Leiva Fernanda, Zakieh Mustafa, Alamrani Marwan, Dhakal Rishap, Henriksson Tina, Singh Pawan Kumar, Chawade Aakash
Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden.
Lantmännen Lantbruk, Svalöv, Sweden.
Front Plant Sci. 2022 Oct 18;13:1010249. doi: 10.3389/fpls.2022.1010249. eCollection 2022.
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 = 0.52 compared with SmartGrain for which = 0.30 for all morphological traits. However, the results combined from both methods showed the greatest prediction performance of = 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.
赤霉病是一种对小麦生产造成经济损失的重要病害，因此对小麦产量构成重大威胁。多项研究评估了利用感染病害的籽粒通过图像分析方法预测赤霉病的有效性；然而，考虑到成本与效益、分辨率和准确性之间的关系，很少有研究关注最终应用。大规模样本的赤霉病抗性常规筛选仍依赖于低通量的目视检查。本研究旨在通过评估冬小麦的16个种子形态特征来预测赤霉病，比较两种成本效益型种子图像分析方法的性能，即免费软件“SmartGrain”和全自动商用仪器“Cgrain Value™”。对一组经目视评估病害严重程度的赤霉病种子进行了分析。数据集由432个温室接种的冬小麦基因型组成。将每种方法的预测结果，以及两种方法结果合并后的预测结果，与病害目视评分进行比较。结果表明，对于所有形态特征，“Cgrain Value™”的预测准确率更高，为 = 0.52，而“SmartGrain”的预测准确率为 = 0.30。然而，两种方法的结果合并后显示出最大的预测性能，为 = 0.58。此外，与其他特征相比，形态特征的一个子部分，即宽度、长度、厚度和颜色特征，与目视评分的相关性更高。总体而言，两种方法都与目视评分相关。本研究表明，这些经济实惠的成像方法可有效预测种子中的赤霉病，并使我们能够区分种子形态的微小差异，从而实现对无病种子/籽粒的精确性能选择。
