MINI PROJECT REPORT [EC65]
Sustainable Development of Intensive
Aquaculture Supervising Model
BACHELOR OF ENGINEERING
in
Electronics and Communication
Bhavana B Rao
1MS19EC026
Keerthana J
1MS19EC052
Khushi
1MS19EC053
Sarangamath
Mallikarjuna
1MS20EC402
Under Guidance of
Sadashiva V Chakrasali
Assistant Professor, Department of E & C
Department of Electronics and Communication
RAMAIAH INSTITUTE OF TECHNOLOGY
(Autonomous Institute, Affiliated to VTU)
Accredited by National Board of Accreditation & NAAC with ‘A+’ Grade
MSR Nagar, MSRIT Post, Bangalore-560054
www.msrit.edu
2022
CERTIFICATE
This is to certify that the Mini Project work entitled “Sustainable
Development of Intensive Aquaculture Supervising Model”
is
carried out by Bhavana B Rao (1MS19EC026), Keerthana J
(1MS19EC052),
Khushi
Sarangamath
(1MS19EC053),
and
Mallikarjuna (1MS20EC402), bonafide students of Ramaiah Institute
of Technology, Bangalore, in Electronics and Communication of
the Visvesvaraya Technological University, Belgaum,during the year
2021 -2022. It is certified that all corrections / suggestions indicated
for Internal Assessment have been incorporated in the report.
Guide
HoD
Sadashiva V Chakrasali
Dr. Maya V Karki
Assistant Professor
Professor and HoD,
Department of E &C
Department of E & C
RIT, Bangalore
RIT, Bangalore
Name & Signature of Examiners with Date: 1)
2)
DECLARATION
We hereby declare that the Mini Project entitled “Sustainable Development
of Intensive Aquaculture Supervising Model” has been carried out
independently at Ramaiah Institute of Technology under the guidance of
Sadashiva V
Chakrasali,
Assistant
Professor,
Electronics and Communication, RIT, Bangalore.
Signature of Students:
1. Bhavana B Rao
1MS19EC026
2. Keerthana J
1MS19EC052
3.Khushi Sarangamath 1MS19EC053
4.Mallikarjuna
Place:
Date:
1MS20EC402
Department
of
ACKNOWLEDGEMENT
The immense satisfaction that accompanies the successful completion of
the project would be incomplete without the mention of the people who
made it possible. We consider it our honour to express our
deepest
gratitude and respect to the following people who always guided and
inspired us during the course of the Project.
We are deeply indebted to Dr. N. V. R. Naidu, Principal, RIT, Bangalore
for providing us with a rejuvenating master course under a very creative
learning environment.
We are much obliged to Dr. Maya V Karki, Professor & HoD, Department
of Electronics and Communication Engineering, RIT, Bangalore for her
constant support and motivation.
We sincerely thank our guide Sadashiva V Chakrasali, Assistant
Professor, Department of Electronics and Communication
Engineering,
RIT, Bangalore and express our humble gratitude for his valuable guidance,
inspiration, encouragement and immense help which made this work a
success.
We sincerely thank the Chairperson of the group, Dr. Lakshmi S and
Sara Mohan George for reviewing our work and providing valuable
suggestions. We also thank all the faculty members of Department of E&C,
RIT for their kind support to carry out this project successfully.
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ABSTRACT
India is the world's third largest producer of fish, with the aquaculture and
fisheries industries playing a major economic role. Aquaculture accounts for
roughly 68 percent of the country's total fish production and 1.07 percent of the
country's GDP. By 2025, India is anticipated to require 1.6 crore tonnes of
fisheries. However, due to sudden regional climatic conditions, aquatic
productivity has reduced in recent times. The number of fish in a shoal can
provide essential information for the design of smart manufacturing management
systems in intensive aquaculture. Traditional fish cultivation techniques and
manual extraction of aquatic organisms on the other hand are not only
cumbersome, labour intensive and time consuming but also, they also put strain
on the fish creating physical stress as it is a disruptive contact method that impacts
the overall fish wellbeing and health. In order to achieve efficient and sustainable
aquaculture, our project concentrates on enhancing fish growth and improving
fish productivity by supervising essential water parameters like pH, turbidity,
temperature, dissolved oxygen and fish population. It also focuses on CNN based
fish detection and classification models (YOLOv4) for greater understanding of
fish behaviour along with fish weight and dimension prediction methods which
provide data that are essential for fish feeding and harvesting.
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Contents
Page Nos.
