SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 3(4); March (2023), pp. 214–223
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

Research Article

Design of Intelligent Fish Box Based on Machine Vision andInternet of Things Technology
Suqing Duan, Jiangyu Wu, Shuai Chen, Yizhun Peng

College of Electronic Information and Automation, Tianjin University of Science and Technology, China

ARTICLE I N FO
Article History
Received 20 November 2022
Accepted 28 June 2023
Keywords
Machine visionInternet of things
Remote control
Neural network
Machine learning

ABSTRACTThe rapid economic development has fueled the demand for enhancing lifestyle and homeaesthetics, leading to the growing popularity of leisure activities and home decoration. As a
response, the ornamental fish industry has flourished, prompting fish enthusiasts to seek efficient
ways to care for their fish. Smart fish box has emerged as popular solutions, offering features suchas remote control and monitoring. Smart fish box incorporates machine vision and Internet ofThings technologies, allowing users to remotely control lighting, water changing, feeding, andoxygen pump operations. Temperature sensors transmit data to a mobile app, enabling users tomonitor and adjust water temperature. These boxes also features built-in cameras for real-timemonitoring and send notifications when fish food is running low. This innovative design addresses
several challenges in ornamental fish care. This paper presents the mechanical structure, control
circuitry, and vision algorithm of the smart fish box. By utilizing collected data, a neural network
is trained on the Raspberry Pi platform, successfully recognizing fish health status.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

1. Introduction

II. The analysis of smart vision fish box functions.
III. The design of the controller for the intelligent visual
fish box.
IV. The design of the sensor system for the smart visionfish box, comprising peripheral sensors and actuators.
V. The software design of the smart fish box includes
control algorithms and a neural network visionalgorithm for fish health identification.
VI. System testing, including debugging and preparation ofeach component.
VII.
Summary of the main contents of the paper.

According to the Ministry of Industry and Information
Technology of China, the application of new technologies
and products is being accelerated across the country tocultivate new economic growth points and explore newmodels. There is a growing demand for intelligent fish
farming equipment and manufacturers around the world
are developing and manufacturing various types ofintelligent fish box controllers. A number of controllers
with simple circuitry, stable operation and automaticintermittent operation have been introduced. However, due
to the independent operation of different devices and theirunique environmental parameters, aquariums require theinstallation of multiple independent controllers, increasing
costs. The development of multifunctional fish box
controllers is therefore becoming increasingly important[1].
The subsequent sections of this paper are organized asfollows.

2. Functional Analysis of the Intelligent Visual Fish
Boxes
2.1. System Scheme Design Overview
The intelligent visual fish box controller is composed ofseveral essential components, including the primarycontrol board Arduino development board, visual

Corresponding author’s E-mail: pengyizhun@tust.edu.cn URL: www.tust.edu.cn.

214recognition module Raspberry Pi and camera, and various
peripheral circuits. These components work together toenable the functionality of the system. Additionally, thesystem incorporates crucial elements such as themechanical structure, switches, relays, temperature sensor,
WIFI module, voice control system, and a range of motors.
To control the system, signals are sent from the network of
the mobile app on the cell phone side to themicrocontroller. Serial communication is establishedbetween the voice control part, the main control board, andthe cell phone. The mechanical part incorporates anelectric control box for the automatic feeding mechanism,
which is directly connected to the box.
The system operates on 5V control electricity and canfunction by connecting to a standard 220V household
socket. After powering on, the fish box can automaticallyconnect to the Internet. Through the cell phone, users cantoggle the water pump, the oxygen pump, and lights on andoff. The temperature sensor collects data, which can betransmitted to the cell phone, allowing users to view thehistorical water temperature change curve on their mobile
device. Furthermore, The voice control module of the fishbox operates by capturing audio signals through a radio
device. These signals are then processed and converted
into corresponding hexadecimal data. This data is
subsequently transmitted to the main control board,
enabling voice control functionality. Additionally, the fishbox is equipped with a fish food bin that has a capacity tostore food for up to 4 days, allowing users to remotely feed
the fish using the mobile app. When the fish food bin isempty, a signal is sent to the user as a reminder to refill thefood bin and reset it to zero positions. Additionally, thecamera provides real-time monitoring of fish health andsends notifications to the user in the event of fish death,enabling timely clean-up to prevent further water pollution[2].
2.2. External Structure Introduction
To ensure durability, the electric control box body is
fabricated from high-quality PVC foam board, known for
its strength and resilience. This material provides a sturdy
and reliable structure for the box. Inside the box, the circuit
wiring is meticulously organized and arranged to optimize
functionality and efficiency. The neat wiring layout helps
prevent any interference or malfunction, ensuring thesmooth operation of the entire system. The schematic
diagram of the electric control box design is illustrated in
Fig1.

