Vineyard leaf roughness characterization by computer vision and cloud computing technics

Thomas Decourselle (1) , Ludovic Journaux (1) , Jean-Claude Simon (2) , Jean-Noël Paoli (2) , Frédéric Cointault (2) , Fan Yang (1) , Johel Miteran (1)

1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]
2 AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement

In the context of vineyard leaf roughness analysis for precision spraying applications, this article deals with its characterization by computer vision and cloud computing techniques. The techniques merge feature extraction, linear or nonlinear dimensionality reduction techniques and several kinds of classification methods. Different combinations are processed and their performances compared in terms of classification error rate, in order to find the best association. However these combinations are hardly processed because of the lack of computing power and the prohibitive time consumption of the algorithms. To overcome these difficulties, we propose a solution: the use of cloud computing, which considerably improves computing power. We conclude on the well performance of vineyard leaf roughness characterization through the combination of features extraction, dimensionality reduction and classification processed in a cloud computing environment.

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]; Environnement et Société

Thomas Decourselle, Ludovic Journaux, Jean-Claude Simon, Jean-Noël Paoli, Frédéric Cointault, et al.. Vineyard leaf roughness characterization by computer vision and cloud computing technics. International Conference on Agricultural Engineering-AgEng 2010, Sep 2010, Clermont-Ferrand, France. pp.309. ⟨hal-00842218⟩