ABSTRACT: In this paper we present applications of different machine learning algorithms in aquaculture. Machine learning algorithms learn models from historical data. In aquaculture historical data are obtained from farm practices, yields, and environmental data sources. Associations between these different variables can be obtained by applying machine learning algorithms to historical data. In this paper we present applications of different machine learning algorithms in aquaculture applications

Akhlaqur Rahman

Sumaira Tasnim

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International Journal of Computer Trends and Technology (IJCTT) – volume 10 number 4 – Apr  2014 
        ISSN: 2231-2803                      http://www.ijcttjournal.org               Page214 
 
Application of Machine Learning Techniques 
in Aquaculture 
 
Akhlaqur Rahman1 and Sumaira Tasnim2 
1Department of Electrical and Electronic Engineering Uttara University, Bangladesh 
 2School of Engineering, Deakin University, Australia 
 
ABSTRACT:  
In this paper we present applications of different machine 
learning algorithms in aquaculture. Machine learning 
algorithms learn models from historical data. In 
aquaculture historical data are obtained from farm 
practices, yields, and environmental data sources. 
Associations between these different variables can be 
obtained by applying machine learning algorithms to 
historical data. In this paper we present applications of 
different machine learning algorithms in aquaculture 
applications.   
 
Keywords: aquaculture, machine learning, decision 
support system 
 
1. INTRODUCTION 
Aquaculture refers to the farming of aquatic 
organisms such as fish and aquatic plants. It 
involves cultivating freshwater and saltwater 
populations under controlled conditions. Use of 
sensor technologies to monitor the environment 
where aquaculture operations take place is a recent 
trend. The sensors collect data about the 
aquaculture environment that are using by farm 
managers for decision making purposes. 
The literature states a number of activities 
related to decision support systems in Aquaculture 
farm operations. A number of decision support 
systems have been developed for. Some of them 
use machine learning and methods and other do 
not. For the sake of completeness we briefly 
discuss these other methods first and detail the 
machine learning based methods in the following 
section. 
Bourke et al. [1] developed a framework where 
real-time water quality indicators, as well as 
operational information were displayed and their 
impact on survival rate, biomass and production 
failure of aquaculture species were evaluated.  
Wang et al. [2] developed an early warning system 
for dangerous growing conditions. Padala and 
Zilber [3] used expert system generated rules to 
reduce stock loss and increase size and quality of 
yields. Ernst et al. [4] focused on managing 
hatchery production using rules and calculations of 
physical, chemical and biological processes. Silvert 
[5] developed a scientific model to evaluate the 
environmental impact. Halide et al. [6] used rules 
that are hand-crafted from domain experts. 
2. MACHINE LEARNING METHODS IN 
AQUACULTURE APPLICATIONS 
In this section we discuss the different 
applications where machine learning methods are 
applied in the aquaculture domain. 
2.1 Shellfish Farm Closure Prediction 
Consumption of contaminated shellfish can 
cause severe illness and even death in humans. The 
authors in [7]–[9] developed a sensor network 
based approach to predict the contamination event 
using machine learning methods. In [7] the authors 
presented a machine learning methods to obtain a 
balance between farm closure and farm opening 
events. The authors in [8] presented a feature 
ranking algorithm to identify the most influential 
cause of closure. In [9] the authors adopted time 
series machine learning approaches like PCA 
(Principal Component Analysis) and ACF (Auto 
Correlation Function) to predict the closure event. 
2.2 Algae Bloom Prediction 
Algae organisms grow widely throughout in the 
world. They provide food and shelter to other 
organisms. When the growth of algae is excessive, 
it can cause oxygen depletion in water and kill fish. 
The authors in [10]–[12] developed machine 
learning based methods to predict algae 
growth/bloom. The authors in [10] extracted a set 
of rules from data gathered by sensor networks to 
find associations between environmental variables 
and algae growth. The authors in [11] designed an 
ensemble method to find the relevant 
environmental variables responsible for algae 
growth and predict the growth. The authors in [12] 
developed machine learning methods to predict the 
propagation of algae patches along the waterway. 
2.3 Missing Values Estimation 
Sensor networks deployed in the field to 
monitor aquaculture environments often suffer 
failure due to sensor bio-fouling, communication 
failure etc. This results in missing sensor readings 
that is required by the machine learning based 
decision making systems. This results in a 
requirement to deal with missing sensor values. 
The authors in [13] and [14] designed a multiple 
classifier based method to deal with missing sensor 
data. Instead of imputing missing data, prediction 
International Journal of Computer Trends and Technology (IJCTT) – volume 10 number 4 – Apr  2014 
        ISSN: 2231-2803                      http://www.ijcttjournal.org               Page215 
 
of events is based on available sensor readings. 
Ensemble approach is shown to perform better than 
imputation methods. The authors assume equal 
weights for all features which may not be true in 
real world scenarios [15][16]. 
2.4 Model Relocation 
In order to train machine learning methods it is 
required to provide historical labeled data. 
However when a farmer aims to setup a new 
aquaculture firm, historical farming data is not 
available for that location. The authors in [17] 
provided a guideline to utilize machine learning 
models trained for a different location to a new 
location based on similarity between the two 
locations. Results show that model relocation can 
significantly reduce the shortcoming generated 
from data unavailability for a particular location. 
2.5 Benthic Habitat Mapping 
In order to get an understanding of the 
aquaculture habitats on seafloor the researchers 
sometimes send Autonomous Underwater Vehicles 
to collect seafloor images. The images are later 
visually analyzed by the researchers to produce the 
habitat map in a region. The authors in [18] 
developed an image processing and machine 
learning based method to automatically produce 
habitat maps from seafloor images. The results 
presented in the paper showed significant accuracy 
in obtaining correct habitat maps. 
2.6 Sensor Data Quality Assessment 
Data produced by the sensors are sometimes 
faulty.  The decisions based on faulty sensor 
reading will result in wrong conclusion. In [19][20] 
the authors have presented a novel ensemble 
classifier approach for assessing the quality of 
sensor data. The base classifiers are constructed by 
random under–sampling of the training data where 
the sampling process is guided by clustering. The 
inclusion of cluster based under–sampling and 
multi–classifier learning has shown to improve the 
accuracy of quality assessment. 
3. CONCLUSIONS 
In this paper we have presented a review of 
machine learning methods in the aquaculture space. 
In future we aim to extend some of these methods n 
time series machine learning methods. 
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Application of Machine Learning Techniques in Aquaculture

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Application of Machine Learning Techniques in Aquaculture

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