information systems for increasing productivity of agriculture · information systems for...

© Hitachi, Ltd. 2014. All rights reserved.

Information Systems for Increasing Productivity of Agriculture

Nov. 5th 2014 Takaomi Nishigaito Director & CTO Hitachi South America Ltda


1 Who am I?

CTO of Hitachi South America and General manager of R&D division.

Came to Brazil from R&D group, Hitachi, Japan to open R&D in Brazil in June 2013.

Focusing on Agriculture and Mining , which is originally strong in Brazil. Planning to make them much more strong using Hitachi`s technologies.


2 Who are we?

Power systems

Automotive systems

Digital media & Consumer products

Social infrastructure & Industrial Systems

Construction machinery

Financial services

Electronics systems & Equipment

High functional materials & Components

*Figures are on a consolidated basis for the FY ending 31 march 2014

JPY 9,616B (USD 93.36B)

14％

10％ 7％

7％

13％

8％

8％ 3％ 12％ 18％ Information &

Telecommunication Systems

We are HITACHI , having IT × OT


3

Hatoyama-machi, Saitama

Hitachi-shi, Ibaraki

Hitachinaka-shi

Kokubunji-shi Tokyo

Yokohama-shi, Kanagawa

R&D Group

High Functional Materials & Components Group

Infrastructure Systems Group

Information & Telecomm. Systems Group

Power Systems Group

Construction Machinery Group

Automotive Systems Group

Healthcare Group

President

Akasaka, Minato-ku, Tokyo

*as at April 2014

Technology strategy planning

Technology Strategy Office

Energy, Societal, Industrial and Life infrastructure, Materials and Key devices

Hitachi Research Laboratory

Information platform technology, Monozukuri technology

Yokohama Research Laboratory

Contribute to expanding regional business Collaboration with key customers

Overseas research centers

Vision design, Experience design

Design Division

Expand business coverage, New areas in anticipation of future societal needs

Central Research Laboratory

R&D Group in HITACHI


4 R&D Organization

Cambridge

Sophia Antipolis

Farmington Hills

USA (Hitachi America, Ltd.) China (Hitachi (China) R&D Corporation) Europe (Hitachi Europe Ltd.)

Rail systems Automotive

systems Energy systems Healthcare

Service design Adv. Physics

Storage systems Automotive equipment Wireless comm. systems

Big data analytics Design

Platform studies on big data & Solution development

Customer-oriented data science

Asia (Hitachi Asia Ltd.)

Munich

Maidenhead

Social systems Information systems Production technology Home appliances

development Design

Singapore

India (Hitachi India Pvt. Ltd.)

Bangalore

Social infrastructure systems Information &

Telecommunication systems

Purpose: Facilitate collaboration with key customers

Beijing

São Paulo

System for agriculture and mining industry Social infrastructure systems

South America (Hitachi South America, Ltda.)

Santa Clara Japan Shanghai


5 Open innovation

Univ. of Campinas (HISA/Brazil R&D)

Study & preempt future market changes

through discussion with university

Pioneer new markets

Tsinghua Univ. (HCR&D)

Green ICT

Internet of Things

Next-generation cloud

Fudan Univ. (HCR&D)

Joint project with Chinese business

through partnership with university

HCR&D: Hitachi (China) R&D Corporation SIR: Semiconductor Innovation Research Project

EERC: Energy and Environmental Research Center HAS: Hitachi Asia Ltd.

DSI: Data Storage Institute A*STAR: Agency for Science, Technology and Research

HISA: Hitachi South America, Ltda.

IIT Hyderabad (HIL/R&D)

Identify India’s unique energy needs by analyzing energy demand curve

within universities Micro-grid

Diesel generator (2 units)

Genome analysis

A*STAR/DSI (HAS/R&D)

Practical verification of genome analytics

platform

Automotive

RWTH Aachen Univ. (HEU/ERD)

Map data linked chassis control

Technische Universität München

(HEU/ERD)

Engine combustion analysis & simulation Semiconductor

measurement

IBM (HAL/R&D SIR)

Joint research with leading consortium in

adv.semiconductor measurement

Univ. of Birmingham (HEU/ERD)

Carriage abnormality detection using

acoustics measurement

Railway maintenance

EERC (HAL/R&D)

Build-up solution portfolio for Oil＆

Gas business

Energy


6 Global collaboration

North America

Europe

Japan

South America

Global Big Data Innovation Laboratory (GBDIL)

US Big Data Lab • Storage solutions • Big data analytics

Central Research Lab. Hitachi Research Lab. Yokohama Research Lab.

Design Division

Overseas research bases leading the expansion of big data business

Brazil R&D Division • Agriculture

European Big Data Lab •Healthcare

• Transportation

Denmark Big Data Lab (to be est. in 2014/4Q)

India

R&D Centre • Analytics workbench


7

≪Past≫

Decision making using historical data.

