FarmVibes: AI, Edge, & IoT For Agriculture

Ranveer Chandra
We need to increase production and decrease 

environmental impact

Over 820 million 

people worldwide 

suffer from hunger

50% more food is 

needed by 2050. 

40% food wasted.

More than 

2 billion people

lack vital nutrients

31% GHG emissions 

from agri-food systems, 

70% fresh water use
Data-driven Agri-Food systems

Bringing data to the cloud enables key insights for individuals and organizations. 

Agriculture retail, 

farm coop, 

agronomist

Agrochemical Farmer Co-ops Food processing Distribution Retailer Consumer

and Seeds

Agriculture 

equipment

When this data is shared, we unlock greater efficiencies

and productivity across the supply chain.

30% 

of Crop inputs 

wasted due to lack 

of reliable data & 

tools

8x

Increase in farm 

data generation in 

next 10 years

500

Hours facility prod 

downtime, on avg 

at $25K/hour

3-10%

Commission fee 

charged by food 

brokers

$10B

Spend by Big CPG 

in 2020 for 

consumer analytics

67%

Consumers want 

full food ingredient 

transparency

>99%

Calories consumed 

from <1% of plants
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Precision & regenerative agriculture has been shown to:

Data-driven agriculture

Reduce cost

Improve yield

Ensure sustainability

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According to USDA, high cost of manual 

data collection prevents farmers from 

using data-driven agriculture.

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Cloud

Challenge 1: Connectivity on farms

Farmers home/office

Soil moisture

sensors

Drone video

Wind speed/

Direction sensors

pH sensors

Too much distance between 

farm and the house/office

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A solution in white space

Base station

Increasing wireless reach 

with TV White Space Cloud

Farmers home/office

Soil moisture

sensors

Drone video

Wind speed/

Direction sensors

pH sensors

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Challenge 2: Sparse sensor deployments

• Physical constraints due to farming practices

• Too expensive to deploy and maintain

How do we get coverage with a 

sparse sensor deployment?

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Panoramic overview Precision map

Use aerial imagery and AI to enhance spatial coverage

Sensors Machine 

Learning

Aerial imagery

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Cloud

Farmers home/office

Soil moisture

sensors

Drone video

Wind speed/

Direction sensors

Edge Compute in the Farm

Azure IoT Edge

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IoT Edge

DRONE CAMERA SENSORS

MQTT Broker

FTP Server

Video Processor

S E N S O R I N T E R F A C E

Cloud Sync

Drone Flight 

Planner

IoT

Hub Web Server Streaming Analytics

• Can run offline

• Unique Gateway services

• Deep Learning at Edge

• Component Migration 

Web Server

(Offline Access & 3-D Walkthrough)

MQTT

TCP

FTP

HeatMap Gen

Panorama Gen

Edge CNN

L O C A L C O M P U T A T I O N A G R I C U L T U R A L S E R V I C E S

Precision Irrigation

Regenerative tech

Yield Prediction

Pest Infestation

Precision Fertilizer

Storage

Storage

HTTP/

AMQP

Web Server

(Offline Access & 3-D Walkthrough)

Edge CNN

Storage
Deployment

Deployments in several locations 

including WA, CA, NY

Farm sizes range from 0.5 – 9000 acres

Sensors:

• DJI Drones 

• FarmBeats sensor boxes with soil moisture, 

temperature, wind speed/direction sensors 

• IP Cameras to capture IR imagery as well as 

monitoring

Cloud Components: Azure IoT Suite

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Micro-Climate Forecasting

Impact:

Knowing microclimate enables better modeling of plant diseases, 

application timing, and risk management.

Challenges:

Forecast important variables for accurate plant disease prediction, 

not included in current weather forecasts (results shown).

Results:

Soil moisture & temperature forecasting error less than 10%. 

Forecast for low temp was 42 degrees. Micro-Climate forecast was 

31 degrees in lower areas of the field. Actual was 30 degrees. 

Instead of spraying grass herbicide, the farmer waited and avoided 

large crop damage in some of the most productive areas.

Goal:

Microclimate weather forecasting model based on FarmBeats

sensors in the field.

Mean Absolute Percentage Error

Number of Forecasted Data

15

10

5

0

1 3 5 1 3 5

Soil Moisture Soil Temperature

*The lower the error, the better the prediction. Model 1 Model 2 Model 3

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Example: Panorama

Water puddle Cow excreta Cow herd Stray cow

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Precision Map: Panorama Generation

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Precision Map: Moisture

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Precision Map: pH

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Application: Cow-Shed Monitor
The Microsoft TechSpark initiative is 

bringing precision agriculture and AI to 

classroom with FarmBeats student kits.

Future Farmers of America + FarmBeats + FarmVibes

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Motivation: existing sensors are expensive

• ~100s of dollars

Affordable sensing

low-cost soil moisture and EC sensing using Wi-Fi

Strobe design: Wi-Fi cards with 2+ antennas

• Relative time-of-flight and amplitude

Wi-fi 

transmitter

Wi-fi 

receiver

Air

Soil

Results: Strobe can accurately detect 

moisture and EC change in soil 

Effective 

dielectric 

permittivity

Soil texture

Bulk density

Organic matter

Wilting point

Porosity

Jian Ding, Umesh Acharya, Rattan Lal, Ranveer Chandra
Landsat-8 Total Frames per Revolution Frame Count

1

10

100

1000

10000

Observation Count 

far exceeds

Frames Downlinked

0 8 16 24 32 40 48 56

Satellite Count

Downlinked Observed

> 50x 

gap

Edge in Space: The Downlink Bottleneck
Key idea: Filter Out Low Value Data at Edge

09/2018, reference image 10/2018, captured image, 

Only download changed areas!

Potential: 16.5x compression when only downloading changes
Soil Carbon Modeling Our Goal:

1. Model cause-and-effect relationship among soil processes.

2. Identify factors that cause changes in soil carbon.

3. Customizing process models for region-specific modeling

Machine Learning Paradigm:

Explicitly learn cause and 

effect relationships.

Incorporate spatial-temporal 

correlations.

Use domain knowledge to 

guide learning.

Accurately models 78.6 % in changes in soil carbon

using weather drivers, green house gas emissions,

soil macro-nutrient information for test farms

Causal Graph

Discovered Causal Relations Reasoning

Soil Temperature → Soil Carbon Higher temperatures decomposes 

organic carbon

Soil Nitrogen → Soil Carbon Nitrogen and Carbon co-metabolize 

in soil

Dissolved Oxygen → Soil Carbon Dissolved oxygen in soil improves 

the organic matter content

Example of Discovered Relations:
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@ranveerchandra