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Smarter Agricultural Decisions A Big Data and Big Compute Approach

Dr. Chid Apte, IBM Research

© 2014 IBM Corporation 2

Trends in Agribusiness

Trend 1

§  Demand for agricultural products is increasing

§  Growing population (9 billion by 2050) will require 50% growth in agricultural output

§  Rise of the middle class in developing countries will increase demand for meat, dairy, vegetable oil, fruits &vegetables, sugars

§  New sources of demand for agricultural products will come from the energy industry, such as bio-fuels and ethanol

§  Growing population will also exacerbate the food security problem

Trend 2

§  Dwindling natural resources are forcing change

§  Agriculture accounts for 70 percent of fresh-water

withdrawals. Over the past 50 years, the use of water has increased 300%

§  Limited remaining arable land due to economic development, environmental damage, climate change and harsher weather patterns. Ratio of arable land to population declined about 40 percent between 1960 and 2000 - faster in developing nations

§  The fertility of land under cultivation decreasing due to poor soil management (inadequate crop rotation and over dependence on fertilizer)

§  The changing climate introduces new challenges for agriculture.

World Population Growth


Trends in Agribusiness (con’t)

Trend 3

§  Rising and increasingly volatile commodity and energy prices are forcing Agribusinesses to make dramatic improvements

§  Input costs for all commodity based agribusinesses

(grain, dairy, protein) have increased sharply since 2000

§  Efficiency gains are imperative to make profits in this challenging environment.

§  Performance in Commodity Trading is increasingly important for profitability

Trend 4

§  Agriculture gains in developing countries are changing global business dynamics

§  The agriculture sector is larger and more important

for the largest growth market countries than for major developed countries §  India agriculture = 17% of GDP: China = 11%:

Brazil = 6%: Russia = 5% §  Major industrialized countries = 1 - 3%

§  Governments are actively investing in agribusiness to improve the quantity, quality and safety of food for domestic consumption and export


Trends in Agribusiness (con’t)

Trend 5

§  Food safety concerns are driving government regulation and consumer demand for greater transparency in the global food supply chain

§  Companies are increasingly implementing

traceability systems for quality, safety and process integrity to ensure access to export markets

§  Developing countries with high percentage of GDP from Agriculture exports (China, Thailand) are moving quickly towards transparency to combat a reputation for lower health and safety standards

Trend 6

§  A wealth of data is becoming readily available

§  Sensors are providing new sources of data

§  Aerial: drones, satellites, airplanes §  Land: soil moisture, water dripping,

nutrients §  Machinery: scales on machines, on-spot

yield, crop quality

Precision by Satellite: Real-time kinematic CPS receivers let Clay Mitchell tend to his crops with centimeter accuracy


Delivering scalable data-driven insights to create novel sustainable productivity and efficiency for Agriculture

§  Agriculture will be the next major industry to experience a data-driven transformation

– Provide the industry with actionable insights that enables more productive and sustainably sound decisions.

– Improve efficient use of resources to drive sustainable food security.


Examples of IBM Research capabilities in Agriculture

6

Genomic Research • Genomic sequencing • Seed development using genomic analytics • Disease monitoring and analysis Crop Planning • Multi objectives decision making • Yield optimization • Weather modeling for optimized crop planning • Sustainability

Smart Farming •  Sensor based physical

analytics •  Precision irrigation •  Precision agriculture

algorithms (fertilizers, spraying, pesticides)

•  Remote sensing Coupled Modeling •  Deep Thunder •  Hydrology simulation Farmer Services •  Spatio temporal

insights •  Sustainability

Logistics •  Smart agri-logistics

including intelligent transport and real-time logistics of crop products

•  Predictive asset management

Farmer Services •  Spatio temporal

insights

Storage efficiency •  Optimized storage

planning •  Spoilage reduction

Logistics •  Smart agri-logistics and

integrated supply chain across farm-to-fork processes

•  Predictive asset management

Traceability •  Food traceability to

consumers Food safety •  Food safety consortium

Finance •  Financial services,

insurance

Pre-planting Planting & growing Harvesting & Store Distribution & Process

Mobile Services Platform for Agribusiness Ecosystem (IoT)

Massive Scale Analytics (Cloud, Big Data, Analytics)

© 2014 IBM Corporation Comparative genomics

Trait Mapping

Cultivar demographics

Improving quality of cultivated plants à  Deciphering genotypic and phenotypic variation à  Adaptation to changing environments à  Marker Assisted Selection and breeding strategies

