Climate change is causing unpredictable weather patterns that threaten agriculture. Climate-smart agriculture (CSA) helps farming adapt to and mitigate climate change to ensure food security. An IoT sensor network can sense local conditions, analyze big data using edge computing, and predict weather to help farming adapt through a continuous feedback loop. Open data standards and an e-government platform can further support CSA goals.

1. :IoT+ Big Data
Enabler in Climate Smart Agriculture
Symposium on Smart Agriculture
18th March 19 | Colombo | Sri Lanka.
Dassana Wijesekara
https://medium.com/@dassana.p.wijesekara

2. Climate
Change
● Enhanced Melting of Cryosphere
● Cloud Circulation and Precipitation Unpredictability
● Carbon Feedback in the Climate System
● Dynamic Climate Extremes
● Groundwater Recharge/Discharge Cycles
● Regional Sea Levels and Coastal Environment Changes

4. Adopt & Mitigate

5. Climate Smart Agriculture
Climate-smart agriculture (CSA) is an approach
that helps to guide actions needed to transform
and reorient agricultural systems to effectively
support development and ensure food security in
a changing climate.

6. Climate Smart Agriculture
Adaptation Mitigation
Productivity
&
Efficiency
Synergies

7. Adaptation ?
Crop Microclimate Feedback
Loop
local/peripheral
network
Sensors data
Data cleansing
Pluggable algorithms
Edge analytics
Aggregation
Resilient data push
Regional Climate Model
Analytics
Prediction models
Real-time feedback
Human task
Sense | Analyse | Predict | Adopt

8. > Sense +
PAR Photosynthetic Active Radiation
% Relative Humidity
mb Barometric Pressure
PYR Total Solar Radiation
mm Total Precipitation
C Air Temperature
kph Wind speed and direction
C Soil Temperature
% Soil Moisture
% Leaf Wetness

9. IoT in Sensor Ecosystem

10. Error Correction and Edge Computing

11. Improve Energy Efficiency in Sensing

12. Open
Source
Open Data
Open
Architecture

13. > Analyse +
Data Cleansing Injesion
Data Lake Storage
Big Data Analytics Analytics
Data Visualization View

14. Big Data
Massive Volumes of data with wide variety of
both structured and unstructured that can be
captured, analysed and used for decision making.

15. Big Data Handling
Multi-source
data
Data preprocessing Techniques
removal of
duplicated data
noise reduction Cleansing Transformation Standardization
Data Analytics Pipe
Analysis Mining Statistics Clustering Fusion

16. Real-time & Batch Analytics
Complex
event
processing
Batch
Analytics
Real-time
view
Batch Job
Feedback
Distributed Processing
Insight

17. Open Data & Data Driven Innovation

18. OADA Road Map

19. Role of e-Government Platform
● Link government agencies
● Share data and information
● Standardization (privacy and security)
● Provide technical and financial packaged services