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

Abstract

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.

Grapevine vigour: a critical factor driving Trunk Disease expression

“…In contrast with perennial inner symptoms, the external incidence of EBDA in vineyards is highly variable (Fernandez et al, 2024), depending on the year, the cultivar, the age of the vineyard (Etienne et al, 2024;Gastou et al, 2024;Grosman & Doublet, 2012), the pruning type and training management (Dal et al, 2008;Kraus et al, 2022;Lecomte et al, 2019;Travadon et al, 2016), and the climate (Bortolami et al, 2021;Calzarano et al, 2018;Larignon, 2009Larignon, , 2020Lecomte et al, 2023;Marchi et al, 2006;Surico et al, 2000). There is a big challenge in understanding what determines disease symptom expression, to try to mitigate the impact of the disease for growers.…”