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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