FarmBeats: An IoT Platform for Data-Driven Agriculture. Chandra_FarmBeats-Overview.pdf · •IP...
Transcript of FarmBeats: An IoT Platform for Data-Driven Agriculture. Chandra_FarmBeats-Overview.pdf · •IP...
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FarmBeats: AI & IoT for Data-Driven Agriculture
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Data-Driven Agriculture
Ag researchers have shown that it:
• Improves yield
• Reduces cost
• Ensures sustainability
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But…
According to USDA, high cost of data collection prevents farmers from using data-driven agriculture
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Challenge 1: Internet Connectivity
(Farmer’s home/office) Cloud
Sensors • Few miles away• Obstructed by crops, canopies, etc
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TV White Spaces in the Farm• What are the TV White Spaces?
• Unused TV channels
• Benefits over Wi-Fi, Zigbee, etc• High throughput at long range
• Key insight for farms:• “lots” of TV spectrum is available, more than 100 MHz
• Just like Wi-Fi router covers the home, TVWS base station can cover the farm
dbm
Frequency
-60
-100
“White spaces”
470 MHz 700 MHz
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Challenge 2: Limited Resources
• Need to work with 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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Idea: Use UAVs to Enhance Spatial Coverage
• Drones are ~1000 dollars and automatic
• Can cover large areas quickly
• Can collect visual data
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Combine visual data from the UAVs with the sensor data from the farm
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Low-cost Aerial Imagery: Tethered Eye (TYE) • UAVs have a few limitations:
• limited battery life
• Regulatory concerns
• Cost > 1000 dollars
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Idea: Use Drones to Enhance Spatial Coverage
Sparse Sensor Data
Precision MapPanoramic OverviewDrone Video
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FarmBeats can use drones to expand the sparse sensor data and create summaries for the farm
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Challenge 3: Internet at Farmer’s House
(Farmer’s home/office)
Base Station
TV White Spaces
Cloud
Few miles
Sensors
• Weak Connectivity• Prone to outages
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Wi-Fi, BLE
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Storage
FTP Server
FarmBeats Gateway (Windows 10 IoT Gateway)Sensors
Camera
Drone
MQTT Broker
Video Processor
Panorama Gen
HeatMap Gen
Sensor Interface Local Computation Ag Services
Precision Irrigation
Precision pH
Yield Prediction
Pest Infection
Precision Fertilizer
…
Streaming Analytics
IoT Hub
Storage
Web Server(Offline Access & 3-D Walkthrough)
Web Server
Drone Flight
PlannerCloud Sync
HTTP/ AMQP
EdgeCNN
• Can run offline• Unique Gateway services• Deep Learning at Edge• Component Migration
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Deployment
• Six months deployment in two farms: Upstate NY (Essex), WA (Carnation)
• The farm sizes were 2000 acres and 5 acres respectively
• Sensors:• DJI Drones
• Particle Photons with Moisture, Temperature, pH Sensors
• IP Cameras to capture IR imagery as well as monitoring
• Cloud Components: Azure Storage and IoT Suite
SensorModule
SensorModule
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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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Precision Map: Accuracy
0
0.2
0.4
0.6
0.8
1
1.2
Temp (F) pH (0-14) Moist (0-6)
Mea
n E
rro
r
FarmBeats LeastCount
FarmBeats can accurately expand coverage by orders of magnitude using a sparse sensor deployment
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Application: Cow-Shed Monitor
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Conclusion
• FarmBeats: End to end IoT system for environments constrained by:• Limited internet connectivity
• Power Variability
• Sparse Sensor Deployment
• Acts as a tool to enhance farm and farmer productivity
• Used by farmers for applications beyond precision farming
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Thank you!Sean Stratman, Dancing Crow Farm, WA Mark & Kirstin Kimball, Essex Farm, NY
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Questions
http://www.microsoft.com/en-us/research/project/farmbeats-iot-agriculture/
Ranveer Chandra, Manohar Swaminathan, Sudipta Sinha, Ashish Kapoor, Akshay Nambi, Raghuram Lanka, Madhu Sudarshan, Cameron Phillips, Heping Shi, Akash Devgun, Raji Kommineni
Interns:
Deepak Vasisht (MIT), Zerina Kapetanovic (UW), Jong-Ho Won (Purdue), Xinxin Jin (UCSD), Vasuki Narasimha Swamy (Berkeley), Michael Grant (WSU), Rahul Sharma (IIIT Hyderabad), Akshit Kumar (IIT Madras), Rohit Shetty (PESET), Aditya Jain (IIIT Delhi)
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