Vineyard leaf roughness characterization by computer vision and cloud computing technics

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.