Effect of Soil Water on GPR Estimation of Bulked Roots, Methods, and Suggestions

Background: Root phenotyping methods are of increasing importance as researchers seek to understand belowground productivity and breeders work to select for root traits. Effective non-destructive root phenotyping methods do not exist for bulked-root and tuber crops such as potato and cassava. Cassava is a tropical crop widely grown by subsistence farmers throughout the tropics and is the fourth most important staple food crop in the world, yet lags in research. It has an extensive growth period sometimes exceeding 12 months. Early maturity is a major goal for breeders, but the ability to select for it is hampered by the lack of non-destructive yield estimation methods. GPR is a tool with potential to aid in bulked root selection, but standard methods have yet to be developed. In this study, we demonstrate good practice in GPR estimation of root mass, which was used as a proxy for cassava root mass, and investigate the effect of soil water content on measurement.Results: Significant correlation between GPR data and daikon root mass was found for three of the five irrigation treatments. Correlation strength improved with increased soil water content and decreased variation of soil water content between plots. Pearson correlation coefficient varied from 0.53 – 0.79.Conclusions: GPR can be used to estimate bulked root mass. Wet soil can improve the predictive quality of GPR data, but water content needs to be homogeneous throughout the study site and period. Determining the optimal soil water content will require further research.

ROOT PHENOTYPING FROM X-RAY COMPUTED TOMOGRAPHY: SKELETON EXTRACTION

Breakthrough imaging technologies are a potential solution to the plant phenotyping bottleneck in marker-assisted breeding and genetic mapping. X-Ray CT (computed tomography) technology is able to acquire the digital twin of root system architecture (RSA), however, advances in computational methods to digitally model spatial disposition of root system networks are urgently required.We extracted the root skeleton of the digital twin based on 3D data from X-ray CT, which is optimized for high-throughput and robust results. Significant root architectural traits such as number, length, growth angle, elongation rate and branching map can be easily extracted from the skeleton. The curve-skeleton extraction is computed based on a constrained Laplacian smoothing algorithm. This skeletal structure drives the registration procedure in temporal series. The experiment was carried out at the Ag Alumni Seed Phenotyping Facility (AAPF) at Purdue University in West Lafayette (IN, USA). Three samples of tomato root at 2 different times and three samples of corn root at 3 different times were scanned. The skeleton is able to accurately match the shape of the RSA based on a visual inspection.The results based on a visual inspection confirm the feasibility of the proposed methodology, providing scalability to a comprehensive analysis to high throughput root phenotyping.