Skip to Main content Skip to Navigation
Journal articles

Plant Leaf Roughness Analysis by Texture Classification with Generalized Fourier Descriptors in different Dimensionality Reduction context

Abstract : In the context of plant leaf roughness analysis for precision spraying, this study explores the capability and the performance of some combinations of pattern recognition and computer vision techniques to extract the roughness feature. The techniques merge feature extraction, linear and nonlinear dimensionality reduction techniques, and several kinds of methods of classification. The performance of the methods is evaluated and compared in terms of the error of classification. The results for the characterization of leaf roughness by generalized Fourier descriptors for feature extraction, kernel-based methods such as support vector machines for classification and kernel discriminant analysis for dimensionality reduction were encouraging. These results pave the way to a better understanding of the adhesion mechanisms of droplets on leaves that will help to reduce and improve the application of phytosanitary products and lead to possible modifications of sprayer configurations
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-00783830
Contributor : Johel Miteran <>
Submitted on : Friday, February 1, 2013 - 5:24:51 PM
Last modification on : Friday, July 17, 2020 - 2:54:05 PM

Links full text

Identifiers

Citation

Ludovic Journaux, Jean-Claude Simon, Marie-France Destain, Frédéric Cointault, Johel Miteran, et al.. Plant Leaf Roughness Analysis by Texture Classification with Generalized Fourier Descriptors in different Dimensionality Reduction context. Precision Agriculture, Springer Verlag, 2011, 12 (3), pp.345-360. ⟨10.1007/s11119-010-9208-z⟩. ⟨hal-00783830⟩

Share

Metrics

Record views

451