CHAPTER 1 INTRODUCTION
1-3
1.1 Introduction
1.2 Problem definition
1.3 Motivation of the work
1.4 Objective & Scope
CHAPTER 2 LITERATURE SURVEY
4-6
CHAPTER 3 METHODOLOGY
7-17
3.1 Wireless Sensor Network
3.1.1. Arduino UNO
3.1.2. DSB1820 Temperature Sensor
3.1.3. Turbidity Sensor
3.1.4. ESP-8266 01 Wi-Fi Module
3.2 Machine Learning Model
3.2.1. Fish Recognition
3.2.2. Fish Classification
3.2.3. Fish Weight Prediction using dimension estimator
CHAPTER 4 RESULTS & DISCUSSION
18-26
4.1 ThingSpeak Results
4.2 Fish Prediction Results
4.3 Fish Classification Results
4.4 Fish Weight Prediction Results
CHAPTER 5 CONCLUSION & FUTURE WORK
27
5.1 Conclusion
5.2 Future work
CHAPTER 6 REFERENCES
28
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Sustainable development of Intensive Aquaculture Supervising Model
CHAPTER 1
INTRODUCTION
1.1. Introduction
Aquaculture often known as aqua farming, is the practise of raising aquatic organisms such
as fish, crabs, mollusks, and plants. In other words, it is the breeding, raising, and harvesting
fish. It entails the controlled cultivation of freshwater and saltwater populations.
Aquaculture has been a flourishing business sector for many decades. Aquaculture and
fisheries industry are important economic resources that provide food for millions of
people. India holds the second place in the international level for freshwater aquaculture
cultivation and is responsible for generating almost 11 million tonnes of aquatic products.
The aquaculture sector accounts for around 68 percent of overall fish production.
Aquaculture contributes 1.07 percent to the GDP of the country. India is expected to
demand 1.6 crore tonnes of fisheries by 2025.
According to the National Fisheries Development Board, the fisheries industry generates
an export earnings of Rs.334.41 billion. Aquatic production has recently decreased as a
result of dramatic regional climate circumstances. Aquatic production has recently
decreased as a result of dramatic regional climate circumstances and also, a large extent of
Indian aquaculture is as yet dependent on customary and traditional cultivation techniques
which are rather inefficient, inaccurate, labour intensive and yield low productivity.
Convolutional neural (CNN’s) have rapidly become the standard study model in
aquaculture. Fig 1.1 Represents the percentage share of fishes obtained from aquaculture,
and percentage of fishes caught from seas and inland water bodies. In 20 years, aquaculture
has increased its share of India's fish production from a third to half. And it will account
for approximately two-thirds of India's fish production by 2030.
Fig 1.1 Percentage of Aquaculture Production
1.2. Problem definition
Traditional artificial sampling and manual testing of aquatic life are not only difficult, hard,
and time-consuming, but it also creates stress on the fish because it is a disruptive contact
and invasive method that has an impact on the fish's wellness and health. There are four
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essential water parameters for aquaculture, those are dissolved oxygen (DO), temperature,
hydrogen potential (pH) and turbidity.
The ideal content for those parameters varies depending on the type of aquaculture and fish
species that are the object of cultivation.
Depleted DO is the leading cause of fish kills, and fish farmers know that low-oxygen
conditions are their worst enemy. The presence of 0.05mg per litre of un-ionized ammonia
can be harmful to fish and can adversely affect their growth. As it reaches this level, it
irritates the fish, causing increased health deterioration and stress. In turn, they become
susceptible to bacterial infection and their resistance to disease would decrease.
Fish can become stressed in water with a pH ranging from 4.0 to 6.5 and 9.0 to 11.0. Fish
growth is limited in water pH less than 6.5, and reproduction ceases and fry can die at pH
less than 5.0. Death is almost certain at a pH of less than 4.0 or greater than 11.0.
Temperature is an important physical factor affecting fish growth and survival of fishes.
For cold-water fish, such as salmon and trout, the optimal temperature range for growth is
between 48-65ºF. Cool water species, like yellow perch, prefer water between 60º and 85ºF
and warm water fish like catfish and tilapia prefer between 75-90ºF.
Turbidity is an important factor on which productivity depends. Turbidity is
caused
due to suspended inorganic and organic substances, such as silt and clay, growth
of algae and phytoplankton and other microscopic organisms which is harmful for fish
culture. High turbidity increases the absorption of sunlight making the water warmer and
hence decreasing DO.
Traditional weight measurement methods involve extracting live fish from the ponds. This
results in stress and the possibility of injury.
The underwater habitat is more complicated, with more interference, and is limited by
light, noise, and disruptions. As a result, it's impossible to tell the fish apart from the
background. Furthermore, fish have complete freedom of movement in the water, resulting
in a variety of shapes and major occlusion issues. Occlusion increases as the number of fish
grows, and estimation accuracy diminishes.
Real time monitoring for 24/7 is near impossible for fish farmers. Due to lack of real time
monitoring immediate action towards hazardous circumstances are not taken which leads
to the decline of fish health.
