Chapter 3 ANALYSIS

3.4 Methodology
The project follows a systematic approach to achieve the stated objectives. The methodology
includes:

• Data Collection and Preprocessing: Acquire a diverse dataset of animal images and
preprocess them by resizing, normalizing, and augmenting to improve model
performance.

• Model Selection and Training: Use the pretrained VGG16 model and apply transfer
learning to fine-tune it on the specific animal image dataset for accurate
classification.

• User Interface Development: Develop a user-friendly interface using Streamlit,

enabling users to upload images and receive real-time classification results.

• Model Evaluation: Evaluate the model's performance using metrics like accuracy,
precision, recall, and F1-score, and conduct cross-validation to ensure generalization.

• Deployment and Testing: Deploy the model and Streamlit application on a cloud
platform or local server, and perform extensive testing with real-world images.

• Documentation and Reporting: Document the entire process, including methodology,

code, and results, and analyze outcomes to identify challenges and propose future
improvements.

Dept. of ISE, RNSIT 2023-2024 5

Chapter 4
METHODS AND ALGORITHMS

Convolutional Neural Network (CNN):

Convolutional Neural Network (CNN) is a specialized deep learning architecture
designed for processing structured grid-like data, such as images. It consists of multiple layers,
including convolutional, pooling, and fully connected layers, that are trained to automatically
extract hierarchical features from the input data. CNNs leverage convolutional operations to
detect patterns and features at different spatial scales, making them particularly effective for
tasks like image classification, object detection, and image segmentation.

In the context of animal image classification, CNNs offer significant advantages in

learning discriminative features from raw image data. By employing convolutional layers to
extract visual features hierarchically, CNNs can effectively capture the unique characteristics of
different animal species, facilitating accurate classification even in the presence of variations in
pose, lighting conditions, and background clutter.

CNNs have become the de facto standard for image classification tasks due to their ability
to learn complex representations directly from raw pixel values, without the need for handcrafted
features. Moreover, advancements in deep learning frameworks and hardware acceleration have
made it feasible to train deep CNN architectures on large-scale image datasets efficiently. As a
result, CNNs have become indispensable tools for tasks like animal image classification, where
the goal is to automatically identify and classify animals from images with high accuracy and
efficiency.

VGG16 Model:

The VGG16 model is a popular choice for animal image classification due to its
pretrained capabilities. This means it's already learned powerful image recognition features from
a massive dataset. By fine-tuning this pre-trained model on your specific animal images, you
can achieve good accuracy without training from scratch. VGG16 is readily available in deep
learning frameworks and offers a solid foundation for your animal classification project.

Dept. of ISE, RNSIT 2023-2024 7

Chapter 3
ANALYSIS
3.1 Problem Statement

Given a dataset of animal images containing features such as image pixels,

dimensions, and other relevant attributes, the objective is to develop a predictive model that
can effectively classify each image into its corresponding animal category. The model should
be trained on historical image data, leveraging visual patterns and characteristics to
accurately classify new images with high accuracy. Additionally, the model should be robust
to handle imbalanced class distributions and adapt to variations in image quality and
background noise. Ultimately, the goal is to deploy a reliable image classification of animals
system that can assist in wildlife monitoring, conservation efforts, and scientific research.

3.2 Objectives

• Develop a machine learning model capable of accurately classifying images of various animal
species.
• Analyze visual patterns and characteristics within image data to identify distinct features for each
animal category.
• Implement a scalable and efficient image classification of animals system capable of handling large
volumes of image data in real-time.
• Optimize the model architecture and hyperparameters to improve the accuracy and robustness of
animal classification.
• Contribute to wildlife monitoring and conservation efforts by accurately identifying and
categorizing animals from images captured in different environments.

3.3 Aim of the Project

Given a dataset of animal images containing features such as image pixels, dimensions,
and other relevant attributes, the objective is to develop a predictive model that can effectively
classify each image into its corresponding animal category. The model should be trained on
historical image data, leveraging visual patterns and characteristics to accurately classify new
images with high accuracy. Additionally, the model should be robust to handle imbalanced class
distributions and adapt to variations in image quality and background noise. Ultimately, the
goal is to deploy a reliable image classification of animals system that can assist in wildlife
monitoring, conservation efforts, and scientific research.
Dept. of ISE, RNSIT 2023-2024 4
 Chapter 3 ANALYSIS
IntIntroducro

 Ensure scalability and real-time performance to handle large-scale data efficiently and
provide timely recommendations.
 Maintain recommendation diversity to balance accuracy and expose users to a broad
range of content, including new and unexpected suggestions.
 Ease of Use: Develop a user-friendly system that can be easily operated by non-experts,
making it accessible to a wide range of users.

