1. Konstantinos Bovolis kbovolis@i2s.gr
Machine Learning methods to
estimate the performance of aquafarms
Konstantinos Bovolis
Supporting Blue Growth with innovative applications based on EU e-infrastructures
14-15 February 2018, Brussels

2. Outline Challenges and Needs BlueBRIDGE Solution
BlueEconomy: Performance Evaluation, Benchmarking and Decision Making
Case Study
Conclusion
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

3. Challenges and Needs Challenges that have to be addressed:
maintaining the economic viability of the sector by reducing costs and increasing production
guaranteeing high quality food and animal welfare
addressing environmental concerns.
All aquaculture producers are concerned about improving the performance of their companies in terms of cost, feed conversion, growth rate and mortality and at the same time, be sustainable and environmental friendly
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

4. It is not only equipment and hardware!
Unfortunately, answering this question is not that simple
Aquafarmers can invest in the latest technology for cages or on the most advanced feeding systems but they cannot forget two key aspects:
an aquaculture comes with its own array of environmental challenges that have a huge impact on production system;
an aquaculture business can be sustainable only if they are able to continuously monitor and improve its performance
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

5. BlueBRIDGE Solution
Provide innovative data analytics and machine learning services that will benefit all the stakeholders of the aquaculture sector
The aim is to support:  
Companies to maximize the growth rate, reduce costs and minimize the impact on the environment
Investors to make efficient identification of strategic locations of interest and select the most profitable investments
Governments and environmental agencies to evaluate the current situation and define policies
Researchers to generate new knowledge and evaluate the practical indicators of aquafarming performance
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

6. Blue Economy Performance Evaluation, Benchmarking & Decision Making
Goal:
Estimate/create KPIs Tables (biol. FCR, SFR, Mortality Rate) based on historical data using Machine Learning Techniques (i.e. GAMs, MARS)
Define a Site:
location
temperature profile
Setup Site
Performance Evaluation
Estimate KPIs:
Collect data
Upload data
Generate models
Setup Model
Benchmarking & Decision Making
Goals:
Create accurate and feasible production plans
Benchmark the performance against the competition
Perform production planning by:
Create scenarios
Assess the KPIs
Benchmarking
What-If Analysis
Decision
GAM : Generalised Additive Models
MARS: Multivariate Adaptive Regression Splines
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

7. Aquafarms engagement
10 Aquaculture companies have already started to utilizing BlueBRIDGE services and tools, via their own VREs:
ARDAG Aquaculture
iLKNAK Aquaculture
GALAXIDI MARINE FARM S.A.
NIREUS AQUACULTURE S.A.
MARKELLOS AQUACULTURE LEROS S.A.
STRATOS AQUACULTURES
ALIEIA S.A.
FORKYS
ELLINIKA PSARIA
KIMAGRO FISH FARMING LTD
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

8. Case Study: Performance Evaluation, Benchmarking &Decision Making
How to evaluate the performance of Sea Bream production at site A over different stocking months?
Define the Site A
(Setup Site) 1
Create a production model for Site A
(Setup Model) 2
Create hypothetical scenarios for Site A
(What-If) 3
Evaluate results:
Production KPIs
Benchmarking 4
Η διαδικασία είναι επαναληπτική
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

9. Case Study: Performance Evaluation, Benchmarking &Decision Making
Step 1: Setup Sites
Aquafarm manager can define the average temperature fortnightly and the geographical location of the site of interest (i.e. Site A)
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

10. Case Study: Performance Evaluation, Benchmarking &Decision Making
Step 2: Setup Model
Aquafarm manager can develop reliable and powerful Machine Leaning models, which are capable to estimate vital production indicators, such as biological FCR, SFR and Mortality Rate, providing real historical production data and details regarding the production of the specific fish species (i.e. Sea Bream) of the site of interest (i.e. Site A)
For the particular case study, aquafarmer needs to upload production data for different stocking periods for the Sea Bream species at the Site A
Note:
Very often data need to be cleaned and preprocessed before the analysis is executed
‘Setup Model’ tool includes processes so as to remove automatically inconsistent entries and outliers from the processed data
However, aquafarmer is responsible to provide to the system good quality data
Μπορώ να μειώσω
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

11. Case Study: Performance Evaluation, Benchmarking &Decision Making
Step 2: Setup Model
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

12. Case Study: Performance Evaluation, Benchmarking &Decision Making
Step 2: Setup Model - Results
The outcome of the modeling process is a simulation of the relationship between growth, feeding and temperature
Development of tables for
Biological FCR,
Feeding Rate and
Mortality Rate
in terms of fish weight (Avg. Weight Categories) and temperatures (Avg. Sea Temperature)
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

13. Case Study: Performance Evaluation, Benchmarking &Decision Making
Step 2: Setup Model – Results of Machine Learning process
Avg. Weight Categories
Avg. Sea Temperature 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 1
3.47
3.09
2.72
2.38
2.07
1.81
1.59
1.42
1.29
1.21
1.16
1.14
1.15
1.17
1.19 3
3.46
3.08
2.37
1.80
1.58
1.41
1.28
1.20
1.13
1.18 8
3.44
3.06
2.70
2.35
2.05
1.78
1.56
1.39
1.26
1.12
1.11
3.39
3.01
2.65
2.31
2.00
1.74
1.52
1.34
1.22
1.09
1.07
1.08
1.10 50
3.32
2.94
2.58
2.24
1.93
1.66
1.45
1.27
1.06
1.02
1.00
1.01
1.04
100
3.53
3.16
2.79
2.45
2.14
1.88
1.49
1.36
1.23
1.24
150
4.10
3.72
3.36
3.02
2.71
2.44
2.22
1.84
1.79
1.82
200
4.48
4.11
3.74
3.40
2.83
2.61
2.23
2.18
2.16
2.17
2.19
2.21
250
4.50
4.12
3.76
3.41
3.11
2.84
2.62
2.32
2.20
300
4.38
4.01
3.64
3.30
2.99
2.73
2.51
2.34
2.13
2.08
2.06
2.09
2.11
350
4.37
3.99
3.63
3.28
2.98
2.49
2.04
400
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

