Big Data-driven Smart Fish Farming

Sarah Benjelloun, Mohamed El Mehdi El Aissi, Yassine Loukili, Younes Lakhrissi

and Safae Elhaj Ben Ali

SIGER Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco

Keywords: Big Data, Smart Aquaculture, Smart Fish Farming, Data-driven.

Abstract: With the fast global population growth, the demand of fishery products is also increasing. Aquaculture is

developed in Asian countries the most, but not enough in countries with the same climate as Morocco. This

study explores the potential of Big Data technologies as fuel for a smart fish farm. By using big Data, the

activity of aquaculture is well managed with a better production level and less wastage. Many research studies

are exploring big Data for agriculture, but there is only a few exploring the potential of these technologies for

fish farming. Moreover, no study has been conducted for the Morocco case. This study is directed to reveal

the importance of investing in a big Data-Driven aquaculture. This paper presents the state of aquaculture in

Morocco to show the area of improvement. Then, it reveals the application of big Data technologies for smart

fish farming with a suggested architecture to solve current challenges. It also highlights Data generation

process, Data collection techniques and analytics methods.

1 INTRODUCTION

With the rapid development of robotics, the Internet

of Things (IoT), fifth-generation (5G), Big Data and

Artificial Intelligence, all fields of activity have gone

through a considerable progression (Bradley, 2019).

Agriculture is the most critical industry. With the

increasing global population, the demand for food is

also increasing (Sarker, 2019a). Human consumption

tends to prefer aliments with a much leaner and

lower-calorie source of protein. Naturally, fish is

preferred and widely consumed.

Aquaculture has a promising potential to help in

securing food safety worldwide. Aquaculture

production is growing more and more around the

world. In fifty years, fish production increased from

1 million tons in the 50s to 55 million tons in 2004 to

90 million tons in 2012 and finally reaching 106

million tons in 2015, according to The World Bank

(The World Bank, 2016). China is the largest

aquaculture producer with other Asian countries like

Indonesia, India, Vietnam, the Philippines,

Bangladesh, South Korea, Thailand and Japan.

Unfortunately, Morocco doesn’t score significant

points in this domain. In general, aquaculture in

Africa remains limited despite the existence of

immense potential.

Big Data has a big potential to assess actions to

increase the productivity in agriculture in general and

aquaculture specifically. Indeed, with the power of

Data management, Data analytics and its

applications, all industries have improved

significantly. According to (Liu, Shanhong, 2020),

Big Data and business analytics revenue worldwide

in 2019 reached 189.1 billion U.S. dollars, and is

forecasted to reach 274.3 billion U.S. dollars in 2022.

These numbers encourage the use of Big Data in order

to obtain its benefits. Fostering smart Data-based

solutions in aquaculture enables innovative

management and making intelligent decisions.

Indeed, Big Data applications support

industries/companies to help make decisions via

managing then analysing huge volumes of Data.

Different organizations from different domains invest

in Big Data technologies for discovering hidden

patterns, market trends, customer preferences and

unknown correlations.

Big Data related technologies did gain one's spurs

in agriculture first (Sarker, 2019b), then in a few

aquaculture commercialized solutions. But no studies

regarding aquaculture have been conducted in

Morocco for this purpose.

In this optic, the current research suggests an

architectural and technical solution based on Big

Data’s technologies. By exploiting the already

512

Benjelloun, S., El Aissi, M., Loukili, Y., Lakhrissi, Y. and Elhaj Ben Ali, S.

Big Data-driven Smart Fish Farming.

DOI: 10.5220/0010738800003101

In Proceedings of the 2nd International Conference on Big Data, Modelling and Machine Learning (BML 2021), pages 512-517

ISBN: 978-989-758-559-3

existing Data, the purpose of this work is to provide a

system in which fish production is not only managed

but also optimized. The objective is to acquire Data,

process it and store it before utilizing it for reports,

dashboards and predictive purposes.

The objective of this paper is to highlight the

current state of art of Moroccan aquaculture to

establish the area of improvement. After that, we

show the different use cases of Big Data in this field

by considering research papers addressing Data-

driven studies. Finally, we present the functional

architecture proposal.

This work is organized into four major sections.

The first section summarizes the state of aquaculture

in Morocco. The following section contains the

methodology that we followed in order to accomplish

the research. In the third section, we emphasize use

cases of Big Data related to fish farming. The

prominent three use cases are management,

optimization and prediction. The fourth chapter is a

suggestion of a Big Data architecture applied to fish

farming Data. We discuss the functional architecture

proposed and explain every component.

2 AQUACULTURE IN MOROCCO

In Morocco, aquaculture remains growing at a very

slow pace compared to several regions in the world.

