AUTHOR=Leiva Fernanda , Zakieh Mustafa , Alamrani Marwan , Dhakal Rishap , Henriksson Tina , Singh Pawan Kumar , Chawade Aakash 

TITLE=Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2022.1010249

DOI=10.3389/fpls.2022.1010249

ISSN=1664-462X

ABSTRACT=Fusarium head blight (FHB) is an economically important disease affecting wheat that poses a major threat to wheat production. Several studies have evaluated the effectiveness of image analysis methods to predict FHB using disease infected grains; however, few have looked at the final application, considering the relationship between cost-benefit, resolution, and accuracy. Conventional screening of the FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performances of two cost-benefit seed image analysis methods, the free software 'SmartGrain' and the fully automated commercially available instrument 'Cgrain Value™' by assessing sixteen seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which were visually assessed for their severity. The dataset is composed of 432 winter wheat genotypes that were greenhouse-inoculated. The predictions from each method, in addition to the predictions combined from the results of both methods were compared to the disease visual scores. The results showed that Cgrain Value™ had a higher prediction accuracy of R2=0.52 compared to SmartGrain for which R2=0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of R2=0.58. Additionally, a subpart of morphological traits, namely: width, length, thickness, and color features showed a higher correlation with the visual scores compared to the other traits. Overall, both methods were related to the visual scores. This study shows that these affordable imaging methods could be effective to predict FHB in seeds and enable to distinguish minor differences in seed morphology, which could enable a precise perform selection of disease-free seeds/grains.