Fig.1 electric control box
The feeding mechanism is created using 3D printing tech
nology instead. Fig2 showcases the design of the 3D print
ed feeding mechanism.

Fig.2 printing feeding mechanism
The practical realization and effectiveness of the intellige
nt functionalities in the assembled fish box are showcasedin Fig3.

Fig.3 The smart fish box effect
3. Smart Visual Fish Box Controller Design
3.1. Central Processing Unit (CPU) Board
The Arduino UNO utilizes the Atmel Atmega328 chip,
which offers ease and speed in project development. As an
open-source controller, it can be programmed, compiled,and burned using the IDE and a USB cable, eliminating theneed for additional programming tools. The main control
board provides a total of 0 to 13 digital inputs/outputs and
0 to 5 analog inputs/outputs. The main control board,
powered by either USB or an external power supply (5V
to 9V), supports Internet service provider downloads. It
provides stability and high efficiency, meeting the control
requirements of the smart fish box. By utilizing this board,the use of Arduino UNO as the main control board can215reduce system design costs and improve program
development efficiency. The PCB design is accomplishedusing specialized software. Fig4 illustrates the schematic
diagram of the Arduino design, which was created using
Altium Designer.

(2) Camera
The camera used in this design is the original Raspberry Pi
camera with a resolution of five megapixel. It is connectedto the CSI interface on the Raspberry Pi motherboard,
enabling real-time video streaming. The camera captures
the live video feed, which is then processed by the
Raspberry Pi's neural network to detect and determine thesurvival status of the fish in the box.The schematic
diagram of the Raspberry Pi camera module is displayedin Fig6.

Fig.4 The arduino schematic diagram completed using
Altium Designer
3.2. Visual Component and Camera(1) Raspberry Pi 4B
This design incorporates the Raspberry Pi 4B, an ARMbased microcomputer motherboard, to handle the visioncomponent of the smart fish box algorithm. The Raspberry
Pi 4B is capable of connecting to devices such as a mouse,
network cable, and keyboard for programming and otheroperations. It can also output high-definition video throughits HDMI interface. The Raspberry Pi 4B utilizes a
MicroSD/SD card as its memory storage. Additionally, themotherboard features an Ethernet port and multiple USB
ports for peripheral connectivity.
The selection of Raspberry Pi 4B is based on its
outstanding performance. With a 64-bit quad-coreprocessor running at 1.5GHz, the Raspberry Pi 4B delivers
high processing speed and offers ample memory capacity.
It also provides an excellent desktop system experience.
The original camera integrated with the Raspberry Pi
satisfies the requirements of this design for accurately
identifying the survival or non-survival of ornamental fish.
The schematic diagram of the Raspberry Pi 4B
motherboard is illustrated in Fig5.

