Novel technology to improve feeding management

Javier Villa, Andromeda Ibérica

Nikos Papandroulakis, HCMR

INDUSTRY FORUM: SUSTAINABLE SOLUTIONS TO ADDRESS SEA BASS AND SEA BREAM FARMING CHALLENGES IN THE MEDITERRANEAN

The problem

• How much should I feed my fish?

Feeding

“All methods and practices employed to deliver nutritionallybalanced and adequate amounts of diets to animals formaintaining normal health and reproduction together withefficient growth and performance (Cho and Bureau, 1998)”.

• Feeds represent a high percentage of the production cost(from 40-60%)

• A lot of work for development of diets and feedingmethods

✓ More efficient formulas

✓ Feeding Automation (up to barge feeding)

✓ Feeding control ( cameras implementation etc)

The environmentTemperature

Dissolved Oxygen

Currents

Wind

(Ammonia, Salinity, Light)

How much to feed?

A KEY POINT: the biomass

✓ The real Nº fishes - Control of introduction, mortality collection & escapes

✓ The real A.W. of the stock – Sampling controls

Husbandry practice• Stocking density• Feeding method

• Distribution ( hand-cannon.-spreaders)• Strategy ( dose/days- table or Adlibitum

feeding)• Feed losses control ( cameras- sensors)• The trend

Big volumes – MORE COMPLEX MANAGEMENT

Biomass simulation – sea bream breamIntro date April

Batch size 280000

AW intro 10gr

Harvest size 450 gr.

Cost Input

Feed €/Kg 0,90 €

production €/month 6.000 €

Fish €/unit 0,31 €

Commercial cost €/Kg. 0,65 €

Sales Price €/Kg. 5,0 €

APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JUL

A.W.- P.M. 10 14.2 26.5 49.3 80.8 120.3 164.3 213.5 251.4 263.8 268.0 264.2 267.6 281.9 317.6 372.4 433.8

€/Kg 24.0 € 14.4 € 8.7 € 6.0 € 4.6 € 3.9 € 3.4 € 3.2 € 3.2 € 3.4 € 3.6 € 3.7 € 3.7 € 3.6 € 3.4 € 3.8 €

-2.0 €

3.0 €

8.0 €

13.0 €

18.0 €

23.0 €

0

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100

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€/K

g.

A.W

. G

r/fi

sh

BREAM SIMULATIONGROWTH & COST A.W.- P.M.

RESULTS

HARVEST 109.230

SURVIVAL 89,9%

FCR 1,7

€/Kg 3,8 €

PROFIT 130.013 €

Stock control– procedure

INTRO CONTROL COUNT BIOMASS

SCANNER

MORTALITY COLLECTION

(<100 gr.)

Every 1-2 DAYS

INTERMEDIATE CONTROL

CONTROL COUNT VACCINATION

GRADING

MORTALITY COLLECTION

(> 100 gr)

Twice week

HARVEST

Packing Count

High CannibalismEscapes Risk

• Scanner control – No 100% Accuracy• Cannibalism losses- High risk (below 100gr)• Escapes risk – Along all the cycle• Losses from Birds – could be critical• Other predators ( turtle- Other fishes)

84

86

88

90

92

94

96

98

100

0

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500

AP

R

MA

Y

JUN

JUL

AU

G

SEP

OC

T

NO

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DEC

JAN

FEB

MA

R

AP

R

MA

Y

JUN

JUL

Survival evolution%

A.W. (gr.)

Effect of Final Biomass variation in KPIs and cost

-80%

-60%

-40%

-20%

0%

20%

40%

60%

80%

-15% -10% -5% 0% 5% 10% 15%

% PROFIT

1.0

1.5

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5.0

-80%

-60%

-40%

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0%

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15% 10% 5% 0% -5% -10% -15%

KPIs

PROFIT SURVIVAL FCR €/Kg

(LOSSES) - Number of real fish compared to expected fish at the end of a cage

LOSSES Final Nº Fishes HARVEST SURVIVAL FCR €/Kg PROFIT % PROFIT

15% 289.600 125.615 103,4% 1,4 3,31 € 211.936 € 63,0%

10% 277.009 120.153 98,9% 1,5 3,46 € 184.628 € 42,0%

5% 264.418 114.692 94,4% 1,6 3,63 € 157.320 € 21,0%

0% 251.826 109.230 89,9% 1,7 3,81 € 130.013 € 0,0%

-5% 239.235 103.665 85,4% 1,7 4,01 € 102.187 € -21,4%

-10% 226.644 98.209 80,9% 1,8 4,24 € 74.907 € -42,4%

-15% 214.052 92.753 76,4% 1,9 4,49 € 47.626 € -63,4%

Objective of the work in PerformFISH

Development of an Accurate System for Biomass Estimation

➢ A parameter in the core of any feeding system ➢ The system will include estimation of

