Hostname: page-component-8448b6f56d-dnltx Total loading time: 0 Render date: 2024-04-19T07:07:54.726Z Has data issue: false hasContentIssue false
  • English
  • Français

Potential of on-board colour imaging for in-field detection and counting of grape bunches at early fruiting stages

Published online by Cambridge University Press:  01 June 2017

F. Abdelghafour ,
B. Keresztes ,
C. Germain  and
J. P. Da Costa
F. Abdelghafour*
Affiliation:
Univ. Bordeaux, IMS UMR 5218, F-33400 Talence, France, CNRS, IMS UMR 5218, F-33400 Talence, France
B. Keresztes
Affiliation:
Univ. Bordeaux, IMS UMR 5218, F-33400 Talence, France, CNRS, IMS UMR 5218, F-33400 Talence, France
C. Germain
Affiliation:
Univ. Bordeaux, IMS UMR 5218, F-33400 Talence, France, CNRS, IMS UMR 5218, F-33400 Talence, France
J. P. Da Costa
Affiliation:
Univ. Bordeaux, IMS UMR 5218, F-33400 Talence, France, CNRS, IMS UMR 5218, F-33400 Talence, France
*
†E-mail: florent.abdelghafour@ims-bordeaux.fr
Get access

Abstract

In order to enable the wine industry to anticipate in field work and marketing strategies, it is necessary to provide early assessments of vine productivity. The proposed method is designed for the detection and the measurement of grape bunches between the flowering season and the early fruition stages, before ‘groat-size’. The method consists of determining the affiliation of a pixel to a grape cluster based on colorimetric and texture features, using an SVM supervised classifier. The eventual affiliation of the pixels is achieved with an average reliability above 75%, which lets us envision in the near future the possibility of estimating the real number of grape bunches.

Keywords

early detectiongrape bunchesimagerytexture analysissupervised classification
Type
Precision Horticulture and Viticulture
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 505 - 509
Copyright
© The Animal Consortium 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Berenstein, R, Shahar, OB, Shapiro, A and Edan, Y 2010. Grape clusters and foliage detection algorithms for autonomous selective vineyard sprayer. Intelligent Service Robotics 3, 233–243.Google Scholar
Chang, CC, Hsu, CW and Lin, CJ 2000. The analysis of decomposition methods for support vector machines. IEEE Trans. on Neural Networks 11, 1003–1008.Google Scholar
Dunstone, RJ 2002. Final Report for Project DNR 02/02. Winegrape Crop Forecasting ModuleCWRDC: Adelaide, Australia 39.Google Scholar
Eerens, H, Haesen, D, Rembold, F, Urbano, F, Tote, C and Bydekerke, L 2014. Image time series processing for agriculture monitoring. Environmental Modelling & Software 53, 154–162.Google Scholar
Grossetete, M, Berthoumieu, Y, Da Costa, JP, Germain, C and Lavialle, O 2012. Early estimation of vineyard yield: site specific counting of berries by using a smartphone. Conference on Agiculture Engineering, Spain pp.C-1915 .Google Scholar
Haralick, RM 1979. Statistical and structural approaches to texture. Proceedings of the IEEE 67, 786–804.Google Scholar
Illingworth, J and Kittler, J 1987. The Adaptive Hough Transform. PAMI-9 5, 690–698.CrossRefGoogle Scholar
Keresztes, B, Da Costa, JP, Grenier, G, Germain, C and David-Beaulieu, X 2014. A depth and color vision system for the automated detection of grape maturity, in: Equipment for Agriculture and Forestry. Presented at the RHEA, Madrid, Spain.Google Scholar
Liu, S, Marden, S and Whitty, M 2013. Towards Automated Yield Estimation in Viticulture. Proceedings of Australasian Conference on Robotics and Automation. University of New South Wales, Australia.Google Scholar
Lorenz, DH, Eichhorn, KW, Bleiholder, H, Klose, R, Meier, U and Weber, E 1995. Growth Stages of the Grapevine: Phenological growth stages of the grapevine (Vitis vinifera L. ssp. vinifera)—Codes and descriptions according to the extended BBCH scale. Australian Journal of Grape and Wine Research 1, 100–103.Google Scholar
Loy, G and Zelinsky, A 2003. Fast radial symmetry for detecting points of interest. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 959–973.Google Scholar
Rosu, R, Donias, M, Bombrun, L, Said, S, Regniers, O and Da Costa, JP 2015. Structure Tensor Riemannian Statistical Models for CBIR and Classification of Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 55, 248–260.Google Scholar
Nuske, S, Gupta, K, Narasimhan, S and Singh, S 2012. Modeling and Calibrating Visual Yield Estimates in Vineyards. Field and Service Robotics 343–356.Google Scholar
Serrano, E, Ontier, L, Dufourcq, T and Roussel, S 2005. Capteurs aux champs: Estimation de Rendement par le Volume (in-field sensors : yield estimation by volumetry). L’innovation En Viticulture/œnologie. Presented at the ‘l’innovation en viticulture/ œnologie. Station Régionale ITV Midi-Pyréné, Gaillac. 40-45.Google Scholar