Fig.6 Raspberry Pi supporting camera(3) The network connectivity component
To establish a network connection in the smart fish box system, the ESP8266 is employed as the wireless network M
CU. This module is specifically chosen for its affordability, seamless integration, and its suitability for Io product development. The ESP8266 demonstrates consistent and reliable performance, even in challenging conditions and extreme temperatures. It boasts a built-in 32-bit Tensilica processor and standard digital peripherals, enabling versatil
e functionality in diverse operational environments.
In terms of hardware, only a few peripheral circuits suchas interfaces, RF BLUN, and a power management module
are required, which helps reduce the PCB size andcomplexity.
The ESP8266's 32-bit Tensilica processor contributes tolow power consumption. It incorporates patented
technologies that enable extremely efficient power usage,
making it suitable for a wide range of low-power
applications.
Furthermore, the ESP8266 features a built-in ultra-low po
wer Tensilica L106 32-bit RISC processor, operating at a
CPU clock rate of 160 MHz. It supports a Real-Time Ope
rating System and the Wi-Fi protocol. This provides 80%of the processing power required for program design anddevelopment, ensuring efficient and effective operation of

Fig.5 Raspberry Pi4B motherboard
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the smart fish boxes system. Fig7. illustrates the schemat
ic diagram of the ESP8266-12E minimum system design.

Fig.9 The built-in microphone
Fig.7 ESP8266-12E minimum system
4. Smart Vision Fish Box Sensor Design
4.1. Voice module
The Su-03T is a compact, low-power, and cost-effective s
peech recognition chip. It is specifically designed for easy
integration into various smart products that require voicecontrol, including smart homes and other applications. De
spite its small size, the Su-03T chip offers efficient speec
h recognition capabilities, enabling seamless voice contro
l functionality in the targeted smart devices. Its low power consumption ensures the energy efficiency, while its aff
ordability makes it an attractive option for manufacturers
seeking to incorporate voice control features into their pro
ducts.The schematic diagram of the SU-03T application c
ircuit design is presented Fig8.

The module operates by utilizing its built-in microphone
to capture voice input. It processes the recorded audio by
analyzing its spectrum and extracting key features. Thesefeatures are then compared against a pre-defined library ofcommands. The module's on-board processor performs
further processing of the recognition results, which may
include tasks like serial transmission. This enables
seamless integration with other components of the system
and allows for efficient execution of commands based onvoice input.
By leveraging the capabilities of online development
platforms, the SU-03T module can be optimized to achieve
higher accuracy and faster voice recognition. Its low
power consumption and streamlined development process
make it an ideal choice for fulfilling the requirements ofthe voice control component in this design. The system
function block diagram is depicted in Fig10.

Fig.8 Design of SU-03T Application Circuit Diagram
The voice module utilizes an on-board electret capacitor
with dimensions of 4.0*1.2mm as the audio input device.
The microphone operates within a voltage range of 1.1V
to 10V and has a sensitivity of 42dB. This microphone
configuration enables the voice module to capture audio
signals effectively for further processing and voicerecognition. The built-in microphone design is illustrated
in Fig.9.

Fig.10 System function block diagram
4.2. Temperature sensor
The temperature sensing element employed in the designis the DS18B20 digital temperature sensor. The probe's water temperature detection line is sealed with waterproof
measures, such as stainless steel. The DS18B20 sensor uti
lizes one-wire bus transmission method to transmit tempe
rature data, ensuring high sensitivity and minimal delay. E
ach DS18B20 sensor has a unique identifier number, allo
wing for individual sensor identification based on these numbers. The length of the sensor wires can be adjusted ac
cording to user requirements. In this design, a waterproof
DS18B20 probe is utilized for water temperature detectio217

n in the fish box [3]. The circuit schematic for the DS18B
20 temperature sensor is depicted in Fig.11.

Fig.11 DS18B20 temperature sensor circuit schematic

4.3. Rudder
The design incorporates the use of a DS3235 digital rudder
for controlling the rudder mechanism. This rudder isequipped with a rudder control panel that is controlled bya microcontroller unit. It provides a single PWM pulse
width to set and lock the desired angle of rotation. The
DS3235 rudder offers high control accuracy, good
linearity, and seamless integration with the control
protocol, allowing for precise control with a minimum
control angle of 0.9°or less.
The rudder is equipped with a digital circuit board, robust
metal gears, a CNC aluminum mid-case, and double ballbearings. These components work together to ensure
efficient heat dissipation and enhance the overall
durability of the system. In the context of this design, therudder is directly linked to the feeding mechanism.
Whenever the main control board receives a feedingcommand, the rudder promptly responds to carry out thedesired action, the rudder rotates by 36°. With a power
supply of 7.4V, the rudder can generate a torque of 35
kg*cm and complete a 60° rotation in just 0.11 seconds.
This rudder's precise angle control, fast heat dissipation,and long lifespan make it suitable for the automatic
feeding function of the fish box. The rudder Interface
design is depicted in Fig.12.

