AUTHOR=Sharma Prakriti , Thilakarathna Imasha , Fennell Anne 

TITLE=Hyperspectral imaging and artificial intelligence enhance remote phenotyping of grapevine rootstock influence on whole vine photosynthesis

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2024.1409821

DOI=10.3389/fpls.2024.1409821

ISSN=1664-462X

ABSTRACT=Rootstocks are gaining importance in viticulture as a strategy to combat abiotic challenges, as well as, enhancing scion physiology. Photosynthetic parameters such as maximum rate of carboxylation of RuBP (Vcmax) and the maximum rate of electron transport driving RuBP regeneration (Jmax) have been identified as ideal targets for potential influence by rootstock and breeding. However, leaf specific direct measurement of these photosynthetic parameters is time consuming, limiting the information scope and the number of individuals that can be screened. In contrast, hyperspectral imaging using the optical properties of the entire vine provides the potential to predict these physiological traits at canopy level. In this study, estimates of Vcmax and Jmax were assessed using canopy reflectance metrics obtained for wavelengths ranging from 400 to 1000nm integrated through artificial intelligence-based (AI) model algorithms. Prediction models were developed using two growing seasons data, from Vitis hybrid ‘Marquette’ grafted to five commercial rootstocks and homografted to ‘Marquette’. For the six different rootstocks, significant differences in photosynthetic efficiency (Vcmax and Jmax) were noted for both direct and indirect measurements, indicating that rootstock genotype has a significant influence on scion photosynthesis. Evaluation of multiple feature-extraction algorithms indicated the proposed Vitis base model incorporating a 1D-Convolution neural Network (CNN) had the best prediction performance with an R2 of 0.60 for Vcmax and Jmax. Inclusion of weather and chlorophyll parameters slightly improved model performance for both both photosynthetic parameters  Integrated AI and hyperspectral remote sensing provide potential for high-throughput whole vine assessment of photosynthetic performance and selection of rootstock genotypes that confer improved photosynthetic performance potential in the scion.