Evaluation of a Line-Scan X-Ray System for Phenotyping Peanut Smut Resistance Using Faux-Infected Pods
Year: 2025
Type: article
Abstract: Peanut smut, caused by the fungus Thecaphora frezzii, is the most problematic disease in Argentina and threatens peanut production worldwide. A major roadblock to breeding for peanut smut resistance is the current practice of manually opening pods to rate disease, which is slow and labor intensive. To address this problem, a line-scan X-ray digital imaging system was developed and evaluated as a potential alternative to hand opening pods. Since the peanut smut pathogen T. frezzii is not present in the U.S., suitable test phantom pods were developed to emulate infected pods. Faux-infected pods were made by replacing all or one seed with finely ground coffee, corn starch, or corn smut (Mycosarcoma maydis) teliospores as a proxy for T. frezzii teliospores. In addition, one seed from some pods were made to contain an artificial sorus made from corn smut teliospores to mimic T. frezzii-infected pods with low levels of disease severity. Over 3,300 pods representing each of the four major market types were scanned using the X-ray system, and the images were scored by ten raters as being healthy or “unhealthy” (faux-infected pods). Rater accuracy varied from 82-89%, and overall, the ten raters correctly classified 85% of the images. Healthy pods were easier to identify (89.5%) than faux-infected pods (82.6%), but raters had the most difficulty in correctly scoring pods with the artificial sorus (21.2%). Because T. frezzii-infected pods with small/few sori and partially damaged seeds appear to be relatively rare in both resistant and susceptible genotypes, the X-ray system’s inability to generate clear images of artificial sori may not be a significant detraction. These results indicate that the X-ray imaging system has potential for more efficient peanut smut phenotyping; however, the system needs to be tested in Argentina with pods grown in T. frezzii-infested fields.
Source: Peanut Science
Authors: N. Wang, P.R. Weckler, Y. Zhang, A. Errabelly, M. Payton, J. Baldessari, K. Chamberlin, A. Rodriquez, R. Koch Bach, M. Maestro, R. Bennett
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: diamond
OpenAlex ID: W4410806322