Transforming Shrimp Farming using

Artificial Intelligence

Sreeram RaaviFounder & CEO

Eruvaka Technologies

Data Analysis

Water Quality

Feed ManagementShrimp Health

Real Time Water Quality Monitoring

• DO, pH , Temperature

• Analytics on diurnal data to check for deviations

Intelligence in the Water Quality Monitoring Equipment

• Self Calibration

• Self Cleaning

• Zero User Interaction

Smartphone based Water Quality Monitoring

• Simplified test procedures to test Ammonia, Nitrite,

Alkalinity, Potassium, Calcium, Magnesium

• Camera and Flash of Smartphone are used as

colorimeter.

• Data is recorded digitally on the app.

Automatic Feeders

• Assessing the feed trays and regulating the

feed is a real pain

• Doesn’t feed the shrimp at right time

• Doesn’t consider the water quality

• Doesn’t consider the shrimp health / molting

Intelligence in the Feeding Equipment

• Feed level sensor in Hopper

• Accuracy in dosing

• Sensors to monitor the wear and tear of motors for predictive maintenance

• Detect feed blockages

Automatic / Acoustic Feeding + Intelligence - AI Feeding

• On-demand feeding of the shrimp based on acoustics, water quality, weekly growth data

• Also feed them on growth models

• Fine Balance between Growth and Profitability

• AI on the edge

• Feeds the shrimp to its full growth potential

Autonomous Feeding of Shrimp

Feeding Response vs Hourly Feed

`• Feeding is regulated based on feeding

response of the Shrimp

• Hourly dispensed feed varies based on the

feeding response

Feeding the shrimp with DO, Temperature

`

22.5

23

23.5

24

24.5

25

25.5

26

0

2

4

6

8

10

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Tem

per

atu

re (

°C)

Feed

(K

g) &

DO

(p

pm

)

Hour

Hourly Feed Vs DO Vs Temperature

Feed (Kg) Dissolved Oxygen (ppm) Temperature (°C)

• DO and Temperature play a major role in feed

consumption and animal metabolism

• Shrimp are poikilothermic, metabolism, growth

and feed intake are tied to temperature

• AI feeding regulates the feeding based on DO,

Temperature

Feeding models for different growth rates

`

• AI Feeding automatically regulates the feeding

based on past few weeks growth data

• AI algorithms learn the data for each pond and

dynamically adjust the models based on pond

potential

• Pond Specific growth models will be developed

over a period of time

0

1

2

3

4

5

6

7

8

9

3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

Fe

ed

(%

)ABW (g)

Feeding Model for Different Growth Rates

1 gm 1.5 gm 2 gm 2.5 gm 3 gm 3.5 gm

AI engine improves the feeding algorithm co-relation

Economic Feeding

0

20

40

60

80

100

120

140

160

50 60 70 80 90 100 110 120 130 140 150

Fe

ed

(k

gs)

Shrimp Appetite

Appetite Feeding Vs Intelligent Feeding

Intelligent Feeding Appetite Feeding Pond Capacity

Weekly Biomass Increment vs Feed Intake

• AI Feeding arrives at a balance between

Weekly Biomass increment and Feed

Dispensed

0

5

10

15

20

25

0

500

1000

1500

2000

2500

1 2 3 4 5 6 7 8 9 10 11

Field Biomass vs Weekly Feed

WeeklyFeed

Week Biomass ABW

AI Feeding Vs Acoustic Feeding Results

S.No Pond

Name

Pond

Size

(Ha)

Feeding Days of

Culture

Harvest

ABW(g)

Survival

(%)

FCR Avg.

Weekly

Growth

(g)

Yield

(lb)

Yield/Ha

(lb/Ha)

1 PS 25 4.39 AI Feeding 108 22.23 73 1.54 1.44 26619 6064

PS 26 4.5 Acoustic

Feeding

112 22.67 66 1.62 1.41 24593 5545

2 PS 6 6.6 AI Feeding 130 22.11 75.38 1.46 1.19 42987 6513

PS 2 7.1 Acoustic

Feeding

130 19.62 64.8 1.72 1.06 33636 4737

Value Beyond

Precision Feeding

- Bio-Mass Estimation

- Assessment of Growth Patterns

- Pond Health Index

- Early disease detection

- Prediction of Yield and Profitability

Pond Automation

- Automatic Feeder

- Water Quality

Monitoring

Precision Feeding

- Acoustic Feeding +

AI Algorithms

Devices – Data Transformation

Bio-Mass Estimate

• Bio-mass estimated based on

average feed intake data

• A downtrend in feed intake indicates

survival drop

Feeding Patterns during White Feces

• High Feed Intake followed by sudden down fall

• 25% drop in survival in 2 weeks, no growth

80

148

96

152168170

154170

122

199200

224

191177

147133139

129

151134130

90 89

125

87

140156

99

72

115

76

113

8773

86

130126145

0

50

100

150

200

250

8-Jun 10-Jun 12-Jun 14-Jun 16-Jun 18-Jun 20-Jun 22-Jun 24-Jun 26-Jun 28-Jun 30-Jun 2-Jul 4-Jul 6-Jul 8-Jul 10-Jul 12-Jul 14-Jul

Feed

(K

g)

Date

Normal White Gut

1362 1390873

2833

7452

1107810429

12922 12970

1410113741

14652

11079

9575

0

10

20

30

40

50

60

70

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

4 5 6 7 8 9 10 11 12 13 14 15 16 17

Surv

ival

(%

)

Bio

mas

s (K

g) /

Pro

fit(

$)

Week

Profit Analysis

Biomass Profit Dead Biomass Survival

Profitability Analysis

Weak Response during disease Infection

Position of feeders inside the pond

134 143124

140 135148

138154 146

309298 293

25-Aug 26-Aug 27-Aug 28-Aug 29-Aug 30-Aug 31-Aug 1-Sep 2-Sep 3-Sep 4-Sep 5-Sep

Feed

(K

g)

Date

Impact of Changes in Feeder Position on Feed Intake Old Position New Feeder Position

• AI Feeding observed huge deviation in the feed

intake vs feeding model

• Response was also poor

• After changing the position feeding response

increased drastically

Autonomous Sampling of Shrimp

• Automated Feed tray lifts out of water from pond bottom

• Images of the shrimp are captured

• Shrimp Size and distribution are analyzed from those

images

Image Processing to predict the Shrimp Diseases

• Analyses the color of Hepato Pancreas, Gut

• Presence of Black Gills

• Disease symptoms are identified via computer vision

Autonomous Feeding Robot

• Feeding Robot navigates in the entire pond and dispenses

the feed in entire pond

• Hydrophone, Dissolved Oxygen Sensor are integrated into

the device

Let’s Strive for Sustainable Shrimp Farming

EruvakaTransforming Aquaculture