The most significant production of aquaculture

products is in Asia and especially China. Moroccan

continental aquaculture production levels are not very

well recorded, but according to FAO, the production

level has increased from 2 500 tons in 2005 to around

15 000 tons in 2015. Most of the production of

continental aquaculture comes from carp production

in reservoirs (dams), lakes and rivers. According to

(Holth, 2018), in 2018, fish production levels in

pools, lakes and rivers were estimated at 13 000

tons/year. In fact, this fish is caught by fishermen and,

more correctly, could be marked “aquaculture-based

fishery” instead of aquaculture. The remaining

production of continental aquaculture is constituted

by:

- Eel (production level estimated in 2018, 350

tons/year),

- Tilapia (around 200 tons/year),

- Trout (100 tons/year),

- An unknown production by reservoir fishery of

carp and other species.

As figure 1 shows, consumption per capita is

increasing. Given the modest production, no one can

deny that Moroccan aquaculture sector is still in its

embryonic phase. Furthermore, the Morocco fish

imports’ values reach 216 032 million American

dollars, according to International Trade Centre (ITC)

in Trade Map (Trademap, 2020), as presented in

figure 2.

Figure 1: Fish and seafood consumption per capita 1991-

2017 (Our World in Data, 2018).



Figure 1: Moroccan Imports’ value 2001-2019 (Trade Map,

2020).

When comparing fish production and fish

consumption amounts, we notice that there is a huge

gap that is not improving with time, and that

translates into the need of importing fish products. In

consequence, there is a real need for moving

traditional fish farming systems into smart fish

farming systems to handle the increasing demand.

The goal of this study is to put light on Big Data

Analysis and how it can be a game-changer in terms

of fish production quality and management through a

smart fish farming ecosystem.

3 METHODOLOGY

In order to conduct the study, a systematic literature

method is used to explore the existing techniques. All

literature available between 2015 and 2021 has been

assessed. Other than the period criteria, we used two

inclusion criteria: Full article publication and

50000

100000

150000

200000

250000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

2014

2015

2016

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2018

2019

Imports' value in millions (American dollar)

Big Data-driven Smart Fish Farming

513

relevance to the research. Also, two exclusion criteria

were used: Language publication in English and work

focusing on technical design. To collect articles from

renewed research databases such as web of science,

Springer and IEEE Xplore, we used the following

query: “Big Data” AND “Morocco” AND

[“Aquaculture” OR “Fish Farming”].

Unfortunately, no paper on Smart fish farming

using Big Data applicated to Morocco were found.

Instead, we found most articles of Asia and precisely

China. To overcome this problem of lack of

references, we based our research on articles from

countries having the same climate as Spain. Besides

Spain, no relevant research from Algeria or Tunisia

were found.

After collecting the research papers, our

methodology consisted of following the process: All

relevant big Data-based aquaculture articles were

gathered; Since Asia is the leader in aquaculture

production, we chose relevant work from China in

order to study different methods used in this country.

We narrowed our study by focusing on the

consideration of the application of some practices in

Spain.

4 BIG DATA IN FISH FARMING

USE CASES

Big Data is a lever in the industrial revolution 4.0 and

is a big game-changer for most industries already

over the last few years. Organizations use Big Data

applications in Healthcare, Manufacturing,

Entertainment, cybersecurity and intelligence, crime

prediction and prevention, scientific research, traffic

optimization, weather forecasting and more. Indeed,

Big Data provides valuable insights and, for

companies, undeniable profitability (Wolfert, 2017).

These technologies can be used in fish farming to

predict patterns and increase production and income,

and also fish quality (Sarker, 2020). Some researchers

are also interested in Big Data for its potential in

helping aquaculture become sustainable (Lioutas,

2020).

In fish farms, Data is generated and collected

continually (Roukh, 2020). The Data value chain of

aquaculture then begins with Data acquisition from

sources, whether it is streaming or batch-based

(Amora, 2020). Pre-processing is for cleaning the

Data gathered for validation purposes. This Data is

then stored in a distributed storage system to perform

Data Analysis (Wolfert, 2017). There are four types

of Data Analysis:

‐ Descriptive Analysis: Track Key Performance

Indicators (KPIs) using dashboards;

‐ Diagnostic Analysis: Detect patterns of

behaviour using insights drill down from

descriptive analysis;

‐ Predictive Analysis: Predict what is likely to

happen based on statistical modelling;

‐ Prescriptive Analysis: Determine actions to take

using insights from all previous analyses.

Alongside, Data Visualization comprises the

graphical representation of Data. It also can be useful

to monitor production and activity state through Data

generated by IoT equipment.

The objective of including Big Data technologies

to fish farms is to enhance productivity per cost quota

and to have better environment print by improving

fish survival and water quality for better sustainability

(Roukh, 2020; Bajpai, 2019). The traits considered

with high economic importance in fish farming are

mainly growth, survival and feed conversion ratio

(FCR) (Mengistu, 2020). Many papers are addressing

these topics to understand the variables affecting

these three traits.