Fig.12 rudder Interface

4.4. Motor Control Relay
The JQC-3FF-S-Z relay is utilized as the motor control
relay in this design. It is responsible for controlling theconnection between the live wire and the motor. The relay
configuration includes a common terminal and a normallyopen contact, which are used as the connection points. The
neutral wire is directly connected to the motor. To control
the relay, the control end is connected to a 5V powersource, GND, and an IO pin of the microcontroller. Thisconfiguration enables the microcontroller to effectively
control the operation of the motor by activating ordeactivating the relay.
When a command is received, and the corresponding pin
on the microcontroller becomes low voltage, the normallyopen contact of the relay will be closed. This action allows
current to flow through the relay, thereby turning on theappliance connected to the relay. Essentially, the relay acts
as a switch that can be controlled by the microcontroller toturn the appliance on or off based on the received
commands. The design of the JQC-3FF-S-Z relay is shownin Fig.13.

Fig.13 JQC-3FF-S-Z relay

4.5. Buzzer
In this design, a 5V buzzer with the model number 12065
is used as an acoustic signaling device. The negative
terminal of the buzzer is connected to the common groundshared with the microcontroller, while the positive
terminal is connected to an IO pin on the main control
board. When the microcontroller detects that the fish foodbin is empty, it drives the corresponding pin to a high level,
activating the buzzer and producing an audible alert for theuser to indicate the empty bin condition.
By controlling the IO pin connected to the buzzer, themicrocontroller can activate or deactivate the buzzer as
needed. The buzzer serves as an audible indicator to notify
the user when the fish food needs to be refilled, ensuring
the timely care and well-being of the fish in the box. The
design of the Buzzer is shown in Fig14.

218

Fig.14 Buzzer

5. Software Design
5.1. Control algorithm for each component of the fishbox(1) Regular section
Arduino IDE is used for developing code for the main
control board, which offers convenience and speed indevelopment and programming, thereby reducing thecomplexity of development. The overall idea of the codeis as follows:
Initial setup: Configure the GPIO pins and serial port baudrate, initialize variables required for the program, andensure that all data structures are in their initial state.
The main loop: Read the data from the serial port and entera nesting structure for condition judgment. If there datum
received on the serial port, the system remains in standby
mode. When data is received on the serial port, it proceeds
to the next level of condition judgment. If the conditions
are met, the corresponding operation will be executed.
The code follows a sequential flow where it constantly
checks for data on the serial port and performs specificactions based on the received signals from the voice
module and ESP8266. The Arduino IDE provides anintuitive platform for coding, compiling, and uploading thecode to the main control board, allowing for efficientdevelopment and execution of the desired functions. The
Control Flow Diagram of the Main Control Board is
depicted in Fig.15.

