Taking the pulse of a plant: dynamic laser speckle analysis of plants

Zhong Xu; Wang Xuezhi; Cooley Nicola; Farrell Peter; Moran Bill

doi:10.3788/IRLA201645.0902002

Volume 45 Issue 9

Oct. 2016

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Zhong Xu, Wang Xuezhi, Cooley Nicola, Farrell Peter, Moran Bill. Taking the pulse of a plant: dynamic laser speckle analysis of plants[J]. Infrared and Laser Engineering, 2016, 45(9): 902002-0902002(12). doi: 10.3788/IRLA201645.0902002

Citation:

Zhong Xu, Wang Xuezhi, Cooley Nicola, Farrell Peter, Moran Bill. Taking the pulse of a plant: dynamic laser speckle analysis of plants[J]. Infrared and Laser Engineering, 2016, 45(9): 902002-0902002(12). doi: 10.3788/IRLA201645.0902002

Taking the pulse of a plant: dynamic laser speckle analysis of plants

doi: 10.3788/IRLA201645.0902002

1.
Department of Infrastructure Engineering,University of Melbourne,VIC 3010,Australia;
2.
School of Electrical and Computer Engineering,Royal Melbourne Institute Technology,VIC 3000,Australia;
3.
Higher Education-Primary Industy,NMIT and Melbourne Polytechnic,NMIT Epping Campus,Victoria 3076,Australia;
4.
Department of Electrical and Electronic Engineering,University of Melbourne,VIC 3010,Australia

More Information

Author Bio:
Zhong Xu (1987-),male,PhD.His research interests lie in the area of laser speckle analysis,plant sensing,and geographic information science.Email:peter.zhong49@gmail.com
Received Date: 2016-06-05
Rev Recd Date: 2016-07-03
Publish Date: 2016-09-25

Abstract

Ideally, to achieve optimal production in agriculture, crop stress needs to be measured in real-time, and plant inputs managed in response. However, many important physiological responses like photosynthesis are difficult to measure, and current trade-offs between cost, robustness, and spatial measurement capacity of available plant sensors may prevent practical in-field application of most current sensing techniques. This paper investigates a novel application of laser speckle imaging of a plant leaf as a sensor with an aim, ultimately, to detect indicators of crop stress:changes to the dynamic properties of leaf topography on the scale of the wavelength of laser light. In our previous published work, an initial prototype of the laser speckle acquisition system specific for plant status measurements together with data processing algorithms were developed. In this paper, we report a new area based statistical method that improves robustness of the data processing against disturbances from various sources. Water and light responses of the laser speckle measurements from cabbage leaves taken by the developed apparatus are exhibited via growth chamber experiments. Experimental evidence indicates that the properties of the laser speckle patterns from a leaf are closely related to the physiological status of the leaf. This technology has the potential to be robust, cost effective, and relatively inexpensive to scale.
- dynamic laser speckle analysis,
- local normal vector,
- leaf activity detection,
- plant water status

