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United States Department of Agriculture

Agricultural Research Service

Research Project: INTEGRATED ORCHARD MANAGEMENT AND AUTOMATION FOR DECIDUOUS TREE FRUIT PRODUCTION

Location: Appalachian Fruit Research Laboratory: Innovative Fruit Production, Improvement and Protection

Title: Active spectral sensor evaluation under varying conditions

Authors
item Kim, Yunseop
item GLENN, D. MICHAEL
item Park, Johnny -
item Ngugi, Henry -
item Lehman, Brian -

Submitted to: Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE)
Publication Type: Proceedings
Publication Acceptance Date: April 25, 2010
Publication Date: May 17, 2010
Citation: Kim, Y., Glenn, D.M., Park, J., Ngugi, H.K., Lehman, B.L. Active spectral sensor evaluation under varying conditions. Proceedings of the American Society of Agricultural and Biological Engineers International (ASABE). David L. Lawrence Convention Center, Pittsburgh, PA, June 20-23, 2010. ASABE Paper No. 10-09111.

Technical Abstract: Plant stress has been estimated by spectral signature using both passive and active sensors. As optical sensors measure reflected light from a target, changes in illumination characteristics critically affect sensor response. Active sensors are of benefit in minimizing uncontrolled illumination effects on optical sensors by producing their own illumination that is reflected from the target and measured by the detector. Although active sensors use modulated radiation that can be differentiated from ambient illumination, in order to validate data and increase the accuracy, sensor performance characteristics must be well understood and examined in different target conditions of plant leaves. The performance of an active NDVI sensor was evaluated to study the effect of: 1) partial canopy coverage, 2) target off-center, 3) standoff distance, 4) target surface tilting, 5) solar bidirectional effect, 6) temperature, and 7) diurnal radiation change. These evaluations provide a valid range of sensor measurement and a motivation to improve the measurement accuracy by using selective data that can be validated by a supplemental vision sensor.

Last Modified: 8/19/2014
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