The intelligent control system of this design utilizes relays
to manage various components, including lighting, oxygen
pump, pumping pump, and heating rod. Upon receiving
the relevant command or when specific conditions are met,
the microcontroller triggers the activation of thecorresponding relays by setting the associated output pins
to a low level. This enables effective control over therespective devices, ensuring their proper functioning as per
the desired commands or conditions.
However, the automatic feeding mechanism operates
slightly differently. The feeding mechanism is controlled
by a rudder motor. Each time a feeding command isreceived, the rudder motor turns by 36°, which isequivalent to one-tenth of a full revolution. Once the fish
food bin reaches its final frame, indicating the depletion offish food, it undergoes a reset process to return to its initial
position. Simultaneously, the attitude variable is reset to its
original value. In conjunction with this, themicrocontroller sets the designated pin connected to thebuzzer to a high level, activating the buzzer to audibly
notify the user about the empty bin.
This approach ensures that the feeding mechanism
operates reliably and accurately. The rudder motor
provides precise control over the angle of rotation,
allowing for controlled dispensing of fish food. The
detection of an empty bin and the subsequent reset andalert mechanism help ensure that the fish in the box isproperly fed and taken care of.
(2) ESP8266 Networking Component Design
1. Temperature Sensor Data Processing Design
The DS18B20 temperature sensor stores temperature valu
es using complement notation, where the low 8 bits are inverted and then incremented by 1 to obtain the original code. The temperature data, in hexadecimal format, is stored
in RAM and converted to BCD format for further storage. By using the commands sensors.requestTemperatures()and sensors.getTempCByIndex(0), the temperature can be obtained from the sensor. The ESP8266 module commu
nicates with a cell phone app via WiFi and displays the temperature data using the Temp component. This allows users to conveniently monitor the water temperature of the
fish box on their mobile devices. The design of the mobil
e phone application showcasing the numerical components and historical temperature effect is depicted in Fig.16.

Fig.15 Control flow diagram of main control board219

Fig.16 Mobile phone APP in the numerical components andhistorical temperature effect

2.Mobile App Interface Design
Using the Blinker platform for secondary development, amobile app interface is created for operation and control.
The app connects to a third-party server to enable network
control. Users can toggle the device on/off, set the desired
temperature for the fish box, and monitor real-time andhistorical temperature data through a temperature curve.
Additionally, users can monitor the water temperature andtrack the remaining days of fish food effortlessly throughthe intuitive app interface.. This provides a convenient way
for users to remotely control and monitor their fish box.
The design of the mobile phone app interface is depicted
in Fig.17.

displayed using the numerical component, similar to theTemp usage described in the previous section [4].
(3) Voice recognition module
The voice control component is implemented in C
programming language. During development, specifickeywords are defined, and the voice chip extracts featuresfrom the voice input and compares them against these
keywords. When a corresponding command is recognized,
Once the code is executed, the relevant data is transmitted
through the serial port to the main control board, enabling
the system to respond to voice commands. Additionally,
the SU-03T module can undergo intelligent training onother platforms to further improve the accuracy of voicerecognition.
The voice recognition chip is seamlessly integrated with an MCU (Microcontroller Unit) and establishes communication using the jeon format. Upon receiving the data, the serial port carefully analyzes it to extract the recognition c
ode corresponding to the current command. Based on thiscode, the system executes the appropriate operation, ensu
ring seamless functionality and accurate response to usercommands.

5.2. Advanced neural network vision algorithm for fish
health identification.
(1) Data collection and processing
To gather images of healthy and dead goldfish from theweb, a crawler was employed. However, during thecrawling process, it was observed that a significant portionof the collected dataset was inaccurate due to various
reasons, such as complex backgrounds. To address thisissue, a data filtering step was implemented to reduce thedataset size and select images that was relatively pure andaligned with the requirements of the design. These selected
images were considered suitable data for further analysis
and utilization in the project. The design of the data set
portion is displayed Fig.18. While the 3D diagram
illustrating the data features is presented in Fig.19.

Fig.17 Mobile phone APP interface

Upon pressing the interface button, ESP8266 transmits
control commands to Arduino based on the status of theswitches. Each component on the mobile phone interface
is associated with a specific function, When a specificcomponent is pressed, the corresponding code associatedwith it is executed. In addition, numerical values are