References

[1]	Jones Hamlyn G, Vaughan Robin A. Remote Sensing of Vegetation:Principles, Techniques, and Applications[M]. Oxford:Oxford University Press, 2010.
[2]	Zhong X, Wang X, Farrell P, et al. Modeling and classifying surface roughness via laser speckle statistics[C]//Proceedings of the 2011 International Conference on Signal and Information Processing, Shanghai China, 2011.
[3]	Buckley T N, Mott K A, Farquhar G D. A hydromechanical and biochemical model of stom-atal conductance[J]. Plant, Cell Environment, 2003, 26(10):1767-1785.
[4]	Bowman William D. The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves[J]. Remote Sensing of Environment, 1989, 30(3):249-255.
[5]	Saliendra Nicanor Z, Sperry John S, Comstock Jonathan P. Influence of leaf water status on stomatal response to humidity, hydraulic conductance, and soil drought in betula occidentalis[J]. Planta, 1995, 196(2):357-366.
[6]	Mohammad R Riahi, Hamid Latifi, Mohsen Sajjadi. Speckle correlation photography for the study of water content and sap flow in plant leaves[J]. Applied Optics, 2006, 45(29):7674-7678.
[7]	Tsukasa Matsuo, Hisashi Hirabayashi, Hiroaki Ishizawa, et al. Application of laser speckle method to water flow measurement in plant body[C]//Proceedings of the 2006 International Joint Conference on SICE-ICASE, 2006:3563-3566.
[8]	Kawamura M, Ishizawa H, Horiguchi T, et al. Laser speckle pattern measurement for plant state monitoring[C]//Proceedings of the 2010 SICE Annual Conference, 2010:2928-2932.
[9]	Wang Xuezhi, Yang Weiping, Ashley Wheaton, et al. Automated canopy temperature estimation via infrared thermography:a first step towards automated plant water stress monitoring[J]. Computers and Electronics in Agriculture, 2010, 73(1):74-83.
[10]	Rabal H J. Dynamic Laser Speckle and Applications[M]. New York:CRC Press, 2008.
[11]	Ricardo Arizaga, Nelly Luci, Marcelo Trivi, et al. Display of local activity using dynamical speckle patterns[J]. Optical Engineering, 2002, 41(2):287-294.
[12]	Briers J David, Webster Sian. Laser speckle contrast analysis (lasca):a nonscanning, full-field technique for monitoring capillary blood flow[J]. Journal of Biomedical Optics, 1996, 1(2):174-179.
[13]	Briers J David. Laser doppler, speckle and related techniques for blood perfusion mapping and imaging[J]. Physiological Measurement, 2001, 22(4):R35.
[14]	Miao Peng, Li Minheng, Fontenelle Hugues, et al. Imaging the cerebral blood flow with enhanced laser speckle contrast analysis (elasca) by monotonic point transformation[J]. Biomedical Engineering, IEEE Transactions on, 2009, 56(4):1127-1133.
[15]	Miao P, Rege A, Li N, et al. High resolution cerebral blood flow imag-ing by registered laser speckle contrast analysis[J]. IEEE Transactions on Biomedical Engineering, 2010, 57(5):1152-1157.
[16]	Forrester K R, Tulip J, Leonard C, et al. A laser speckle imaging technique for measuring tissue perfusion[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(11):2074-2084.
[17]	Stewart J B. Modelling surface conductance of pine forest[J]. Agricultural and Forest Meteorology, 1988, 43(1):19-35.
[18]	Elaine Miles, Ann Roberts. Non-destructive speckle imaging of subsurface detail in paper-based cultural materials[J]. Optics Express, 2009, 17(15):12309-12314.
[19]	Zhong Xu, Wang Xuezhi, Nicola Cooley, et al. Normal vector based dynamic laser speckle analysis for plant water status monitoring[J]. Optics Communications, 2014, 313:256-262.
[20]	Braga Jr R A, Horgan G W, Enes A M, et al. Biological feature isolation by wavelets in biospeckle laser images[J]. Computers and Electronics in Agriculture, 2007, 58(2):123-132.
[21]	Nobre C M B, Braga Jr R A, Costa A G, et al. Biospeckle laser spectral analysis under inertia moment, entropy and cross-spectrum methods[J]. Optics Communications, 2009, 282(11):2236-2242.
[22]	Zhong Xu, Wang Xuezhi, Nicola Cooley, et al. Dynamic laser speckle analysis via normal vector space statistics[J]. Optics Communications, 2013, 305(313):27-35.
[23]	Tuzet A, Perrier A, Leuning R. A coupled model of stomatal conductance, photosynthesis and transpiration[J]. Plant, Cell Environment, 2003, 26(7):1097-1116.
[24]	Galle Damour, Thierry Simonneau, Herv Cochard, et al. An overview of models of stomatal conductance at the leaf level[J]. Plant, Cell Environment, 2010, 33(9):1419-1438.
[25]	Susanna Von Caemmerer. Biochemical Models of Leaf Photosynthesis[M]. Austrilia:Csiro Publishing, 2000.
[26]	Driscoll S P, Prins A, Olmos Enrique, et al. Specification of adaxial and abaxial stomata, epidermal structure and photosynthesis to CO2 enrichment in maize leaves[J]. Journal of Experimental Botany, 2006, 57(2):381-390.
[27]	Belinda E Medlyn, Remko A Duursma, Derek Eamus, et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance[J]. Global Change Biology, 2011, 17(6):2134-2144.
[28]	Xavier Chone, Cornelis Van Leeuwen, Denis Dubourdieu. Stem water potential is a sensitive indicator of grapevine water status[J]. Annals of Botany, 2001, 87(4):477-483.
[29]	Frangi A, Niessen W, Vincken K, et al. Multiscale vessel enhancement filtering[J]. Medical Image Computing and Computer-Assisted Interventation-MICCAI'98, 1998:130-137.
[30]	James Collatz G, Timothy Ball J, Cyril Grivet, et al. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration:a model that includes a laminar boundary layer[J]. Agricultural and Forest Meteorology, 1991, 54(2):107-136.
[31]	Leuning R. A critical appraisal of a combined stomatal-photosynthesis model for C3 plants[J]. Plant, Cell Environment, 1995, 18(4):339-355.
[32]	Gabriel Katul, Stefano Manzoni, Sari Palmroth, et al. A stomatal optimization theory to describe the effects of atmospheric CO2 on leaf photosynthesis and transpiration[J]. Annals of Botany, 2010, 105(3):431-442.
[33]	Farquhar G D, von Caemmerer S, Berry J A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species[J]. Planta, 1980, 149(1):78-90.
[34]	Kirschbaum MUF, Kppers M, Schneider H. Modelling photosynthesis in fluctuating light with inclusion of stomatal conductance, biochemical activation and pools of key photosynthetic intermediates[J]. Planta, 1997, 204(1):16-26.
[35]	Uwe Rascher, Ladislav Nedbal. Dynamics of photosynthesis in fluctuating light[J]. Current Opinion in Plant Biology, 2006, 9(6):671-678.
[36]	Kirschbaum MUF, Gross L J, Pearcy R W. Observed and modelled stomatal responses to dynamic light environments in the shade plant alocasia macrorrhiza[J]. Plant, Cell Environment, 1988, 11(2):111-121.
[37]	Silvre Vialet-Chabrand, Erwin Dreyer, Oliver Brendel. Performance of a new dynamic model for predicting diurnal time courses of stomatal conductance at the leaf level[J]. Plant, Cell Environment, 2013, 8:1529-1546.