Genome assembly

Breeding strategies & simulations Differential gene expression Genomic selection & prediction

Agriculture Genomics (methods)

© 2014 IBM Corporation

à Improving cacao, avocado quality: Discovering markers for selection, utilizing genetic and phenotypic variation in mapping populations

à Improving robustness of bioenergy grasses: Discovering mechanisms for drought, flood, salt stress resistance

Agriculture Genomics (applications & collaborators/clients)

à Improving quality of cultivated plants: Generating smarter breeding decisions, guided by accurate genomic selection methods and simulations

Ongoing research directions •  Detection and modeling of epistasis, multi-trait prediction and mapping •  Phasing and using haplotype data for improving prediction and trait mapping •  Metagenome for prediction •  Differential gene expression in polyploid plants with no reference genome •  Population generation models for accurate breeding simulations


Opportunity •  Advanced Polymer Materials are increasingly used in agricultural innova5ons to enhance cul5va5on of crops in adverse weather and soil condi5ons for crop protec5on and for improving the yield and quality of crops in shorter 5me at lower cost.

Innova-on •  IBM Research has the poten5al for providing polymer materials innova5ons in smarter agriculture via it’s ongoing ac5vi5es in adjacent research spaces:

-‐ e.g. Controlled Release Polymers (CRP) for the precision deployment and performance of moisture control agents and site-‐localized release of chemical ac5ves

-‐  e.g. An-microbial Polymers which are currently being evaluated for use in comba5ng citrus greening of orange orchards in Florida

-‐  e.g. Exis-ng Block Co-‐polymers and Hydrogels that are poten5ally adaptable towards soil reclama5on research

Impact •  Up to 90% of agrichemicals deployed are wasted – precision CRP agrichemicals have poten5al for significant financial and environmental savings

•  Up to 10% of harvested grain held in long term storage is lost through pests. CRP pest repellents are a poten5al innova5on in reducing this cost

•  IBM polymers present innova5ve research solu5ons to emerging problems such as Citrus Greening which has cost the state of Florida alone $4.5B to date

Materials Innovation for Smarter Agriculture

Advanced Polymer Synthesis

New Polymer Nanostructures

Innovative Structure- Function

Relationships

Technology Integration


Deep Thunder and the Flint River Partnership Advances in Precision Agriculture & Water Optimization

In Georgia, where agriculture has a $72 billion economic impact, farmers are turning to ground-breaking technology from IBM to help meet ever-increasing food production demands and leading the way in conservation measures, improving agricultural efficiency by up to 20 percent.

Published April 23, 2014

http://www.research.ibm.com/articles/precision_agriculture.shtml


BRUNEI Reference: http://www.research.ibm.com/articles/brunei.shtml

Weather modeling and data analytics empower an island nation to save its natural resources

Goal: Improve food security for its citizens by improving local agriculture. •  Only 3% of Brunei’s rice is grown in the country today •  Brunei hopes to increase domestic rice production by 60% by

2015.

• Apply deep expertise in using data analytics and weather modeling to improve agriculture and energy development.

• Using a Blue Gene-P system, show specific conditions in an area as small as 1.5 x 1.5 square kilometers, and reflect changes over 10 minute increments for a 48 hour period.

Farm Management

Increase Yields


Seasonal Weather Forecasting for Maximizing Yields

Opportunity •  The weather condi5ons during the lifecycle of a crop play a profound role in determining yields.

•  Use seasonal (mul5-‐month) weather forecast along with knowledge of op5mal condi5ons required by the crop to determine op5mal 5me for sowing / transplan5ng so as to maximize yield.

Innova-on •  On-‐going research project is a first-‐of-‐its-‐kind integrated seasonal weather and crop modeling.

•  Advisories through tablets, smartphones. Expected Impact •  Poten5al of up to 15% improvement in yields have been reported, transla5ng into significant revenue savings.

Multi-Dimensional Matching between forecasted data and ideal conditions: Main challenge is to find the best feasible offset.


Physical analytics to improve crop yields while reducing water consumption

Opportunity

Impact

•  Increase in yield by 2.12 tons/acre (~23 %)

•  Water conservation 20%. •  Less variability, which reduces “re-mixing” of

grapes at the winery; higher quality grapes

§  Perennial crop yields and quality are severely limited by temporal and spatial variability (due to soil differences etc.)

§  To overcome this variability, real-time measurements of crop conditions, analytics to determine optimum water /fertilization and a different irrigation/ fertilization system are required

Innovation

Evapo-transpiration modeling & plant model to derive optimal irrigation schedule


Grain Silo Measurement and Optimization

Complex three

dimensional surface!

