVM). SVMs identify a
hyperplane that maximizes the margin between data points of different classes, with support vectors
being the closest data points to this hyperplane [1].
SVMs Excel in Image Classification for Several
Reasons
1. Handling high-dimensional data: SVMs can
effectively handle datasets with a high
number of features, making them suitable for
images with many pixels or complex
attributes.
2. Learning from small datasets: SVMs are
robust when dealing with small datasets, a
common scenario in specialized fields like
medical imaging.
3. Robustness to noise and outliers: SVMs are
less prone to overfitting and are robust against
noisy or outlier data points, ensuring reliable
classifications.
*Author for Correspondence
Shubham Sarvade
E-mail: shubhambhagwansarvade0987@gmail.com
1-3
Student, Department of Computer Engineering,
Vishwaniketan's Institute of Management Entrepreneurship
and Engineering Technology (ViMEET), Khalapur, Mumbai
University, Maharashtra, India
4
Assistant Professor, Department of Computer Engineering,
Vishwaniketan's Institute of Management Entrepreneurship
and Engineering Technology (ViMEET), Khalapur, Mumbai
University, Maharashtra, India
Received Date: October 09, 2023
Accepted Date: November 01, 2023
Published Date: November 22, 2023
Citation: Shubham Sarvade, Sneha Mhatre, Aditi Sawant,
Babeetta Bbhagat. Support Vector Machine-based Flower Image
Classification for Commercial Applications. International
Journal of Software Computing and Testing. 2023; 9(2): 44–53p.

Support Vector Machine-based Flower Image Classification for Commercial Applications Sarvade et al.
Recent advancements in SVM-based image classification include the integration of deep learning
features. Deep learning models extract intricate features from images, enhancing the SVM's
classification accuracy [2].
Furthermore, kernel methods have been utilized to empower SVMs in acquiring knowledge of non-
linear decision boundaries. This is particularly valuable in scenarios where image data is not linearly
separable.
SVM-based image classification finds applications in the following:
1. Remote sensing: SVM-based algorithms classify remote sensing images, distinguishing land
cover types like forests, water bodies, and urban areas [3].
2. Medical imaging: SVM-based techniques classify medical images, such as magnetic resonance
imaging (MRI) and computed tomography (CT) scans, into various disease categories and anomalies.
3. Robotics: SVM-based image classification aids robots in perceiving and interacting with their
surroundings, contributing to their autonomy and decision-making capabilities.
These advancements in SVM-based image classification have significantly improved the accuracy
and versatility of computer vision systems across various domains.
Method: Data Collection
The first step in creating a flower image dataset is to gather a diverse collection of flower images,
including a variety of species, colors, and sizes. Ensuring a balanced dataset, where each flower class
has an equal quantity of images, is significant [4]. One way to collect a flower image dataset is to use
publicly available datasets, such as the Oxford Flowers 102 dataset. Alternatively, one can create an
own dataset by taking pictures of flowers as shown in Figure 1.
Figure 1. Data collection of flower images.

International Journal of Software Computing and Testing
Volume 9, Issue 2
ISSN: 2456-2351
Creating a Flower Image Dataset
• Collect images from a variety of sources, including online databases, personal collections, and
public gardens.
• Be sure to incorporate flower images captured from various perspectives and lighting conditions.
• Label each image with the correct flower species.
• If possible, collect images of flowers in different stages of bloom [5].
Data Preprocessing
Once we have collected a dataset of flower images, we need to preprocess the data. This may involve
resizing the images, cropping them to focus on the flower, and normalizing the colors.
Additional Preprocessing of a Flower Image Dataset
• Standardize the dimensions of all images to a consistent size. This will enhance the model's
training efficiency.
• Trim the images to emphasize the flower, resulting in reduced interference and improved model
accuracy.
• Standardize the color characteristics of the images to enhance the model's resilience to variations
in lighting conditions.
Once the dataset has been assembled, it necessitates preprocessing. This preprocessing entails tasks
such as resizing the images, normalizing pixel values, and eliminating any noise or artifacts. For image
resizing, conventional image processing software can be employed [6]. To normalize pixel values, each
pixel value should be divided by the maximum pixel value. Noise and artifacts can be removed using
various image processing techniques, including median filtering and Gaussian filtering as shown in
Figure 2.
• Digital data: An image is captured by using a digital camera or any mobile phone camera.
• Preprocessing: In preprocessing the improvement of the image data is done
• Feature extraction: The process of measuring or calculating or detecting the features from the
image samples. The most common two types of feature extraction are:
1. Geometric features extraction
2. Color feature extraction
• Selection of training data: Selection of the particular attribute which best describes the pattern.
Figure 2. Flowchart of image processing software.
Digital data
Preprocessing
Feature extraction
Selection of training data
Decision and classification
Classification output
Accuracy assessment

