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Deep Learning In Agriculture
- 1. DEEP LEARNING IN AGRICULTURE Erik Andrejko Head, Data Science —The Climate Corporation SiliconValley Machine Learning Meetup Mar 28 2014
- 2. OUTLINE • The Climate Corporation • The Agricultural Challenge • The Role of Deep Learning
- 4. OUR MISSION To help all the world’s people and businesses manage and adapt to climate change… starting with agriculture
- 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.
- 8. 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)
- 9. AGRICULTURAL DEMAND POPULATION MORE PEOPLE CONSUMPTIONHIGHER DEMAND / CAPITA
- 10. NORMAN BORLAUG IS CREDITED WITH SAVING BILLION LIVES1 GREEN REVOLUTION
- 11. 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.
- 12. 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. *
- 13. 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)
- 14. 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
- 16. 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 –
- 17. 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
- 18. MACHINE LEARNING GENETICS PRACTICES ENVIRONMENT YIELD T R A I N T E S T predictive model problem: curse of dimensionality
- 19. 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.
- 21. CHALLENGES Spatio-‐Temporal Data Sparse Data Latent Features Curse of Dimensionality DATA CHALLENGES LEARNING CHALLENGES Missing Data Multi-‐task Learning Noisy Data
- 22. DATA POTENTIAL YIELD MONITOR DATA 14B OBSERVATIONS REMOTE SENSING DATA 260B OBSERVATIONS WEATHER DATA 20B OBSERVATIONS one season, one crop, one country
- 23. FEATURE ENGINEERING Zea mays (corn) Genetics, Environment, Practices Soil Processes Nutrient Processes Crop Processes Yield LATENT SPACE
- 24. LATENT FEATURE SPACE Environment, Genetics and Practices Physical Processes Yield Outcome }{Engineered Features Learned Features
- 26. THE ROLE OF DEEP LEARNING
- 27. 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
- 28. 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
- 29. 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.
- 30. 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
- 31. 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
- 32. PROTECT & IMPROVE REDUCE RISK INCREASE YIELDS Goal: optimize global food production POSSIBILITIES