Precision Agriculture

, Volume 14, Issue 6, pp 586–605 | Cite as

Plant growth parameter estimation from sparse 3D reconstruction based on highly-textured feature points

  • Ran Nisim Lati
  • Sagi Filin
  • Hanan Eizenberg


Crop canopy spatial parameters are indicative of plant phenological growth stage and physiological condition, and their estimation is therefore of great interest for modeling and precision agriculture practices. Rapid increases in computing power have made stereovision models an attractive alternative to common single-image-based 2D methods, by allowing detailed estimation of the plant’s growth parameters regardless of imaging conditions. Models that have been proposed thus far are still limited in their application because of sensitivity to outdoor illumination conditions and the inherent difficulty in modeling complex plant shapes using only radiometric information. Assuming that not all of the plant-related pixels are essential for growth estimation, this study proposes a 3D reconstruction model that focuses on selected salient features on the plant surface, which are sufficient for obtaining growth characteristics. In addition, by introducing a hue-invariant model, the proposed algorithm shows robustness to diverse outdoor illumination conditions. The algorithm was tested under greenhouse and field conditions on corn, cotton, sunflower, tomato and black nightshade plants, from young seedlings to fully developed plant growth stages, and accurately estimated height (error ~4.5 %) and leaf cover area (error ~5 %). Furthermore, a strong correlation (r2 ~0.92) was found between the plant’s estimated volume and measured biomass, yielding an accurate biomass estimator in the validation tests (error ~4.5 %). This estimation ability remained stable while applying the model on plants with varying densities (overlapping leaves) and imaging setups where the standard 2D based analyses failed, thus showing the 3D modeling contribution to robust growth estimation models.


