ArticleFrontiers in plant science2022
Phenotyping Fusarium head blight through seed morphology characteristics using RGB imaging.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Rapid acquisition of ionomic and morphological data from plant seeds through fast X-ray fluorescence microscopy and computer vision.Plant phenomics (Washington, D.C.) · 2025Article
- CRISPR activation: identifying and using novel genes for plant disease resistance breeding.Frontiers in genome editing · 2025Review
- Controlling plant pests with lasers.Precision agriculture · 2025Review
- A new bacterial consortia for management of Fusarium head blight in wheat.Scientific reports · 2024Article
- ScabyNet, a user-friendly application for detecting common scab in potato tubers using deep learning and morphological traits.Scientific reports · 2024Article
- Image-based phenotyping of seed architectural traits and prediction of seed weight using machine learning models in soybean.Frontiers in plant science · 2023Article
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Authors and funding
7 authors.
Funding
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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
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