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通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142

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Taking the pulse of a plant: dynamic laser speckle analysis of plants

1. Department of Infrastructure Engineering,University of Melbourne,VIC 3010,Australia;

2. School of Electrical and Computer Engineering,Royal Melbourne Institute Technology,VIC 3000,Australia;

3. Higher Education-Primary Industy,NMIT and Melbourne Polytechnic,NMIT Epping Campus,Victoria 3076,Australia;

4. Department of Electrical and Electronic Engineering,University of Melbourne,VIC 3010,Australia

Author Bio:

Received Date: 2016-06-05

Rev Recd Date: 2016-07-03

Publish Date: 2016-09-25

Key words:

Abstract: Ideally, to achieve optimal production in agriculture, crop stress needs to be measured in real-time, and plant inputs managed in response. However, many important physiological responses like photosynthesis are difficult to measure, and current trade-offs between cost, robustness, and spatial measurement capacity of available plant sensors may prevent practical in-field application of most current sensing techniques. This paper investigates a novel application of laser speckle imaging of a plant leaf as a sensor with an aim, ultimately, to detect indicators of crop stress:changes to the dynamic properties of leaf topography on the scale of the wavelength of laser light. In our previous published work, an initial prototype of the laser speckle acquisition system specific for plant status measurements together with data processing algorithms were developed. In this paper, we report a new area based statistical method that improves robustness of the data processing against disturbances from various sources. Water and light responses of the laser speckle measurements from cabbage leaves taken by the developed apparatus are exhibited via growth chamber experiments. Experimental evidence indicates that the properties of the laser speckle patterns from a leaf are closely related to the physiological status of the leaf. This technology has the potential to be robust, cost effective, and relatively inexpensive to scale.