1.3. Motivation of the work
A large extent of Indian aquaculture is as yet dependent on customary and traditional
cultivation techniques which are inefficient, invasive and labour intensive. With the
continued expansion of aquaculture scale and density, modern aquaculture technologies
have been compelled to overproduce, resulting in an accelerated pace of water environment
imbalance, the occurrence of frequent fish diseases, and a reduction in aquatic product
quality. Furthermore, because the average age profile of agricultural workers in many
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regions of the world is on the rise, fishery output will confront a labour crisis, and
aquaculture technologies will need to evolve immediately.
The concept of an intelligent fish farm has begun to take shape as modern
information technology has steadily entered numerous fields of agriculture.
Hence, through the concept of “Assisting human with machine” , we are able to achieve
intelligent fish farming methods like the precise task of raising oxygen ,monitoring the
other essential water parameters in aquaculture namely turbidity, temperature, pH,
ammonia and dissolved oxygen ,optimising feeding, lowering disease occurrences,
maintaining the overall fish health, providing a clean and healthy environment to aid fish
growth and properly harvesting in order to accomplish green and sustainable
aquaculture.
1.4. Objective & Scope
Prime objectives of aquaculture include production of protein- rich, nutritious, and
appetising food that benefits the entire society by providing ample food supply at a low or
fair cost, while also improving current fish stocks in natural and man-made water bodies,
generation of ornamental and sport fish.
Our project aims at enhancing fish growth and improving their productivity by supervising
essential water parameters like pH, turbidity, temperature, dissolved oxygen and fish
population in order to accomplish sustainable aquaculture. Thus, improving the quality of
life of fishes. It also focuses on CNN based fish detection and classification models
(YOLOv4) with weight and dimension prediction.
With respect to Fish Classification & weight prediction, a high accuracy fish classification
is required for greater understanding of fish behaviour. The approximation of fish size and
weight provides data that are essential for fish feeding and harvesting. The count and
distribution of the various species of fishes can give valuable insights about the health of
the ecological system and can be used as a parameter for monitoring environmental
changes. Visual classifying of fishes can also help trace their movement and give patterns
and trends in their activities providing a deeper knowledge about the species as a whole.
There is a requirement for an automated fish sorting system capable of recording species,
length and weight data. This requirement is driven by the need to reduce labour onboard
vessels and to automate logging of the catch, which is required for regulatory purposes and
also to enable increased traceability.
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CHAPTER 2
LITERATURE SURVEY
2.1. Cong Wang, Zhen Li, Tan Wang, Xianbao Xu, Xiaoshuan Zhang, Daoliang
Li, " Intelligent fish farm—the future of aquaculture", Aquaculture
International, pp. 2681–2711 ,2021
The intelligent fish farm tries to deal with the precise work of increasing oxygen, optimising
feeding, and reducing disease incidences. It reviews the application of fishery intelligence
equipment, IoT, edge computing, 5G, and artificial intelligence algorithms in modern
aquaculture.
The sensing equipment is responsible for collecting the environmental data such as DO,
pH, temperature, salinity, ammonia nitrogen, nitrite, water level, etc., as well as the working
status of the device and aquaculture video image information. The control equipment
includes aerator (oxygen cone), feeder, pump valve and other aquaculture equipment.
Fig 2.1 Conceptual design of IOT system
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2.2. Chang, CC., Wang, JH., Wu, JL. "Applying Artificial Intelligence (AI)
Techniques to Implement a Practical Smart Cage Aquaculture Management
System. " Journal of Medical and Biological Engineering, pp.652-658, 2021
This paper implements a management system that successfully integrates AI and IoT
technologies and is applied in cage culture.
It integrates sensors, underwater cameras and a communication system into a platform and
places it in a cage. Data from an autogyro and a remotely operated vehicle (ROV) were
integrated into an Omni IoT system, which can integrate the monitoring and sensing system
of feed delivery.
The data is transmitted to a cloud system, here the National Taiwan Ocean University is
used as the data collection sensor for AI computations of the feeding system, fish behaviour
monitoring and analysis system and the ROV system.
Fig 2.2 Smart cage Aquaculture Management system
2.3. S. Luo, X. Li, D. Wang, J. Li and C. Sun, "Automatic Fish Recognition and
Counting in Video Footage of Fishery Operations," 2015 International
Conference on Computational Intelligence and Communication Networks
(CICN), pp. 296-299, 2015
This paper presents an algorithm for recognizing and counting fish in video footage of
fisheries operations that is both accurate and automatic. In order to fully harness the
advantages of these algorithms, the strategy combines machine learning techniques with
statistical methodologies. The images acquired by the camera are pre- processed by
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smoothing operations. The frames are smoothed by averaging neighbouring pixels to
reduce the impression of motion. Machine learning methods are used to recognize the fish.