Biomass Height Invariant transformation Leaf cover area Stereovision 


  1. Andersen, H. J., Reng, L., & Kirk, K. (2005). Geometric plant properties by relaxed stereo vision using simulated annealing. Computers and Electronics in Agriculture, 49(2), 219–232.CrossRefGoogle Scholar
  2. Berge, T., Aastveit, A., & Fykse, H. (2008). Evaluation of an algorithm for automatic detection of broad leaved weeds in spring cereals. Precision Agriculture, 9(6), 391–405.CrossRefGoogle Scholar
  3. Boegh, E., Soegaard, H., Broge, N., Hasager, C. B., Jensen, N. O., Schelde, K., et al. (2002). Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture. Remote Sensing of Environment, 81(2–3), 179–193.CrossRefGoogle Scholar
  4. Donald, W. W. (2006). Estimated corn yields using either weed cover or rated control after pre-emergence herbicides. Weed Science, 54(2), 373–379.Google Scholar
  5. Ehlert, D., Adamek, R., & Horn, H. J. (2009). Laser rangefinder-based measuring of crop biomass under field conditions. Precision Agriculture, 10(5), 395–408.CrossRefGoogle Scholar
  6. Ehlert, D., & Dammer, K. H. (2006). Wide-scale testing of the Crop-Meter for site-specific farming. Precision Agriculture, 7(2), 101–115.CrossRefGoogle Scholar
  7. Ehlert, D., Heisig, M., & Adamek, R. (2010). Suitability of a laser rangefinder to characterize winter wheat. Precision Agriculture, 11(6), 650–663.CrossRefGoogle Scholar
  8. Ehlert, D., Horn, H., & Adamek, R. (2008). Measuring crop biomass density by laser triangulation. Computers and Electronics in Agriculture, 61(2), 117–125.CrossRefGoogle Scholar
  9. Ehsani, M. R., Upadhyaya, S. K., & Mattson, M. L. (2004). Seed location mapping using RTK-GPS. Transactions of the ASAE, 47(3), 909–914.Google Scholar
  10. Ephrath, J. E., Wang, R. F., Terashima, K., Hesketh, J. D., Huck, M. G., & Hummel, J. W. (1993). Shading effects on soybean and corn. Biotronics, 22, 15–24.Google Scholar
  11. Finlayson, G., Hordley, S., & Drew, M. (2002). Removing shadows from images. In Lecture notes in computer science: Vol. 2353. ECCV 2002: European conference on computer vision (pp. 823–836). Berlin: Springer.Google Scholar
  12. Fusiello, A., Roberto, V., & Verri, A. (2000). Symmetric stereo with multiple windowing. International Journal of Pattern Recognition and Artificial Intelligence, 14(8), 1053–1066.CrossRefGoogle Scholar
  13. Gee, C. H., Bossu, J., Jones, G., & Truchetet, F. (2008). Crop/weed discrimination in perspective agronomic images. Computers and Electronics in Agriculture, 60(1), 49–59.CrossRefGoogle Scholar
  14. Griepentrog, H. W., Nørremark, M., Nielsen, H., & Blackmore, B. S. (2005). Seed mapping of sugar beet. Precision Agriculture, 6(2), 157–165.CrossRefGoogle Scholar
  15. Hansen, P. M., & Schjoerring, J. K. (2003). Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression. Remote Sensing of Environment, 86(4), 542–553.CrossRefGoogle Scholar
  16. Harris, C., & Stephens, M. (1988). A combined corner and edge detector. In Fourth Alvey vision conference (pp. 147–151). Manchester, UK.Google Scholar
  17. Hartley, R., & Zisserman, A. (2004). Multiple view geometry in computer vision. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
  18. Hernández-Andrés, J., Lee, R. L., Jr, & Romero, J. (1999). Calculating correlated color temperatures across the entire gamut of daylight and skylight chromaticities. Applied Optics, 38(27), 5703–5709.PubMedCrossRefGoogle Scholar
  19. Jin, J., & Tang, L. (2009). Corn plant sensing using real-time stereo vision. Journal of Field Robotics, 26(6–7), 591–608.CrossRefGoogle Scholar
  20. Johnson, C. K., Mortensen, D. A., Wienhold, B. J., Shanahan, J. F., & Doran, J. W. (2003). Site-specific management zones based on soil electrical conductivity in a semiarid cropping system. Agronomy Journal, 95(2), 303–315.CrossRefGoogle Scholar
  21. Kise, M., & Zhang, Q. (2008). Creating a panoramic field image using multi-spectral stereovision system. Computers and Electronics in Agriculture, 60(1), 67–75.CrossRefGoogle Scholar
  22. Kraus, K. (2007). Photogrammetry (2nd ed., Vol. 1). Bonn, Germany: Dümmler.CrossRefGoogle Scholar
  23. Lati, R. N., Filin, S., & Eizenberg, H. (2011). Robust methods for measurement of leaf cover area and biomass from image data. Weed Science, 59(2), 276–284.CrossRefGoogle Scholar
  24. Lindquist, J. L., & Knezevic, S. Z. (2001). Quantifying crop yield response to weed populations: Applications and limitations. In R. K. D. Peterson & L. G. Higley (Eds.), Biotic stress and yield loss (pp. 205–232). Boca Raton, FL, USA: CRC Press.Google Scholar
