6533b827fe1ef96bd12858d3

RESEARCH PRODUCT

Vine leaf roughness estimation by image processing

subject

description

International audience; The application of plant protection product has an important role in agricultural production processes. With current pesticides management, a huge amount of them are applied to worldwide orchards. In precision spraying, spray application efficiency depends on the pesticide application method, the phytosanitary product as well as the leaf surface properties. For environmental and economic reasons, the global trend is to reduce the pesticide application rate of the few approved active substances. Under these constraints, one of the challenges is to improve the efficiency of pesticide application. Different parameters can influence pesticide application such as nozzle types, liquid viscosity and leaf surface. In this paper, we focus on the vine leaf surface properties and discrimination between two kinds of vine leaves (Pinot and Chardonnay) in different stages of development for following their roughness growth. This discrimination allows studying the impact of the product behavior, and allows to adjusting the product viscosity and spraying parameters according to the roughness and the stage of vine leaf development. In this context, we propose to explore the performance of combination of Generalized Fourier Descriptor with Kernel Discriminant Analysis methods using neural network. The result appears provide sufficient information to characterize vine leaves.