 Deliver personalized movie suggestions tailored to individual user preferences

and viewing histories.
3.2.1 Aims of the Project
 Ensure Scalability: Develop a system capable of efficiently handling large-scale
data to provide real-time performance..
 Ensure Non-Invasive Testing: Utilize foundprocessing to assess freshness without
altering or damaging the moviesamples.
 Improve Scalability: Design the solution to be easily scalable across different
conditions.
 Integrate User-Friendly Technology: Create a practical and intuitive application for
end-users to efficiently .

3.4 Software Requirement Specifications

A Software Requirements Specification (SRS) is a description of a software system to

be developed. It lays out functional and non-functional requirements and may include a set

cases that describe user interactions that the software must provide.

3.3.1 Software Requirement Specification

 Machine Learning frameworks (e.g., TensorFlow, PyTorch)
 Data analysis tools (e.g., Pandas, NumPy)
 IDE (e.g., PyCharm, Jupyter Notebook)
 Database management (e.g., SQL)
 Version control (e.g., Git)
 ‘

3.3.2 Hardware Requirement Specification

 Processor: Intel or AMD
 High-performance CPU/GPU (e.g., NVIDIA RTX)
 Sufficient storage (SSD preferred)
 Computer with 16GB

Dept. of ISE, RNSIT 2023-2024 5

Chapter 8

CONCLUSION AND FUTURE WORK

8.1 Conclusion
The application of AI and foundprocessing for determining moviefreshness represents
asignificant advancement in seafood quality control. By providing a rapid, accurate, and
non-invasive assessment method, this approach addresses the limitations of traditional
freshness evaluation techniques. Implementing such a system can enhance food safety,
reduce waste, and streamline quality control processes in the seafood industry. Future
enhancements, including expanding datasets, integrating real-time monitoring, and
improving user interfaces, will further strengthen the system's effectiveness and
applicability, leading to even greater benefits in freshness assessment and overall quality
management.

This technology not only reduces subjectivity and human error associated with sensory
evaluations but also speeds up the assessment process, making it practical for real-time
application in the supply chain. Additionally, it contributes to reducing waste by
identifying spoilage earlier and improving overall operational efficiency.

In summary, the development and deployment of AI and foundprocessing solutions for

moviefreshness determination offer significant benefits for ensuring product quality and
safety. Continued innovation in this field promises to drive further improvements in the
efficiency and accuracy of seafood quality control, ultimately benefiting both consumers
and industry stakeholders.

8.2 Future Enhancements

Expand Dataset Diversity: Increase the dataset to include a wider variety of movie species,
sizes, and environmental conditions to enhance model robustness and generalizability.
Real-Time Monitoring Integration: Incorporate real-time data collection methods, such as
IoT sensors, to provide continuous monitoring and dynamic freshness assessments.
Advanced Model Improvement: Explore and integrate advanced deep learning techniques,
such as transfer learning or ensemble methods, to further improve model accuracy and
reliability.

Dept. of ISE, RNSIT 2023-2024 22

21
47
63
Chapter 3 ANALYSIS

3.5 Software Requirement Specifications

A Software Requirements Specification (SRS) is a description of a software system to

be developed. It lays out functional and non-functional requirements and may include a set
of use cases that describe user interactions that the software must provide.

3.5.1 Software Requirement Specifications

• Operating System: The code can run on any major operating system, including Windows,
macOS, and Linux.
• Python: Python 3.x (preferably the latest version) must be installed on the system. Python
serves as the primary programming language for executing the code.
• Python Libraries: The following Python libraries need to be installed: base64, streamlit,
requests, keras, VGG16.
• Integrated Development Environment (IDE) Any Python-compatible IDE or text editor
can be used for writing and executing the code. Popular choices include PyCharm, Jupyter
Notebook, Visual Studio Code, and Spyder.
• Data File: The code expects a dataset of animal images in a specific format. Ensure that
the image files are accessible and located in the same directory as the code, or specify the
correct path to the directory containing the image files.

3.5.2 Hardware Requirement Specifications

• Processor: A modern multi-core processor (e.g., Intel Core i5 or higher, AMD Ryzen) is
recommended for faster data processing.
• RAM: A minimum of 4GB of RAM is recommended for handling moderate-sized datasets
efficiently. However, having 8GB or more RAM will provide better performance,
especially when dealing with larger datasets.
• Storage: Adequate storage space is needed to store the dataset and any additional files or
outputs generated during the analysis. A few gigabytes of free disk space should be
sufficient for most cases.
• Internet Connection: An internet connection may be required for installing Python
libraries and accessing external resources or datasets.
 Chapter 5
DATASETS

The dataset used in this project is publicly available on the Kaggle website and is sourced
from a dataset related to animal image classification. It contains a collection of animal images with
corresponding labels for different animal species. Each image is represented by its pixel values
and metadata attributes, including dimensions and file format.

