Dipl.-Ing. Ralph Ostermeierufrrj.br/institutos/it/deng/varella/Downloads/IT... · 30 km north of Munich 10 minutes from the international airport of Munich About 50.000 citizens Fantastic

Crop Production Engineering

Ostermeier, 03/2006 (1) – UFRRJ-2006ostermeier.ppt© 2006

• Electrical Engineering and Information Technology studies at theTechnische Universitaet Muenchen,

Sector Information Technology [1989 to 1995]

• Diploma thesis at the chair of Hochfrequenztechnik (High-Frequency Engineering) (Prof. Dr. techn. P.

Russer) in cooperation with the institute for Agricultural Engineering of the TUM-Weihenstephan

(Prof. Dr. agr. H. Auernhammer) [1995]

• Systems engineer, GEO TEC eletronics GmbH, 85399 Hallbergmoos [1996 to 2002 ]:

Product development (LBS terminal, computer systems for navigation and process control,

RTK-(P)DGPS-receiver-module, mobile GPS-reference station);

Agricultural off-road vehicle retrofitting and systems integration; representation of interests within LAV

(Member of the technical committee electronics and of German and international standard commitees)

• Research Associate and doctorand at the Technische Universitaet Muenchen, sector Crop Production

Engineering [since 2002 ]:

Dipl.-Ing. Ralph Ostermeier

DFG DFG researchresearch groupgroup "" IKBIKB --DuernastDuernast " " subprojectsubproject 8; 8; mainmain interestsinterests : : Process control, Multisensor Data Fusion, ISOBUS, "Intelligent Sensors"



RealReal--time process control for a sensor based fertilizer time process control for a sensor based fertilizer application system using application system using multisensormultisensor data fusiondata fusion

Ostermeier, R., Prof. Dr. Auernhammer, H.

Center of Life Sciences WeihenstephanDepartment of Bio Resources and Land Use Technology


UFRRJ, March 2006



Outline

1. Where do I come from ?Europe -> Germany -> Bavaria -> TUM

2. IKB subproject 8: Introduction and objectives

3. Multisensor Data FusionFunctional model

Process model

System architecture

Simulation

4. Conclusions, open questions and outlook



Winter in Freising-Weihenstephan, 5 March 2006



Europe, Germany, Bavaria



Germany, Bavaria



Munich, Neuschwanstein castle, MUC airport



Oktoberfest



The Olympic park



Allianz Arena - FIFA World Cup Germany 2006



8 Universities with „Facultiesof Agriculture“ and Chairs of

„Agricultural Mechanization“

4 +2 Universities with Chairsof „Agricultural Engineering“

2 Lately closed Chairs of Agricultural Engineering / Agricultural Mechanization

Universities in Germany



8 Universities with „Facultiesof Agriculture“ and Chairs of

„Agricultural Mechanization“

4 +2 Universities with Chairsof „Agricultural Engineering“

2 Lately closed Chairs of Agricultural Engineering / Agricultural Mechanization

Universities in Germany

The only„Agricultural University“



12 Faculties

Technische Universitaet Muenchen



Technische Universitaet Muenchen

•• 12 faculties12 faculties

•• three main campusesthree main campuses

•• about 20.000 students (20 % from abroad)about 20.000 students (20 % from abroad)

•• 396 professors, 4000 total scientific staff396 professors, 4000 total scientific staff

•• 26 courses leading to a German 26 courses leading to a German ““DiplomDiplom”” degreedegree

•• 24 Bachelor24 Bachelor’’s and 50 Masters and 50 Master’’s coursess courses

Munich

Garching

Weihenstephan



Weihenstephan

The 3 locations of TUM



30 km north of Munich

10 minutes from the international airport of Munich

About 50.000 citizens

Fantastic connections to Munich by fast train services

“City of education”“City of education”

Centre of Freising with market every Wednesday and Saturday

Freising – A small, lovely old city



2.500 students (+ 2.500 students in the University of applied Sciences)

500 students in Agriculture & Horticulture

Campus “Weihenstephan”



Close to the City

≈ 3 km to theexperimental station

Our Lab and experimental station “Duernast”



Prof. Dr. Auernhammer and team

Good-bye Dr. Pedro Machado



Real-time process control for a

sensor based fertilizer application system

using multisensor data fusion



(Auernhammer, 2002)

System Approaches – Process control for mobile application systems



(Auernhammer, 2002)




(Auernhammer, 2002)




In-fieldController

Real-time process control for a sensorbased fertilizer application system

R. Ostermeier, H. AuernhammerCrop Production Engineering

IKB Duernast Integrated Research Project

„Information System Site Specific

Crop Management Duernast“

Sub-project 8:



a, Theoretical Framework

Basis for integrated specification and design process

Facilitate communication and understanding

Selection of appropriate problem solving paradigm

b, ISO 11783 (ISOBUS) and DIN 9684 (LBS) compliant

c, Site-specific intensive N-fertilization

Objectives

Implementation “Real-time approach with map overlay”



Expert-KnowledgeEcological-economical

optimum

Plant, Surrounding

Fertilization

Input OutputProcess (System)

Information

decide

act

observe

statestate Sta

te Inte

r-ve

ntio

n

"Precision FarmingMaps"

DocumentationOn-line

sensor technology

Methods - Control engineering view

Activation, Control, Feedback

(according to Auernhammer)



DeductionAction

Methods - Multisensor Data Fusion

"Data Fusion is the process of combining data or

information to estimate or predict entity states"

Steinberg and Bowman (2001)

human brain and

perception system



How to analyze, specify and design Multisensor Data Fusion systems ?

