Animal Repellent Document
Animal Repellent Document
Animal Repellent Document
(DNN) and IoT for the development and deployment of many controlling, monitoring and
tracking applications at a fine-grained level. In this rapidly evolving scenario, managing the
relationship with the elements external to the agriculture ecosystem, such as wildlife, is a
relevant open issue. One of the main concerns of today's farmers is protecting crops from
wild animals’ attacks. There are different traditional approaches to address this problem
which can be lethal (e.g., shooting, trapping) and non-lethal (e.g., scarecrow, chemical
repellents, organic substances, mesh, or electric fences). Nevertheless, some of the traditional
methods have environmental pollution effects on both humans and ungulates, while others
are very expensive with high maintenance costs, with limited reliability and limited
effectiveness. In this project, we develop a system, that combines AI Computer Vision using
DCNN for detecting and recognizing animal species, and specific ultrasound emission (i.e.,
different for each species) for repelling them. The new system requires communication,
computation and storage capabilities, and therefore designed and developed an infrastructure
that integrates ad-hoc IoT devices, Edge and Cloud Computing. The edge computing device
activates the camera, then executes its DCNN software to identify the target, and if an animal
is detected, it sends back a message to the Animal Repelling Device including the type of
ultrasound to be generated according to the category of the animal. The ``activity'' message is
also transmitted from the repellent device via LoRa to the LoRa gateway, which then
2. Problem Definition
As human and livestock population has increased immensely, forest encroachment has been
effected, which paved a floor to grazing activities, cultivation of wastelands and
deforestation. Due to such human disturbances and habitat loss the wild animals have been
affected to a great extent, some became endangered, some extinct, and those who survived,
learnt to live in man designed habitat successfully. For many years, the natural habitats of the
country have been altered because of human. .Most of the highlands and some of the
lowlands have been modified into agricultural and pastoral land. This has led to
encroachment into wildlife habitats. The constriction of wildlife habitats resulted in severe
competition for natural resources between wild animals and the local communities. This in
turn resulted in wildlife human conflict. These animals enter human settlement and their
fields for food and causes damage to agriculture and horticulture crops. Until recently, there
has been little attention given to vertebrate species that damage crops, particularly crops of
small-scale farmers in tropical and sub-tropical regions. Birds are also recognized as
significant pests. The main damage is caused by their feeding activities. Herbivorous and
omnivorous species of birds damage the agriculture. The solution to the above problem is
detecting the animals and repellent them(animals) based on AI and IOT. The process flow is
admin add the user details and animals details in the system then if the system detect the
animals, then the system sends the message or notification to the farmer or user and also it
including the type of ultrasound to be generated according to the category of the animal is
used for repellent the animal.
3. Modules List
1. Animal Repellent Web Dashboard
2. Animal Recognition
3. Repellent
4. Monitoring and Visualizing
5. Notification
6. Performance Analysis
4. System Analysis
4.1 Existing System
Wild animals are a special challenge for farmers throughout the world. Animals such as deer,
wild boars, rabbits, moles, elephants, monkeys, and many others may cause serious damage
to crops. They can damage the plants by feeding on plant parts or simply by running over the
field and trampling over the crops. Therefore, wild animals may easily cause significant yield
losses and provoke additional financial problems. Another aspect to consider is that wild
animal crop protection requires a particularly cautious approach. In other words, while
utilizing his crop production, every farmer should be aware and take into consideration the
fact that animals are living beings and need to be protected from any potential suffering.
Farmers Traditional Approach
There are different existing approaches to address this problem which can be lethal (e.g.,
shooting, trapping) and non-lethal (e.g., scarecrow, chemical repellents, organic substances,
mesh, or electric fences), firecrackers, bright lights, fire, beating drums, and dogs. Non-
chemical control of pocket gophers. 22 rimfire rifle or a shotgun can be used to dispatch
woodchucks. Some motion-activated water sprayers have been developed that spray birds
when they break the motion-detecting
1. Agricultural fences
Fencing is a popular wild animal protection practice for that can last for many years.
