Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging

Abstract

Fusarium head blight (FHB) is an economically important disease affecting wheat and thus 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 and benefit, resolution, and accuracy. The conventional screening of FHB resistance of large-scale samples is still dependent on low-throughput visual inspections. This study aims to compare the performance of two cost–benefit seed image analysis methods, the free software “SmartGrain” and the fully automated commercially available instrument “Cgrain Value™” by assessing 16 seed morphological traits of winter wheat to predict FHB. The analysis was carried out on a seed set of FHB which was visually assessed as to the 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 with the disease visual scores. The results showed that Cgrain Value™ had a higher prediction accuracy of R2 = 0.52 compared with 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 the morphological traits, namely, width, length, thickness, and color features, showed a higher correlation with the visual scores compared with 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 us to distinguish minor differences in seed morphology, which could lead to a precise performance selection of disease-free seeds/grains.

ScabyNet, a user-friendly application for detecting common scab in potato tubers using deep learning and morphological traits

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Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean

“…SmartGrain (version 1.2), an open-source image analysis software, was downloaded ( http://www.kazusa.or.jp/phenotyping/smartgrain/index.html ) for image processing and analysis ( Tanabata et al., 2012 ; Gürsoy, 2019 ; Leiva et al, 2022 ). The raw images were loaded into the software, and the developer’s recommended procedure was followed for image segmentation, processing, and data mining.…”

Novel Methods for Resistance Breeding in Winter Wheat

“…However, visual screening for FHB resistance is a labour-and timeconsuming process with low reproducibility, and possible subjectivity leading to investigate the use of image analysis methods to evaluate FHBdamaged kernels (FDK) (Maloney et al, 2014). The morphological seed traits, such as colour, thickness, length, and width, can be functional for predicting FHB (Leiva et al, 2022). In this context, image-based methods could be developed to examine their consistency and to predict FHB with the assigned traits in relation to the phenotype-genotype association.…”

“…Summary of a multiple linear regression model utilizing 16 morphological characteristics from Cgrain Value™ and SmartGrain(Leiva et al, 2022). The most significant characteristics concerning Fusarium head blight (FHB) disease infection according to the P-value has an *.…”