Grapevine flower estimation by applying artificial vision techniques on images with uncontrolled scene and multi-model analysis

Autor: Aquino Martín, ArturoMillán Prior, Borja; Gutiérrez, S.; Tardáguila Laso, Javier

Tipo de documento: Artículo de revista

Revista: Computers and Electronics in Agriculture. ISSN: 0168-1699. Año: 2015. Volumen: 119. Páginas: 92-104.

JCR (datos correspondientes al año 2014):
Science  Área: AGRICULTURE, MULTIDISCIPLINARY  Quartil: Q1  Lugar área: 06/56  F. impacto: 1,761 
Science  Área: COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS  Quartil: Q2  Lugar área: 06/56  F. impacto: 1,761 

SCIMAGO (datos correspondientes al año 2014):
,895  SNIP: 1,997 



  • Burt, P.J., Fast filter transforms for image processing (1981) Comput. Graph. Image Process., 16, pp. 20-51
  • Carbonneau, A., Deloire, A., Jaillard, B., (2007) La Vigne: Physiologie, Terroir, Culture, , Éditions Dunod, Paris
  • Coombe, B.G., Adoption of a system for identifying grapevine growth stages (1995) Aust. J. Grape Wine Res., 1, pp. 104-110
  • Diago, M.P., Correa, C., Millán, B., Barreiro, P., Valero, C., Tardáguila, J., Grapevine's yield and leaf area estimation using supervised classification methodology on RGB images taken under field conditions (2012) Sensors, 12, pp. 16988-17006
  • Diago, M.P., Sanz-Garcia, A., Millan, B., Blasco, J., Tardaguila, J., Assessment of flower number per inflorescence in grapevine by image analysis under field conditions (2014) J. Sci. Food Agric., 94, pp. 1981-1987
  • Dry, P.R., Longbottom, M.L., McLoughlin, S., Johnson, T.E., Collins, C., Classification of reproductive performance of ten winegrape varieties (2010) Aust. J. Grape Wine Res., 16, pp. 47-55
  • Dunn, G.M., Martin, S.R., Yield prediction from digital image analysis: a technique with potential for vineyard assessments prior to harvest (2004) Aust. J. Grape Wine Res., 10, pp. 196-198
  • Font, D., Pallejà, T., Tresanchez, M., Teixidó, M., Martinez, D., Moreno, J., Palacín, J., Counting red grapes in vineyards by detecting specular spherical reflection peaks in RGB images obtained at night with artificial illumination (2014) Comput. Electron. Agric., 108, pp. 105-111
  • Galet, P., (1983) Precis De Viticulture, , Dehan, Montpellier
  • Keller, M., Kummer, M., Vasconcelos, M.C., Reproductive growth of grapevines in response to nitrogen supply and rootstock (2001) Aust. J. Grape Wine Res., 7, pp. 12-18
  • May, P., From bud to berry, with special reference to inflorescence and bunch morphology in Vitis vinifera L. (2000) Aust. J. Grape Wine Res., 6, pp. 82-98
  • May, P., (2004) Flowering and Fruitset in Grapevines, , Lythrum Press, Adelaide
  • Nuske, S., Achar, S., Bates, T., Narasimhan, S., Singh, S., Yield estimation in vineyards by visual grape detection (2011) 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2352-2358. , E.E.U.U., New York
  • Nuske, S., Wilshusen, K., Achar, S., Yoder, L., Narasimhan, S., Singh, S., Automated visual yield estimation in vineyards (2014) J. Field Robotics, 31 (5), pp. 837-860
  • Otsu, N., A threshold selection method from gray-scale histogram (1978) IEEE Trans. Syst. Man Cybern., 8, pp. 62-66
  • Poni, S., Casalini, L., Bernizzoni, F., Civardi, S., Intrieri, C., Effects of early defoliation on shoot photosynthesis, yield components, and grape composition (2006) Am. J. Enol. Vitic., 57, pp. 397-407
  • Roscher, R., Herzog, K., Kunkel, A., Kicherer, A., Töpfer, R., Förstner, W., Automated image analysis framework for high-throughput determination of grapevine berry sizes using conditional random fields (2014) Comput. Electron. Agric., 100, pp. 148-158
  • Serra, J., (1982) Image Analysis and Mathematical Morphology, 1. , Academic, London, U.K
  • Soille, P., (2004) Morphological Image Analysis - Principles and Applications, , Springer-Verlag, Berlin, Germany