Fig.18 Data set part of the data
220

Fig.19 3D diagram of data features

The images were initially represented in RGB format. To
prepare the images for training, a normalization operation
was applied to each pixel channel, converting the values tofloating-point numbers ranging from 0 to 1. Before
training, all images were resized to a uniform size of
320x320 pixels using the shortest axis fit method, ensuringconsistency in the dataset.
Once the data processing steps were completed, the dataset
was divided into a test set and a training set according to acertain ratio, typically two to eight. Each image in thedataset was labeled with the appropriate class label,indicating whether the goldfish in the image was healthy
or dead. Finally, relevant features were extracted from theimages for further analysis.
It is worth noting that after the data filtering and processing, most of the surviving data features were found to be loc
ated in the upper right region of the coordinate space, whi
le the majority of the features from the dead fish data wer
e concentrated in the lower right region. These spatial dist
ributions of features could potentially serve as discriminat
ive factors for distinguishing between healthy and dead goldfish.
(2) Utilizing the MobileNetV2 architecture, an object
detection model is employed in this design to achieve
accurate detection of fish survival. The Raspberry Pi
executes the object detection algorithm, capturing images
and providing detailed information about the detected
objects such as their class, quantity, and precise locations
within the images. This approach ensures effective
monitoring and analysis of the fish population in thesystem.
Building a functional computer vision model from scratch
requires significant effort, as it necessitates a diverse ran
ge of input data to ensure robust generalization. Consequently, training such a model can be time-consuming, often
spanning several days. To alleviate this process and exped
ite it, the fish box design incorporates transfer learning. B
y leveraging pre-trained models and retraining only the up

per layers of the neural network, the process becomes mo
re manageable and faster. This approach yields more relia
ble models that can be trained within a short timeframe an
d using smaller datasets.
To achieve robust object detection in this design, theMobileNetV2 SSD FPN-Lite pre-trained model is utilized.
This model has been trained on the COCO 2017 dataset,
which consists of images scaled to 320x320 pixels. The
architecture of MobileNetV2 SSD FPN-Lite consists ofthree main components: the base network (MobileNetV2),
the detection network, and the feature extractor (FPN-Lite).
This combination of components enables accurate andefficient object detection, making it well-suited for thedetection of fish survival in the system.
The base networks, including MobileNet, VGG-Net,
LeNet, AlexNet, and others, serve as the foundation forneural networks. These base networks offer advanced
capabilities for tasks such as classification or detection. By
incorporating fully connected layers and softmax layers at
the end of these networks, a classification network can becreated [5]. Alternatively, the fully connected layers andsoftmax layers can be eliminated and replaced with
detection networks such as SSD, Faster R-CNN, and othersimilar architectures to enable object detection capabilities.
The classification network schematic is depicted in Fig.20.

Fig.20 Schematic of classification network

SSD and RPN is two commonly used object detection net
works. SSD is capable of detecting multiple objects in animage with a single shot. In contrast, single-shot object detection methods, like SSD, offer advantages over region p
roposal-based methods such as the R-CNN series. With si
ngle-shot methods, there is no need for separate stages ofregion proposal generation and object detection. Instead, t
he detection is performed directly on the convolutional fe
ature maps, resulting in faster processing speeds. This effi
ciency is particularly beneficial in real-time applications
where quick object detection is essential, but they may
trade off some accuracy for real-time processing speed. ss
s also encounter challenges in detecting objects that are too close or too small.

221

To address these challenges, Feature Pyramid Network
introduces the concept of feature pyramid design. FPN aim
at improve recognition accuracy and processing speed,
especially for targets of different sizes. The use of feature
pyramids helps in handling smaller targets more
effectively and enhances the overall performance of object
detection systems.
6. Testing the system's operational functionality
6.1.

The setup and preparation of each component

The feeding mechanism was adjusted by utilizing a cellph
one app for debugging purposes. After the debugging pro
cess, the initial angle of the rudder was set to 42°, resultin
g in improved alignment between the feeding mechanism
and the chassis hole position.
Following the adjustment of the feeding mechanism, thecellphone app was used to control the fish box and perform
various actions. After testing, it was determined thatfunctions such as pumping, oxygen pumping, automatic
feeding, and lighting were operating normally. The
temperature sensor section exhibited minimal difference
between the processed data and the actual watertemperature. Moreover, the heating equipment functioned
as expected, activating when the water temperature fell
below the user's set temperature to facilitate automatic
control of the water temperature.
In the voice control part, the hardware construction wascompleted, and the focus shifted towards program writing
and burning. The sU-03T module required the assistanceof the UniOneUpdateTool burning tool to complete theprogram burning process. The firmware burning was
carried out initially, followed by the power supply to themodule. Sometimes, To ensure the successful burning of
the firmware, multiple power supply attempts and power
failure simulations were conducted. The process involved
careful management of power supply and deliberate powerinterruptions. Please refer to Fig.21. for a visual
representation of the Firmware burn-in design.

