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DEEP LEARNING IN
AGRICULTURE
Erik Andrejko	

Head, Data Science —The Climate Corporation	

SiliconValley Machine Learning ...
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
GROWERAPPLICATIONS
Yield Potential
Profitability
Yield Expectation
TWI
Total Weather Insurance: p...
THE AGRICULTURAL
CHALLENGE
WORLD POPULATION
Alexandratos N, Bruinsma J. 2012.World agriculture towards 2030/2050, the 2012 revision. ESA Working Pape...
AGRICULTURAL DEMAND
POPULATION
MORE PEOPLE
CONSUMPTIONHIGHER DEMAND / CAPITA
NORMAN BORLAUG
IS CREDITED WITH SAVING
BILLION LIVES1
GREEN REVOLUTION
YIELD INCREASES
Ray DK, Mueller ND,West PC, Foley JA. 2013.YieldTrends Are Insufficient to Double Global Crop Production by...
crop yield must
I 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.YieldTrends Are I...
EXAMPLE: BEEF
2,500M 700M
Source: FAO and USDA (assuming 2kg of cereal of 500g of beef)
Current worldwide cereal
productio...
IT’S POSSIBLE
I now say that the
world has the
technology… to feed
on a sustainable basis 

a population of 

10 billion p...
DATA SCIENCE MEETS
AGRICULTURE
NEXT REVOLUTION ?
INTENSIFY
Apply breeding, fertilization 

to increase yields.
OPTIMIZE
Apply data science to optimize
ma...
DATA SCIENCE
Computer Science
Domain Science Statistics
What is important?
How can it be built?
How can predictions be mad...
MACHINE LEARNING
GENETICS PRACTICES ENVIRONMENT YIELD
T	

R	

A	

I	

N
T	

E	

S	

T
predictive
model
problem: curse of d...
YIELD OPTIMIZATION
OPTIMIZED YIELD
Yield optimized for
environment by optimization of
genetics and management using
predic...
EASY! RIGHT?
unfortunately, no
CHALLENGES
Spatio-­‐Temporal	
  Data
Sparse	
  Data
Latent	
  Features
Curse	
  of	
  Dimensionality
DATA CHALLENGES LEARN...
DATA POTENTIAL
YIELD MONITOR DATA
14B OBSERVATIONS
REMOTE SENSING DATA
260B OBSERVATIONS
WEATHER DATA
20B OBSERVATIONS
one...
FEATURE ENGINEERING
Zea mays (corn)
Genetics,	
  Environment,	
  Practices
Soil	
  Processes
Nutrient	
  Processes
Crop	
 ...
LATENT FEATURE SPACE
Environment, Genetics and Practices
Physical Processes
Yield Outcome
}{Engineered
Features
Learned
Fe...
CAUSAL DESCRIPTION
THE ROLE OF DEEP
LEARNING
FEATURE LEARNING
genetics, environment and practices
soil processes
nutrient processes
crop processes
yield
Hierarchical
D...
SPATIAL DATA
Lee, Honglak, et al. "Convolutional deep belief networks for scalable unsupervised learning of hierarchical
r...
MULTI-TASK LEARNING
DEEP NEURAL NETWORK
Hidden layers (latent features) shared
across tasks	

!
Multi-task informs latent ...
MISSING DATA
Hinton, Geoffrey E., Simon Osindero, andYee-WhyeTeh. "A fast learning algorithm for deep belief nets."
Neural...
additional applications of deep-learning in agriculture
OTHER APPLICATIONS
Crop	
  identification
Disease	
  detection
Pra...
PROTECT & IMPROVE
REDUCE RISK INCREASE YIELDS
Goal: optimize global food production
POSSIBILITIES
QUESTIONS ?
erik@climate.com
Deep Learning In Agriculture
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Deep Learning In Agriculture

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A confluence of factors have converged to afford the opportunity to apply data science at large scale to agricultural production. The demand for agricultural outputs is growing and there is a need to meet this demand by utilizing increasingly mechanized precision agriculture and enormous data volumes collected to intelligently optimize agriculture outputs. We will consider the machine learning challenges related to optimizing global food production.

