6533b85efe1ef96bd12bf9c5

RESEARCH PRODUCT

Analyses spectrale et texturale de données haute résolution pour la détection automatique des maladies de la vigne

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‘Flavescence dorée’ is a contagious and incurable disease present on the vine leaves. The DAMAV project (Automatic detection of Vine Diseases) aims to develop a solution for automated detection of vine diseases using a micro-drone. The goal is to offer a turnkey solution for wine growers. This tool will allow the search for potential foci, and then more generally any type of detectable vine disease on the foliage. To enable this diagnosis, the foliage is proposed to be studied using a dedicated high-resolution multispectral camera.The objective of this PhD-thesis in the context of DAMAV is to participate in the design and implementation of a Multi-Spectral (MS) image acquisition system and to develop the image pre-processing algorithms, based on the most relevant spectral and textural characteristics related to ‘Flavescence dorée’.Several grapevine varieties were considered such as red-berried and white-berried ones; furthermore, other diseases than ‘Flavescence dorée’ (FD) such as Esca and ‘Bois noir’ (BN) were also tested under real production conditions. The PhD work was basically performed at a leaf-level scale and involved an acquisition step followed by a data analysis step.Most imaging techniques, even MS, used to detect diseases in field crops or vineyards, operate in the visible electromagnetic radiation range. In DAMAV, it is advised to detect the disease as early as possible. It is therefore necessary to investigate broader information in particular in the infra-red. Reflectance responses of plants leaves can be obtained from short to long wavelengths. These reflectance signatures describe the internal constituents of leaves. This means that the presence of a disease can modify the internal structure of the leaves and hence cause an alteration of its reflectance signature.A spectrometer is used in our study to characterize reflectance responses of leaves in the field. Several samples at different growth stages were used for the tests. To define optimal reflectance features for grapevine disease detection (FD, Esca, BN), a new methodology that designs spectral disease indices based on two dimension reduction techniques, coupled with a classifier, has been developed. The first feature selection technique uses the Genetic Algorithms (GA) and the second one relies on the Successive Projection Algorithm (SPA). The new resulting spectral disease indices outperformed traditional vegetation indices and GA performed in general better than SPA. The features finally chosen can thus be implemented as filters in the MS sensor.In general, the reflectance information was satisfying for finding infections (higher than 90% of accuracy for the best method) but wasn’t enough. Thus, the images acquired with the developed MS device can further be pre-processed by low level techniques based on the calculation of texture parameters injected into a classifier. Several texture processing techniques have been tested but only on colored images. A method that combines many texture features is elaborated, allowing to choose the best ones. We found that the combination of optimal textural information could provide a complementary mean for not only differentiating healthy from infected grapevine leaves (higher than 85% of accuracy), but also for grading the disease severity stages (higher than 73% of accuracy) and for discriminating among diseases (higher than 72% of accuracy). This is in accordance with the hypothesis that a multispectral camera can enable detection and identification of diseases in grapevine fields.