Agricultural fences are quite an effective wild animal protection technology. However,
utilizing fences as a practice is often regulated. Some local and state entities may restrict or
prevent the use of certain types of fences. Therefore, before deciding on a suitable fence, it's
important to check local law regulations. The quality of fencing depends on the material and
structure. Depending on how it is made and what it is made of, some permanent fences can
last up to 30 years. Farmers usually use one of the following types of fences:
Wire fences
constructed of metal wires woven together forming a physical barrier. The fences
are effective, long lasting, and require relatively little maintenance. However, they are
expensive and recommended only for the protection of high-value crops.
Plastic fences
polypropylene fences are generally less expensive and easier to install and repair than other
types. Additionally, these fences are widely acceptable and meet various regulations. Their
disadvantage includes their short lifespan (up to 10 years) and questionable effectiveness in
areas with a higher possibility of wild animal crop damage.
Disadvantages
Its disadvantages include the potential for the entire fence to be disabled due to a
break in the conducting wire, shorting out if the conducting wire contacts any non-
electrified component that may make up the rest of the fence, power failure, or forced
disconnection due to the risk of fires starting by dry vegetation touching an electrified
wire.
Other disadvantages can be lack of visibility and the potential to shock an
unsuspecting human passer-by who might accidentally touch or brush the fence
Bee fence disadvantages are that it is only restricted to elephants and humans can also
become targets of the bees
Percentage of all intrusions in the detection area that was detected was relatively low
Sensor Failure
Expensive
Advantages
Wide area surveillance
Accurate and Fast prediction
Cost effectiveness of available Crop protection systems.
Easy to use and with less maintenance.
Robust and reliable system.
Complete security or full proof system.
Less or no labor requirement.
Easily adaptable by the farmers
Remote Monitor
Low energy consumption
Warns and tracks
Fully automated system
Integra table with third-party cameras
5. SYSTEM SPECIFICATION
5.1. Hardware specification
Processors: Intel® Core™ i5 processor 4300M at 2.60 GHz or 2.59 GHz (1
socket, 2 cores, 2 threads per core), 8 GB of DRAM
Disk space: 320 GB
Operating systems: Windows® 10, macOS*, and Linux*
5.2. Software specification
PHP 5 & Python 3.2
MySQL
WAMP Server 2.0
Fkask
Embedded C
Arduino IDE
Chapter 6
SOFTWARE DESCRIPTION
Python 3.7.4
Python is a general-purpose interpreted, interactive, object-oriented, and high-level
programming language. It was created by Guido van Rossum during 1985- 1990. Like Perl,
Python source code is also available under the GNU General Public License (GPL). This
tutorial gives enough understanding on Python programming language.
Python is a high-level, interpreted, interactive and object-oriented scripting language. Python
is designed to be highly readable. It uses English keywords frequently where as other
languages use punctuation, and it has fewer syntactical constructions than other languages.
Python is a MUST for students and working professionals to become a great Software
Engineer specially when they are working in Web Development Domain. I will list down
some of the key advantages of learning Python:
Python is Interpreted − Python is processed at runtime by the interpreter. You do not need to
compile your program before executing it. This is similar to PERL and PHP.
Python is Interactive − You can actually sit at a Python prompt and interact with the
interpreter directly to write your programs.
Python is Object-Oriented − Python supports Object-Oriented style or technique of
programming that encapsulates code within objects.
Python is a Beginner's Language − Python is a great language for the beginner-level
programmers and supports the development of a wide range of applications from simple text
processing to WWW browsers to games.
The Python Package Index (PyPI) hosts thousands of third-party modules for Python. Both
Python's standard library and the community-contributed modules allow for endless
possibilities.
The most basic use case for Python is as a scripting and automation language. Python isn’t
just a replacement for shell scripts or batch files; it is also used to automate interactions with
web browsers or application GUIs or to do system provisioning and configuration in tools
such as Ansible and Salt. But scripting and automation represent only the tip of the iceberg
with Python.