The dataset includes images of various animals such as dogs, cats, birds, and mammals,
among others. Each image is labelled with its corresponding animal category, allowing for
supervised learning tasks in image classification.

The images are stored in a structured format, such as JPEG or PNG, and can be easily
loaded into a dataset using libraries like OpenCV or PIL in Python. Additionally, the dataset may
include metadata information such as image resolution, file size, and image quality metrics to assist
in preprocessing and analysis.

Figure 5.1: Example snapshot of the dataset

 CHAPTER 5 IMPLEMENTATION

Requirements:

Figure 6.1: Required modules

Functions:

Figure 6.2: Functions

Main driver:

Figure 6.3: Main Driver

 VISVESVARAYA TECHNOLOGICAL UNIVERSITY
JNANA SANGAMA, BELAGAVI – 590 018, KARNATAKA

A Mini Project Report on

IMAGE CLASSIFICATION OF ANIMAL

Submitted in partial fulfillment of the requirements for the VI Semester of degree of

Bachelor of Engineering in Information Science and Engineering of Visvesvaraya
Technological University, Belagavi

by

NEHA VENKATESH KURDEKAR PURUSHOTHAM N K

(1RN21IS189) (1RN21IS110)

Under the Guidance of

Dr. Bhagyashree Ambore
Associate Professor
Department of ISE

Department of Information Science and Engineering

RNS INSTITUTE OF TECHNOLOGY
Dr. Vishnuvardhan Road, Rajarajeshwari Nagar Post,Channasandra,
Bengaluru – 560 098

2023-2024
 RNS INSTITUTE OF TECHNOLOGY
Dr. Vishnuvardhan Road, Rajarajeshwari Nagar PostChannasandra,
Bengaluru – 560 098

DEPARTMENT OF INFORMATION SCIENCE AND ENGINEERING

CERTIFICATE

Certified that the Mini Project work (ISMP67) entitled Image Classification Of Animals has been
successfully completed by Neha Venkatesh Kurdekar (1RN21IS189), Purushotham N K
(1RN21IS110) bonafide students of RNS Institute of Technology, Bengaluru in partial fulfillment
of the requirements for the award of degree Bachelor of Engineering in Information Science and
Engineering of Visvesvaraya Technological University, Belagavi during academic year 2023- 2024.
The Mini Project report has been approved as it satisfies the academic requirements in respect of
project work for the said degree.

Dr. Bhagyashree Ambore Dr. Suresh L Dr. Ramesh Babu HS

Project Guide Professor and Principal
Associate Professor Head of Department
 DECLARATION
We, K TRESHA [1RN21IS062], KAVYA [1RN21IS066] and RISHIKA R [1RN22IS411],
students of VI Semester B.E. in Information Science and Engineering, RNS Institute of Technology
hereby declare that the mini project (ISMP67) entitled potato disease detection has been carried out
by us and submitted in partial fulfillment of the requirements for the VI Semester of degree of Bachelor
of Engineering in Information Science and Engineering of Visvesvaraya Technological University,
Belagavi during academic year 2023-2024.

Place: Bengaluru
Date: 26-07-2024

K TRESHA [1RN21IS062]

KAVYA [1RN21IS066]

RISHIKA R [1RN22IS411]

i
 ABSTRACT

The "Potato Disease Detection Using Deep Learning" project aims to address critical
agricultural challenges by leveraging advanced technologies. This innovative solution
employs convolutional neural networks (CNNs) to accurately identify diseases in potato
leaves. The deep learning model is trained on a comprehensive dataset of annotated potato
leaf images, which undergo preprocessing steps like resizing, normalization, and
augmentation to enhance the model's robustness. This approach ensures high precision in
recognizing disease patterns, thus enabling effective disease management in potato
cultivation.

The system architecture integrates a user-friendly frontend developed with React JS,
allowing farmers to upload images of potato leaves for analysis. The backend, powered by
FastAPI, handles image processing and communicates with the deep learning model for
inference. This seamless interaction between the frontend and backend ensures real-time
feedback on the health status of the potato leaves, providing immediate and actionable
insights to the users. The intuitive web application design ensures accessibility for users
with varying technical expertise, promoting widespread adoption.

Overall, the project aims to provide an efficient and cost-effective solution for early disease
detection in potato crops. By automating the detection process, it reduces the dependency on
human experts, minimizes the risk of misdiagnosis, and ensures timely intervention. The
system's high accuracy, combined with its ease of use and rapid feedback capabilities,
makes it a valuable tool for farmers, helping them mitigate crop loss and improve yield
quality.

ii
 DECLARATION

We, Neha Venkatesh Kurdekar [1RN21IS189], Purushotham N K [1RN21IS110], students of VI

Semester B.E. in Information Science and Engineering, RNS Institute of Technology hereby declare that
the Mini Project (ISMP67) entitled Image Classification Of Animal has been carried out by us and
submitted in partial fulfillment of the requirements for the award of degree of Bachelor of Engineering
in Information Science and Engineering of Visvesvaraya Technological University, Belagavi during
academic year 2023-2024.

