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Approche multi-critère pour la caractérisation des adventices

Abstract : The objective of this thesis is to develop a way to detect weeds in a field using multispectral images, in order to determine which weeds should be eliminated during the current crop cycle and more particularly at the early stages. The multi-criteria approach focuses on the spatial arrangement, the spectral signature, the morphology and the texture of the plants located in the plots. This work proposes a method for selecting the best criteria for optimal discrimination for a given setup. Prior to the extraction of these criteria, a set of methods was developed in order to correct the errors of the acquisition device, to precisely detect the vegetation and then to identify within the vegetation the individuals on which the different criteria can be computed. For the individual detection step, it appears that leaf scale is more suitable than plant scale. Vegetation detection and leaf identification are based on deep learning methods capable of processing dense foliage. The introduction of these methods in a usual processing chain constitutes the originality of this manuscript where each part was the subject of an article. Concerning the acquisition device, a method of spectral band registration was developed. Then, new vegetation indices based on artificial intelligence constitute one of the scientific advances of this thesis. As an indication, these indices offer a mIoU of 82.19% when standard indices ceil at 63.93%-73.71%. By extension, a leaf detection method was defined and is based on the detection of their contours, this method seems advantageous on our multispectral data. Finally, the best property pairs were defined for crop/weed discrimination at leaf level, whith classification performances up to 91%.
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Contributor : jehan-antoine vayssade Connect in order to contact the contributor
Submitted on : Friday, June 3, 2022 - 5:00:31 PM
Last modification on : Wednesday, June 8, 2022 - 3:38:04 AM
Long-term archiving on: : Sunday, September 4, 2022 - 7:55:36 PM


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  • HAL Id : tel-03688127, version 1



Jehan-Antoine Vayssade. Approche multi-critère pour la caractérisation des adventices. Intelligence artificielle [cs.AI]. Université Bourgogne Franche-Comté, 2022. Français. ⟨NNT : ⟩. ⟨tel-03688127⟩



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