China being the worldwide leader in the

aquaculture segment, many research papers approach

aquaculture using big Data. The relevant applications

can be divided into six categories (Yang, 2021): live

fish identification, species classification, behavioural

analysis, feeding decisions, size or biomass

estimation, and water quality prediction. It is

esteemed that maintaining an ecological environment

with good water quality is the most critical link to

ensure production efficiency with the quality other

than focusing on economic aspects (Hu, 2020).

Therefore, many pieces of researche have been

conducted in the optic of water quality management

using big Data applications (Hu, 2020; Wen, 2020;

Peng, 2020; Song, 2019; Yang, 2021). Whereas,

Spain research articles focus on water quality

management and feeding strategies for improving the

economic efficiency of the operational process (Parra,

2018; Luna, 2019; O’Donncha, 2019).

5 FISH FARMING BIG DATA

ARCHITECTURE

The functional architecture has evolved from a mono-

zone to a multi-zone, depending on the technical

need.

BML 2021 - INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML’21)

514

Figure 3: Functional architecture proposal for Data-Driven fish farming system.

From our perspective, the Data Lake should allow

us to store all the Data coming from the sources, and

it may be structured, semi-structured or even

unstructured. As a result, mono-zone Data Lake was

efficient to store the Data in its native format.

However, in most cases, we may perform some

transformations on the stored Data at the Data Lake

level. Under these circumstances, we started using the

multi-zone Data Lakes by creating more than one

Data storage level. This approach will allow us to

manage Data more efficiently.

For this main reason, our proposed Data Lake

architecture for fish farming context contains three

Data storage layers, as shown in figure 3.

5.1 Data Sources

Fish farming systems can have many Data sources,

and each Data source has its own Data structure. The

primary Data source for our system is the sensors that

collect real-time Data (Pressure, Temperature, PH,

Humidity, Dissolved Oxygen, salinity...).

The second Data source is the manual Data stored

in flat files (CSV, TXT) by the business people. It

contains marketing and measures Data that cannot be

automated by sensors.

One more Data source are APIs; they provide

Data about weather, average global fish price and

information about the fish farming market.

5.2 Data Lake

The adopted Data Lake architecture have three Data

storage level. The first Data storage level is the Raw

Zone; it contains Data as-is from the source without

any transformations. The Data ingestion may be on

real-time or batch. This Data Lake Zone allows

finding the original Data version by the Data

engineers. It has to be noted that the stored raw Data

format can be slightly different from the original

format.

The second Data storage level is the Refined zone.

In this zone, we can transform Data according to the

needs and store the intermediate Data. In the Refined

zone, Data can be processed either in batch or stream.

Users can perform selections, joins, aggregations

depending on the use case.

The third Data storage level is the Access zone,

which offers access to the stored Data for Data

analytics purposes. This zone provides self-service

Data consumption for machine learning algorithms,

Reporting, Business Intelligence, statistical analysis,

etc.

Lastly, all the previous Data Lake zones are covered

by Data Governance zone to ensure Data quality,

metadata management and Data access.

5.3 Data Consumption

The Data can be consumed in many types as Data

Visualization through dashboards presenting KPIs for

each uses case. We can perform some predictive

analysis through Machine learning or even some

statistical analysis.

6 CONCLUSION AND FUTURE

WORK

Nowadays, Big Data techniques are almost applied in

all fields as insurance, banking, industry, marketing

and medicine. And it has been proved effective as it

helped enhance traditional operational processes and

increase profit. However, Big Data is not widely

applied in agriculture in general and in aquaculture

Big Data-driven Smart Fish Farming

515

especially. This study focuses on fish farming

production in Morocco. It shows the vast gap between

fish production amount and significant market need,

which generate an essential quantity of fish

importation.

To handle this increasing demand, applying Big

Data becomes a necessity for migrating from

traditional fish farming systems to Data-driven

systems, allowing fish farmers and stakeholders

effective Data exploitation for enhanced fish

production and quality.

We propose a functional architecture of the

dedicated fish farming system that relies on three

levels, mainly Data sources, Data lake, Data

consumption. The Data source level comprises the

streaming Data generated by sensors, flat files

containing additional operational Data and Data from

APIs. The Data lake layer involves raw zone, refined

zone and access zone, and Data governance for

availability, usability, integrity and security of Data.

Lastly, the Data consumption layer is for Data

analysis and visualization.

Now that we have expended the functional

architecture of the Data-driven fish farming system,

our future works are focused on proposing a technical

architecture as a proof of concept as well as applying

Big Data analysis to predict results based on the

explanatory variables to be able to take actions

accordingly.

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