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Reference (37)

[1]	Jones Hamlyn G, Vaughan Robin A. Remote Sensing of Vegetation:Principles, Techniques, and Applications[M]. Oxford:Oxford University Press, 2010.
[2]	Zhong X, Wang X, Farrell P, et al. Modeling and classifying surface roughness via laser speckle statistics[C]//Proceedings of the 2011 International Conference on Signal and Information Processing, Shanghai China, 2011.
[3]	Buckley T N, Mott K A, Farquhar G D. A hydromechanical and biochemical model of stom-atal conductance[J]. Plant, Cell Environment, 2003, 26(10):1767-1785.
[4]	Bowman William D. The relationship between leaf water status, gas exchange, and spectral reflectance in cotton leaves[J]. Remote Sensing of Environment, 1989, 30(3):249-255.
[5]	Saliendra Nicanor Z, Sperry John S, Comstock Jonathan P. Influence of leaf water status on stomatal response to humidity, hydraulic conductance, and soil drought in betula occidentalis[J]. Planta, 1995, 196(2):357-366.
[6]	Mohammad R Riahi, Hamid Latifi, Mohsen Sajjadi. Speckle correlation photography for the study of water content and sap flow in plant leaves[J]. Applied Optics, 2006, 45(29):7674-7678.
[7]	Tsukasa Matsuo, Hisashi Hirabayashi, Hiroaki Ishizawa, et al. Application of laser speckle method to water flow measurement in plant body[C]//Proceedings of the 2006 International Joint Conference on SICE-ICASE, 2006:3563-3566.
[8]	Kawamura M, Ishizawa H, Horiguchi T, et al. Laser speckle pattern measurement for plant state monitoring[C]//Proceedings of the 2010 SICE Annual Conference, 2010:2928-2932.
[9]	Wang Xuezhi, Yang Weiping, Ashley Wheaton, et al. Automated canopy temperature estimation via infrared thermography:a first step towards automated plant water stress monitoring[J]. Computers and Electronics in Agriculture, 2010, 73(1):74-83.
[10]	Rabal H J. Dynamic Laser Speckle and Applications[M]. New York:CRC Press, 2008.
[11]	Ricardo Arizaga, Nelly Luci, Marcelo Trivi, et al. Display of local activity using dynamical speckle patterns[J]. Optical Engineering, 2002, 41(2):287-294.
[12]	Briers J David, Webster Sian. Laser speckle contrast analysis (lasca):a nonscanning, full-field technique for monitoring capillary blood flow[J]. Journal of Biomedical Optics, 1996, 1(2):174-179.
[13]	Briers J David. Laser doppler, speckle and related techniques for blood perfusion mapping and imaging[J]. Physiological Measurement, 2001, 22(4):R35.
[14]	Miao Peng, Li Minheng, Fontenelle Hugues, et al. Imaging the cerebral blood flow with enhanced laser speckle contrast analysis (elasca) by monotonic point transformation[J]. Biomedical Engineering, IEEE Transactions on, 2009, 56(4):1127-1133.
[15]	Miao P, Rege A, Li N, et al. High resolution cerebral blood flow imag-ing by registered laser speckle contrast analysis[J]. IEEE Transactions on Biomedical Engineering, 2010, 57(5):1152-1157.
[16]	Forrester K R, Tulip J, Leonard C, et al. A laser speckle imaging technique for measuring tissue perfusion[J]. IEEE Transactions on Biomedical Engineering, 2004, 51(11):2074-2084.
[17]	Stewart J B. Modelling surface conductance of pine forest[J]. Agricultural and Forest Meteorology, 1988, 43(1):19-35.
[18]	Elaine Miles, Ann Roberts. Non-destructive speckle imaging of subsurface detail in paper-based cultural materials[J]. Optics Express, 2009, 17(15):12309-12314.
[19]	Zhong Xu, Wang Xuezhi, Nicola Cooley, et al. Normal vector based dynamic laser speckle analysis for plant water status monitoring[J]. Optics Communications, 2014, 313:256-262.