Colours are retrieved from photos as characteristics for classification. An artificial neural
network (ANN) is developed and used as the algorithm to determine whether a pixel
belongs to a fish or not. An Error Back Propagation (BP) ANN classifier is employed to
differentiate the fish from the background. A statistical shape model is also employed to
distinguish the real fish from other items. After the fish have been identified, the counting
method is used to accurately count them. It considers real fish as well as fishlike things in
one or more frames. Neighbouring frames are taken into account when removing the fishlike items. Non-fish things that resemble fish normally appear in no more than three
adjacent frames at a time. At least three adjacent frames will have real fish in them at the
same time. To eliminate false positives, this rule is applied. Finally, based on the actual fish
number in one frame, fish counting is done. The recognition accuracy of this method was
found to be 89.6%. And this method also demonstrated that fish with different colours and
shapes can be recognized simultaneously.
Fig 2.3 Input and Output of an ANN Classifier
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CHAPTER 3
METHODOLOGY
Fig 3.1 Methodology
Figure 3.1 represents the methodology of the project. Data from the fish farms are collected
by a network of sensors including but not limited to Temperature Sensor (ds18b20),
Turbidity Sensor, pH Sensor, Dissolved Oxygen Sensor and an underwater camera
(GoPro). In order to record, monitor, or communicate temperature changes, a temperature
sensor is an electronic device that monitors the temperature of its surroundings and turns
the input data into electronic data. The amount of light scattered by the suspended solids in
water is measured by turbidity sensors. The turbidity level (and cloudiness or haziness) of
water increases together with the amount of total suspended solids (TSS) in the water. The
level of acidity and alkalinity in water and other solutions is measured by a pH sensor.
Optical DO sensors, sometimes referred to as luminescent DO sensors (LDO) or fluorescent
sensors, quantify the amount of dissolved oxygen in water based on the luminescence's
quenching in the presence of oxygen. They can assess either the luminescence's duration
or its intensity because oxygen has an impact on both. The Microcontroller used to control
the functioning of the entire system is an Arduino UNO. A low-cost, adaptable, and simpleto-use programmable microcontroller board called Arduino UNO is available for use in a
range of electronic applications. All the sensor data is sent to the Arduino for processing.
The GoPro underwater camera provides images of fish in the farm to a CNN model, which
will count the number of fishes in the farm. An ESP8266 Wi-Fi Module is used to transmit
the sensor data to the cloud platform, ThingSpeak. With the help of the IoT analytics
platform service ThingSpeak, you can gather, visualise, and examine real-time data streams
online. This data can be remotely accessed by fish farmers and take necessary actions when
fish face hazardous conditions.
The entire project is divided into two main categories:
Wireless Sensor Network and the Machine Learning Models.
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3.1. Wireless Sensor Network
The circuit diagram of the sensor network is as depicted in figure 3.2. The controller used
is Arduino UNO. Arduino UNO is based on an ATmega328P microcontroller. The Arduino
Uno board is connected to the DS18B20 Waterproof Temperature sensor that detects the inwater temperature and the Turbidity sensor along with its module. The Arduino is also
connected to an ESP-8266 01 WiFi Module for transmitting the sensor data to ThingSpeak,
the cloud platform.
Fig 3.2 Circuit diagram
3.1.1. Arduino UNO
Arduino UNO is a microcontroller board based on the ATmega328P. It has 14 digital
input/output pins (of which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz
ceramic resonator, a USB connection, a power jack, an ICSP (In-Circuit Serial
Programming) header and a reset button. It is programmed based on IDE, which stands for
Integrated Development Environment. It can run on both online and offline platforms.
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Fig 3.3 Pinout diagram of Arduino UNO
Technical specifications of Arduino UNO:
•
•
•
•
•
•
•
•
•
•
•
•
Microcontroller: ATmega168
Operating Voltage: 5V
Input Voltage (recommended): 7-12V
Input Voltage (limits): 6-20V
Digital I/O Pins: 14
Analog Input Pins: 6
DC Current per I/O Pin: 40mA
DC Current for 3.3V Pin: 50mA
Flash Memory: 32KB (ATmega328)
SRAM: 2KB (ATmega328)
EEPROM: 1KB (ATmega328)
Clock speed: 16MHz
3.1.2. DS18B20 Temperature Sensor
The DS18B20 is a waterproof temperature sensor which supplies 9-bit to 12-bit readings
of temperature. The communication of this sensor can be achieved through a one-wire bus
protocol which uses one data line to communicate with the microprocessor. Additionally,
this sensor gets the power supply directly from the data line connected to the Arduino UNO
so that the need for an external power supply can be eliminated.
