%0 Journal Article
%T End-to-end multimodal 3D imaging and machine learning workflow for non-destructive phenotyping of grapevine trunk internal structure
%+ Géno-vigne® (UMT Géno-vigne®)
%+ Diversité, Adaptation et Amélioration de la Vigne [AGAP] (DAAV)
%+ Tridilogy SARL, Saint-Gély du Fesc
%+ Plateforme d'histocytologie et d'imagerie cellulaire végétale (PHIV)
%+ Département Systèmes Biologiques (Cirad-BIOS)
%+ Comité Interprofessionnel du Vin de Champagne (CIVC)
%+ Institut Français de la Vigne et du Vin (IFV)
%+ Université de Montpellier (UM)
%+ Laboratoire Charles Coulomb (L2C)
%A Fernandez, Romain
%A Le Cunff, Loïc
%A Mérigeaud, Samuel
%A Verdeil, Jean‐Luc
%A Perry, Julie
%A Larignon, Philippe
%A Spilmont, Anne-Sophie
%A Chatelet, Philippe
%A Cardoso, Maïda
%A Goze-Bac, Christophe
%A Moisy, Cédric
%Z This work was supported by the French Ministry of Agriculture and Food, France AgriMer, the Comite National des Interprofessions des Vins d'appellation d'origine et d'indication geographique (CNIV), and the Institut Francais de la Vigne et du Vin (IFV) within VITIMAGE and VITIMAGE-2024 projects (program Plan National Deperissement du Vignoble); and by Agropolis fondation-APLIM Etendard project (contract 1504-005). Imaging acquisitions were performed at Tridilogy, Groupe CRP-Imaneo; and the MRI platform member of the national infrastructure France-BioImaging supported by the French National Research Agency << Investments for the Future >> (ANR-10-INBS-04), and of the Labex CEMEB (ANR-10-LABX-0004) and NUMEV (ANR-10- LABX-0020).
%Z The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. The extension of the Trainable Segmentation plugin is open-source, and available as a fork of Trainable Segmentation on GitHub: https://github.com/Rocsg/Trainable_Segmentation/tree/Hyperweka.
%< avec comité de lecture
%@ 2045-2322
%J Scientific Reports
%I Nature Publishing Group
%V 14
%P 5033
%8 2024-02-29
%D 2024
%R 10.1038/s41598-024-55186-3
%M 38424155
%K 3D imaging
%K Image processing
%K MRI
%K X-ray CT
%K Plant phenotyping
%K Plant diseases
%K Trunk diseases
%K Vitis vinifera L.
%Z Life Sciences [q-bio]/Biochemistry, Molecular Biology/Genomics [q-bio.GN]
%Z Computer Science [cs]/Bioinformatics [q-bio.QM]
%Z Life Sciences [q-bio]/Agricultural sciences/AgronomyJournal articles
%X Quantifying healthy and degraded inner tissues in plants is of great interest in agronomy, for example, to assess plant health and quality and monitor physiological traits or diseases. However, detecting functional and degraded plant tissues in-vivo without harming the plant is extremely challenging. New solutions are needed in ligneous and perennial species, for which the sustainability of plantations is crucial. To tackle this challenge, we developed a novel approach based on multimodal 3D imaging and artificial intelligence-based image processing that allowed a non-destructive diagnosis of inner tissues in living plants. The method was successfully applied to the grapevine ( Vitis vinifera L.). Vineyard’s sustainability is threatened by trunk diseases, while the sanitary status of vines cannot be ascertained without injuring the plants. By combining MRI and X-ray CT 3D imaging with an automatic voxel classification, we could discriminate intact, degraded, and white rot tissues with a mean global accuracy of over 91%. Each imaging modality contribution to tissue detection was evaluated, and we identified quantitative structural and physiological markers characterizing wood degradation steps. The combined study of inner tissue distribution versus external foliar symptom history demonstrated that white rot and intact tissue contents are key measurements in evaluating vines’ sanitary status. We finally proposed a model for an accurate trunk disease diagnosis in grapevine. This work opens new routes for precision agriculture and in-situ monitoring of tissue quality and plant health across plant species.
%G English
%2 https://hal.inrae.fr/hal-04501077/document
%2 https://hal.inrae.fr/hal-04501077/file/Fernandez_et_al_SciRep_2024_Imaging_4D_vines.pdf
%L hal-04501077
%U https://hal.inrae.fr/hal-04501077
%~ CIRAD
%~ CNRS
%~ L2C
%~ GIP-BE
%~ AGREENIUM
%~ UNIV-MONTPELLIER
%~ INSTITUT-AGRO-MONTPELLIER
%~ INRAE
%~ INRAEOCCITANIEMONTPELLIER
%~ ANR
%~ AGAP
%~ IPSIM
%~ UM-2015-2021
%~ UM-EPE
%~ RESEAU-EAU
%~ INSTITUT-AGRO
%~ BIOLOGIE_ET_AMELIORATION_DES_PLANTES