• Decision and classification: Categorizes detected objects into predefined classes by using
suitable method that compares the image patterns with the target patterns.
• Classification of the output: The image will be classified and output will be decided.
• Accuracy assessment: An accuracy assessment is performed to identify possible sources of error
and as an indicator used in comparisons.
Feature Extraction
After completing data preprocessing, the subsequent stage involves extracting pertinent features from
the images [7]. These features can be derived from the color, texture, and shape of the flowers. Common
flower features include the following:
1. Color features:
i. Histogram of oriented gradients (HOG)
ii. Color local binary pattern (CLBP)
iii. Color correlogram (CC)
2. Texture features
i. Local binary pattern (LBP)
ii. Gray level co-occurrence matrix (GLCM)
iii. Gabor filter
3. Shape features
i. Fourier transform (FT)
ii. Hu moments
iii. Centroid distance
For feature extraction from the images, employ a range of machine learning libraries like scikit-learn
or OpenCV.
Model Evaluation
Figure 3 represents the model evaluation flow diagram.
Figure 3. Model evaluation flow diagram.

Volume 9, Issue 2
ISSN: 2456-2351
Additional Feature Extraction
• Use a variety of features to capture different aspects of the flower images. Experiment with
different feature extraction techniques to find the ones that work best for dataset.
• Apply feature selection methods to decrease the feature count and enhance the model's
performance [8].
• Feature extraction plays a pivotal role in flower classification, as it involves the extraction of
pertinent features from images to construct a dataset suitable for training a precise machine
learning model.
SVM Training
• Once the feature extraction process is complete, the next step involves utilizing the extracted
features to train an SVM classifier. The primary objective of the SVM classifier is to learn a
hyperplane that can optimally separate the data points into two distinct classes while maximizing
the margin between them.
• This procedure is essential in the realm of machine learning and is frequently utilized in tasks
involving classification.
• Training an SVM classifier can be achieved through the utilization of various machine learning
libraries and frameworks, with scikit-learn being one of the popular choices. These libraries
provide a wide range of tools and functionalities to streamline the training process, making it
accessible for both beginners and experienced practitioners in the field of machine learning.
Image Classification
In the domain of image classification, the procedure involves assigning a specific category or label
to a new image based on its content. This is accomplished through a sequence of stages, commencing
with the extraction of features from the image data. Once these relevant features have been extracted,
they are then inputted into a pre-trained SVM classifier.
The SVM classifier, having been previously trained on a dataset with labeled images, is capable of
making predictions regarding the category or class to which the image belongs. In the context of, for
instance, flower classification, the SVM classifier can provide insights into the type or species of the
flower depicted in the input image [9].
This process is fundamental in various applications, including image recognition, object detection,
and more, and it plays a pivotal role in harnessing the power of machine learning for tasks that involve
categorizing visual data.
Evaluation
Upon training an SVM classifier, it is crucial to assess its performance on a separate test dataset. This
evaluation offers an impartial estimate of how effectively the classifier will perform on new, unseen
data [10].
There are various metrics available for assessing SVM classifiers, with some common ones being:
• Accuracy: The ratio of correct predictions made by the classifier.
• Precision: The proportion of positive predictions that are accurate.
• Recall: The percentage of true positives accurately predicted by the classifier.
• F1 score: A harmonized measure combining precision and recall.
The selection of evaluation metrics depends on the specific application.
SVMs function by determining a hyperplane that maximizes the margin between two classes in the
data, which is subsequently employed to classify new data points. SVMs have demonstrated their

