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Computational Science as an enablerfor sustainable FEW Systems

Baskar GanapathysubramanianIowa State University

NSF FEW Workshop: Oct 12-13, 2015, ISU

NSF FEW Workshop: Oct 12-13, 2015, ISU2

Computational Science and Engineering Group

What do we do:1) Algorithm design and software implementation2) Application driven research: Curiosity driven group

Overview of research activities related to Plant Sciences

NSF FEW Workshop: Oct 12-13, 2015, ISU3

Feature extraction: Data for crop models

Spatial coverage

(Dimensions of field)Temporal Coverage

(Crop Cycle)

Data for validation/input/calibration

Data deluge due to sensor advances and data collection improvements

Heterogeneous, multi length and time scale data

Noisy, gappy data

Need to extract traits used for various ‘down stream’ tasks

Have to do this in an automated, high throughput, and efficient way

Similar issues faced by other disciplines: Astronomy, Particle physics, Driverless automobiles, security and defense applications

Machine learning approaches very promising

NSF FEW Workshop: Oct 12-13, 2015, ISU4

Machine Learning

Goal of ML is to generalize beyond training data

Pattern recognition, perception and control tasks

Very difficult to manually encode all features

From opsrules.com

MNIST dataset

TIMIT dataset

Breakthrough in learning algorithms. Prominent examples include ‘deep networks’

NVIDIA cuDNN website

More data, Better computing infrastructure

NSF FEW Workshop: Oct 12-13, 2015, ISU5

Learning feature labels in scenes: Convolution networks

From Le Cun group, Hinton group, Ng group

Machine Learning Examples

NSF FEW Workshop: Oct 12-13, 2015, ISU6

From Le Cun group, Hinton group, Ng group

Machine Learning ExamplesLearning a hierarchy of features: Feature extractions using auto-encoders, sparse encoders, Deep Belief networks, Deep Neural Networks

Basic hypothesis: Use high throughput phenotyping to enable extraction of detailed characteristics of tassels.

Challenges: Identification of tassel locations, followed by extraction of tassel features of close to a million images!

ML: Agricultural Examples

P. Schnable

Basic hypothesis: Use high throughput phenotyping to understand features affecting (a)biotic stress tolerance

A. Singh

A. Singh

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Standard Area Diagram

Example Application: Iron Deficiency Chrolosis (IDC)

IDC: Inability of plants to absorb iron from soil

Current Methods are Visual:- Time consuming- Labor Intensive- Reliability/Consistency

issues

ML tools for rapid identification. Deploy as apps

ML: Agricultural Examples

S. Sarkar

NSF FEW Workshop: Oct 12-13, 2015, ISU9

ML for Yield Prediction

Goal: 1) Collect and curate dataset of economic, agricultural, meteorological, and crop management traits that is used to make predictions. 2) Develop and deploy suite of statistical and ML tools on data3) Create a workflow that will enable the larger community to utilize data and test methods

Yield forecasting: Combination of knowledge-based computer programs (that simulate plant-weather-soil-management interactions) along with soil and environment data and targeted surveys.

D. HayesCompanies such as Climate Corp and other big data firms may now be able to beat the USDA at yield forecasting, leading to detrimental asymmetric markets.

A publicly available high quality yield prediction tool will enable the producers to make informed decisions thereby ensuring a symmetrical market.

S. Sarkar

D. Nettleton

D. Attinger

M. Gilbert

Simple physiological model of adult maize plant.

Validated in field by Matthew Gilbert (UC Davis)

Several field-testable traits: stomatal conductance, root, stem, leaf conductance.

Input: Hourly weather data.

Outputs: Water use, Photosynthetic yield

Optimization: Trait identification for productivity

Software engineering

Code optimization

Integrate with parallel optimization framework Deploy on HPC systems

NSF FEW Workshop: Oct 12-13, 2015, ISU11

Optimization: Trait identification for productivity

Pareto front with more than 3 million configurations tested. Ran on XSEDE TACC and local HPC resources (unpublished, 2015).

Explored traits that perform under well irrigated vs drought conditions.

Concluding Observations

1) Leverage (rapid) machine learning developments

2) Learn from progress/best practices in other fields

3) Fast ML models as surrogate models for exploration, uncertainty quantification

4) Visualization and data management become important

5) Data exchange/sharing/interoperability protocols have to be set.

6) Critical to incorporate software engineering practices into the workflow (code reuse,

modularity).

7) Need sustained support for software development and maintenance

8) Need to be ready for next generation cyber infrastructure

9) Community based approach?