|
|
|
|
|
| Identify Plant Drought Stress by 3D-Based Image |
| ZHAO Yan-dong, SUN Yu-rui, CAI Xiang, LIU He, Peter Schulze Lammers |
1.School of Technology, Beijing Forestry University, Beijing 100083, P.R.China
2.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, P.R.China
3.Department of Agricultural Engineering, University of Bonn, Bonn 53115, Germany |
|
|
|
摘要 Plants respond to drought stress with different physical manners, such as morphology and color of leaves. Thus, plants can be considered as a sort of living-sensors for monitoring dynamic of soil water content or the stored water in plant body. Because of difficulty to identify the early wilting symptom of plants from the results in 2D (two-dimension) space, this paper presented a preliminary study with 3D (three-dimension)-based image, in which a laser scanner was used for achieving the morphological information of zucchini (Cucurbita pepo) leaves. Moreover, a leaf wilting index (DLWIF) was defined by fractal dimension. The experiment consisted of phase-1 for observing the temporal variation of DLWIF and phase-2 for the validation of this index. During the experiment, air temperature, luminous intensity, and volumetric soil water contents (VSWC) were simultaneously recorded over time. The results of both phases fitted the bisector (line: 1:1) with R2=0.903 and REMS=0.155. More significantly, the influence of VSWC with three levels (0.22, 0.30, and 0.36 cm3 cm-3) on the response of plant samples to drought stress was observed from separated traces of DLWIF. In brief, two conclusions have been made: (i) the laser scanner is an effective tool for the non-contact detection of morphological wilting of plants, and (ii) defined DLWIF can be a promising indicator for a category of plants like zucchini.
Abstract Plants respond to drought stress with different physical manners, such as morphology and color of leaves. Thus, plants can be considered as a sort of living-sensors for monitoring dynamic of soil water content or the stored water in plant body. Because of difficulty to identify the early wilting symptom of plants from the results in 2D (two-dimension) space, this paper presented a preliminary study with 3D (three-dimension)-based image, in which a laser scanner was used for achieving the morphological information of zucchini (Cucurbita pepo) leaves. Moreover, a leaf wilting index (DLWIF) was defined by fractal dimension. The experiment consisted of phase-1 for observing the temporal variation of DLWIF and phase-2 for the validation of this index. During the experiment, air temperature, luminous intensity, and volumetric soil water contents (VSWC) were simultaneously recorded over time. The results of both phases fitted the bisector (line: 1:1) with R2=0.903 and REMS=0.155. More significantly, the influence of VSWC with three levels (0.22, 0.30, and 0.36 cm3 cm-3) on the response of plant samples to drought stress was observed from separated traces of DLWIF. In brief, two conclusions have been made: (i) the laser scanner is an effective tool for the non-contact detection of morphological wilting of plants, and (ii) defined DLWIF can be a promising indicator for a category of plants like zucchini.
|
|
Received: 12 May 2011
Accepted:
|
| Fund: This work was supported by the Chinese-German Center for Scientific Promotion (Chinesisch-Deutsches Zentrum für Wissenschaftsförderung) under the Project of Sino- German Research Group (GZ494), the Beijing Municipal Education Commission for Building Scientific Research and Scientific Research Base (2008BJKY01), the German Academic Exchange Service (DAAD), and China Scholarship Council (CSC) for enhancing our cooperation, and the International Cooperation Fund of Ministry of Science and Technology, China (2010DFA34670). |
|
Corresponding Authors:
SUN Yu-rui, Mobile: 13520452042, E-mail: pal@cau.edu.cn
E-mail: pal@cau.edu.cn
|
| About author: ZHAO Yan-dong, E-mail: yandongzh@bjfu.edu.cn; |
Cite this article:
ZHAO Yan-dong, SUN Yu-rui, CAI Xiang, LIU He, Peter Schulze Lammers.
2012.
Identify Plant Drought Stress by 3D-Based Image. Journal of Integrative Agriculture, 12(7): 1207-1211.
|
| [1]Bettina M, Engelbrecht J, Melvin L, Tyree T, Kursar T A. 2007. Visual assessment of wilting as a measure of leaf water potential and seedling drought survival. Journal of Tropical Ecology, 23, 497-500. [2]Ehlert D, Horn H, Adamek R. 2008. Measuring crop biomass density by laser triangulation. Biosystem Engineering, 61, 117-125. [3]Foucher P, Revollon P, Vigouroux B, Chassériaux G. 2004. Morphological image analysis for the detection of water stress in potted Forsythia. Biosystems Engineering, 89, 131-138. [4]Hong S H, Jang J S, Javidi B. 2004. Three-dimensional volumetric object reconstruction using computational integral imaging. Optics Express, 12, 483-491. [5]Kacira M, Ling P P. 2001. Design and development of an automated and non-contact sensing system for continuous monitoring of plant health and growth. Transaction of the ASAE, 44, 989-996. [6]Kacira M, Ling P P, Short T H. 2002a. Establishing crop water stress index (CWSI) threshold values for early non-contact detection of plant water stress. Transactions of the ASAE, 45, 775-780. [7]Kacira M, Ling P P, Short T H. 2002b. Machine vision extracted plant movement for early detection of plant water stress. Transactions of the ASAE, 45, 1147-1153. [8]Kraft M, de Freitas N R S, Munack A. 2010. Test of a 3D time of flight camera for shape measurements of plant. CIGR Workshop on Image Analysis in Agriculture. pp. 108-115. [9]Linker R, Seginer I. 2003. Water stress detection in a greenhouse by a step change of ventilation. Biosystems Engineering, 84, 79-89. [10]Matoba O, Naughton T J, Frauel Y, Bertaux N, Javidi B. 2002. Real-time three-dimensional objective reconstruction by use of a phase-encoded digital hologram. Applied Optics, 41, 6187-6192. [11]Nyakwende E, Paull C J, Atherton J G. 1997. Nondestructive determination of leaf area in tomato plants using image processing. Journal of Horticultural Science, 722, 255-262. [12]Omasa K, Hosoi F, Konish A. 2007. 3D lidar imaging for detecting and understanding plant responses and canopy structure. Journal of Experimental Botany, 58, 881-898. [13]Preusser T, Pumpf M. 2002. A level set method for anisotropic geometric diffusion in 3D image processing. SIAM Journal on Applied Mathematics, 62, 1772-1793. [14]Revollon P, Chasseriaux G, Riviere L M, Gardet R. 1998. The use of image processing for tracking the morphological modification of forsythia following an interruption of watering. In: Proceedings of the International Conference on Agricultural Engineering. AgEng, Oslo, Norway. [15]Seginer I, Elster R T, Goodrum J W, Rieger M W. 1992. Plant wilt detection by computer-vision tracking of leaf tips. Transactions of the ASAE, 35, 1563-1567. [16]Testi L, Goldhamer D A, Iniesta F, Salinas M. 2008. Crop water stress index is a sensitive water stress indictor in pistachio tress. Irrigation Science, 26, 395-405. [17]Yang Y, Ling P D, Fleisher D H, Timlin D J, Reddy V R. 2008. Non-contacting techniques for plant drought stress detection. Transactions of the ASAE, 51, 1483-1492. [18]Zhang L, Tam W J. 2005. Stereoscopic image generation based on depth images for 3D TV. IEEE Transactions on Broadcasting, 51, 191-199. |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
| |
Shared |
|
|
|
|
| |
Discussed |
|
|
|
|