[20]	Braga Jr R A, Horgan G W, Enes A M, et al. Biological feature isolation by wavelets in biospeckle laser images[J]. Computers and Electronics in Agriculture, 2007, 58(2):123-132.
[21]	Nobre C M B, Braga Jr R A, Costa A G, et al. Biospeckle laser spectral analysis under inertia moment, entropy and cross-spectrum methods[J]. Optics Communications, 2009, 282(11):2236-2242.
[22]	Zhong Xu, Wang Xuezhi, Nicola Cooley, et al. Dynamic laser speckle analysis via normal vector space statistics[J]. Optics Communications, 2013, 305(313):27-35.
[23]	Tuzet A, Perrier A, Leuning R. A coupled model of stomatal conductance, photosynthesis and transpiration[J]. Plant, Cell Environment, 2003, 26(7):1097-1116.
[24]	Galle Damour, Thierry Simonneau, Herv Cochard, et al. An overview of models of stomatal conductance at the leaf level[J]. Plant, Cell Environment, 2010, 33(9):1419-1438.
[25]	Susanna Von Caemmerer. Biochemical Models of Leaf Photosynthesis[M]. Austrilia:Csiro Publishing, 2000.
[26]	Driscoll S P, Prins A, Olmos Enrique, et al. Specification of adaxial and abaxial stomata, epidermal structure and photosynthesis to CO2 enrichment in maize leaves[J]. Journal of Experimental Botany, 2006, 57(2):381-390.
[27]	Belinda E Medlyn, Remko A Duursma, Derek Eamus, et al. Reconciling the optimal and empirical approaches to modelling stomatal conductance[J]. Global Change Biology, 2011, 17(6):2134-2144.
[28]	Xavier Chone, Cornelis Van Leeuwen, Denis Dubourdieu. Stem water potential is a sensitive indicator of grapevine water status[J]. Annals of Botany, 2001, 87(4):477-483.
[29]	Frangi A, Niessen W, Vincken K, et al. Multiscale vessel enhancement filtering[J]. Medical Image Computing and Computer-Assisted Interventation-MICCAI'98, 1998:130-137.
[30]	James Collatz G, Timothy Ball J, Cyril Grivet, et al. Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration:a model that includes a laminar boundary layer[J]. Agricultural and Forest Meteorology, 1991, 54(2):107-136.
[31]	Leuning R. A critical appraisal of a combined stomatal-photosynthesis model for C3 plants[J]. Plant, Cell Environment, 1995, 18(4):339-355.
[32]	Gabriel Katul, Stefano Manzoni, Sari Palmroth, et al. A stomatal optimization theory to describe the effects of atmospheric CO2 on leaf photosynthesis and transpiration[J]. Annals of Botany, 2010, 105(3):431-442.
[33]	Farquhar G D, von Caemmerer S, Berry J A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species[J]. Planta, 1980, 149(1):78-90.
[34]	Kirschbaum MUF, Kppers M, Schneider H. Modelling photosynthesis in fluctuating light with inclusion of stomatal conductance, biochemical activation and pools of key photosynthetic intermediates[J]. Planta, 1997, 204(1):16-26.
[35]	Uwe Rascher, Ladislav Nedbal. Dynamics of photosynthesis in fluctuating light[J]. Current Opinion in Plant Biology, 2006, 9(6):671-678.
[36]	Kirschbaum MUF, Gross L J, Pearcy R W. Observed and modelled stomatal responses to dynamic light environments in the shade plant alocasia macrorrhiza[J]. Plant, Cell Environment, 1988, 11(2):111-121.
[37]	Silvre Vialet-Chabrand, Erwin Dreyer, Oliver Brendel. Performance of a new dynamic model for predicting diurnal time courses of stomatal conductance at the leaf level[J]. Plant, Cell Environment, 2013, 8:1529-1546.

Taking the pulse of a plant: dynamic laser speckle analysis of plants

doi: 10.3788/IRLA201645.0902002

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通讯作者: 陈斌, bchen63@163.com

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