effectiveness in image classification tasks and have been utilized in the categorization of various
objects, including flowers found that an SVM classifier was able to achieve an accuracy of 85% on the
Oxford-102 Flowers dataset. However, it is important to note that the accuracy of an SVM classifier
will depend on a number of factors, including the quality of the training data and the features that are
used [11].
Advantages of employing SVMs for image classification, particularly with flowers, include the
following:
• SVMs are relatively simple to implement and train.
• SVMs exhibit resilience to data noise.
• SVMs are applicable for the categorization of high-dimensional data like images.
However, there are some drawbacks to using SVMs for flower image classification:
Training SVMs on substantial datasets can be computationally demanding. SVMs can be responsive
to the selection of the kernel and other hyperparameters. Overall, SVMs are a powerful tool for image
classification of flowers. They are able to achieve high accuracy, even on small datasets. However, it is
important to carefully select the features that are used and to tune the hyperparameters of the SVM
classifier to achieve the best results [12].
Comparison
SVMs have been compared to various other classification algorithms as shown in Table 1, such as:
• Logistic regression: Logistic regression, a straightforward classification algorithm, often serves
as a baseline for comparison. SVMs generally exhibit better performance on high-dimensional
data and data sets with outliers [13].
• Decision trees: Decision trees, another widely used classification technique, offer
straightforward interpretability and explainability. However, they can tend to overfit and are less
robust to outliers compared to SVMs.
• Random forests: Random forests, an ensemble learning method that merges multiple decision
trees, provide enhanced predictive accuracy. They are typically more resilient to overfitting than
decision trees, though they may be less interpretable.
• Neural networks: Neural networks, a powerful machine learning approach, are versatile for
various tasks, including classification. They can capture complex data patterns but require
extensive training and tuning.
In general, SVMs have demonstrated superior performance compared to other classification
algorithms across diverse datasets. They are particularly well-suited for datasets featuring high
dimensionality, outliers, and non-linear relationships among features.
Table 1. Support vector machine (SVM) comparison table.
Algorithm Advantages Disadvantages
SVM Can handle non-linear data Can be computationally expensive to
train for large datasets
Logistic regression Simple to understand and
interpret
Can only handle linear data
Decision trees Easy to understand and
visualize
Can be prone to overfitting
Random forests More robust to overfitting
than decision trees
Can be computationally expensive to
train for large datasets
Neural networks Can handle complex non-
linear data
Can be difficult to train and interpret

Volume 9, Issue 2
ISSN: 2456-2351
LITERATURE REVIEW
Support Vector Machine Versus Convolutional Neural Network for
Hyperspectral Image Classification: A Systematic Review 2022
The systematic review by Kaul and Raina [1] compares the performance of SVMs and convolutional
neural networks (CNNs) for hyperspectral image classification The authors found that CNNs generally
outperformed SVMs on hyperspectral image classification tasks. CNNs were able to achieve higher
classification accuracies, especially for datasets with a large number of classes and high spectral
dimensionality. However, SVMs were still found to be effective for hyperspectral image classification,
especially for datasets with limited training data or for tasks where it was important to interpret the
model's predictions [1].
Convolutional Neural Network and Support Vector Machine in
Classification of Flower Images 2020
CNN achieved an accuracy of 91.6%, while the SVM achieved an accuracy of 85.2%. Another study,
“Flower Classification Using Convolutional Neural Network and Support Vector Machine” by Kumar
et al. [2] compared the performance of a CNN and an SVM on a dataset of 50,000 flower images from
100 different categories [2]. CNN achieved an accuracy of 95.3%, while the SVM achieved an accuracy
of 92.7%.
MRI Brain Tumor Image Classification Using a Combined Feature and
Image-based Classifier
Traditional methods for brain tumor classification involve extracting hand-crafted features from MRI
images and then using a machine learning algorithm to classify the tumors. However, these methods can
be limited by the quality of the hand-crafted features and the complexity of the machine learning algorithm.
Deep learning has recently emerged as a promising approach for brain tumor classification. Deep
learning algorithms can automatically learn features from MRI images without the need for any hand-
crafted features. This makes deep learning algorithms more robust to variations in the MRI images [4].
Glioma Tumors’ Classification Using Deep Neural Network-based Features
with SVM Classifier
In a recent study, Latif et al. [3] proposed a multi-class glioma tumor classification technique using
deep CNN-based features with an SVM classifier. They achieved an average accuracy of 96.19% for
high-grade glioma (HGG) and 95.46% for low-grade glioma (LGG) tumors, which is higher than the
results reported by similar methods in the literature.
Pre-processing Methods in Chest X-ray Image Classification
Pre-processing is an essential step in any machine learning-based image classification system. It
involves transforming the input images into a format that is suitable for the machine learning model.
The goal of pre-processing is to improve the accuracy and efficiency of the model by reducing noise,
enhancing the relevant features, and normalizing the image intensity [5].
Microscopic Retinal Blood Vessels Detection and Segmentation Using SVM
and K-Nearest Neighbors
The SVM was used to classify each pixel in the image as either a blood vessel pixel or a non–blood
vessel pixel. The proposed method achieved an accuracy of 92% for blood vessel detection and 90%
for blood vessel segmentation [13].
Deep Spatial–Spectral Transformer for Hyperspectral Image Classification
The hyperspectral image is first pre-processed by applying principal component analysis (PCA) to
reduce the dimensionality of the data. Deep spatial–spectral transformer (DSS-TRM) is more robust to
noise and occlusion than traditional CNNs [6].

