End-to-end multimodal 3D imaging and machine learning workflow for non-destructive phenotying of grapevine trunk internal structure

Fernandez Romain, Le Cunff Loïc, Merigeaud Samuel, Verdeil Jean-Luc, Perry Julie, Larignon Philippe, Spilmont Anne-Sophie, Chatelet Philippe, Cardoso Maida, Goze-Bac Christophe, Moisy Cédric. 2024. End-to-end multimodal 3D imaging and machine learning workflow for non-destructive phenotying of grapevine trunk internal structure. Scientific Reports, 14:5033, 14 p.

Url - autres données associées : https://github.com/Rocsg/Trainable_Segmentation/tree/Hyperweka

Résumé : 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.

Mots-clés Agrovoc : Vitis vinifera, maladie des plantes, vigne, apprentissage machine, diagnostic, tissu végétal, phénotype

Mots-clés géographiques Agrovoc : France

Mots-clés libres : Image registration, Multimodal imaging, Vineyard, Grapevine trunk disease, Machine Learning

Classification Agris : H20 - Maladies des plantes
U30 - Méthodes de recherche

Champ stratégique Cirad : CTS 4 (2019-) - Santé des plantes, des animaux et des écosystèmes

Agences de financement hors UE : Ministère de l'Agriculture et de l'Alimentation, France AgriMer, Comité National des Interprofessions des Vins à Appellation d'Origine, Institut Français de la Vigne et du Vin, Agence Nationale de la Recherche

Projets sur financement : (FRA) VITIMAGE, (FRA) VITIMAGE-2024, (FRA) Mediterranean Center for Environment and Biodiversity, (FRA) Digital and Hardware Solutions and Modeling for the Environement and Life Sciences

Auteurs et affiliations

• Fernandez Romain, CIRAD-BIOS-UMR AGAP (FRA)
• Le Cunff Loïc, IFV (FRA)
• Merigeaud Samuel, Tridilogy (FRA)
• Verdeil Jean-Luc, CIRAD-BIOS-UMR AGAP (FRA)
• Perry Julie, CIVC (FRA)
• Larignon Philippe, IFV (FRA)
• Spilmont Anne-Sophie, IFV (FRA)
• Chatelet Philippe, Université de Montpellier (FRA)
• Cardoso Maida, Université de Montpellier (FRA)
• Goze-Bac Christophe, Université de Montpellier (FRA)
• Moisy Cédric, IFV (FRA) - auteur correspondant

Source : Cirad-Agritrop (https://agritrop.cirad.fr/609641/)

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