Stereo Spectral Imaging System for Plant Health Characterization
Three-dimensional (3D) measurements of whole plants may provide detailed structure information about plant growth patterns and also complement existing X-ray systems for the below-ground part. In addition to this structural characterization of plants, spectral information may also biochemically characterize plants' health. A stereo vision technique is a cost-effective and rapid imaging technique for measuring and reconstructing 3D structures. The Normalized Difference Vegetation Index (NDVI) requiring measurements of two spectral wavelengths in the NIR and red spectral regions has been widely used in remote sensing as an index to estimate various vegetation properties including chlorophyll concentration in leaves, leaf area index, biomass, and plant productivity. We integrated both stereo vision and NDVI techniques and developed a stereo spectral imaging (SSI) system for chlorophyll and biomass quantification of plants in 3D space. We used a stereo vision camera system and custom designed a dual-wavelength filter system at 690 nm and 750 nm to develop the SSI system. Calibration techniques for NDVI computation using two spectral band images were developed by referencing a diffuse reflectance panel. We also developed a texture mapping technique for rendering NDVI values in 3D space. In this paper, the performance of the SSI system was evaluated with an artificial plant with spectral properties similar to real plant’s green leaves.
KeywordsNormalize Difference Vegetation Index Point Cloud Stereo Vision Stereo Match Relative Reflectance
Unable to display preview. Download preview PDF.
- M.T. Lemmon, P.H. Smith, C. Shinohara, R. Tanner, P. Woida, A. Shaw, J. Hughes, R. Reynolds, R. Woida, J. Penegor, C. Oquest, S.F. Hviid, M.B. Madsen, M. Olsen, K. Leer, L. Drube, R.V. Morris, D. Britt, “The Phoenix surface stereo imager (SSI) investigation”, in Proc. 39th Lunar and Planetary Science Conf. (Lunar and Planetary Science XXXIX), vol.1391, pp. 2156-2157, 2008.Google Scholar
- C.L. Jones, N.O. Maness, M.L. Stone, and R. Jayasekara, “Chlorophyll estimation using multispectral reflectance and height sensing”, Transactions of the ASABE, vol. 50, no. 5, pp. 1867-1872, 2007.Google Scholar
- R.B. Boone, K.A. Galvin, N.M. Smith, and S.J. Lynn, “Generalizing El Nino effects upon Maasai livestock using hierarchical clusters of vegetation patterns”, Photogrammetric Engineering and Remote Sensing, vol. 66, no. 6, pp. 737-744, 2000.Google Scholar
- R.E. Plant, D.S. Munk, B.R. Roberts, R.L. Vargas, D.W. Rains, R.L. Travis, and R.B. Hutmacher, “Relationships between remotely sensed reflectance data and cotton growth and yield”, Transactions of the ASAE, vol. 43, no. 3, pp. 535-546, 2000.Google Scholar
- E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision, Upper Saddle River, N.J.: Prentice Hall, 1998.Google Scholar
- D. Shreiner, M. Woo, J. Neider, and T. Davis, OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R), Version 2. 5th ed., Reading, MA: Addison-Wesley Professional, 2005.Google Scholar
- D.A. Roberts, Y. Yamaguchi, and R.J.P. Lyon, “Comparison of various techniques for calibration of AIS data”, in Proc. of the Second Airborne Imaging Spectrometer Data Analysis Workshop, JPL Publ. 86-35, pp. 21-30, 1986.Google Scholar