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The pin configuration of DS18B20 is as given below
• Pin1 (Ground): This pin is used to connect to the GND terminal of the circuit
• Pin2 (Vcc): This pin is used to give the power to the sensor which ranges from 3.3V
or 5V
• Pin3 (Data): The data pin supplies the temperature value, which can communicate
with the help of 1-wire method.
Fig 3.4 Pinout of DS18B20
Technical specifications of Arduino UNO:
•
•
•
•
•
•
•
•
•
•
•
Microcontroller: ATmega168
Operating Voltage: 5V
Input Voltage (recommended): 7-12V
Input Voltage (limits): 6-20V
Digital I/O Pins: 14
Analog Input Pins: 6
DC Current per I/O Pin: 40mA
DC Current for 3.3V Pin: 50mA
Flash Memory: 32KB (ATmega328)
SRAM: 2KB (ATmega328)
EEPROM: 1KB (ATmega328)
3.1.3. Turbidity sensor
The Arduino turbidity sensor detects water quality by measuring turbidity, also known as
opaqueness. It detects suspended particles in water using light by measuring light
transmittance and scattering rate, which vary with the amount of total suspended solids
(TSS) in the water. The level of liquid turbidity rises as the TTS rises. Turbidity sensors
are used to assess water quality in rivers and streams, as well as wastewater and effluent
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measurements, settling pond control instrumentation, sediment transport research, and
laboratory measurements. This liquid sensor can output both analogue and digital signals.
When in digital signal mode, the threshold can be adjusted. You can choose the mode based
on your MCU.
Specifications of turbidity sensor:
•
•
•
•
•
•
•
•
•
Operating Voltage: 5V DC
Operating Current: 40mA (MAX)
Response Time: <500ms
Insulation Resistance: 100M (Min)
Output Method:
 Analog output: 0-4.5V
 Digital Output: High/Low level signal (you can adjust the threshold value
by adjusting the potentiometer)
Operating Temperature: 5℃~90℃
Storage Temperature: -10℃~90℃
Weight: 30g
Adapter Dimensions: 38mm*28mm*10mm/1.5inches *1.1inches*0.4inches
Fig 3.5 Connection diagram
3.1.4. ESP- 8266 01 WiFi Module
ESP8266EX (simply referred to as ESP8266) is a system-on-chip (SoC) which integrates
a 32-bit Tensilica microcontroller, standard digital peripheral interfaces, antenna switches,
RF balun, power amplifier, low noise receive amplifier, filters and power management
modules into a small package. It provides capabilities for 2.4 GHz Wi-Fi (802.11 b/g/n,
supporting WPA/WPA2), general-purpose input/output (16 GPIO), Inter-Integrated Circuit
(I²C), analog-to-digital conversion (10-bit ADC), Serial Peripheral Interface (SPI), I²S
interfaces with DMA (sharing pins with GPIO), UART (on dedicated pins, plus a transmitonly UART can be enabled on GPIO2), and pulse-width modulation (PWM). The processor
core, called "L106" by Espressif, is based on Tensilica's Diamond Standard 106Micro 32Department of Electronics & Communication
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bit processor controller core and runs at 80 MHz (or overclocked to 160 MHz). It has a 64
KiB boot ROM, 32 KiB instruction RAM, and 80 KiB user data RAM. (Also, 32 KiB
instruction cache RAM and 16 KiB ETS system data RAM.) External flash memory can be
accessed through SPI.
Specifications:
• 8Mbit external QSPI flash memory
• 32-bit Tensilica Xtensa LX106 CPU running 80MHz
• 3.3V supply
• PCB-trace antenna
• 2 x 4 dual-in-line pinout
• 14.3 x 24.8mm
• 1.5g
Fig 3.6 Pinout of ESP-8266 01
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3.2. Machine Learning Models
3.2.1. Fish Recognition
Fig 3.6 Fish Recognition Algorithm
The proposed idea uses Convolutional Neural Networks for detection of fish. A deep
learning neural network called a convolutional neural network, or CNN, is made for
processing structured arrays of data, like photographs. The state-of-the-art for many visual
applications, such as image classification, convolutional neural networks are widely
employed in computer vision. They have also found success in natural language processing
for text classification. The patterns in the input image, such as lines, gradients, circles, or
even eyes and faces, are very well recognised by convolutional neural networks.
Convolutional neural networks are extremely effective for computer vision because of this
quality. Convolutional neural networks do not require any preparation and can operate
immediately on a raw image, in contrast to older computer vision methods. A CNN feedforward neural network with up to 20 or 30 layers. The convolutional layer is a unique kind
of layer that gives convolutional neural networks its power. Many convolutional layers are
placed on top of one another in convolutional neural networks, and each layer is capable of
identifying more complex structures. Handwritten digits can be recognised with three or
four convolutional layers, while human faces can be distinguished with 25 layers. A
convolutional neural network uses convolutional layers to process input images and
recognise progressively more complex elements, mimicking the organisation of the human
visual cortex.