Published in: Science, Technology, Education
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Deep Learning In Agriculture

  1. 1. DEEP LEARNING IN AGRICULTURE Erik Andrejko Head, Data Science —The Climate Corporation SiliconValley Machine Learning Meetup Mar 28 2014
  2. 2. OUTLINE • The Climate Corporation • The Agricultural Challenge • The Role of Deep Learning
  3. 3. THE CLIMATE 
 CORPORATION
  4. 4. OUR MISSION To help all the world’s people and businesses manage and adapt to climate change… starting with agriculture
  5. 5. PROTECT & IMPROVE MP TWI GROWERAPPLICATIONS Yield Potential Profitability Yield Expectation TWI Total Weather Insurance: parametric supplemental crop insurance product MP Government subsidized loss-adjusted insurance program, integrated risk management GROWER APPLICATIONS Collection 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.
  6. 6. THE AGRICULTURAL CHALLENGE
  7. 7. 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)
  8. 8. AGRICULTURAL DEMAND POPULATION MORE PEOPLE CONSUMPTIONHIGHER DEMAND / CAPITA
  9. 9. NORMAN BORLAUG IS CREDITED WITH SAVING BILLION LIVES1 GREEN REVOLUTION
  10. 10. YIELD INCREASES Ray DK, Mueller ND,West PC, Foley JA. 2013.YieldTrends Are Insufficient to Double Global Crop Production by 2050. PLoS ONE 8(6), doi:10.1371/journal.pone.0066428.
  11. 11. crop yield must I 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.YieldTrends Are Insufficient to Double Global Crop Production by 2050. PLoS ONE 8(6), doi:10.1371/journal.pone.0066428. *
  12. 12. 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)
  13. 13. 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
  14. 14. DATA SCIENCE MEETS AGRICULTURE
  15. 15. NEXT REVOLUTION ? INTENSIFY Apply breeding, fertilization 
 to increase yields. OPTIMIZE Apply data science to optimize management. GREEN REVOLUTION GREEN DATA REVOLUTION 1960 – 2010 – BIOTECH Marker assisted selection. BIOTECH REVOLUTION 1980 –
  16. 16. 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
  17. 17. MACHINE LEARNING GENETICS PRACTICES ENVIRONMENT YIELD T R A I N T E S T predictive model problem: curse of dimensionality
  18. 18. YIELD OPTIMIZATION OPTIMIZED YIELD Yield optimized for environment by optimization of genetics and management using predictive model. YIELD Yield optimized for environment by optimization 
 of genetics and management traditional practices.
  19. 19. EASY! RIGHT? unfortunately, no
  20. 20. CHALLENGES Spatio-­‐Temporal  Data Sparse  Data Latent  Features Curse  of  Dimensionality DATA CHALLENGES LEARNING CHALLENGES Missing  Data Multi-­‐task  Learning Noisy  Data
  21. 21. DATA POTENTIAL YIELD MONITOR DATA 14B OBSERVATIONS REMOTE SENSING DATA 260B OBSERVATIONS WEATHER DATA 20B OBSERVATIONS one season, one crop, one country
  22. 22. FEATURE ENGINEERING Zea mays (corn) Genetics,  Environment,  Practices Soil  Processes Nutrient  Processes Crop  Processes Yield LATENT SPACE
  23. 23. LATENT FEATURE SPACE Environment, Genetics and Practices Physical Processes Yield Outcome }{Engineered Features Learned Features
  24. 24. CAUSAL DESCRIPTION
  25. 25. THE ROLE OF DEEP LEARNING
  26. 26. FEATURE LEARNING genetics, environment and practices soil processes nutrient processes crop processes yield Hierarchical Dimensionality Reduction Deep Neural Network yield genetics, environment and practices hiddenlayers physicalmodels
  27. 27. 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
  28. 28. MULTI-TASK LEARNING DEEP 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 HiddenLayers 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.
  29. 29. MISSING DATA Hinton, Geoffrey E., Simon Osindero, andYee-WhyeTeh. "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
  30. 30. additional applications of deep-learning in agriculture OTHER APPLICATIONS Crop  identification Disease  detection Practice  classifications Remote  sensing Image  segmentation  /   clustering Nutrient  deficiency   detection Cloud  detection Environment  classification
  31. 31. PROTECT & IMPROVE REDUCE RISK INCREASE YIELDS Goal: optimize global food production POSSIBILITIES
  32. 32. QUESTIONS ? erik@climate.com

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