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3/18/2015
Bio-Informatics & High
Performance Computing (HPC)
for Realization & Advancement
of Precision Agriculture
Kudang B. Seminar
e-mail: [email protected]
Head of Bio-informatics Engineering Laboratory Fateta IPB
Honorary Member of AFITA
Member of ASICTA
Honorary Member HIPI
Member of PERTETA
The creation and
development of
advanced
information and
computational
techniques for
solving problems
in biology
https://www.osc.edu/press/ohio_supercomputer_center_ralph_regula_s
chool_to_power_choose_ohio_first_bioinformatic s
http://www.babraham.ac.uk/our-research/scienceservices/bioinformatics/
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The use of super computers and parallel
processing techniques for solving complex
computational problems
1. Fundamental Concepts
Stephen Hawking (January 2000),
The Newton Professor of Physics at Cambridge:
"I think the next century will be
the century of complexity"
George Rzevski: www.rzevski.net
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http://www.necsi.edu/projects/mclemens/cs_char.gif
What is Complexity?

It consists of a large number of diverse components
engaged in unpredictable interaction (Variety and
Uncertainty)

Its global behaviour emerges from the interaction of
local behaviours of its components (Emergence)

It self-organises to accommodate unpredictable
external or internal events and therefore its global
behaviour is “far from equilibrium” or “at the edge of
chaos” (Adaptability and Resilience)

It co-evolves with its environment (Irreversibility)
George Rzevski: www.rzevski.net
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Dramatic Change!
Biology has changed
dramatically and highthroughput experimental
methods such as produce
masses of data.
Superlinear Grow &
Challenging Problems

The bioinformatics
databases grow
superlinearly!

The masses of data
led to numerous
challenging
bioinformatics
problems &
solutions
http://www.benjaminwicks.com/portfolio/
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Big Data Analysis & Optimism
Tens of thousands of ESTs (Expressed
Sequence Tags) available for study
Most methods to study message
distributions are low throughput AND
time consuming
“Genomics” necessitates the large scale
study of gene expression
Optimism: Biology is finite…
~30,000 human genes; ~1000 protein superfamilies
…but computers speeds keep increasing => HPC
Future Extension of Bio-informatics
Integrated
Data
Genotype
Data
Phenotype
Data
Gographical
Indication
Data
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Why Integration is needed?
Genotype: genetic constitution of an
individual organism
 Phenotype: expressed physical traits of
an individual organism.
 Geographical indication (GI): specific
characters (signature) of an individual
organism that correspond to a specific
geographical location or origin.

Relating Genotype & Phenotype Data
Chromosome Genotype
data
DNA
Phenotype
data
Leaf
Tuber
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cDNA Collections (Libraries)
• Various tissues are collected from the plant,
and messages are extracted
Leaf
Messages
Tuber
Messages
Unifying GI,Phenotype,
Genotype Data
Origin
Genotype +
Phenotype
Data
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What about
Precision Agriculture (PA)?
 An agricultural system that provides precise
treatments to all agribusiness chain from
upstream (onfarm) to downstream (off farm).
agribusiness
chain
from
Field
To
Table
Why is Precision Agriculture hard?
Life
Systems
Non-Life
Systems
Simple behavior & attributes
Complex behavior & attributes
Involve genetics
Autonomous
Do not involve genetics
Non-autonomous
CNC
BDA
(Computer Numerical Control)
Expert system
Finite Element System
Adaptive System
(Biosistem-Derived Algorithm)
Fuzzy control
Fractals
Pattern recognition
Genetic algorithm
Artificial Neural Network
Photosynthetic algorithm
Immune system algorithm
Ant Colony Algorithm
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General Model of
Precision Agriculture
Bio-Process
Control System
Complex
Computation &
Decision Making
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Agro-products
Bio-Products
Related Values
Timely &
Conditionally
Valuable Data
Tecnology
for specific
bioproducts
Environment
Related Values
Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
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Food Chain/Network
Tools for
making
transparency
& precision
work?
Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
Tools for Transparency &
Precision Support
Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
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Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
ICT Cloud to Support PA
Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.`
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Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
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Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
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Linking production with Consumers
Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
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Gerhard Schiefer. 2014. Transparency for Sustainability: Food chain challenge & Future
Internet opportunities, AFITA Conference 2014.
Plant Health Monitoring and Control
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Tomato Grading
Grading Technology
Precision Agriculture (Off Farm):
Minimizing Loss in Tomato Ketchup Supply Chain
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THE DEVELOPMENT OF IDENTIFICATION SYSTEM AND GENOME ANALYSIS
SINGLE NUCLEOTIDE POLYMORPHISM UNTUK PEMULIAAN
SOYBEAN PLANT
Tim Peneliti:
Dr. Wisnu Ananta Kusuma (Ilmu Komputer, IPB)
Dr. Agus Buono (Ilmu Komputer, IPB)
Dr. Ir. I Made Tasman (BB Biogen)
Habib Rijzaani (BB Biogen)
Mukhlis Hidayat, M.Kom (Matematika, Unsyiah)
Research Contributions
•Creation of superior strains
• Utilization of genomic analysis for
breeding
•Utilization of information
technology to support the analysis of
genomic-based breeding
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The Supervised
Multi-Agent
Systems for
GreenhouseBased Crop
Industry
Kudang B. Seminar
Herry Suhardiyanto
UIJI Seminar, 23-25 July 2011, Ehime University
Dept. of Mechanical & Biosystem Eng.
Bogor Agricultural University
A Large Scale Crop Production Plant
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Concurrent Engineering for Environment
and Plant Control
USER
Greenhouse
s
USER
SUPERVISO
RY AGENT
INTERFACE
USER’S PREFERENCE
SELECTION MODULE
Array of
Controller
s
Greenhouse
Controllers
Supervis
ed
Agents
Greenhouse
Controllers
Greenhouse
Controllers
Modes
of
control
Parameters Optimality
of control
Criteria
Supervisory Control Engine
Control
Knowledg
e
I/O
Knowledg
e
Climatic
Knowledge
Crop
Knowledg
e
CONCLUDING REMARKS (1)


The role of bio-informatics & HPC is
becoming very crucial and strategic to the
realization and advancement of precision
agriculture (PA).
intergrating and interelating the knowledge
of genotype, phenotype and GI will be very
useful for formulating and establishing better
paradigms, solutions and tools for better &
smarter agricultural system particularly in
Indonesia
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CONCLUDING REMARKS (2)
Collaborative & interdisciplinary research
can be extended coherently towards
better & competitive agriculture
production
through PA & bioinformatics
CONCLUDING REMARKS
 There have been many applications of
bioinformatics for agriculture, partcularly
food production but there are more
challenging research & applications due to
biosystem diversity & complexity

CONCLUDING REMARKS (3)
The applications of bioinformatics heavily
rely on the advancement of HPC (High
Performance Computing Technology) &
that of superintelligent computations.
 The development & use of bio-derived
computational algorithms will be more
aggresive due to nature of bio-systems.

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THANK YOU
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