A shovel-omics facelift: exploring inexpensive and simple root phenotyping techniques in peanut

Authors: BUCIOR, ERIKA - North Carolina State University; Sorensen, Ronald

Submitted to: Peanut Science
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/25/2025
Publication Date: 8/20/2025
Citation: Bucior, E.R., Sorensen, R.B. 2025. A shovel-omics facelift: exploring inexpensive and simple root phenotyping techniques in peanut. Peanut Science. 52(1)106-114. https://doi.org/10.3146/0095-3679-52.1-PS1654.
DOI: https://doi.org/10.3146/0095-3679-52.1-PS1654

Interpretive Summary: This manuscript documents the design, materials, assembly, soil filing, and washing of peanut roots in a box used to characterize peanut roots. A wooden box is assembled out of plywood and 2 inch by 6 inch boards to make a box that is open on each end (6 inches thick, 2 feet wide and 4 feet tall). The box is then filled with soil and either nails or screws are inserted into the back plate of the box. Peanuts are planted and grown in the soil. When the peanuts are mature, the box is positioned almost horizontal (slight pitch), the face plate of the box is removed exposing the roots and soil. The soil is washed away leaving the roots hanging on the nails. The roots are then characterized using photography, AI software, and validated with root-scanning techniques. Root metrics, such as depth, branching frequency, and surface area, were successfully captured for two peanut cultivars, Georgia 06G and Georgia 18RU. Data analysis using RhizoVision was straightforward and accurately matched the ground truth data that we collected from the physical root systems with only about a 4% error rate for GA06G and 5.2% error in Ga18RU. It quickly provided us with morphology metrics such as root length, surface area, and volume; architecture metrics such as number of root tips, and number of root crossings, as well as color-based metrics which can be used to infer root health. This method proved cost-effective, easy to implement, and adaptable to large-scale applications. Additionally, it offers significant potential for breeding, agronomy, and extension research, particularly in studies related to drought resilience, nutrient acquisition, and soil health. The root box method serves as an accessible tool for researchers, agronomists, and growers, providing critical insights into root architecture that can inform crop management strategies and improve agricultural sustainability.

Technical Abstract: Accurately characterizing root systems in mature, field-grown crops presents a significant challenge due to the complexity of root architecture and the limitations of existing phenotyping techniques. While recent technological advancements, such as in-situ photographing, have improved root assessment, widely accessible and cost-effective methods remain scarce. Root phenotyping in peanut is typically limited to terminal excavation or expensive, spatially constrained imaging systems. In this exploratory methods paper, we describe a simplified, low-cost approach—referred to as the 'root box method'—to visualize and characterize root architecture in peanut under controlled conditions. We document the construction and use of a box system made from widely available materials, enabling manual root washing, high-contrast imaging, and basic image-based phenotyping. We compare root metrics obtained with this system to those generated by a commercial root scanner to explore its utility as an accessible alternative for small-scale or early-stage research. While the method has clear limitations, it offers a practical starting point for observing root architectural variation in peanut genotypes. Our goal is to provide a transparent evaluation of this approach to support broader participation in root research and tool development in resource-limited contexts. Additionally, it offers significant potential for breeding, agronomy, and extension research, particularly in studies related to drought resilience, nutrient acquisition, and soil health. The root box method serves as an accessible tool for researchers, agronomists, and growers, providing critical insights into root architecture that can inform crop management strategies and improve agricultural sustainability.