Comparison of leaf surface roughness analysis methods by sensitivity to noise analysis

Abstract

Surface roughness is of great interest in agricultural spraying because it is used to characterise leaf surface wettability to predict the behaviour of droplets on a leaf surface. In recent years, the use of texture analysis to estimate surface roughness has emerged. In this paper we propose to estimate leaf surface roughness by using an optimisation of the Generalized Fourier Descriptors method. This approach is then compared with two other standard methods in the literature, one based on grey level intensity variation and the other on wavelet decomposition. Since roughness has many definitions and each method is calculated differently, we propose a new approach to compare the results based on the sensitivity of each method according to surface roughness variations. These variations were introduced by adding different kinds of noise to the image. Gaussian and salt & pepper noise are added to simulate rapid changes and peak impulses on the surface topography, whereas a Structural noise (sinusoidal signal) is added to simulate depth on the surface topography. The novelty of this contribution is the use of a new approach and procedure for agronomic application (leaf surface roughness). The results obtained are expected to be used to characterise the adhesion mechanisms of liquid droplets on a leaf surface.