General application programming with Python
You can create both command-line and cross-platform GUI applications with Python and
deploy them as self-contained executables. Python doesn’t have the native ability to generate
a standalone binary from a script, but third-party packages like cx_Freeze and PyInstaller can
be used to accomplish that.
Data science and machine learning with Python
Sophisticated data analysis has become one of fastest-moving areas of IT and one of Python’s
star use cases. The vast majority of the libraries used for data science or machine learning
have Python interfaces, making the language the most popular high-level command interface
to for machine learning libraries and other numerical algorithms.
Web services and RESTful APIs in Python
Python’s native libraries and third-party web frameworks provide fast and convenient ways to
create everything from simple REST APIs in a few lines of code to full-blown, data-driven
sites. Python’s latest versions have strong support for asynchronous operations, letting sites
handle tens of thousands of requests per second with the right libraries.
Metaprogramming and code generation in Python
In Python, everything in the language is an object, including Python modules and libraries
themselves. This lets Python work as a highly efficient code generator, making it possible to
write applications that manipulate their own functions and have the kind of extensibility that
would be difficult or impossible to pull off in other languages.
Python can also be used to drive code-generation systems, such as LLVM, to efficiently
create code in other languages.
“Glue code” in Python
Python is often described as a “glue language,” meaning it can let disparate code (typically
libraries with C language interfaces) interoperate. Its use in data science and machine
learning is in this vein, but that’s just one incarnation of the general idea. If you have
applications or program domains that you would like to hitch up, but cannot talk to each other
directly, you can use Python to connect them.
Python 2 vs. Python 3
Python is available in two versions, which are different enough to trip up many new users.
Python 2.x, the older “legacy” branch, will continue to be supported (that is, receive official
updates) through 2020, and it might persist unofficially after that. Python 3.x, the current and
future incarnation of the language, has many useful and important features not found in
Python 2.x, such as new syntax features (e.g., the “walrus operator”), better concurrency
controls, and a more efficient interpreter.
Python 3 adoption was slowed for the longest time by the relative lack of third-party library
support. Many Python libraries supported only Python 2, making it difficult to switch. But
over the last couple of years, the number of libraries supporting only Python 2 has
dwindled; all of the most popular libraries are now compatible with both Python 2 and
Python 3. Today, Python 3 is the best choice for new projects; there is no reason to pick
Python 2 unless you have no choice. If you are stuck with Python 2, you have various
strategies at your disposal.
Python’s libraries
The success of Python rests on a rich ecosystem of first- and third-party software. Python
benefits from both a strong standard library and a generous assortment of easily obtained and
readily used libraries from third-party developers. Python has been enriched by decades of
expansion and contribution.
Python’s standard library provides modules for common programming tasks—math, string
handling, file and directory access, networking, asynchronous operations, threading,
multiprocessors management, and so on. But it also includes modules that manage common,
high-level programming tasks needed by modern applications: reading and writing structured
file formats like JSON and XML, manipulating compressed files, working with internet
protocols and data formats (webpages, URLs, email). Most any external code that exposes a
C-compatible foreign function interface can be accessed with Python’s ctypes module.
The default Python distribution also provides a rudimentary, but useful, cross-platform GUI
library via Tkinter, and an embedded copy of the SQLite 3 database.
The thousands of third-party libraries, available through the Python Package Index (PyPI),
constitute the strongest showcase for Python’s popularity and versatility.
For example:
The Beautiful Soup library provides an all-in-one toolbox for scraping HTML—even tricky,
broken HTML—and extracting data from it.
Requests makes working with HTTP requests at scale painless and simple.
Frameworks like Flask and Django allow rapid development of web services that encompass
both simple and advanced use cases.
Multiple cloud services can be managed through Python’s object model using Apache
Libcloud.
