Assessment of flower number per inflorescence in grapevine by image analysis under field conditions

Journal of the Science of Food and Agriculture

Volumn 94, Issue 10, 2014, Pages 1981-1987

  Diago, Maria P ^a   Sanz Garcia, Andres ^a   Millan, Borja ^a   Blasco, Jose ^b   Tardaguila, Javier ^a  

^a UNIVERSITY OF LA RIOJA   (Spain)

^b INSTITUTO VALENCIANO DE INVESTIGACIONES AGRARIAS IVIA   (Spain)

Author keywords

Berry number per cluster; Flowering; Fruit set; Non invasive technologies; Vineyard; Vitis vinifera L

Indexed keywords

VITACEAE; VITIS; VITIS VINIFERA;

ALGORITHM; FLOWER; FRUIT; GROWTH, DEVELOPMENT AND AGING; IMAGE PROCESSING; INFLORESCENCE; PROCEDURES; SPECIES DIFFERENCE; VITIS;

ALGORITHMS; FLOWERS; FRUIT; IMAGE PROCESSING, COMPUTER-ASSISTED; INFLORESCENCE; SPECIES SPECIFICITY; VITIS;

EID: 84901924023     PISSN: 00225142     EISSN: 10970010     Source Type: Journal    
DOI: 10.1002/jsfa.6512     Document Type: Article

References (29)

1. May P, Flowering and Fruitset in Grapevines. Lythrum Press, Adelaide (2004).
2. Matthews MA and Nuzzo V, Berry size and yield paradigms on grapes and wines quality. Acta Hortic 754:423-436 (2007).
3. Dry PR, Longbottom ML, McLoughlin S, Johnson TE and Collins C, Classification of reproductive performance of ten winegrape varieties. Aust J Grape Wine Res 16:47-55 (2010).
4. Galet P, Precis De Viticulture. Dehan, Montpellier (1983).
5. Carbonneau A, Deloire A and Jaillard B, La Vigne: Physiologie, Terroir, Culture. Éditions Dunod, Paris (2007).
6. Coombe BG, The effect of removing leaves, flowers, and shoot tips on fruit-set in Vitis vinifera L. J Hortic Sci 37:1-15 (1962).
7. Friend AP and Trought MCT, Delayed winter spur-pruning in New Zealand can alter yield components of Merlot grapevines. Aust J Grape Wine Res 13:157-164 (2007).
8. Skene KGM, A comparison of the effects of 'cycocel' and tipping on fruit set in Vitis vinifera L. Aust J Biol Sci 22:1305-1311 (1969).
9. Collins C and Dry PR, Response of fruitset and other yield components to shoot topping and 2-chlorethyltrimethyl-ammonium chloride application. Aust J Grape Wine Res 15:256-267 (2009).
10. Poni S, Casalini L, Bernizzoni F, Civardi S and Intrieri C, Effects of early defoliation on shoot photosynthesis, yield components, and grape composition. Am J Enol Vitic 57:397-407 (2006).
11. Brown K, Jackson DI and Steans GF, Effects of Chlormequat, girdling, and tipping on berry set in Vitis vinifera L. Am J Enol Vitic 39:91-94 (1988).
12. Coombe BG, Fruit set in grape vines: the mechanism of the CCC effect. J Hortic Sci 45 415-425 (1970).
13. Christensen P, Timing of zinc foliar sprays. I. Effects of application intervals preceding and during the bloom and fruit-set stages. II. Effects of day vs. night application. Am J Enol Vitic 31:53-59 (1980).
14. Longbottom ML, Dry PR and Sedgley M, A research note on the occurrence of 'star' flowers in grapevines: observations during the 2003-2004 growing season. Aust J Grape Wine Res 10:199-202 (2004).
15. May P, From bud to berry, with special reference to inflorescence and bunch morphology in Vitis vinifera L. Aust J Grape Wine Res 6:82-98 (2000).
16. Keller M, Kummer M and Vasconcelos MC, Reproductive growth of grapevines in response to nitrogen supply and rootstock. Aust J Grape Wine Res 7:12-18 (2001).
17. Cubero S, Aleixos N, Moltó E, Gómez-Sanchis J and Blasco J, Advances in machine vision applications for automatic inspection and quality evaluation of fruits and vegetables. Food Bioprocess Tech 4:487-504 (2011).
18. Ji W, Zhao D, Cheng F, Xu B, Zhang Y and Wang J, Automatic recognition vision system guided for apple harvesting robot. Comput Electr Eng 38:1186-1195 (2012).
19. Lorente D, Aleixos N, Gómez-Sanchis J, Cubero S, García-Navarrete OL and Blasco J, Recent advances and applications of hyperspectral imaging for fruit and vegetable quality assessment. Food Bioprocess Tech 5:1121-1142 (2012).
20. Blasco J, Aleixos N, Cubero S, Gómez-Sanchís J and Moltó E, Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features. Comput Electron Agric 66:1-8 (2009).
21. López-García F, Andreu-García G, Blasco J, Aleixos N and Valiente J-M, Automatic detection of skin defects in citrus fruits using a multivariate image analysis approach. Comput Electron Agric 71:189-197 (2010).
22. Cubero S, Diago MP, Blasco J, Tardaguila J, Millan B and Aleixos N, A new method for pedicel/peduncle detection and size assessment of grapevine berries and other fruits by image analysis. Biosyst Eng 117:62-72 (2014).
23. Dunn GM and Martin SR, Yield prediction from digital image analysis: a technique with potential for vineyard assessments prior to harvest. Aust J Grape Wine Res 10:196-198 (2004).
24. Grossetête M, Berthoumieu Y, Da-Costa JP and Germain C, Early estimation of vineyard yield: site specific counting of berries by using a smartphone, in International Conference of Agricultural Enginweering (CIGR-AgEng2012), Valencia, Spain (2012).
25. Lorenz DH, Eichhorn KW, Bleiholder H, Klose R, Meier U and Weber E, Phenological growth stages of the grapevine (Vitis vinifera L. ssp. vinifera): codes and descriptions according to the extended BBCH scale. Aust J Grape Wine Res 1:100-110 (1995).
26. CIE Colorimetry (2nd edn). Publication 15.2, CIE, Vienna (1986).
27. Soille P, Morphological Image Analysis: Principles and Applications. Springer, Berlin (1999).
28. Witten IH, Frank E and Holmes G, Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington, MA (2011).
29. Nuske S, Achar S, Bates T, Narasimhan S and Singh S, Yield estimation in vineyards by visual grape detection, in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, New York, pp. 2352-2358 (2011).