A dataset of 1000 fish images was collected using the GoPro underwater camera. The
images were annotated. Image annotation is the process of assigning labels to digital
photographs, usually requiring human input but occasionally requiring machine assistance.
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In order to teach the computer vision model about the items in the image, labels are
predetermined by a machine learning engineer. By putting bounding boxes around the
important objects, an image annotation tool (such as makesense.ai) is used to apply a
number of labels, thereby annotating the picture. The data is split into train and test dataset
in the ratio 9:1. When machine learning algorithms are used to make predictions on data
that was not used to train the model, their performance is estimated using the train-test split
technique. The initial set of data needed to train machine learning models is known as
training data (or a training dataset). Machine learning algorithms are taught how to generate
predictions or complete a specified task using training datasets. After a machine learning
software has been trained on an initial training data set, it is tested using a secondary (or
tertiary) data set called a test set. A label file as well as a configuration file was created
along with it. A label list file represents all the classes that may be present in an image
because it contains all the labels you would want to attach to your bounding boxes. They
are, therefore, your target demographic. In this instance, there is only one label: "Fish." For
the intended model, the configuration file contains the attributes for each convolutional
layer. These files are used to train the model based on pretrained weights from the darknet
framework. Darknet is an open source, high-performance neural network implementation
framework. This model will act as a one class classifier which will be used to detect fish.
When images of the fish are taken by the underwater camera, it detects fish by surrounding
it with a bounding box.
3.2.2. Fish Classification
Fig 3.7 Fish Classification Algorithm
Convolutional Neural Networks are used in the proposed notion to classify fish based on
their species. The GoPro underwater camera was used to acquire a dataset of 500 fish
images. The first 250 images were of one species, ‘Molly,’ and the remaining 250 images
were of another species, ‘Goldfish’. The images went through an augmentation process for
one cycle. By modifying the existing data, image augmentation creates new data that can
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be used for model training. In other words, it is the process of enhancing the dataset that is
made available for deep learning model training. This resulted in a data set of 1000 photos.
The photos were annotated and divided into 9:1 train and test datasets as mentioned in
section 3.2.1. Along with it, a labels file and a configuration file were created. The labels
file contains information about class names. There are two labels in this case: “Molly” and
“Goldfish.” The configuration file provides the attributes for each convolutional layer for
the planned model. These files are used to train the model based on pre-trained weights
from the darknet framework. Darknet is an open source, high-performance neural network
implementation framework. This model will act as a two-class classifier which will be used
to classify fish. When images of the fish are taken by the underwater camera are passed
through the model it detects fish by surrounding it with a bounding box and classifies it as
either ‘Molly’ or ‘Goldfish’. This achieves the objective of classification.
3.2.3. Fish Weight Prediction using Dimension Estimator
Fig 3.8 Fish Weight Prediction Algorithm
Images from a GoPro underwater camera are processed using computer vision in a
dimension estimator. An area of artificial intelligence known as computer vision enables
computers and systems to extract useful information from digital photos, videos, and other
visual inputs and to act or offer suggestions in response to that information. If AI gives
computers the ability to think, computer vision gives them the ability to see, observe, and
comprehend. The dimension estimator makes use of an image's Hue, Saturation, and Value
pictures.
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Fig 3.9 Image Processing
When combining the three elements of a colour, hue is more precisely defined by the
dominant wavelength and is the first thing we mention (i.e., "yellow"). Hue is a word that
refers to the aspect of colour that we can most easily perceive when we look at it, or to
colour in its most basic form; it simply represents a colour that is fully saturated, as in the
following examples: White, black, or grey are never added to "pigment primary" (CMY)
when they are completely pure. The ratio of the dominant wavelength to other wavelengths
in the colour determines a pure hue similar to complete saturation when considering
spectral "light primaries' ' (RGB). Value describes a colour's lightness or blackness. It
shows how much light was reflected. Dark values with black added are referred to as
"shades'' of the specified hue name when referring to pigments. The term "tints'' of the
colour name refers to light values that have white pigment applied. The intensity and
brilliance of a colour are determined by its saturation. In order to lessen the saturation of a
pigment hue, white and black (or grey) are added to the colour. However, under the
"additive" light colour model, saturation is determined by how much or how little the colour
is made up of other hues. The major colour qualities that allow us to distinguish between
different colours are hue, saturation, and value. The image is also contoured in order to
discern the edge of the fish. The line connecting all the points along an image's edge that
have the same intensity is referred to as the contour. Contours are useful for object
detection, determining the size of an object of interest, and shape analysis. Since it relies
on pixel intensity, binary images—particularly those with a black background and white
objects—perform better. The camera measurements must first be calibrated in order to
determine the mm per pixel value before we can estimate the actual (physical) dimensions.