Fig.21 Firmware burn-in

To enhance training efficiency, the number of training iter
ations is progressively increased from 20 to 50. By setting
the number of rounds to 50, the model will undergo moreiterations and have the opportunity to learn and optimize
its parameters more effectively. Increasing the number oftraining rounds can potentially lead to better performanceand higher accuracy in the trained model.
6.2.

Execute and obtain outcomes

Once the control code is written, the smart fish box can be
activated by issuing the command "Little Fish." Upon
receiving the "lighting" command, the fish box will
perform the corresponding action related to the lighting
feature. To put the fish box back into the standby state, the"end" command is used. However, if further commands
are to be executed, the fish box needs to be awakened again.
The "end" command serves to return the fish box to thestandby state, and subsequent commands would require
the box to be awakened again.
Regarding the vision part, once the parameters are set, themodel training process can commence. The model istrained using the specified parameters to learn andoptimize its performance for vision-related tasks. The
design of the Confusion matrix is shown in Fig.22.

Fig.22 Confusion matrix

The algorithm for the vision part is executed, and themodel is deployed on Raspberry Pi4 to identify thesurvival status of fish. By running the command in theterminal, the model is compiled with full hardware
acceleration, downloaded to Raspberry Pi, and thedetection process is initiated. When an object is detected,
the object's coordinates and labels are output, correctly
indic ating whether the fish is dead or healthy in theinterface[6]. The Code running interface is displayed in222

Fig.23. While the Target detection effect is demonstrated
in Fig.24.

4. Kang Rongxian,Wang Xun,Wang Xiaotong et al. Research on
intelligent control system of fish box based on Internet of Things,
Information Technology,2022,12(09).
5. Zhang Jing, Nong Changrui, Yang Zhiyong. A review of target
detection algorithms based on convolutional neural networks,
Journal of Arms and Equipment Engineering,2022,05-07:1-12.
6. Liu Rujun, Zheng Zekai, Zhao Min. Design and implementati
on of an intelligent aquaculture system based on Raspberry Pi, I
nternet of things technology,2022,12(04):67-70.

Authors Introduction
Ms. Suqing Duan
Currently, she is pursuing her
undergraduate studies at the School ofElectronic
Information
andAutomation, Tianjin University of
Science and Technology, with aresearch focus on embedded systems.

Fig.23 Code running interface

Mr. Jiangyu Wu

Fig.24 Target detection effect

7. Conclusion

Undoubtedly, the field of smart home and Internet ofThings is rapidly evolving in the contemporary world. The
proposed design of the intelligent fish box in this researchembraces this technological advancement. Through theamalgamation of diverse disciplines including
microcontroller, machine vision, artificial intelligence, andvoice control, this design showcases the synergy ofknowledge domains, a comprehensive system is formed. It
is anticipated that smart fish box, with their automation
capabilities, will find broader applications in fish rearing
in the future.
This design represents the integration of multiple
disciplines to enhance the user's experience of humancomputer interaction. As a result, there is a growing
market demand for products of this nature. The integrationof technologies and disciplines opens up new possibilities
and opportunities for creating innovative and user-friendly
smart fish box.

Mr. Shuai Chen

In 2021, he completed his academic
degrees at the School of Electronic
Information and Automation, Tianjin
Universityof
ScienceandTechnology.

Currently, he is enrolled in the Schoolof Electronic Information andAutomation at Tianjin University of
Science and Technology, pursuing his
undergraduate degree. His researcharea primarily revolves aroundsoftware design.

Dr. Yizhun Peng
Currently, he is an Associate Professor at
Tianjin University of Science &Technology, specializing in intelligent
robots and intelligent control. He received
his doctoral degree in control theory andcontrol engineering from the Institute ofAutomation, Chinese Academy of
Sciences, in 2006..

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