NumPy, Pandas, and Matplotlib accelerate math and statistical operations, and make it easy
to create visualizations of data.
Python’s compromises
Like C#, Java, and Go, Python has garbage-collected memory management, meaning the
programmer doesn’t have to implement code to track and release objects. Normally, garbage
collection happens automatically in the background, but if that poses a performance problem,
you can trigger it manually or disable it entirely, or declare whole regions of objects exempt
from garbage collection as a performance enhancement.
An important aspect of Python is its dynamism. Everything in the language, including
functions and modules themselves, are handled as objects. This comes at the expense of
speed (more on that later), but makes it far easier to write high-level code. Developers can
perform complex object manipulations with only a few instructions, and even treat parts of an
application as abstractions that can be altered if needed.
Python’s use of significant whitespace has been cited as both one of Python’s best and worst
attributes. The indentation on the second line below isn’t just for readability; it is part of
Python’s syntax. Python interpreters will reject programs that don’t use proper indentation to
indicate control flow.
Syntactical white space might cause noses to wrinkle, and some people do reject Python for
this reason. But strict indentation rules are far less obtrusive in practice than they might seem
in theory, even with the most minimal of code editors, and the result is code that is cleaner
and more readable.
Another potential turnoff, especially for those coming from languages like C or Java, is how
Python handles variable typing. By default, Python uses dynamic or “duck” typing—great for
quick coding, but potentially problematic in large code bases. That said, Python has recently
added support for optional compile-time type hinting, so projects that might benefit from
static typing can use it.
The Apache Web Server
In addition to PHP, MySQL, JavaScript, and CSS, there’s actually a fifth hero in the dynamic
Web: the web server. In the case of this book, that means the Apache web server. We’ve
discussed a little of what a web server does during the HTTP server/client exchange, but it
actually does much more behind the scenes. For example, Apache doesn’t serve up just
HTML files—it handles a wide range of files, from images and Flash files to MP3 audio files,
RSS (Really Simple Syndication) feeds, and more. Each element a web client encounters in
an HTML page is also requested from the server, which then serves it up. But these objects
don’t have to be static files, such as GIF images. They can all be generated by programs such
as PHP scripts. That’s right: PHP can even create images and other files for you, either on the
fly or in advance to serve up later. To do this, you normally have modules either precompiled
into Apache or PHP or called up at runtime. One such module is the GD library (short for
Graphics Draw), which PHP uses to create and handle graphics.
Apache also supports a huge range of modules of its own. In addition to the PHP module, the
most important for your purposes as a web programmer are the modules that handle security.
Other examples are the Rewrite module, which enables the web server to handle a varying
range of URL types and rewrite them to its own internal requirements, and the Proxy module,
which you can use to serve up often-requested pages from a cache to ease the load on the
server. Later in the book, you’ll see how to actually use some of these modules to enhance
the features provided by the core technologies we cover. About Open Source Whether or not
being open source is the reason these technologies are so popular has often been debated, but
PHP, MySQL, and Apache are the three most commonly used tools in their categories. What
can be said, though, is that being open source means that they have been developed in the
community by teams of programmers writing the features they themselves want and need,
with the original code available for all to see and change. Bugs can be found and security
breaches can be prevented before they happen. There’s another benefit: all these programs are
free to use. There’s no worrying about having to purchase additional licenses if you have to
scale up your website and add more servers. And you don’t need to check the budget before
deciding whether to upgrade to the latest versions of these products.
What Is a WAMP, MAMP, or LAMP?
WAMP, MAMP, and LAMP are abbreviations for “Windows, Apache, MySQL, and PHP,”
“Mac, Apache, MySQL, and PHP,” and “Linux, Apache, MySQL, and PHP,” 13 www.it-
ebooks.info respectively. These abbreviations describe a fully functioning setup used for
developing dynamic Internet web pages. WAMPs, MAMPs, and LAMPs come in the form of
a package that binds the bundled programs together so that you don’t have to install and set
them up separately. This means you can simply download and install a single program and
follow a few easy prompts to get your web development server up and running in the quickest
time with the minimum hassle. During installation, several default settings are created for
you. The security configurations of such an installation will not be as tight as on a production
web server, because it is optimized for local use. For these reasons, you should never install
such a setup as a production server. However, for developing and testing websites and
applications, one of these installations should be entirely sufficient.
