Modeling dynamic laser speckle patterns of plant leaves
AuthorZhong, X; Wang, X; Farrell, P; Cooley, N; Moran, B
EditorLiu, XH; Zhang, KF; Li, MZ
Source TitleMANUFACTURING PROCESS AND EQUIPMENT, PTS 1-4
PublisherTRANS TECH PUBLICATIONS LTD
University of Melbourne Author/sWang, Xuezhi; Farrell, Peter; Cooley, Nicola; Moran, William; ZHONG, XU; ZHONG, XU
AffiliationElectrical And Electronic Engineering
Document TypeJournal Article
CitationsZhong, X., Wang, X., Farrell, P., Cooley, N. & Moran, B. (2013). Modeling dynamic laser speckle patterns of plant leaves. MANUFACTURING PROCESS AND EQUIPMENT, PTS 1-4, 694-697, pp.1363-+. https://doi.org/10.4028/www.scientific.net/AMR.694-697.1363.
Access StatusThis item is currently not available from this repository
C1 - Journal Articles Refereed
<jats:p>t is known that dynamic speckle patterns can be used to identify the temporal evolution of an active sample and some desired properties of the sample may therefore be detected via the dynamic laser speckle analysis. Mathematically understand the structure of sample surface which gives arise to the dynamic speckle patterns is crucial to the analysis. In this paper, a simulation model of the dynamic speckle patterns for plant leaves is proposed. The developed model is derived from the principle of coherent electromagnetic wave scattering off a randomly textured and time varying surface and is demonstrated via the simulation of dynamic speckle patterns of a fresh unattached leaf surface along the true laser speckle measurements. Both simulated and true measurements of the dynamic speckle patterns of the leaf are analyzed via several statistical techniques and a consistent agreement between the results of the two is observed. The proposed statistical model is helpful to gain the insight of the relationship between speckle dynamics and the activity of the leaf surface which is supposed to be an measurement of plant water stress and water status.</jats:p>
KeywordsAgriculture; Land and Farm Management not elsewhere classified; Sensory Processes; Perception and Performance; Signal Processing; Intelligence
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