The amount of pixels in each unit index, for instance, 1 cm on the image contains 100
pictures, is known as the "pixels per metric." It actually performs the same job as a reference
object. For instance, we can utilise a reference object on the map whose dimensions are
known to determine the sizes of other things on the map. The proposed model employs the
metric of inches. When the image is passed, the length and breadth of the fish in the image
are returned by counting the number of pixels and evaluating it to the corresponding
predefined metric value. Using the values of length and breadth, the diagonal length can
also be calculated. We utilise a pre-existing dataset to forecast weight based on the
dimensions provided. This dataset includes the fish's length, height, and diagonal lengths,
as well as their weights. Our primary variable is weight. We train three models, Decision
Tree, Random Forest, and Extreme Gradient Booster, and save the best model in a best
model.json file. The best model is picked and utilised to generate a prediction function,
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which is saved in the prediction.py file. Both of these files are then used to generate the
main.py file, which contains the code for the ML-to-App interface. These files have all
been uploaded to a GitHub repository. Streamlit.io is then used to create an app from this
repository. Streamlit is an opensource app framework for Machine Learning and Data
Science projects that creates an app from the contents of the GitHub repository. This app
will be able to forecast the weight of the fish based on the dimension predicted by the
dimension estimator.
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CHAPTER 4
RESULTS & DISCUSSION
4.1 Sensor Results
The circuit connections of the implemented sensors are shown in Fig. 4.1. It involves
connecting the turbidity sensor, temperature sensor, Arduino uno, and other support
modules necessary for the sensors to operate correctly.
Fig 4.1 Circuit Connections
The readings from the temperature sensor are shown in Fig. 4.2. On the serial monitor, the
temperature readings from the temperature sensor are shown. A commercial mercury
thermometer is used to measure the temperature concurrently. It is discovered that they
both provide the same values.
Fig 4.2 Temperature Sensor Readings
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Figures 4.3 and 4.4 show how the turbidity sensor in water operates. On the Serial Monitor,
Fig. 4.3 displays the raw voltage readings made while the turbidity sensor was submerged
in clear water. The voltage value decrease that occurs when the turbidity sensor is
submerged in murky water is shown in Fig. 4.4. The rise in turbidity is inversely
proportional to the decrease in voltage. Turbidity is therefore calculated using the proper
formula.
Fig 4.3 Turbidity Sensor readings in Clear Water
Fig 4.4 Turbidity Sensor readings in unclear water
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4.2. ThingSpeak Result
The temperature and turbidity sensor have been successfully installed. The values of
temperature and turbidity in water have been collected and sent over to the cloud platform
“ThingSpeak” through ESP8266-01 Wi-Fi module.
Fig 4.5 ThingSpeak readings
Figure 4.5 represents the temperature and turbidity sensor readings in ThingSpeak
Platform. The data has been collected 5 times per minute. The temperature fluctuation is in
the range of 240C to 300C. The turbidity value varies between 4 to 5 NTU.
4.3. Fish Recognition Result
The CNN layers detect the presence of the fish when an image is passed through the model..
Figure 4.6 shows the result of fish recognition model. Each fish is surrounded by a
bounding box with its corresponding confidence score. The confidence score indicates how
certain the model is that the data contained within the bounding box is that of a fish. The
following images shows a group of black molly fish and gold fish, being recognized
individually.
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Fig 4.6 Fish Recognition Result
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The loss chart in fig 4.7 shows how training loss error and mean average precision at 50%
have changed throughout the course of the iterations for the fish detection model. The
training loss or mistake on the training dataset is shown by the blue curve (specifically
Complete Intersection-Over-Union or CIoU loss for YOLOv4). The mean average
precision at the 50% intersection-over-union threshold (mAP@0.5), shown in red,
measures how effectively the model generalises to new datasets or validation sets.
Fig 4.7 Loss Chart
Precision measures how accurate the predictions are. i.e. the percentage of the predictions
are correct. The precision for the model trained as shown in Table 4.1 is 0.73. We calculate
the overlap between the predicted bounding box and the actual bounding box for each
bounding box. IoU is used to measure this (intersection over union). IoU for the fish
detection model is 50.98%. Recall gauges how well we are able to identify every
advantage. In the proposed model, the recall value is 0.80. Depending on the various
detecting obstacles that are present, the mean Average Precision, or mAP score, is
computed by taking the mean AP over all classes and/or overall IoU thresholds. Mean
Average Precision for this model is 0.73. The harmonic mean of recall and precision is used
to calculate the F1 score. F1 score of the model is 0.76
Mean Average Precision
0.7357
0.73
Precision
0.80
Recall
0.76
F1 Score
50.98%
Mean IoU
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Table 4.1
4.4. Fish Classification Result
The classification of fishes into their corresponding classes is shown in Fig 4.8. The model
can classify six different fish species, including "Black Molly," "White Molly," "Chocolate
Molly," "Guppi," "Southern Platyfish," and "Gold Fish." The model has high levels of
confidence in its ability to distinguish between these classes.