14. Case Study: Performance Evaluation, Benchmarking &Decision Making
Step 2: Setup Model – Results of Machine Learning process
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

15. Case Study: Performance Evaluation, Benchmarking &Decision Making
Step 2: Setup Model – Results of Machine Learning process
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

16. Case Study: Performance Evaluation
Step 3: What-If Analysis
Aquafarm manager can draw a hypothesis and evaluate it, using an already existing production model
The ‘What-If Analysis’ tool:
calculates production indicators which are able to estimate the performance of the fish growth
presents the results in a meaningful tables and interactive graphs
benchmark the production performance against competition over the same hypothesis
production indicators such as LTD Biological/Economical FCR, LTD SGR, LTD Growth, LTD Mortality, monthly feed consumption and average weight per day
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

17. Case Study: Performance Evaluation
Step 3: What-If Analysis
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

18. Case Study: Performance Evaluation
Step 3: What-If Analysis – Case Study
Baseline scenario: evaluate the Sea Bream production performance whether a population of fish will be stocked at “Site A” in December (01/12) with initial average weight 2 grs and they are cultivated for 18 months (harvest date 31/05)
Alternative scenario: stock the fish 3 months later, namely on March (01/03). Thus, the harvest date will be at the end of August (31/08). The other conditions are similar with baseline scenario
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

19. Case Study: Performance Evaluation
Step 3: What-If Analysis – Case Study Results
Baseline Scenario
Alternative Scenario
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

20. Case Study: Performance Evaluation
Step 3: What-If Analysis – Case Study Results
Baseline Scenario
Alternative Scenario
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

21. Case Study: Performance Evaluation
Step 3: What-If Analysis – Case Study Results
Monthly Feed Consumption
Baseline Scenario
Alternative Scenario
Dec-17
Mar-18
Jan-18
Apr-18
Feb-18
May-18
Jun-18
Jul-18
Aug-18
Sep-18
Oct-18
Nov-18
Dec-18
Jan-19
Feb-19
Mar-19
Apr-19
May-19
Jun-19
Jul-19
Aug-19 18
317.91
444.13
tons saving around 11% comparing with the total feed consumption at baseline scenario ( tons)
However, after the cultivation of a duration of 18th months (31/05) the average weight in baseline scenario is estimated to be grs against grs of the alternative scenario
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

22. BlueBRIDGE Conclusions IT Providers Aquaculture
Gain knowledge from the aquaculture domain
New approaches to face problems
Combine production and techno-economical models
Aquaculture
New perspectives to overcome production problems
Enrich capabilities to process historical production data
Benchmarking – change mentality towards to open sector
Encourage to use innovative cloud-based apps, such as BlueBRIDGE
BlueBRIDGE
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

23. Any Questions? http://www.bluebridge-vres.eu/
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

24. Case Study: Performance Evaluation, Benchmarking &Decision Making
Step 2: Setup Model – Sample Data
Periodic or “Sampling to Sampling” dataset contains data which are gathered from sequential samplings by an aquaculture company:
datefrom: the date when the sampling period is started
dateto: the date when the sampling period is terminated
openweight: the fish average weight at the beginning of the sampling period
closeweight: the fish average weight at the end of the sampling period
avgtemperature: the average sea temperature of the sampling period
openfishno: the number of fish at the begin of the sampling period
closefishno: the number of fish at the end of the sampling period
Sampling is a common procedure in the aquaculture sector, in order to estimate crucial production KPIs as well as the number and the average weight of fish in cages/units. This kind of datasets can be generated by monitoring systems used by Aquaculture companies. Each dataset contains measurements as well as estimations of basic parameters regarding the fish growth in a period of time. The time between samplings is not prefixed and it is determined by each aquaculture company. However, it should not be exceeded the two months.
*Note: The “Periodic” (“Sampling to Sampling”) dataset should be in Microsoft Excel (xls, xlsx) format
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

25. Case Study: Performance Evaluation, Benchmarking &Decision Making
Step 2: Setup Model – Sample Data
calculated attributes (KPIs production indicators):
fcr: Biological Feed Conversion Rate, which is calculated from the number of kilograms of feed used to produce one kilogram of fish, measured at the end of the sampling period
mortalityrate: ratio of dead fishes at the end of the sampling period
sfr: Suggested Feed Ratio, which indicates the quantity of feed given to the fishes over the period, measured at the end of the sampling period
sgr: Specific Growth Rate, which indicates the growth of the fish in a particular period, measured at the end of the sampling period
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels

26. Case Study: Performance Evaluation, Benchmarking &Decision Making
Step 2: Setup Model – Weight limits
FCR
SFR
SGR
Mortality 1
0.50 3 8 2 20 50 5
100
150 10
200 15
250
300 30
350
400
1000
120
450
500
600
Weight Categories Dataset:
contains user-defined categories of average fish weight which are corresponded to each production KPI (FCR, SFR, SGR and Mortality Rate)
24/8/2019
“Supporting Blue Growth with innovative applications based on EU e-infrastructures”, February 2018, Brussels