Big Data: Challenges in Agriculture

Big Data Summit, November 2014 Moorea Brega: Agronomic Modeling Lead – The Climate Corporation

Outline

THE AGRICULTURAL

CHALLENGE

THE ROLE OF DATA SCIENCE

DATA SCIENCE MEETS

AGRICULTURE

The Agricultural Challenge

The Agricultural Challenge

Forty decisions, Forty outcomes

Key Decisions

equipment selection results analysis seed selection crop selection

fertility management water management

planting logistics planting practices

scouting inputs fertility

harvest logistics harvest timing grain marketing

Each season about 40 management decisions are made

Sequential Multi-Arm Bandit

Bayesian network

Each season about 40 management decisions made

many decisions

one outcome

Forty outcomes A typical farmer will manage 40 seasons

a typical farmer

manages 40 seasons

providing 40 outcomes

Data Science Meets Agriculture

The Next Revolution?

GREEN REVOLUTION GREEN DATA REVOLUTION

INTENSIFY Apply breeding, fertilization

to increase yields.

OPTIMIZE Apply data science to optimize

management.

BIOTECH Marker assisted selection, traits,

chemistries, microbials.

BIOTECH REVOLUTION

Data Available One Crop, One Season, One Country

YIELD MONITOR DATA 14B OBSERVATIONS

REMOTE SENSING DATA 260B OBSERVATIONS

WEATHER DATA 20B OBSERVATIONS

Yield Modeling

yield genetics environment practices variability

y = f (g, e, p) + ε

Yield is a function of genetics, the environment & farming practices

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.

Challenges in Applying Data Science

Challenges

Data Challenges • Spatio-Temporal Data

• Heterogeneous Data

• Missing Data

• Noisy Data

Learning Challenges • Latent Features

• Curse of Dimensionality

• Multi-task Learning

2005

1950

1870

Multi-sensor Data high spatial resolution gridded data observed data

Reanalysis Data coarse spatial resolution gridded data produced by deterministic weather models

Gauge Data sparse spatially and temporally observed data

Data Challenges Spatial Misalignment

Data Challenges Spatial and Temporal Misalignment

Low resolution, higher temporal frequency

High resolution, lower temporal frequency

Data Challenges Heterogeneous Data

Data Challenges Missing Data

Yield data can be missing due to: ● pest/disease ● low yield due to

other causes (heat, drought, frost, ponding)

● equipment malfunction

● data post-processing by an outside entity

Data Challenges Noisy Data

Noise can come from many sources: ● Clouds and

atmospheric disturbance

● Equipment malfunction/equipment calibration issues

● Measurement error ● Human error ● Mislabeled data or data

with no labels

Inherent Complexity

Zea mays (corn)

Genetics, Environment, Practices

Soil Processes

Nutrient Processes

Crop Processes

YIELD

The Role of Data Science

The Role of Data Science A Coherent View of the Field

weather sensors remote sensors ground sensors

Utilizing multiple sources of data before and during the growing season to provide growers with insights and recommendations.

The Role of Data Science Identifying Crop Stress

Insights When is the crop under stress? Recommendations What actions can I take to correct this?

The Role of Data Science Nutrient Applications

Grower Practices

weather data

Insights How much nitrogen is available to my crop?

Recommendation How much fertilizer should I apply?

The Role of Data Science

Bayesian network

Optimize each decision for risk adjusted return

step-wise optimization of conditional expected utility

The Next Revolution?

GREEN REVOLUTION GREEN DATA REVOLUTION

INTENSIFY Apply breeding, fertilization

to increase yields.

OPTIMIZE Apply data science to optimize

management.

BIOTECH Marker assisted selection, traits,

chemistries, microbials.

BIOTECH REVOLUTION

Questions? moorea@climate.com