Imagers as sensors: Correlating plant CO2 uptake with digital visible-light imagery
Venue
Data Management for Sensor Networks (2007)
Publication Year
2007
Authors
Josh Hyman, Eric Graham, Mark Hansen, Deborah Estrin
BibTeX
Abstract
There exist many natural phenomena where direct measurement is either impossible or
extremely invasive. To obtain approximate measurements of these phenomena we can
build prediction models based on other sensing modalities such as features
extracted from data collected by an imager. These models are derived from
controlled experiments performed under laboratory conditions, and can then be
applied to the associated event in nature. In this paper we explore various
different methods for generating such models and discuss their accuracy,
robustness, and computational complexity. Given sufficiently computationally simple
models, we can eventually push their computation down towards the sensor nodes
themselves to reduce the amount of data required to both flow through the network
and be stored in a database. The addition of these models turn in-situ imagers into
powerful biological sensors, and image databases into useful records of biological
activity.