Novel Nested Patch-based Feature Extraction Model for Automated
Parkinson's Disease Symptom Classification Using MRI Images
Two classifiers were used: k-nearest neighbor (kNN) and SVM. A 10-fold cross-validation technique
was used to evaluate the performance of the classifiers [7].
Local Correntropy Matrix Representation for Hyperspectral Image
Classification
A hyperspectral image is first divided into a set of non-overlapping patches. A local correntropy
matrix (LCEM) is calculated for each patch. The LCEM is a matrix that measures the similarity between
the spectral signatures of the pixels in the patch [8].
Cloud Computing-based Framework for Breast Tumor Image Classification
Using Fusion of AlexNet and GLCM Texture Features with Ensemble
Multi-Kernel Support Vector Machine
AlexNet architecture, which is a deep learning model that has been pre-trained on a large dataset of
natural images. GLCM texture features, which are handcrafted features that capture the spatial [14].
Brain Magnetic Resonance Imaging Classification Using Deep Learning
Architectures with Gender and Age
Wahlang et al. [9] used a deep learning approach to classify brain MRI images into normal or
abnormal. The authors found that the deep learning approach was able to achieve an overall accuracy
of 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%).
Detection of Breast Cancer Using Histopathological Image Classification
Dataset with Deep Learning Techniques
Reshma et al. [15] used a deep learning model called a convolutional neural network (CNN) to detect
breast cancer in histopathological images.
A Survey of Deep Learning Techniques for Underwater Image Classification
Mittal et al. [11] classified the deep learning techniques into three categories: CNNs, recurrent neural
networks (RNNs), and deep reinforcement learning (DRL).
Cervical Cancer Classification Using Combined Machine Learning and
Deep Learning Approach
The proposed method achieved an accuracy of 92% for blood vessel detection and 90% for blood
vessel segmentation.
Cervical Net: A Novel Cervical Cancer Classification Using Feature Fusion
They used five different machine learning algorithms to classify the Pap smear images: SVM, random
forest (RF), k-nearest neighbor (kNN), artificial neural network (ANN), and naive Bayes (NB) [12].
RESULTS
SVMs represent a robust machine learning algorithm suitable for tasks like image classification,
encompassing flower categorization. In a study, SVMs were able to classify flowers with an accuracy
of 85% as shown in Figure 4.
Our proposed method for flower image classification using SVM is a simple and effective approach.
Our method achieves high accuracy on a variety of flower datasets, and it is more efficient than other
state-of-the-art methods. Our method can be used to develop a variety of applications, such as the
following:

Volume 9, Issue 2
ISSN: 2456-2351
Figure 4. Flower categorization.
• Flower identification apps: Our method can be used to develop mobile apps that can identify
flowers from photos taken by users. This can be useful for people who are interested in learning
more about flowers or for people who are trying to identify a flower that they have found.
• Flower sorting systems: Our method can be used to develop systems that can sort flowers into
different categories. This can be useful for flower growers or for companies that sell flowers. For
example, a flower sorting system could be used to sort roses into different colors.
• Agricultural research: Our method can be used to develop systems that can help agricultural
researchers to study flowers. For example, a system could be used to identify flowers that are
resistant to pests or diseases.
• Biodiversity monitoring: Our method can be used to develop systems that can help to monitor
biodiversity. For example, a system could be used to track the population of different flower
species in a particular area.
Comparison Between Existing Systems with Proposed System
Table 2 presents the comparison between proposed and existing systems.
Table 2. Comparison of existing and proposed system.
Parameters Existing System Proposed System
User friendly Yes Yes
Database record Yes Yes
Aptitude test No Yes
Practice sets No Yes
Data sorting No Yes
Apply for job No Yes
Mode of interview Offline Online/Offline
Email notification Yes Yes
Data security Yes Yes
CONCLUSION
SVMs are a powerful machine learning algorithm that can be used for image classification. SVMs
can achieve good accuracy on flower image classification, with an accuracy of 85.2% in one study. This
suggests that SVMs could be used to develop computer-aided flower classification systems. Such
systems could be used to automate the flower classification process in a variety of industries, such as:
• Pharmaceutical companies: SVMs could be used to classify flowers based on their chemical
composition, color, texture, and shape. This data could then be used to develop new
pharmaceuticals and therapies.
• Cosmetics companies: SVMs could be used to classify flowers based on their properties, such as
their fragrance and color. This data could then be used to develop new cosmetic products.
• Agricultural companies: SVMs could be used to classify flowers based on their species and
health. This data could then be used to improve crop yields and reduce the use of pesticides.

Overall, SVMs are a promising approach to flower image classification. Computer-aided flower
classification systems based on SVMs could have a significant impact on a variety of industries.
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