Location: Vegetable Crops ResearchTitle: Integrating spectroscopy with potato disease management
|COUTURE, JOHN - Purdue University|
|SINGH, ADITYA - University Of Florida|
|CHARKOWSKI, AMY - Colorado State University|
|GROVES, RUSSELL - University Of Wisconsin|
|TOWNSEND, PHIL - University Of Wisconsin|
Submitted to: Plant Disease
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/26/2018
Publication Date: 5/1/2018
Citation: Couture, J.J., Singh, A., Charkowski, A., Groves, R., Gray, S.M., Bethke, P.C., Townsend, P. 2018. Integrating spectroscopy with potato disease management. Plant Disease. https://doi.org/10.1094/PDIS-01-18-0054-RE.
Interpretive Summary: Spectral phenotyping, an imaging technology based on reflected light, is an effective and efficient method for the non-destructive characterization of plants. We examined the ability of leaf-based spectral phenotyping to 1) detect Potato virus Y (PVY) and physiological effects of the disease in visually asymptomatic plants, 2) classify different strains of PVY, and 3) identify specific potato varieties. The results of this study indicate that spectral phenotyping can be used to identify disease presence before the onset of visual symptoms, estimate specific biochemical and physiological responses to PVY, and discriminate between varieties of potato. These findings could be used by potato seed certification agencies to evaluate the incidence of PVY in seed lots and to identify seed lots that are mislabeled. Furthermore, this approach can be used by researchers who need to have a greater understanding of how PVY affects the physiology of virus infected plants.
Technical Abstract: Spectral phenotyping is an effective and efficient method for the non-destructive characterization of plant biochemical and physiological status. We examined the ability of full range (350-2500 nm), foliar spectral data to 1) detect Potato virus Y (PVY) and physiological effects of the disease in visually asymptomatic plants, 2) classify different strains of PVY, and 3) identify specific potato varieties. Across varieties, foliar spectral profiles of PVY-infected leaves were statistically different (F=96.1, P=<0.001) from non-infected leaves. Partial-least squares discriminate analysis (PLS-DA) accurately classified leaves as PVY-infected (validation kappa=0.73) and the shortwave infrared spectral regions displayed the strongest correlations with infection status. While spectral profiles of different PVY strains were statistically different (F=6.4, P=<0.001), PLS-DA did not classify different strains well (validation kappa = 0.12). Spectroscopic retrievals revealed that PVY infection decreased photosynthetic capacity and increased leaf lignin content. Spectral profiles of potato varieties also differed (F=9.2, P=<0.001); while average spectral classification was high (validation kappa=0.76), the accuracy of classification varied among varieties. Our study expands the current knowledge base by 1) identifying disease presence before the onset of visual symptoms, 2) providing specific biochemical and physiological responses to PVY, and 3) discriminating between multiple varieties within a single plant species.