Rachid Sabre

ROUGHNESS EVALUATION OF VINE LEAF BY IMAGE PROCESSING

The study of leaf surface roughness is very important in the domain of precision spraying. It is one of the parameters that allow to reduce costs and losses of phytosanitary prod- ucts and to improve the spray accuracy. Moreover, the leaf roughness is related to adhesion mechanisms of liquid on a surface. It can be used to define leaf nature surface (hy- drophilic/hydrophobic). The main goal of this study is thus to estimate and to follow the evolution of leaf roughness using image processing and computer vision. The develop- ment and application of computer vision for measurement of surface leaf roughness using artificial neural networks will be described. The system for image acquisition of leaf surface consists of scanning electron microscope (SEM). The images of leaf surface are captured and analyzed to estimate the optical roughness. 2-D Fast Fourier Trans- form (FFT) algorithm and Co-occurrence Matrix are used for texture analysis. A multilayer perceptron (MLP) neural network is used to model and predict the optical roughness values.

Evolutionary spectrum for random field and missing observations

There are innumerable situations where the data observed from a non-stationary random field are collected with missing values. In this work a consistent estimate of the evolutionary spectral density is given where some observations are randomly missing.