≪Current≫

Decision making using growth model + climate data

≪Future≫

Decision making using Combined model (Climate data Farm data + Satellite data )

IT in Agriculture fields


8 What can we know from Satellite ? A

mo

un

t o

f li

gh

t re

flecte

d

Wave length of Light (nm)

Wave length of Light (nm)

Abso

rbed L

ight

Chlorophyll A

Chlorophyll B

Healthy

NOT Healthy

Absorb Coastal Blue, Blue and Red for creating chlorophyll

NIR1, NIR2 indicate health

Able to know the growth (Chlorophyll), and health by analyzing spectrum of photos taken from space


9 Signal processing

Vegetation index is calculated using the relations between the intensity of specific frequency bands.

Vegetation Index

Calculation

Wave length (nm) Wave length (nm)

CB

B G

Y R

RE NIR1

NIR2

NDVI (Conventional) : Activities PNVI (HITACHI) : Activities according to Growth Stage * Absorption for chlorophyll gradually changes according to the growth stage. Our method considers this phenomena for increasing accuracy.


10 Harvest Scheduling

Able to know growth condition for Harvest scheduling.

PNVI : Hitachi NDVI : Conventional

Original

Field to be Harvested

Clear and Precise

Growth stage index ,which can be used to determine the harvesting, can be clearly obtained by our method.


11 Quality Control

Able to control quality by controlling fertilization.

Protein quantity (affect the quality of Rice. The less protein causes deliciousness) can be analyzed by using our classification method. Additional fertilization can be considered to control the quality before harvesting.

Original Classified Vegetation Index much

little

medium

medium low

©DigitalGlobe


12 Crop Classification

Able to classify crop variety

Original Estimated Actual

© Digital Globe

Wheat Rice

Carrot Other

Spectrum of crops

420 520 620 720 820 920

Pasture1 Pasture2 Barley Wheat1 Wheat2 Rice1 Rice2

Our spectrum analysis technology can be applied to find very tiny deference to distinguish varieties.


13 Fertility Estimation

Able to estimate soil condition

Actual Total Nitrogen[%]

Est

imate

d T

ota

l N

itro

gen[%

]

0

0,05

0,1

0,15

0,2

0,25

0,3

0,35

0,4

0 0,1 0,2 0,3 0,4

Total Nitrogen Map

Hi

Low ©DigitalGlobe

Good Correlation

Soil condition (total nitrogen) can also be estimated from satellites by observing corresponding bands.


14 Crop Yield Prediction

Able to PREDICT the Yield before harvesting.

1st Step : ≪Learning≫ Accumulate past harvested data, field data and modify growth curve. 2nd Step : ≪Prediction≫ Take one shot photo and predict yield.

Planting Growth Maturation Harvest

Cro

p G

row

th

time

Map update and farming monitoring

Images just before harvest

Satellite image acquisition

Yield data investigation of typical farm lands

Field data survey

Rice growth cycle

1st Step

2nd Step


15 Yield prediction flow

2 steps for yield prediction

Statistical analysis is executed in the predictor using Bayesian Model with the fusion of image and field data.

Map

Farming Datas

Crop map

Satellite image

Satellite Image

Location matching

Field extraction

Yield prediction

Correspond with survey data

Input Yield map

Predictor Learning

Prediction

Bayesian Model


16 Bayesian Model

Accumulated field data , including operation factor and climate factor, and satellite data are combined in Bayesian network for accurate estimation and prediction.

Spectral Signature

(PNVI, NDVI_XY)

Growth Status

Amount of Chlorophyll, etc

Moisture

Biomass （LAI、Height）

EAT: Effective Accumulated Temperature fPAR: Fraction of Photosynthetically Active Radiation LAI: Leaf Area Index

Satellite Data

Water Retaining Capacity

Years from Cultivation

Effective Rainfall

Rainfall fPAR

Growth Environment

Variety

Disease Weed

Fertilization

Soil Fertility

EAT

Operation Factor

Climate Factor

Accumulated Field Data

Planting Date

Yield


17 Estimated results for Soy beans

Very good agreement between actual and estimated yield had obtained in the experiments done in Brazil

Actual Estimated

High Low


18 Future in Agriculture fields

Soil Analysis

Plowing/ Fertilizer

Planting

Fertilizer/ Pesticide

Harvest

Agricultural

Cycle

Planting

Planning Harvest

Control

Cultivation

Planning

Shipping

1. Fertilizer Planning Support

・ Land improvement planning

・ Optimize fertilizer amount

4. Crop Quality Control

・ Crop yields, quality control

5. Harvest Control

・ Optimize harvest time

・ Judge crop loss

Production

Support Cycle

Quality

Control

2. Planting Management

・ Optimize the planting

inspection

・ Support planting plans

3. Additional Fertilizer Planning Support

・ Improve crop yields, stabilize

quality

・ Optimize additional fertilizer

Enhance IT × OT to all the phase in agriculture.

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