Using an IDE
As good as dedicated program editors can be for your programming productivity, their utility
pales into insignificance when compared to Integrated Developing Environments (IDEs),
which offer many additional features such as in-editor debugging and program testing, as
well as function descriptions and much more.
Web Framework
Web Application Framework or simply Web Framework represents a collection of libraries
and modules that enables a web application developer to write applications without having to
bother about low-level details such as protocols, thread management etc.
Flask
Flask is a web framework. This means flask provides you with tools, libraries and
technologies that allow you to build a web application. This web application can be some
web pages, a blog, a wiki or go as big as a web-based calendar application or a commercial
website.
Flask is often referred to as a micro framework. It aims to keep the core of an application
simple yet extensible. Flask does not have built-in abstraction layer for database handling,
nor does it have formed a validation support. Instead, Flask supports the extensions to add
such functionality to the application. Although Flask is rather young compared to
most Python frameworks, it holds a great promise and has already gained popularity among
Python web developers. Let’s take a closer look into Flask, so-called “micro” framework for
Python.
Flask was designed to be easy to use and extend. The idea behind Flask is to build a solid
foundation for web applications of different complexity. From then on you are free to plug in
any extensions you think you need. Also, you are free to build your own modules. Flask is
great for all kinds of projects. It's especially good for prototyping.
Flask is part of the categories of the micro-framework. Micro-framework is normally
framework with little to no dependencies to external libraries. This has pros and cons. Pros
would be that the framework is light, there are little dependency to update and watch for
security bugs, cons is that some time you will have to do more work by yourself or increase
yourself the list of dependencies by adding plugins. In the case of Flask, its dependencies are:
WSGI
Web Server Gateway Interface (WSGI) has been adopted as a standard for Python web
application development. WSGI is a specification for a universal interface between the web
server and the web applications.
Werkzeug
It is a WSGI toolkit, which implements requests, response objects, and other utility functions.
This enables building a web framework on top of it. The Flask framework uses Werkzeug as
one of its bases.
Jinja2
Jinja2 is a popular templating engine for Python. A web templating system combines a
template with a certain data source to render dynamic web pages.
built-in development server and fast debugger
integrated support for unit testing
RESTful request dispatching
Jinja2 templating
support for secure cookies (client-side sessions)
WSGI 1.0 compliant
Unicode based
Plus, Flask gives you so much more CONTROL on the development stage of your project.
It follows the principles of minimalism and lets you decide how you will build your
application.
Flask has a lightweight and modular design, so it easy to transform it to the web
framework you need with a few extensions without weighing it down
ORM-agnostic: you can plug in your favorite ORM e.g., SQL Alchemy.
Basic foundation API is nicely shaped and coherent.
Flask documentation is comprehensive, full of examples and well structured. You
can even try out some sample application to really get a feel of Flask.
It is super easy to deploy Flask in production (Flask is 100% WSGI 1.0
compliant”)
HTTP request handling functionality
High Flexibility
To sum up, Flask is one of the most polished and feature-rich micro frameworks available.
Still young, Flask has a thriving community, first-class extensions, and an elegant API.
Flask comes with all the benefits of fast templates, strong WSGI features, thorough unit
testability at the web application and library level, extensive documentation. So next time
you are starting a new project where you need some good features and a vast number of
extensions, definitely check out Flask.
System Architecture
Animal Detection
DCNN Model
Test Sample
Animal Dataset
DL Model DL Model
Capture Live
Preprocessing
Frame
Segmentation Preprocessing
Animal Type
Repellent
Farmer
A data flow.
