This project analyses the optimal parameters for the shrimp farming, trying to help the aquaculture of Ecuador, using a cyberphysical system, which includes temperature, salinity, dissolved oxygen, and pH sensors to monitor the water conditions and an embedded system to control it using an XBee andATMega328p microcontrollers to remotely activate and deactivate aerators to maintain the quality of each pool in neat conditions.

1. Raspberry Pi-based IoT for Shrimp Farms Real-
time Remote Monitoring with Automated System
Erick Ruiz-Cedeño, Jesús Capelo, Víctor Asanza , Tony Toscano-Quiroga, Nadia N. Sánchez-
Pozo, Leandro L. Lorente-Leyva and Diego Hernan Peluffo-Ordóñez.
Escuela Superior Politécnica del Litoral, ESPOL, Guayaquil, Ecuador
Facultad de Ingeniería Mecánica y Ciencias de la Producción, FIMCP
Facultad de Ingeniería en Electricidad y Computación, FIEC
Smart Data Analysis Systems Group (SDAS Research Group)
Mohammed VI Polytechnic University

2. Published in: 10.23919/AE51540.2021.9542907

3. Topics
• Introduction
• Related Works
• Methodology
• Results
• Discussion and Conclusions
Raspberry Pi-based IoT for shrimp farms Real-
time remote monitoring with automated system

4. Introduction
• After bananas, shrimp is the most exported non–
oil product of Ecuador.
• Increase its production is a need.
• Quality of water is one of the most important
factors to consider in shrimp rearing.
• A healthy shrimps means a good harvest.
22.4%
15.5%
10.3%
8.0%
6.8%
5.3%
5.2%
2.8% 2.5% 1.5%
% of World Exports
India Ecuador Vietnam Indonesia Argentina
Thailand China Bangladesh Mexico Netherlands

5. Related Works
• Fuzzy Logic Based Control System Temperature, pH and Water
Salinity on Vanammei Shrimp Ponds.
• Focus on the importance of salinity, temperature, and pH
control, for shrimp survival
• Iot based automated shrimp farm aquaculture system.
• Aims to change the traditional monitoring system in shrimp
pools.
• ZigBee based wireless temperature monitoring system for
shrimp farm
• Decided to use Zigbee technology to interconnect all pools
• Water Quality Monitoring System for Vannamae Shrimp
Cultivation Based on Wireless Sensor Network In Taipa,
Mappakasunggu District, Takalar
• Monitored the pH of the water, the temperature at which
it is located, and the turbidity of it.
• Automatic monitoring and control of shrimp aquaculture and
paddy field based on embedded system and IoT
• Propose a real-time monitoring and control system for
shrimp pools

6. Methodology
Parameter Minimum value Maximum value
Dissolved oxygen 5 mg/L 10 mg/L
Temperature 20°C 30°C
Salinity 15 ppm 25 ppm
pH 6.5 8.5
Table I. Range of accepted values for the optimum shrimp farming.

7. Methodology
Sensor Measurement Range Tolerance
LM-35 Temperature -55 a 150 °C ±0.5
SEN0237-a Dissolved oxygen 0 a 20 mg/L ±0.04
PH-BTA pH 0 a 14 ±0.02
DRF0300 Conductivity 5 a 20 mS/cm ±0.02
Table II. Selected sensors for the measurement process

8. Methodology
Figure 1. Network topologies used in this project.

9. Methodology
Figure 2. Gateway’s Algorithm

10. Methodology
Figure 4. Electronic circuit of the Project described.

11. Results
Table IV. Power consumed by all devices.
Device Voltage [V]
Current
[mA]
Power
[W]
Raspberry Pi 3
Idle
5 25 0.125
Raspberry Pi 3
Active
5 250 1.25
Raspberry Pi 3 GPIO 5 16 0.08
XBee-Pro S2 receiving plots 3.3 35 0.1155
Xbee-Pro S2 sending plots 3.3 232 0.7656
Xbee-Pro S2 idle 3.3 15 0.0495
ATMEGA328p 5 1.5 0.0075

12. Results
Conf. # of End Devices by pool Time in between plots Idle Time
1 1 2s 90s
2 4 1s 60s
3 2 5s 90s
4 1 1s 45s
Table III. Possible configurations for the solution.

13. Discussion and Conclusions
• From Figure 5, the best configurations in terms of energy
expenditure vs time are configuration 1 and configuration
4, this is because using a single End Device per pool saves
the process of sending frames for each XBee used, a
process that requires a lot of energy to be performed,
compared to others.
• Thus, the second most important factor would be the
downtime used by each sensor, which is why the best
possible configuration would be the first, followed by the
fourth, then a little higher are the third and finally the
second one.
• These collected values are important to consider for the
application, since depending on the need you can use a
configuration with more measurement efficiency or one
with more energy efficiency.
• However, it is also important to analyze what is the best
configuration in terms of efficacy and efficiency, so that it
can be a standard model for any application. Therefore,
based on the initial configurations and the results
obtained, configuration 4 is recommended
Figure 5. Power vs Time graphic comparing each
configuration.

14. For more information
Víctor Asanza
Mail: vasanza@espol.edu.ec
Tony Toscano-Quiroga
Mail: ttoscano@espol.edu.ec
Facultad de Ingeniería en
Electricidad y Computación, FIEC
Escuela Superior Politécnica del
Litoral, ESPOL
Campus Gustavo Galindo Km 30.5
Vía Perimetral, P.O. Box 09-01-
5863
090150 Guayaquil, Ecuador
Erick Ruiz-Cedeño
Mail: eriaruiz@espol.edu.ec
Jesús Capelo
Mail: jesfacap@espol.edu.ec
Facultad de Ingeniería Mecánica y
Ciencias de la Producción, FIMCP
Escuela Superior Politécnica del
Litoral, ESPOL
Campus Gustavo Galindo Km 30.5
Vía Perimetral, P.O. Box 09-01-
5863
090150 Guayaquil, Ecuador
Nadia N. Sánchez-Pozo
Mail:
nadia.sanchez@sdas-group.com
Leandro L. Lorente-Leyva
Mail:
leandro.lorente@sdas-group.com
Diego Hernan Peluffo-Ordóñez
Mail:
diego.peluffo@sdas-group.com
Smart Data Analysis Systems Group
(SDAS Research Group - www.sdas-
group.com),
Ben Guerir 47963, Morocco

15. Thank you!