• fish size, hence growth (using optical methods and image analysis) and

• total biomass in the cage (using echo sounders)

Total biomass in the cage

Develop a system that

➢ accurately measures total biomass in cages using echosounders

➢ Preliminary trials and on going research

Day

Night

Fish spatial distribution

Individual Fish Size Measurement

Estimation of fish size

Develop a system that

➢ accurately measures fish size (Length/Shape) using 3D photogrammetry and computer vision algorithms and techniques

➢ operate remotely, so that no dedicated personnel will be necessary on site

Some theory …

➢ Stereoscopic Vision imitate human’s visual system withtwo cameras• Estimate the “depth” (distance from the camera) from

two overlapping images of the same scene (slightly separated from each other)

Left Input Frame Right Input Frame

Software to measure fish-length

Solutions exist! As the Vidsync open source Software

But…Manual selection of fish in both L+R images

The pipeline of our method

• Obtain a stereoscopic image • Calculate “depth”

• Create a 3-D model of your fish• Apply the 3D model pose

• Match the model with a specimen to measure• Apply a PSO (particle swarm optimization) algorithm using

the “depth” data

• Train a neural network to specify the candidate specimen

• Obtain a stereoscopic image • Calculate “depth”

• Create a 3-D model of your fish• Apply the 3D model pose

• Match the model with a specimen to measure• Apply a PSO (particle swarm optimization) algorithm using

the “depth” data

• Train a neural network to specify the candidate specimen

• Camera and its calibration

0

5

10

15

20

25

30

35

Real Length (cm)

Camera Measurement(cm)

Error (%)

Meas 1 Meas 2 Meas 3 Meas 4 Meas 5

The pipeline of our method

• Obtain a stereoscopic image • Calculate “depth”

• Create a 3-D model of the fish• Apply the 3D model pose

• Match the model with a specimen to measure• Apply a PSO (particle swarm optimization) algorithm using

the “depth” data

• Train a neural network to specify the candidate specimen

➢ Based on available knowledge/algorithms

3D Model for Fish

➢ Bones to allow fish movement• Each joint with 3 DOF to rotate the model

➢ Scale that can adjust to the given coordinates in an image and the depth

European sea bass Gilthead sea bream

The pipeline of our method

• Obtain a stereoscopic image • Calculate “depth”

• Create a 3-D model of the fish• Apply the 3D model pose

• Match the model with a specimen to measure• Apply a PSO (particle swarm optimization) algorithm using

the “depth” data

• Train a neural network to specify the candidate specimen

Particle Swarm Optimization algorithm

Fish measurement

1. Apply a PSO algorithm using the “depth” data to match the model on a

specimen

Fish measurement

Several different sized fishes were placed in a tankSpecimens were measured (TL) and tagged with different colors for identification

TL (cm)

FL (cm)

WEIGHT (gr)

TAG SAMPLE

27.5 26.5 410.2 G

Depth mapping calculated from original Image Darker fish silhouettes are closer than lighter ones

• The system presents accuracy with reasonable deviation from the actual size

• Candidate image to measure based only on “depth” data

Test of the system

• A cage differs from a tank!

The pipeline of our method

• Obtain a stereoscopic image • Calculate “depth”

• Create a 3-D model of the fish• Apply the 3D model pose

• Match the model with a specimen to measure• Apply a PSO (particle swarm optimization) algorithm using

the “depth” data

• Train a neural network to specify the candidate specimen

Determine points that fit the model

Reference Image

Feature Matching

* Optional

After “training” the systemCandidate Images

Next steps

• Test in real conditions and fine-tuning

• Continue with E. sea bass

Thanks you for your attention!

Nikos Papandroulakis

HCMR

npap@hcmr.gr

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 727610. This output reflects the views only of the author(s), and the European Union cannot be held responsible for any use which may be made of the information contained therein.

V. Chalkiadakis, D. Voskakis, A. Makris,

M. Sfakiotakis, V. Trigonis, A. Kapelonis,

C. Schellewald, Walter Caharija

Javier Villa

Andromeda Ibérica

jvilla@andromedagroup.es