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Machine Learning methods to estimate the performance of aquafarms

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BlueBRIDGE workshop: "Supporting Blue Growth with innovative applications based on EU e-infrastructures" - Brussels February 2018

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Machine Learning methods to estimate the performance of aquafarms

  1. 1. BlueBRIDGE receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 675680 www.bluebridge-vres.eu Konstantinos Bovolis kbovolis@i2s.gr Machine Learning methods to estimate the performance of aquafarms Supporting Blue Growth with innovative applications based on EU e-infrastructures 14-15 February 2018, Brussels
  2. 2. Outline Challenges and Needs BlueBRIDGE Solution BlueEconomy: Performance Evaluation, Benchmarking and Decision Making Case Study Conclusion 15/2/2018 2 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  3. 3. 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 Challenges and Needs 15/2/2018 3 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  4. 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: i. an aquaculture comes with its own array of environmental challenges that have a huge impact on production system; ii. an aquaculture business can be sustainable only if they are able to continuously monitor and improve its performance 15/2/2018 4 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  5. 5. 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 BlueBRIDGE Solution 15/2/2018 5 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  6. 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 15/2/2018 6 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  7. 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 15/2/2018 7 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  8. 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 15/2/2018 8 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  9. 9. 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) Case Study: Performance Evaluation, Benchmarking &Decision Making 15/2/2018 9 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  10. 10. 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 Case Study: Performance Evaluation, Benchmarking &Decision Making 15/2/2018 10 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  11. 11. Step 2: Setup Model Case Study: Performance Evaluation, Benchmarking &Decision Making 15/2/2018 11 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  12. 12. 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) Case Study: Performance Evaluation, Benchmarking &Decision Making 15/2/2018 12 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  13. 13. 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 1.14 1.15 1.17 1.19 3 3.46 3.08 2.72 2.37 2.07 1.80 1.58 1.41 1.28 1.20 1.15 1.14 1.13 1.15 1.16 1.18 8 3.44 3.06 2.70 2.35 2.05 1.78 1.56 1.39 1.26 1.18 1.13 1.12 1.11 1.13 1.14 1.16 20 3.39 3.01 2.65 2.31 2.00 1.74 1.52 1.34 1.22 1.13 1.09 1.07 1.07 1.08 1.10 1.11 50 3.32 2.94 2.58 2.24 1.93 1.66 1.45 1.27 1.15 1.06 1.02 1.00 1.00 1.01 1.02 1.04 100 3.53 3.16 2.79 2.45 2.14 1.88 1.66 1.49 1.36 1.28 1.23 1.21 1.21 1.22 1.24 1.26 150 4.10 3.72 3.36 3.02 2.71 2.44 2.22 2.05 1.93 1.84 1.80 1.78 1.78 1.79 1.80 1.82 200 4.48 4.11 3.74 3.40 3.09 2.83 2.61 2.44 2.31 2.23 2.18 2.16 2.16 2.17 2.19 2.21 250 4.50 4.12 3.76 3.41 3.11 2.84 2.62 2.45 2.32 2.24 2.19 2.17 2.17 2.18 2.20 2.22 300 4.38 4.01 3.64 3.30 2.99 2.73 2.51 2.34 2.21 2.13 2.08 2.06 2.06 2.07 2.09 2.11 350 4.37 3.99 3.63 3.28 2.98 2.71 2.49 2.32 2.19 2.11 2.06 2.04 2.04 2.05 2.07 2.09 400 4.48 4.11 3.74 3.40 3.09 2.83 2.61 2.44 2.31 2.23 2.18 2.16 2.16 2.17 2.19 2.21 Step 2: Setup Model – Results of Machine Learning process Avg. Sea Temperature Avg. Weight Categories Case Study: Performance Evaluation, Benchmarking &Decision Making 15/2/2018 13 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  14. 