Effects of major vein blockage and aquaporin inhibition on leaf hydraulics and stomatal conductance.

The density and architecture of leaf veins determine the network and efficiency of water transport within laminae and resultant leaf gas exchange and vary widely among plant species. Leaf hydraulic conductance ( Kleaf) can be regulated by vein architecture in conjunction with the water channel protein aquaporin. However, our understanding of how leaf veins and aquaporins affect leaf hydraulics and stomatal conductance ( gs) remains poor. By inducing blockage of the major veins and inhibition of aquaporin activity using HgCl2, we examined the effects of major veins and aquaporins on Kleaf and gs in species with different venation types. A vine species, with thick first-order veins and low vein density, displayed a rapidly declined gs with high leaf water potential in response to vein blockage and a greatly reduced Kleaf and gs in response to aquaporin inhibition, suggesting that leaf aquaporins are involved in isohydric/anisohydric stomatal behaviour. Across species, the decline in Kleaf and gs due to aquaporin inhibition increased linearly with decreasing major vein density, possibly indicating that a trade-off function between vein architecture (apoplastic pathway) and aquaporin activity (cell-to-cell pathway) affects leaf hydraulics.

Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components.

BACKGROUND: Classification and phenotype identification of lettuce leaves urgently require fine quantification of their multi-semantic traits. Different components of lettuce leaves undertake specific physiological functions and can be quantitatively described and interpreted using their observable properties. In particular, petiole and veins determine mechanical support and material transport performance of leaves, while other components may be closely related to photosynthesis. Currently, lettuce leaf phenotyping does not accurately differentiate leaf components, and there is no comparative evaluation for positive-back of the same lettuce leaf. In addition, a few traits of leaf components can be measured manually, but it is time-consuming, laborious, and inaccurate. Although several studies have been on image-based phenotyping of leaves, there is still a lack of robust methods to extract and validate multi-semantic traits of large-scale lettuce leaves automatically. RESULTS: In this study, we developed an automated phenotyping pipeline to recognize the components of detached lettuce leaves and calculate multi-semantic traits for phenotype identification. Six semantic segmentation models were constructed to extract leaf components from visible images of lettuce leaves. And then, the leaf normalization technique was used to rotate and scale different leaf sizes to the "size-free" space for consistent leaf phenotyping. A novel lamina-based approach was also utilized to determine the petiole, first-order vein, and second-order veins. The proposed pipeline contributed 30 geometry-, 20 venation-, and 216 color-based traits to characterize each lettuce leaf. Eleven manually measured traits were evaluated and demonstrated high correlations with computation results. Further, positive-back images of leaves were used to verify the accuracy of the proposed method and evaluate the trait differences. CONCLUSIONS: The proposed method lays an effective strategy for quantitative analysis of detached lettuce leaves' fine structure and components. Geometry, color, and vein traits of lettuce leaf and its components can be comprehensively utilized for phenotype identification and breeding of lettuce. This study provides valuable perspectives for developing automated high-throughput phenotyping application of lettuce leaves and the improvement of agronomic traits such as effective photosynthetic area and vein configuration.