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!