14. Step 2: Setup Model – Results of Machine Learning process 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 1 3 8 20 50 100 150 200 250 300 350 400 BiologicalFCR Average Weight Categories 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 Case Study: Performance Evaluation, Benchmarking &Decision Making 15/2/2018 14 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  15. 15. Step 2: Setup Model – Results of Machine Learning process 0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 BiologicalFCR Temperature 1 3 8 20 50 100 150 200 250 300 350 400 Case Study: Performance Evaluation, Benchmarking &Decision Making 15/2/2018 15 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  16. 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 15/2/2018 16 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  17. 17. Case Study: Performance Evaluation Step 3: What-If Analysis 15/2/2018 17 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  18. 18. Case Study: Performance Evaluation Step 3: What-If Analysis – Case Study • Baseline scenario: evaluate the Sea Bream production performance whether a population of 500.000 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 15/2/2018 18 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  19. 19. Case Study: Performance Evaluation Step 3: What-If Analysis – Case Study Results Baseline Scenario Alternative Scenario 15/2/2018 19 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  20. 20. “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels Case Study: Performance Evaluation Step 3: What-If Analysis – Case Study Results Baseline Scenario Alternative Scenario 15/2/2018 20
  21. 21. Case Study: Performance Evaluation Step 3: What-If Analysis – Case Study Results Monthly Feed Consumption Baseline Scenario Alternative Scenario Dec-17 2360.23 Mar-18 1741.49 Jan-18 3520.59 Apr-18 2837.87 Feb-18 3269.62 May-18 4128.76 Mar-18 4231.18 Jun-18 4615.31 Apr-18 2181.23 Jul-18 12295.91 May-18 3752.54 Aug-18 25072.67 Jun-18 9849.37 Sep-18 32578.39 Jul-18 22691.04 Oct-18 39285.06 Aug-18 34990.53 Nov-18 38698.9 Sep-18 41002.50 Dec-18 32145.91 Oct-18 45299.59 Jan-19 14659.85 Nov-18 41002.40 Feb-19 13555.94 Dec-18 30098.66 Mar-19 15467.33 Jan-19 16653.27 Apr-19 19664.4 Feb-19 9741.25 May-19 26381.92 Mar-19 9689.18 Jun-19 42909.94 Apr-19 14294.53 Jul-19 56629.56 May-19 23285.18 Aug-19 61458.86 18 317912.89 18 444128.07 317.91 444.13  283.130 tons saving around 11% comparing with the total feed consumption at baseline scenario (317.91 tons)  However, after the cultivation of a duration of 18th months (31/05) the average weight in baseline scenario is estimated to be 347.78 grs against 305.32 grs of the alternative scenario 15/2/2018 21 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  22. 22. Conclusions IT Providers Gain knowledge from the aquaculture domain New approaches to face problems Combine production and techno- economical models 15/2/2018 22 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels 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
  23. 23. Any Questions? 15/2/2018 23 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels http://www.bluebridge-vres.eu/
  24. 24. Step 2: Setup Model – Sample Data A. 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 Case Study: Performance Evaluation, Benchmarking &Decision Making 15/2/2018 24 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  25. 25. 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 Case Study: Performance Evaluation, Benchmarking &Decision Making Step 2: Setup Model – Sample Data 15/2/2018 25 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels
  26. 26. Step 2: Setup Model – Weight limits B. Weight Categories Dataset: contains user-defined categories of average fish weight which are corresponded to each production KPI (FCR, SFR, SGR and Mortality Rate) FCR SFR SGR Mortality 1 0.50 1 1 3 1 3 3 8 2 8 8 20 3 20 20 50 5 50 50 100 8 100 100 150 10 150 150 200 15 200 200 250 20 250 250 300 30 300 300 350 50 350 350 400 100 400 400 1000 120 1000 1000 150 200 250 300 350 400 450 500 600 Case Study: Performance Evaluation, Benchmarking &Decision Making 15/2/2018 26 “Supporting Blue Growth with innovative applications based on EU e-infrastructures”, 14-15 February 2018, Brussels

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