Dynamic segmentation to estimate vine vigor from ground images

V. Sáiz-Rubio, F. Rovira-Más


The geographic information required to implement precision viticulture applications in real fields has led to the extensive use of remote sensing and airborne imagery. While advantageous because they cover large areas and provide diverse radiometric data, they are unreachable to most of medium-size Spanish growers who cannot afford such image sourcing. This research develops a new methodology to generate globally-referenced vigor maps in vineyards from ground images taken with a camera mounted on a conventional tractor. This monocular camera was able to sense in the visible, NIR, and UV spectra, selectively isolated with bandpass filters. The versatility of the system was further enhanced by implementing two sampling levels: intensive coverage of 1 m2 and super-intensive for 0.1 m2. The core of the procedure resides in the algorithm for automatically segmenting the filtered images in such a way that relative differences in canopy vigor were objectively quantified. The calculation of the dynamic threshold involved the mathematical concepts of gradient and curvature. Field results showed that relative differences in vine vigor can be detected from NIR-filtered images and intensive sampling. Furthermore, individual images were successfully merged into a global vigor map that can be directly employed by end-users. Super-intensive sampling and UV perception were not appropriate for building vigor maps, but could be of interest for other agronomical purposes as the early detection of diseases. Field tests proved the feasibility of building global vigor maps from ground-based imagery, and showed the potential of this technique as a predictive instrument for modest-size producers.


dynamic threshold; GPS; NIR; precision viticulture; UV; vigor map

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Best S, Len L, Flores F, Aguilera H, Quintana R, Concha V, 2011. Handbook Agricultura de Precisin. Progap-INIA. Available in http://www.elsitioagricola.com/CultivosExtensivos/LibroIniaAP/libro3.asp. [1 Feb 2012]. [In Spanish].

Chaerle L, Van der straeten D, 2000. Imaging techniques and the early detection of plant stress. Trends Plant Sci 5(11): 495-501.

Drissi R, Goutouly JP, Forget D, Gaudillere JP, 2009. Non-destructive measurement of grapevine leaf area by ground normalized difference vegetation index. Agron J 101(1): 226-231.

Gausman HW, 1977. Reflectance of leaf components. Remote Sens Environ 6: 1-9.

Hahn F, 2009. Actual pathogen detection: sensors and algorithms- A review. Algorithms 2(1): 301-338.

Hall A, Lamb DW, Holzapfel B, Louis J, 2002. Optical remote sensing applications in viticulture- A review. Aust J Grape Wine Res 8: 36-47.

Johnson L F, Roczenc E, Youkhanac SK, Nemanid RR, Bosche DF, 2003. Mapping vineyard leaf area with multispectral satellite imagery. Comput Electron Agr 38(1): 33-44.

Kim Y, Reid JF, 2007. Bidirectional effect on a spectral image sensor for in-field crop reflectance assessment. Intl J Remote Sensing 28(21): 4913-4926.

Lamb DW, Weedon MM, Bramley R, 2004. Using remote sensing to predict grape phenolics and colour at harvest in a Cabernet Sauvignon vineyard: Timing observations against vine phenology and optimising image resolution. Aust J Grape Wine Res 10: 46-54.

Lu R, Ariana D, 2002. A near-infrared sensing technique for measuring internal quality of apple fruit. Appl Eng Agric 185: 585-590.

Mazzeto F, Calcante A, Mena A, 2009. Comparing commercial optical sensors for crop monitoring tasks in precision viticulture. J Agr Eng Res 40(1): 11-18.

Noble SD, Crowe TG, 2005. Analysis of crop and weed leaf diffuse reflectance spectra. T ASAE 48(6): 2379-2387.

Noh H, Zhang Q, Han S, Shin B, Reum D, 2005. Dynamic calibration and image segmentation methods for multispectral imaging crop nitrogen deficiency sensors. T ASAE 48(1): 393-401.

Nuske S, Achar S, Bates T, Narasimhan S, Singh S, 2011. Yield estimation in vineyards by visual grape detection. Proc 2011 IEEE/RSJ Int Conf on Intelligent Robots and Systems, San Francisco, CA (USA), Sept 25-30.

Otsu N, 1979. A threshold selection method from gray-level histogram. IEEE T Syst Man Cyb 9: 62-66.

Peuelas J, Gamon JA, Fredeena AL, Merino J, Fielda CB, 1994. Reflectance indices associated with physiological changes in nitrogen-and water-limited sunflower leaves. Remote Sens Environ 48(2): 135-146.

Praat J, Bollen F, Irie K, 2004. New approaches to the management of vineyard variability in New Zealand. Proc 12th Tech Conf on Australian Wine Industry, managing vineyard variation (precision viticulture). Aust Wine Ind, Melbourne (Australia), Jun 24-29. pp: 24-30.

Ramalingam N, Ling PP, Derksen RC, 2003. Dynamic segmentation for automatic spray deposits analysis on uneven leaf surfaces. T ASAE 46(3): 893-900.

Stamatiadis S, Taskos D, Tsadilas C, Christofides C, Tsadila E, Schepers JS, 2006. Relation of ground-sensor canopy reflectance to biomass production and grape color in two Merlot vineyards. Am J Enol Viticult 57(4): 415-422.

Tardguila J, Barragn F, Yanguas R, Diago MP, 2008. Estimacin de la variabilidad del vigor del viedo a travs de un sensor ptico lateral terrestre. Aplicacin en la viticultura de precisin. VI World Wine Forum, Logroo (Spain). April 23-25. 7 pp. [In Spanish].

Tucker CJ, 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 2: 127-150.

Verhoeven GJ, Schmitt, KD, 2010. An attempt to push back frontiers-digital near-ultraviolet aerial archaeology. J Archaeol Sci 37: 833-845.

Weekley JG, 2007. Multispectral imaging techniques for monitoring vegetative growth and health. Master thesis. Virginia Polytech Inst Stat Univ, Blacksburg, VA, USA. 45 pp.

DOI: 10.5424/sjar/2012103-508-11