Laurent Audeguin

Transposable Elements Are a Major Cause of Somatic Polymorphism in Vitis vinifera L.

Through multiple vegetative propagation cycles, clones accumulate mutations in somatic cells that are at the origin of clonal phenotypic diversity in grape. Clonal diversity provided clones such as Cabernet-Sauvignon N°470, Chardonnay N° 548 and Pinot noir N° 777 which all produce wines of superior quality. The economic impact of clonal selection is therefore very high: since approx. 95% of the grapevines produced in French nurseries originate from the French clonal selection. In this study we provide the first broad description of polymorphism in different clones of a single grapevine cultivar, Pinot noir, in the context of vegetative propagation. Genome sequencing was performed using 454 GS-FLX methodology without a priori, in order to identify and quantify for the first time molecular polymorphisms responsible for clonal variability in grapevine. New generation sequencing (NGS) was used to compare a large portion of the genome of three Pinot noir clones selected for their phenotypic differences. Reads obtained with NGS and the sequence of Pinot noir ENTAV-INRA® 115 sequenced by Velasco et al., were aligned on the PN40024 reference sequence. We then searched for molecular polymorphism between clones. Three types of polymorphism (SNPs, Indels, mobile elements) were found but insertion polymorphism generated by mobile elements of many families displayed the highest mutational event with respect to clonal variation. Mobile elements inducing insertion polymorphism in the genome of Pinot noir were identified and classified and a list is presented in this study as potential markers for the study of clonal variation. Among these, the dynamic of four mobile elements with a high polymorphism level were analyzed and insertion polymorphism was confirmed in all the Pinot clones registered in France.

Categorizing plant images at the variety level

Two new datasets were created for the evaluation of plant varieties recognition.An experimental study was conducted with todays best performing techniques.Recognizing rice seeds variety appears to be feasible in controlled environment.Recognizing grape varieties from their leaves is still an open problem.Results show that convolutional neural networks perform the best on such problems. This paper addresses the problem of categorizing plant images at the variety level, i.e. at a finer taxonomic grain than state-of-the-art studies usually working at the species level. It therefore introduces two new evaluation datasets of agro-biodiversity interest, each being related to concrete scenarios on large-scale plant resources. They have been chosen so as to involve very different acquisition protocols and visual patterns in order to evaluate if state-of-the-art image classification techniques can generalize to such specific contexts and avoid the cost of building specific ad-hoc solutions. The first one is a collection of 2071 pictures of loose rice seeds built from 95 accessions kept in a bank of seeds. The second one is a collection of 2037 pictures of grape leaves taken in the fields and belonging to 34 varieties among the most commonly ones used in viticulture. Both datasets exhibit a very low inter-class variability resulting in two challenging fine-grained classification tasks, even for expert human operators. A baseline experimental study was conducted on the two datasets using the two most effective families of classification techniques in the state-of-the-art, i.e. convolutional neural networks on one side and fisher vectors-based discriminant models on the other side. It shows that the achieved classification performance is very different between the two problems. It is actually pretty bad for the grape leaves collection but much better in the case of the rice seeds collection for which the acquisition protocol was much more constrained and the morphological variability more visible. The conclusion is that automatically identifying plant varieties might already be feasible for some specific scenarios and in controlled environments but that it is still an open problem in the general case.