Image Analysis in FishProduction

Project:

Fish in vivo online Monitoring (FIVOM)for Flatfish-Aquacultur

in Cooperaton with bbe Moldaenke, KielFounded by ISH und EU

Eberhard Hartung and EIKO THIESSEN

Institute of Agricultural Engineering

AquaLife 2008

1st – 3rd July

Kiel

Introduction

Tillet et al, 2000

(Aquaculture, Fishery) AgricultureLivestock farming 1000 B.C. – Aquaculture today

n

Introduction

Definition•Aquatic organism (fish, shellfish, shrimp, algea)•Population is owned by the company

Versions•Fish farming in pond or raceway (carp, trout) in Freshwater•Nearshore net-contained (Salmon), ponds in mangrove (Shrimp) insaltwater•Onshore recirculating systems with filters (sturgeon, turbot)

Food for the carnivorous species made out of fish meal

Aquaculture

Turbotproduction

Ecomares, Büsum, Germany

In 1-2 years

1-2 kg

40 kg/m²

10 % newwater per day

High manual effort in recirculations systems

Problem

Fishsorting every fewweeks due to differentgrowing of fish

Size-distribution isunknown before sorting, i.e.date can be too early(homogenous distribution:needless stress and task)or too late(very heterogenous distribution:growth decrease of the smaller fish)

Monitoring the sizedistribution with acamera system

Image analysis in Aquaculture

• Size-measuring for management-decision (sorting–and slaughter date, feedsize and –amount, growth,heterogeneity)

• Automatic sorting (lock, picking belt)

• Behavior analysis (disease, feeding control)

Applications

Automatic, continuous estimation of the fish size

Development of a camerasystem for estimation the size (e.g.length, area, …) and derived parameter (weight) of flatfish ina well-defined distance

• Hardware:a) camerasystem optimised for recirculation systems

• Software:b) Database of the geometric parameters and weightc) Algorithm for fishdetection and measuring

Aims

TrialsCommercial plant: Ecomares, Büsum

Laboratory: ILV, Kiel

Material

Material & Methods

a) Camerasystems: Lab

Camera-Setups• Camera above water• Camera in glasbottombox• Underwatercamera

2. 3.

camera

1.

Validation in respect to accuracy, repeatability and suitability underchanging environmental conditions

camera mitfisheye-bjectiv

Light-proof boxwith transparentplate on bottom

Methods

b) Database: Commercial productionEntire basin (8 m x 8 m)Time lapse: some weeks, differentproduction phase

Expected results:Fish-distribution in the bassin, „haunts“

Typical shape and weight-relation

Single fishWeighing, measuring length andtaking a picture

Box with transparent bottom

Methods

c) Algorithm sequenceImage Analysis should estimate the size (length, area) auf singleunmoved turbot.

Therefor:

•Image acquisition optimised with lighting and placing

•Discarding of pictures with fish movement (Motiondetection withdifference-images)•Object-Extraction with imagefilters (edgedetection, thresholds)•Calibration in Real World Coordinates (i.e. cm-unit)

•Selection of fish shapes by means of typical geometric parameters

•Measuring (length, weight, …)

•Post-Processing (statistical analysis, ignore repeated measurements ofone fish)

Results

a) Validating the SetupsWell known referencefish was measured under different conditions

0.410.57

Without waves:0.75with waves:1.4

Accuracy mean quadr.deviation to reference[cm]

0.250.12

Without waves:0.32with waves:1.2

Repeatibility Stdev ofdifferent positions [cm]

Small area, onlyincreases with slope

Difficult to handle (20 kg),partly shadow, airbubbles on the bottom

Reflexion on the surface,image is disturbed due towaves

contra

No reflexionNo reflexionEasy installation, big areapro

1.151.351.45area [m²]

underwatercamera,slopedglasbottomboxcamera above bassin

Results

a) Light and camera

0

20

40

60

80

100

120

140

160

0 50 100 150 200 250 300 350 400 450 500

illuminance [lux]

mea

n gr

eyva

lue

[bits

]

typ. candlepower in production

difference

background

fish

Automatic brightness-controll leads to constant contrast inthe typical light conditions

Results

b) Distribution

Time lapse at Ecomares (every 5 min, 4 weeks)

Mean raw images mean analysed images(fish = white, background = black)

Spatial probability distribution 0 50 90 %

Results

b) distribution

Time lapse at Ecomares (every 5 min, 4 weeks)

Mean raw images

Results

A single fish marked with LED,Points indicate stop of min. 30 s during 3 days

b) Distribution

Bassin border

(deformed du tooptics)

Results

b) Shape-weight-relationSingle fish weighed, length measured and image aquired

lengthy = 0.01x3.3

R2 = 0.98

areay = 0.036x1.6

R2 = 0.99

0

200

400

600

800

1000

1200

1400

1600

1800

2000

100 200 300 400 500 600 700 800 900

area [cm²]

wei

ght [

g]

0 5 10 15 20 25 30 35 40 45 50

length [cm]

n = 55

Results

b) Shape-parameter

L: length U: contour A: areaU²/F, F/L and U/L constant in thepopulation?