Wireless Interface Silo

Capacity Scanner!

Processing Server

Mobile Device

Wireless Interface Integrated

Chain of Operations!

Single Sensor Per Silo Local Data Delivery for ‘Loader’ Minimizes On-Site Footprint Raw Data for Analytics

Computer


Drone Farming

15

CARMEL: http://ibmresearchnews.blogspot.com/2014/06/first-response-made-faster-with-video.html


Watson Life for Agriculture

Swarna: suitable for Alluvial soils, canal irrigated, single cropped wet land Suraksha: suitable for red loamy soils, canal irrigated

Where can I buy Swarna seeds?

What are the latest maize crops?

Use Case 1: Crop Selection Scenario: A district-level employee receives the following query from a farmer:

“What are the new varieties of rice available?”

16

Rice

Groundnut

Maize

Brinjal

Ladyfinger

Tomato

Crops Rice Varieties



Use Case 2: Market Price Scenario: A farmer wants to know the current market price for different varieties of rice

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Use Case 3: Crop Protection Scenario: A field-level employee receives the following query from a farmer: “What are the latest pesticides available for Swarna?”

Pesticides

Weedicides

Diseases

Crop Protection

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Rice

Groundnut

Maize

Brinjal

Ladyfinger

Tomato

MCPA: MCPA is used as an herbicide, generally as its salt or esterified forms Cycloxidime: Cycloxydim is an active substance in the plant protection product

Where can I buy MCPA?

What are the diseases affecting rice?

Pesticides


Opportunity •  Methods enabling high-‐level decision makers make a more informed and public-‐aware decision with respect to land-‐use, sustainability, and other trade offs when growing and purchasing bio-‐fuels

•  Project funded by the European government as the ul5mate beneficiary. Collabora5ve project with Universi5es (Athens, Konstantz), research centers (Greece), interna5onal data and model suppliers (IIASA), and end-‐user environmental non-‐profit organiza5ons (WWF, Oxfam)

Innova-on •  Enable complex and high-‐stakes decision making through u5liza5on of various technologies such as mul5-‐objec5ve Pareto fron5er visualiza5on, gamifica5on, and more.

•  MOOV (Mul5-‐Objec5ve Op5miza5on and Visualiza5on) from Watson group asset will be enhanced and used. Papers and patents will be published

Impact •  Expected impact in making poli5cal decisions on land use and bio-‐fuel import and produc5on based on more informed methodologies, while keeping public opinion at the center. Expected results in more sustainable world wide land use and less nega5ve impact of bio-‐fuel produc5on on tradi5onal agriculture and food availability

•  30 month project ending on March 2016, with major 2014 deliverables at March and October

Multi-Objective Decision Making Through Citizens Engagement The Consensus Project


Jefferson Project at Lake George

http://fundforlakegeorge.org/JeffersonProject


PAIRS: Physical Analytic Information Retrieval and Services Platform

•  PAIRS is an IBM Research big data repository of pre-processed physical data with − a common formats and projections − spatial & temporal joins − a global reference system

•  Big data bus and scheduler provides near real-time updates −  More than 1 Terabyte per day

•  Accessible via Data as a Service

through an integration layer at Softlayer −  APIs to run queries

11/11/14 IBM Confidential 21

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Underlying Big Data / Big Compute Technologies

Analytics

§  Models of physical and biological processes for scenario based planning and prediction.

§  Integration of Physical and Statistical models for decision making.

§  Knowledge extraction

and deep learning from agronomic sources to understand the impact of carbon, nutrients, energy and water cycle in agriculture processes

Services

§  Invent IT services to drive efficiencies in poorly automated social-physical processes.

§  Industry models for connecting human intensive information gathering at various points of inputs and outputs of food supply chain.

Cloud

§  Technology to fuse information from multiple sensing systems: physical, biological and human

§  Efficient storage and processing of peta bytes of geo-spatial and scientific data at scale.

§  Enablement of geo-physical decision support system using geo-physical data mining and analytics.


Genomics

Precision Agriculture

Supply Chain

Preventive Asset Maintenance

Food Safety and Traceability

Marketplace Analytics

Crop Insurance

Sustainability

IBM Research is innovating data-driven transformations across the Agriculture value chain


China

Watson Almaden

Austin

Tokyo Haifa Zurich

India

Dublin

Melbourne

Brazil

Kenya

IBM Research: The World is Our Lab

Taiwan

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