Automated Minirhizotron and Arrayed Soil Sensors AMARSS

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Automated Minirhizotron and Arrayed Soil
SensorsAMARSS
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Mycorrhizal fungi different phenologies in
tropical forests
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The mycorrhizosphere
Michael F. Allen lab, U.C. Riverside
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The problems we encounter
How many mycologists does it take to run a
minirhizotron?
Sevilleta LTER
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Recording data

• Counts of root tips and characterization by color
• Multiple formats -gt digital jpeg
• Sevilleta LTER alone gt30,000 jpeg files so far!
• Extremely time-consuming, although faster than
  image analysis software
• Monotonous!
• Inconsistencies in interpretation
• Problems with recording methods and field notes

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What we would like to do

• Integrate data at multiple scales
• within a pixel
• short- and long-term
• Record above- and below-ground environmental
  conditions
• Automate data collection
• greater frequency
• simultaneous
• response to events (e.g., precipitation)
• remote-control
• Automate data interpretation/recording
• pattern-recognition
• screening of uninteresting images

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Scales of measurementspace
Sevilleta LTER, grassland
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Soil array schematic
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Robotic Design (Mike T)
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Proposed remote minirhizotron nodes
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Egg Design
signal
camera
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How do we automate image analysis?
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Sensor Array
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Field Deployment preliminary
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CO2 concentrations in experimental soils
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DNA Microarray

• Community fingerprint
• How do we sample in the field?
• Which DNA probes should we use?

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Recap

• We seek to understand the contributions and
  responses of mycorrhizosphere organisms to larger
  scale processes
• Current methods have a high resource-investment-to
  -information-gain ratio
• Networked sensing has the potential to lead to
  solutions
• Frequent, responsive, simultaneous data
  collection
• Collection of related soil data
• Software filtering of data, pre- and
  post-collection