LEVEL 0
A level 0 data flow diagram (DFD), also known as a context diagram, shows
a data system as a whole and emphasizes the way it interacts with external entities. This DFD
level 0 example shows how such a system might function within a typical retail business.
LEVEL 1:
A context level DFD is the most basic form of DFD. It aims to show how the
entire system works at a glance. There is only one process in the system and all the data flows
either into or out of this process. Context level DFD's demonstrates the interactions between
the process and external entities.
LEVEL 2:
A level 2 data flow diagram (DFD) offers a more detailed look at the processes that
make up an information system than a level 1 DFD does. It can be used to plan or record the
specific makeup of a system. ... You can then input the particulars of your own system.
UML Diagrams
Use-Case Diagram
Register
Login
Admin
user
ultrasound
Notification
Class Diagram
Admin User
Register Login
Login View
View Notification
Animal repellent
search
detected Database
Ultrasound store
Notification Access
Activity Diagram
Register
Login
searching the
animals
detecting the
animal
animal get
detected
ultrasound to
repellent the animal
alert message
to the user
Sequence Diagram
Admin Animal user
repellent
Register
Login
Login
Collaboration Diagram
UML Diagrams
E-R diagrams
7. TESTING
PROJECT TESTING
Software testing is a method of assessing the functionality of a software program.
There are many different types of software testing but the two main categories are dynamic
testing and static testing. Dynamic testing is an assessment that is conducted while the
program is executed; static testing, on the other hand, is an examination of the program's code
and associated documentation. Dynamic and static methods are often used together.
It is a complex process of testing the presence of potentially harmful bugs in developed
or developing e-learning software.
In the proposed model, we create a function detect(frame) to resize the image so that we
can call the function at appropriate places. We use try and except method, wherein in try, we
resize the image into 64x64 dimension and expanding the dimension. If the ranges of the
image are greater than one, then we rescale it to range 0 to1 and we predict the output
using ‘predict’ function. Then, to know the class in which the image belongs, we use
predict_classes. Shape not found is printed if there is an Attribute Error i.e. if my testing
data/frame doesn’t match the training set we get this indication.
To know whether the prediction is right or not, initially give a testing image to find
whether it belongs to the appropriate class or not. In the proposed model the class indices are
{boar : ‘0’, elephant: ‘1’, monkey: ‘2’} When a boar image is given to test, the output
will be [0] and if my test image is a goat, then it should return ‘shape not found’. For this,
we have to read the image using imread and call the detect function.
To detect the animals in the image. In order to check the correctness of the model, a
sample image is taken and we create a directory to save all the frames that is created.
Frames are named automatically and then we read each frame using imread and we call the
detect function.to find whether there is an animal in the particular frame. Then, we give the
appropriate sound according to the animal detected by importing playsound. This is
achieved by using else-if case by giving the class indices as condition. In the proposed
model, crackers, high pitch sounds and gun shots were used to drive the particular animal
away. After every image is read from the datasets the space and all the windows are released
at once in order to save memory.
CONCLUSION
Agricultural farm security is widely needed technology nowadays. In order to accomplish
this, a vision based IoT system is proposed and implemented using Python and OpenCv and
fabricated a AniRep Device to blow out the animals. The implementation of the application
required the design and development of a complex system for intelligent animal repulsion,
which integrates newly developed HW and SW components and allows to recognize the
presence and species of animals in real time and also to avoid crop damages caused by the
animals. Based on the category of the animal detected, the edge computing device executes
its DCNN model to identify the target, and if an animal is detected, it sends back a message
to the Animal Repelling Device including the type of ultrasound to be generated according to
the category of the animal. The ``activity'' message is also transmitted from the repeller
device via LoRa to the LoRa gateway, which then forwards the packet to the TTN server.
FUTURE ENHANCEMENT
Further in the proposed architecture, some image compression techniques can be developed
to reduce the time taken for notification to reach user as described above.
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