big data for smart agriculture · -smac stack (social, mobile, analytics and cloud computing) -data...
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BIG DATA FOR SMART AGRICULTURE
Dr. Azeem KhanCo-PI Precision Agri and Analytics Lab
University of Agriculture Faisalabad
INTRODUCTION
Agriculture and food systems must undergo significant transformations in order to meet the related challenges of food security and climate change
Increasing resource efficiency is essential both to increase and ensure food security on the long term and to contribute to mitigate climate change.
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF
Population growth Global warming Water scarcity Loss of fertilizer and nutrients Environmental degradation Food prices
Rural population Availability of land and
water Land and soil quality Seed germination Profitability Land and water productivity
Low land and water productivity
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF
Big Data and Analytics
Big Data>> V-Volume, V-Velocity, V-Variety, Variability, Complexity Data can be big in volume as well as by lasting significance (e.g. AEZs, Soil Surveys) Digital data: easily shared and replicated, so re-combinable Digital data presents tremendous reuse opportunities--accelerating current science--take benefits
from previous investments-----(Common dataset for everyone) Life Cycle management of Big data presents many challenges and opportunities
(Disciplinary repositories, common databanks, data sharing mechanisms etc)Analytics Farm analytics can add value to the lives of farmers Large amount of data collected at farm---farmers have limited time and capacity to digest A farm management tool can translate that Big data to actionable solution A farm management tool with right blend of data and machine learning
Reduce the amount of inputs required to grow crops &
increase harvestable crop yield = LOWER COSTS
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF
BIG DATA SCENARIO
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF
Poor computing capabilities
Difficulty in acquiring technology, (Embargos)
Unattended, scattered and missing data--lack of continuity
Absence of data clearing house
Inability to generate more data in less time
Lack of high throughput phenotyping
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF
LESA
MESA
Estimating cropwater stress at field scales
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF
Estimating actual cropwater use and crop yields at regional scales
(Kg ha-1)
AN EXPERIMENT ON WHEAT (2017-18) AT UAF
Plant Papulation increase : 8.7%Fuel efficiency : 6%Working hours: 24 hours
Drill sowing without auto steer
S321 orE2020
Portable RTK Base Station
ST4 orIron1
On-board Display
GNSS Antenna
A45
Navigation Controller
MC2 RTK Corrections
+WAS
Precision Planting
Smart irrigation systembased on sensing variabilityof soil moisture
University of Agriculture Faisalabad – uaf
Precision irrigation
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF
• Cost reduction of smart field sensing devices• Linkage with diverse information systems - Satellite and UAV data– spatial, spectral, radiometric, temporal- Meteorological data, grid based and time series data- SMAC stack (social, mobile, analytics and cloud computing) - Data analytics and Information Repository• Decision support tools and modelling to fill data gaps and climate risk
management• Capacity building
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF http://cropmetrics.com/2018-irrigation-summit/
Farm Analytics
UNIVERSITY OF AGRICULTURE FAISALABAD – UAF Thanks!