Place: Bengaluru
Date: 09-08-2024

NEHA VENKATESH KURDEKAR - [1RN21IS189]

PURUSHOTHAM N K - [1RN21IS110]

i
 ABSTRACT

The project serves as a comprehensive framework for conducting

exploratory data analysis and initial preprocessing in an Image classification of
animals project utilizing machine learning and deep learning techniques. The code
begins by importing a suite of essential libraries, including numpy for numerical
computations, pandas for structured data manipulation, and visualization tools like
matplotlib and seaborn to create insightful plots. Additionally, it incorporates
powerful machine learning modules from scikit-learn for data processing, and
leverages the VGG16 model from the keras platform, alongside utility functions such
as load_img, img_to_array, preprocess_input, and decode_predictions for image
handling and feature extraction.

The dataset, which consists of images from various animal categories, is loaded
into the environment, allowing for in-depth exploration of its characteristics. Through
the use of visualization techniques, the code facilitates the analysis of class
distribution, revealing potential imbalances that might influence model performance.
It further delves into the exploration of image features, examining dimensions, color
distributions, and other properties across different animal categories. By plotting the
relationship between image dimensions and corresponding animal classes, the code
aids in uncovering patterns or anomalies that could impact classification accuracy.

Additionally, the code calculates the fraction of each animal category present in
a sampled subset of the data, providing valuable insights into the dataset's
representativeness and diversity. The preprocessing phase involves loading the
images, converting them to arrays, and applying necessary transformations to
standardize the input for model training. It also separates the image features from their
associated labels, ensuring that the data is well-organized and ready for the subsequent
stages of machine learning model development. Overall, these meticulous EDA and
preprocessing steps are crucial in laying a strong foundation for building, training, and
evaluating effective animal image classification models.

ii
 ACKNOWLEDGMENT

The fulfillment and rapture that go with the fruitful finishing of any assignment would be
inadequate without specifying the people who made it conceivable, whose steady direction and
support delegated the endeavors with success.

We would like to profoundly thank the Management of RNS Institute of Technology for
providing such a healthy environment to carry out this project work.

We would like to express our gratitude to our Director, Dr. M K Venkatesha, and
Principal, Dr. Ramesh Babu H S, for their support and inspiration in guiding us towards the
pursuit of knowledge.

We wish to place on record our words of gratitude to Dr. Suresh L, Professor and Head
of the Department, Information Science and Engineering, for being the enzyme and mastermind
behind our project work.

We place our heartfelt thanks to our Faculty In charge Dr. Bhagyashree Ambore,
Associate Professor, Department of Information Science and Engineering for valuable guidance
in project work.

We would like to thank all other teaching and non-teaching staff of Information Science
& Engineering who have directly or indirectly helped us to carry out the project work.

And lastly, we would hereby acknowledge and thank our parents who have been a source of
inspiration and also instrumental in carrying out this project work.

NEHA VENKATESH KURDEKAR - [1RN21IS189]

PURUSHOTHAM N K - [1RN21IS110]

iii
 TABLE OF CONTENTS
CERTIFICATE
DECLARATION i

ABSTRACT ii
ACKNOWLEDGMENT iii
TABLE OF CONTENTS iv
LIST OF FIGURES v
1. INTRODUCTION 1
2. LITERATURE SURVEY 2
3. ANALYSIS 4
3.1 Problem Statement 4
3.2 Objectives 4
3.3 Aim of project 4
3.4 Methodology 5
3.5 Software Requirement Specifications 5
3.5.1 Software Requirement Specifications 5
3.5.2 Hardware Requirement Specifications 6
4. METHODS AND ALGORITHMS 7

5. DATASETS 8

6. IMPLEMENTATION 9
6.1 Required modules 10
6.2 Functions 10
6.3 Main Driver 11
7. RESULT 12
7.1 Result Snapshot 1 12
7.2 Result Snapshot 2 13
7.3 Result Snapshot 3 13
8. CONCLUSION AND FUTURE ENHANCEMENT 14
REFERENCES

iv
 LIST OF FIGURES

Figure No Descriptions Page

5.1 Example Snapshot of the Dataset 8
6.1 Required Modules 10
6.2 Functions 10
6.3 Main Driver 11
7.1 Result Snapshot 1 12
7.2 Result Snapshot 2 13
7.3 Result Snapshot 3 13