ROUGHNESS EVALUATION OF VINE LEAF BY IMAGE PROCESSING

摘要

The study of leaf surface roughness is very important in therndomain of precision spraying. It is one of the parametersrnthat allow to reduce costs and losses of phytosanitary productsrnand to improve the spray accuracy. Moreover, the leafrnroughness is related to adhesion mechanisms of liquid onrna surface. It can be used to define leaf nature surface (hydrophilic/rnhydrophobic). The main goal of this study is thusrnto estimate and to follow the evolution of leaf roughnessrnusing image processing and computer vision. The developmentrnand application of computer vision for measurementrnof surface leaf roughness using artificial neural networksrnwill be described. The system for image acquisition of leafrnsurface consists of scanning electron microscope (SEM).rnThe images of leaf surface are captured and analyzed tornestimate the optical roughness. 2-D Fast Fourier Transformrn(FFT) algorithm and Co-occurrence Matrix are usedrnfor texture analysis. A multilayer perceptron (MLP) neuralrnnetwork is used to model and predict the optical roughnessrnvalues.