Methods - Theoretical Framework

System architecture HW-SW ?

AbstractionFunctional model WHAT ?

Process model HOW ?



Revised JDL data fusion model(1998) (JDL = Joint Directors of Laboratories)

Level 0Processing

Sub-ObjectAssessment

DATA FUSION DOMAIN

Level 1Processing

ObjectAssessment

Level 3Processing

ImpactAssessment

Level 4Processing

ProcessRefinement

Database Management System

SupportDatabase

FusionDatabase

Human / ComputerInterface

EXTERNAL

DISTRIBUTED

SOURCES

LOCAL

SensorsDocuments

People***

Data stores

Level 2Processing

SituationAssessment

Multisensor Data Fusion - Functional Model

different types of estimation process




EXTERNAL

DISTRIBUTED

SOURCES

LOCAL

SensorsDocuments

People***

Data stores

Level 0Processing


DATA FUSION DOMAIN

Level 1Processing

ObjectAssessment

Level 3Processing

ImpactAssessment

Level 4Processing

ProcessRefinement


SupportDatabase

FusionDatabase

Level 2Processing

SituationAssessment

Revised JDL data fusion model (1998)

Focus: Situation Assessment (Level 2)

Realtime approach with map overlay

= assessment of current on-line sensortechnology measurements withcontext-sensitive interpretation.



Level 0Processing


Level 1Processing

ObjectAssessment

Level 3Processing

ImpactAssessment

Level 4Processing

ProcessRefinement


SupportDatabase

FusionDatabase


EXTERNAL

DISTRIBUTED

SOURCES

Level 2Processing

SituationAssessment

Situation Assessment (Level 2 Processing)

Diagnose of current crop and soil condition based on plant and soil attributes, weather

Enlargement of solution space , more detailed “image of the reality”. (yield potential)

Take constraints into account. Enviromental protection, operator inputs,

State of tractor-implement combination Technical constraints

"Comparision" to a model-based

perception of a

economic and

ecological optimum

with regard to

technical feasibility

Estimation and prediction of entity states on the basis of inferred organisa-tional, causal, biological and spatio-temporal relations among the objects:

application set point



Process model adopts biologically motivated perception

Taxonomy of 16 canonical problem-solving forms

Multisensor Data Fusion - Process Model

Richard T. Antony (1995)

"Principles of Data Fusion Automation"

Member of JDL Data Fusion Group since 1987



Data flow

human memory

Multisensor Data Fusion - Process Model

(according to Antony)

medium-term memoryKnowledge

Sensor Input

Short-termdeclarative

Fusion Process, F

Long-termprocedural

Database

Long-termdeclarative

Situation Description

Medium-termdeclarativeShort-term memory

long-termmemory

Durability of information



New plant and soil attributes

Process Model - Real-time approach with map overlay

Sensor Input


Fusion Process, F

Long-termprocedural

Database



Medium-termdeclarative

Knowledge




Sensor Input


Fusion Process, F

Long-termprocedural

Database




Results of recent fusion processes:

• Location and amount of (just) applied fertilizer

• State and tracks of tractor-implement combination

Knowledge



Sensor Input


Fusion Process, F

Long-termprocedural

Database




Knowledge of:

• yield and soil maps

• static domain constraintsdue to enviromental protectionor topography


Knowledge



Sensor Input


Database




Relationship/procedure for crop production and agricultural engineering assessment

in order to

• derive application set point

• achieve sufficientreaction time forapplication action

Knowledge

=fusion algorithm(Level 2 Processing)


Fusion Process, F

Long-termprocedural



Sensor Input


Database




Relationship/procedure for crop production and agricultural engineering assessment

in order to

• derive application set point

• achieve sufficientreaction time forapplication action

Knowledge

=fusion algorithm(Level 2 Processing)


Fusion Process, F

Long-termprocedural

Sensor Input


ExpertSystem

canonicalproblem solving form

IX



System architecture - Agricultural BUS-System ISOBUS

ISOBUS (ISO 11783)




ISOBUS (ISO 11783) distributed

sensor network





sensor network

long term knowledge

In-field Controller

central fusion nodemedium term &





sensor network

long term knowledge

In-field Controller


declarative





sensor network

long term knowledge

In-field Controller


declarative

ExpertSystem+

procedural



Implementierung Prototyp



Conclusions

• An integrated specification and design approach for a real-time

process control for mobile application systems using multisensor

data fusion has been pointed out.

• The development of a real-time process control for a sensor based

fertilizer application system within the IKB Duernast research

project gives an example for multisensor data fusion architecture

which is in compliance with ISOBUS and demonstrates the potential

and usability of multisensor data fusion for agricultural engineering.



Open Questions and Outlook

There is no substitute for a good sensor. No amount of data fusion

can substitute for a single, accurate sensor that measures the

phenomena that you want to observe (Hall and Steinberg, 2001)

Performance AssessmentMeasures of Performance (MOP) and Measures of Effectiveness (MOE)

(Level 4 Processing "Process Refinement")

Process control of multivariable systems



Funding of

Research Group "IKB-Dürnast"

„Information System Site Specific Crop Management Dürnast“

by

German Research Council

(Deutsche Forschungsgemeinschaft (DFG))

E-mail: [email protected] http://ikb.weihenstephan.de

Acknowledgments