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Phenotyping wheat structural traits from millimetric resolution RGB imagery in field conditions

Abstract : Genetic progress is one of the major leverage used to increase food production and satisfy the needs for the increasing human population under global change issues. Selecting or creating the optimal cultivar for a given location is quite challenging considering the very large spatial and temporal variability of the environmental conditions. Field phenotyping, i.e. the quantitative monitoring of crop state variables and canopy functioning, was recognized as the bottleneck to accelerate genetic progress and increase crop yield. This multidisciplinary study develops statistical and image processing methods to estimate the several structural traits of wheat to be applied to crop breeding. Further, this thesis was undertaken in the context of rapid hardware and software technological advancements illustrated by the increasing accessibility to UAV (Unmanned Aerial Vehicle) and UGV (Unmanned Ground Vehicle) platforms, the decreasing cost of processing units (GPUs, cloud computing) and the boom in the development of deep learning algorithms. This manuscript is divided into five chapters: The first chapter introduces the motivation behind the study as well as the current needs for high throughput phenotyping. A state of the art on phenotyping is also achieved by drawing attention to image processing methods and convolutional neural networks. The second chapter presents the development of methodologies for estimating the crop height. The feasibility of two main technologies and platforms were compared and proven: LiDAR mounted on a UGV and RGB (Red Green Blue) images acquired by a UAV. The next two chapters address the problem of estimating the density of wheat ears and stems from spatial high-resolution images. The results show the potential and limitations of deep learning for this application. Emphasis is also put on the study of the different possible acquisition configurations and the throughput of the method. The last chapter summarizes the pipelines developed and draws different perspectives of high throughput phenotyping to replace or supplement in-situ measurements as well as the improvement facilitated by the methods developed.
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Submitted on : Wednesday, March 4, 2020 - 9:25:09 AM
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  • HAL Id : tel-02498009, version 1



Simon Madec. Phenotyping wheat structural traits from millimetric resolution RGB imagery in field conditions. Agricultural sciences. Université d'Avignon, 2019. English. ⟨NNT : 2019AVIG0707⟩. ⟨tel-02498009⟩



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