Essential insights into the roots of Salvia miltiorrhiza for medicinal plant breeding

Essential insights into the roots of Salvia miltiorrhiza for medicinal plant breeding

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Workflow for studying the roots of S. miltiorrhiza. credit: Plant phenomics

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Workflow for studying the roots of S. miltiorrhiza. credit: Plant phenomics

Plant phenology, an emerging field that uses image recognition and advanced algorithms, focuses on understanding and measuring plant traits to improve crop breeding. Great strides have been made with the advent of automated systems and machine learning techniques.

However, this field faces challenges in fully capturing the complexity of plant physiology, especially in root system phenotyping. Salvia miltiorrhiza (Danshin), highly appreciated for its medicinal properties and in demand in the market, epitomizes this research void.

Despite advances in understanding its bioactive compounds, there remains a significant gap in broader physiological and phenotypic studies, especially of its roots. Addressing this gap is crucial, as it could revolutionize the breeding and cultivation practices of this and similar medicinal plants.

Plant phenomics He published a research article titled “Phenotyping of Salvia miltiorrhiza roots reveals association between root traits and bioactive components.”

In this study, a comprehensive workflow was used to dissect the complex phenotypic landscape of Salvia miltiorrhiza roots. Using WinRHIZO and RhizoVision Explorer, the research extracted agronomic features from high-resolution scanned images, resulting in 81 parameters across 102 root images.

These parameters, especially total length, surface area and volume, showed a strong linear relationship with actual biomass, indicating their effectiveness in predicting root biomass. In addition, the study used Rootscan for anatomical analysis and RootScape for detailed root system architecture (RSA) classification using a landmark-based approach. This led to the clustering of roots into distinct RSA clusters, which are also characterized by diameter classification and K-means clustering.

Metabolic profiling revealed the distribution of primary active components such as phenolic acids and tanshinones across root tissues. Notably, some metabolites showed significant correlations with specific phenotypic traits, such as size band 4 and gross surface, suggesting that these traits can influence metabolite production.

Furthermore, machine learning algorithms, especially Random Forest (RF) and Gradient Boosting (GB), were used to evaluate the classification accuracy of roots. RF and GB have emerged as more effective models, surpassing other machine learning and deep learning models. Ultimately, the study established a significant linear regression relationship between the content of specific bioactive compounds and digital biomass based on total surface. This result indicates that the production of these compounds can be predicted quantitatively without using conventional chemical methods.

In conclusion, this study established a multidimensional S. miltiorrhiza root phenotyping workflow that successfully predicted biomass and metabolite content from phenotypic traits. In contrast to traditional methods, it integrated different analytical tools for a comprehensive approach, highlighting the need for species-specific software.

The research not only advances our understanding of root traits and their association with bioactive compounds, but also paves the way for future applications in dynamic root modeling, stress response analysis, and cultivation optimization, providing valuable insights for breeding and cultivation strategies.

more information:
Junfeng Chen et al., Phenotyping of Salvia miltiorrhiza roots reveals associations between root traits and bioactive components, Plant phenomics (2023). doi: 10.34133/plantvenomics.0098

Introduction to plant phenomics

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