Deep Learning In Agriculture

DEEP LEARNING IN AGRICULTURE

Erik Andrejko Head, Data Science — The Climate Corporation Silicon Valley Machine Learning Meetup Mar 28 2014

OUTLINE• The Climate Corporation

• The Agricultural Challenge

• The Role of Deep Learning

THE CLIMATE CORPORATION

OUR MISSION

To help all the world’s people and businesses manage and adapt to climate change…

starting with agriculture

PROTECT & IMPROVE

MP

TWI

GR

OW

ER A

PPLI

CAT

ION

S

Yield Potential

Profitability

Yield Expectation

TWITotal Weather Insurance: parametric supplemental crop insurance product

MPGovernment subsidized loss-adjusted insurance program, integrated risk management

GROWER APPLICATIONSCollection of grower management advisors using agronomic and climatological models

PROTECT

IMPROVE

The Climate Corporation aims to help farmers around the world protect and improve their farming operations & profitability.

THE AGRICULTURAL CHALLENGE

WORLD POPULATION

Alexandratos N, Bruinsma J. 2012. World agriculture towards 2030/2050, the 2012 revision. ESA Working Paper No. 12-03, June 2012. Rome: Food and Agriculture Organization of the United Nations (FAO)

AGRICULTURAL DEMAND

POPULATIONMORE PEOPLE

CONSUMPTIONHIGHER DEMAND / CAPITA

NORMAN BORLAUGIS CREDITED WITH SAVING

BILLION LIVES1

GREEN REVOLUTION

YIELD INCREASES

Ray DK, Mueller ND, West PC, Foley JA. 2013. Yield Trends Are Insufficient to Double Global Crop Production by 2050. PLoS ONE 8(6), doi:10.1371/journal.pone.0066428.

crop yield mustI N C R E A S E

60% to meet demand

b y 2 0 5 0Ray DK, Mueller ND, West PC, Foley JA. 2013. Yield Trends Are Insufficient to Double Global Crop Production by 2050. PLoS ONE 8(6), doi:10.1371/journal.pone.0066428.

*

EXAMPLE: BEEF

2,500M 700M

Source: FAO and USDA (assuming 2kg of cereal of 500g of beef)

Current worldwide cereal production (tonnes)

Corn demand to support current US per-capita beef consumption for a population of 7B (tonnes)

IT’S POSSIBLE

I now say that the world has the technology… to feed on a sustainable basis a population of 10 billion people. ”

“

Normal Borlaug

DATA SCIENCE MEETS AGRICULTURE

NEXT REVOLUTION ?

INTENSIFYApply breeding, fertilization to increase yields.

OPTIMIZEApply data science to optimize management.

GREEN REVOLUTION

GREEN DATA REVOLUTION

1960 –

2010 –

BIOTECHMarker assisted selection.

BIOTECH REVOLUTION1980 –

DATA SCIENCE

Computer Science

Domain Science Statistics

What is important?

How can it be built?

How can predictions be made?

SCIENTIFIC DATA SCIENCE

use software engineering to enable domain science maximizing use of data

MACHINE LEARNINGGENETICS PRACTICES ENVIRONMENT YIELD

T R A I

N

T E S T

predictive model

problem: curse of dimensionality

YIELD OPTIMIZATION

OPTIMIZED YIELD

Yield optimized for environment by optimization of genetics and management using predictive model.

YIELDYield optimized for environment by optimization of genetics and management traditional practices.

EASY! RIGHT?unfortunately, no

CHALLENGES

Spatio-­‐Temporal  Data

Sparse  Data

Latent  Features

Curse  of  Dimensionality

DATA CHALLENGES LEARNING CHALLENGES

Missing  Data Multi-­‐task  Learning

Noisy  Data

DATA POTENTIALYIELD MONITOR DATA

14B OBSERVATIONS

REMOTE SENSING DATA

260B OBSERVATIONS

WEATHER DATA

20B OBSERVATIONS

one season, one crop, one country

FEATURE ENGINEERING

Zea mays (corn)

Genetics,  Environment,  Practices

Soil  Processes

Nutrient  Processes

Crop  Processes

Yield

LATENT SPACE

LATENT FEATURE SPACE

Environment, Genetics and Practices

Physical Processes

Yield Outcome

}{Engineered Features

Learned Features

CAUSAL DESCRIPTION

THE ROLE OF DEEP LEARNING

FEATURE LEARNINGgenetics, environment and practices

soil processes

nutrient processes

crop processes

yield

Hierarchical Dimensionality

ReductionDeep Neural

Network

yield

genetics, environment and practices

hidde

n lay

ers

phys

ical m

odels

SPATIAL DATA

Lee, Honglak, et al. "Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations." Proceedings of the 26th Annual International Conference on Machine Learning. ACM, 2009.

CONVOLUTIONAL DBN

Hierarchical representation of spatial data !High-dimensional, scalable visible layer !Unsupervised hierarchical learning

MULTI-TASK LEARNINGDEEP NEURAL NETWORK

Hidden layers (latent features) shared across tasks !Multi-task informs latent features

deep neural network in multi-task setting

y

genetics, environment and practices

Hid

den

Laye

rs

w

Deng, Li, Geoffrey Hinton, and Brian Kingsbury. "New types of deep neural network learning for speech recognition and related applications: An overview." Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on. IEEE, 2013.

MISSING DATA

Hinton, Geoffrey E., Simon Osindero, and Yee-Whye Teh. "A fast learning algorithm for deep belief nets." Neural computation 18.7 (2006): 1527-1554.

DEEP BELIEF NETWORK

Greedy layer-wise training algorithm !Robust to noisy inputs !Generative process (MRF)

Alternating Gibbs sampling in deep belief network

additional applications of deep-learning in agriculture

OTHER APPLICATIONSCrop  identification

Disease  detection

Practice  classifications

Remote  sensing

Image  segmentation  /  clustering

Nutrient  deficiency  detection

Cloud  detection Environment  classification

PROTECT & IMPROVEREDUCE RISK INCREASE YIELDS

Goal: optimize global food production

POSSIBILITIES

QUESTIONS [email protected]