Fig 4.8 Classification Result
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The training loss error and mean average precision at 50% have altered during the course
of the iterations for the fish classification model, as shown by the loss curve in fig. 4.9. The
blue curve displays the training error or loss on the training dataset (specifically Complete
Intersection-Over-Union or CIoU loss for YOLOv4). The model's ability to generalise to
new datasets or validation sets is indicated by the mean average precision at the 50%
intersection-over-union threshold (mAP@0.5), which is displayed in red.
Fig 4.9 Loss chart
Precision measures how accurate the predictions are. i.e. the percentage of the predictions
are correct. The model trained as indicated in Table 4.2 has a precision of 0.85. For each
bounding box, we determine the overlap between the predicted and real bounding boxes.
This is measured using IoU. (intersection over union). IoU is 63.43 percent for the fish
detection model. Recall measures how effectively we are able to recognise each benefit.
The recall value in the suggested model is 0.90. The mean Average Precision, or mAP
score, is calculated by calculating the mean AP over all classes and/or overall IoU
thresholds, depending on the numerous detecting obstacles that are present. This model's
mean average precision is 0.943. The F1 score is computed using the harmonic mean of
recall and precision. The model's F1 score is 0.88.
Mean Average Precision
0.942
0.85
Precision
0.9
Recall
0.88
F1 Score
63.24%
Mean IoU
Table 4.2
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4.5. Fish Dimension Estimation and Weight Prediction Result
The outcomes of the fishes' dimension estimation are shown in Figure 4.10. The suitable
computer vision model is used to calculate the length and width of each fish. The
calibrated dimensions of the fish are returned by this model. Inches are the unit of
measurement used to calculate dimensions, as indicated.
Fig 4.10 Result of fish dimension estimation
The different parameters of the three machine learning models used on the weight
estimation dataset are shown in Fig. 4.11. Random Forest, Decision Tree, and XGBoost
Regressor are the three models. The model's parameters, including mean squared error,
mean absolute error, and r2 score, were identified. The loss function for least squares
regression is the average squared error, or MSE. The size of the discrepancy between an
observation's true value and its prediction is referred to as the absolute error. The
calculation of the variation in the predictions that the dataset can explain is known as the
r2score. The optimum model for future implementations is chosen using these parameters.
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Fig 4.11 Weight Prediction Algorithm on Three models
The weight prediction app created with steamlit.io is shown in Fig. 4.12. The estimated fish
weight is presented when the programme receives the vertical length, diagonal length, and
height inputs through the sliders and the "Predict Fish Weight" button is pressed.
Fig 4.12 Weight Prediction App
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CHAPTER 5
CONCLUSION & FUTURE WORK
5.1. Conclusion
· Aquaculture ensures that the supply of fish meets the demand of customers. It provides
employment to millions of people in India. Many people in India and other industries rely
on aquaculture for their Livelihoods. India’s overall fish production is 55 per cent
freshwater aquaculture, making it one of the world’s major exporters of seafood and local
fish. Aquaculture is critical to the economy's development.
By implementing this proposed system, we will be able to increase the quantity and quality
of aquaculture production while reducing labour costs. By adding an autonomous feeding
system and cameras, this system can be further improved and developed. As a result, the
framework in place will reach farmers in order to reduce the negative effects of climatic
change and to ensure the growth and health of aquatic life. This raises the country's GDP
by increasing productivity and facilitating foreign trade.
The effective utilisation of modern technologies such as CNN based model for fish
identification/detection and fish classification, fish dimension and weight prediction along
with remote monitoring of the essential water parameters leads to effective aquaculture.
5.2. Future Work
Successful integration of temperature and turbidity sensors that have met the objective of
monitoring the respective parameters has been achieved. The proposed method of
classification of fish species gives an accuracy of 92% after 4000 epochs. An automatic
fish counting method is obtained that makes use of image processing techniques and edge
detection. Creation of fish weight prediction app using fish dimensions namely vertical
length, diagonal length and height measurements has been deployed using streamlit.
In the coming future, we plan on integrating pH and DO sensors and transmitting data to
cloud. Our focus will also be on recognition and fish counting in video footage, fish
classification for more than 2 classes (fish species) and to validate results for improving
efficiency of the CNN model. We also plan on building an actuation system, that could
keep the parameters measured within limits.
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CHAPTER 6
REFERENCES
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