  25. Lowe, D. G. (2004). Distinctive image features from scale-invariant key points. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
  26. Malacara, D. (Ed.). (2002). Color vision and colorimetry, theory and applications. Bellingham, WA, USA: SPIE Press.Google Scholar
  27. McCarthy, C. L., Hancock, N. H., & Raine, S. R. (2010). Applied machine vision of plants—a review with implications for field deployment in automated farming operations. Intelligent Service Robotics, 3(4), 209–217.CrossRefGoogle Scholar
  28. Meyer, G. E., & Davison, D. A. (1987). An electronic image plantgrowth measurement system. Transaction of the ASAE, 30(1), 242–248.Google Scholar
  29. Murphy, C., Lemerle, D., Jones, R., & Harden, S. (2002). Use of density to predict crop yield loss between variable seasons. Weed Research, 42(5), 377–384.CrossRefGoogle Scholar
  30. Ngouajio, M., Lemieux, C., Fortier, J. J., Careau, D., & Leroux, G. D. (1998). Validation of an operator-assisted module to measure weed and crop leaf cover by digital image analysis. Weed Technology, 12(3), 446–453.Google Scholar
  31. Ngouajio, M., Lemieux, C., & Leroux, G. D. (1999). Prediction of corn (Zea mays) yield loss from early observations of the relative leaf area and the relative leaf cover of weeds. Weed Science, 47(3), 297–304.Google Scholar
  32. Nørremark, M., Søgaard, H. T., Griepentrog, H. W., & Nielsen, H. (2007). Instrumentation and method for high accuracy geo-referencing of sugar beet plants. Computers and Electronics in Agriculture, 56(2), 130–146.CrossRefGoogle Scholar
  33. Pagés, J., Salvi, J., Collewet, C., & Forest, J. (2005). Optimized De Bruijn patterns for one-shot shape acquisition. Image and Vision Computing, 23(8), 707–720.CrossRefGoogle Scholar
  34. Piron, A., Leemans, V., Lebeau, F., & Destain, M. F. (2009). Improving in-row weed detection in multispectral stereoscopic images. Computers and Electronics in Agriculture, 69(1), 73–79.CrossRefGoogle Scholar
  35. Pollefeys, M., Nister, D., Frahm, J. M., Akbarzadeh, A., Mordohai, P., Clipp, B., et al. (2008). Detailed real-time urban 3D reconstruction from video. International Journal of Computer Vision, 78(2–3), 143–167.CrossRefGoogle Scholar
  36. Rasmussen, J., Norremark, M., & Bibby, B. M. (2007). Assessment of leaf cover and crop soil cover in weed harrowing research using digital images. Weed Research, 47(4), 299–310.CrossRefGoogle Scholar
  37. Ruiz–Ruiz, G., Gómez-Gil, J., & Navas-Gracia, L. M. (2009). Testing different color spaces based on hue for the environmentally adaptive segmentation algorithm (EASA). Computers and Electronics in Agriculture, 68(1), 88–89.CrossRefGoogle Scholar
  38. Slaughter, D. C., Giles, D. K., & Downey, D. (2008). Autonomous robotic weed control systems: A review. Computers and Electronics in Agriculture, 61(1), 63–78.CrossRefGoogle Scholar
  39. Snavely, N., Seitz, S. M., & Szelinski, R. (2008). Modelling the world from internet photo collections. International Journal of Computer Vision, 80(2), 189–210.CrossRefGoogle Scholar
  40. Søgaard, H. T. (2005). Weed classification by active shape models. Biosystems Engineering, 93(3), 271–281.CrossRefGoogle Scholar
  41. Tang, L., & Tian, L. (2008a). Real-time crop row image reconstruction for automatic emerged corn plant spacing measurement. Transactions of the ASABE, 51(3), 1079–1087.Google Scholar
  42. Tang, L., & Tian, L. (2008b). Plant identification in mosaicked crop row images for automatic emerged corn plant spacing measurement. Transactions of the ASABE, 51(6), 2181–2191.Google Scholar
  43. Tian, L. F., & Slaughter, D. C. (1998). Environmentally adaptive segmentation algorithm for outdoor image segmentation. Computers and Electronics in Agriculture, 21(3), 153–168.CrossRefGoogle Scholar
  44. Van Evert, F. K., Polder, G., Van der Heijden, G., Kempenaar, C., & Lotz, L. A. P. (2009). Real-time vision-based detection of Rumex obtusifolius in grassland. Weed Research, 49(2), 164–174.CrossRefGoogle Scholar
  45. Xue, L., Cao, W., Luo, W., Dai, T., & Zhu, Y. (2004). Monitoring leaf nitrogen status in rice with canopy spectral reflectance. American Society of Agronomy, 96(1), 135–142.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Mapping and Geo-Information EngineeringTechnion-Israel Institute of TechnologyHaifaIsrael
  2. 2.Department of Weed Research and Plant Pathology, Agricultural Research OrganizationNewe Ya’ar Research CenterRamat YishayIsrael

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