0

2

4

6

8

10

12

14

16

U²/L (Rundheit) F/L U/L

n=55

COV :16 % 22 % 7 %

Single fish weighed, length measured and image aquiredImages analysed concerning shape parameters

Results

c) MotiondetectionFishmovement was analysed with the stdev. in the difference from two

frames during feeding

0

1

2

3

4

5

6

7

8

9

10

0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 160 170 180

time [s]

activ

ity (s

tdev

. diff

.)

still every 10 s feed

greyvalue, 1 s mean

Results

c) CalibrationPlate with points in defined distance is presentated to camera andcalibrationparameters (objectiv focus and distortion, position of thecamera in the real world coordinatesystem x, y, z, ß, ?) are calculatedfor transformation to cm-units

Raw image corrected image(projection in x-y-plane)

Realisation

c) Screen-ShotSoftware for calibration, objectdetection und –measuring anddocumentation programed in HDevelop

original difference +contours

corrected

Variables Source text

control

Summary and discussion

Automatic Image Analysis can reduce manuel effort in fishproduction andserves important information directly related to fish for the producingmanagement.

Underwatercamera minimises problems with watersurface and providesfishlength with an accuracy of better than 1 cm.

Turbots prefer the border of the bassin and changes position hourly over thewhole bassin.

Fishdetection works well with edgefilter but problems if fish overlap withsmall contrast.

Statistic for calculating the mean and variation of fishsize is influenced byrepeated measuremnet of the same fish

Thank you for your Attention!Dieses Projekt ist Teil von „e-region Schleswig-Holstein plus“,

ein Programm des Ministeriums für Wissenschaft, Wirtschaft und Verkehr und derInnovationsstiftung Schleswig-Holstein – gefördert von der ISH und der EU aus dem

Europäischen Fonds für Regionale Entwicklung (EFRE)

Institute of Agricultural Engineering

Fish „size“:

geometricparameters

Definition

Length L (longest distance along to fish axis )Width W (longest distance perpendicular to Length)Diameter D (longest distance in the area )Area AContour U (Outline of the fish)

U

L

A

W

D

Kamerasdigitale CMOS, Mono, 756x480 Pixeldigitale CCD, Farbe 640x480 und Mono 1280x960 PixelSony Analog CCD + FramegrabberObjektive12 + 16 mm C Mount F1.4 (Pentax)2.5 mm CS Mount F1.21.4 – 3.1 Vario CS Mount F1.4 (Fujinon)5 mm C Mount (Compar)FilterPol-, IR-Sperr- und DurchlassfilterPC und SoftwareIntel Pentium 4 @ 1.6 GHz und 768 MB Ram, Intel Pentium Dual-Coreund 2 x Intel Dual-Core Xeon @ 1.6 GHz und 2 GB RAMWindows XP und Halcon 7.1 (unterstützt Parallelprozessoren)

Bildanalyse (Datenaufnahme und –verarbeitung)

Material

Ergebnisse

b) Genauigkeit KameraKamera (55 Bilder) gegen Zollstock

y = 0.99x + 0.59R2 = 0.996

n = 55

15

20

25

30

35

40

45

15 20 25 30 35 40 45

Länge Zollstock [cm]

Läng

e K

amer

a [c

m]

Institut für Landwirtschaftliche Verfahrenstechnik

Institut für Landwirtschaftliche Verfahrenstechnik

RieselfilterI

Becken IIT, pH, Redox, O2, PegelSMS Alarm

Abfluss

PumpeOR6500

EHEIM2260

Druckluft

Eiw

eiss

absc

häum

er T

5000

PumpeOR2500

Strömungs-wächterATK

Aqu

aCris

tal

U

V-C

RieselfilterIIBecken I

1000 lKühlerT4000

Ozonisator

Institut für Landwirtschaftliche Verfahrenstechnik

Institut für Landwirtschaftliche Verfahrenstechnik