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Experimental and statistical modeling of relations between morphological characteristics of grapevine and spraying deposits : application to precision agriculture.

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The social demand for a reduction in the use of plant protection products (PPP) is a driver to reconsider the vine protection process. Today agro-chemical doses are expressed as a fixed quantity per unit area of ground in the field. Dose rates are independent of the conditions of application, and this includes the quantity of foliage to be protected. The agro-environmental performance of pesticides application in viticulture exhibits great variability according to the sprayer type and the growth stage, and training of vegetation. The aim of this thesis was to develop models for predicting the quantities and distributions of deposits in grapevines according to the canopy structure and sprayer characteristics. The canopy structure, according to its dimensions and density, was characterised on a set of vine blocks using a proximal on-the-go LiDAR sensor (Light Detection And Ranging). Data about PPP distributions in the different strata of the canopy was acquired on the same set of plots. LiDAR sensors have been shown to be of interest in previous works related to viticulture, but their use is still scarce in commercial perennial crops, primarily due to the lack of robust protocols for processing and interpreting the data. One of the results of the thesis is an automated method for filtering and classifying point clouds from a LiDAR sensor. This makes it easier to estimate vine canopy dimensions (height, thickness) and its apparent density. Importantly, the method requires scanning on only one side (half) of the canopy. The developed method was compared to a non-automated reference method and to classical manual canopy measurements with good agreement. The proposed method was shown to be able to characterise canopy dimensions from LiDAR data in an automated and robust manner throughout the growing season. A second set of results of the thesis deals with the study at the vine scale of the statistical distribution of leaf deposits intercepted onto leaves and artificial targets within the vine canopy. It is the first such high-resolution study of the variability of within canopy spray depositions. The observed distribution of deposits was shown to be highly variable. It follows therefore, that in order to manage the parts of the canopy that are under treated, and more susceptible to pathogens, using the mean of the deposits, even at plant scale, is insufficient information. Precision spraying should be based on the statistical distribution of deposits, not the mean. The thesis proposed a novel multivariate statistical modelling approach to predict the distribution of deposition taking into account the sprayer application technology and the structure of the vegetation evaluated by a LiDAR sensor. The new model was compared with existing univariate models and offered better accuracy and robustness in predicting deposition distributions, especially under evolving and varying canopy sizes. The proposed novel modelling approach is general enough to be applied to other spraying technologies. Finally, a chapter of the thesis is devoted to assess the interest of developing variable rate precision spraying technologies, in terms of PPP savings and their ability to control the phytosanitary risk. The canopy of a vineyard estate was assessed periodically over an entire growing season with a LiDAR sensor. The multivariate statistical models constructed were inverted with the high-resolution canopy information at different dates in order to produce site-specific PPP prescription maps. These prescription maps were interpreted at different decision scales, according to different technological scenarios.
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Article
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Thesis
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