期刊
AGRONOMY-BASEL
卷 8, 期 5, 页码 -出版社
MDPI
DOI: 10.3390/agronomy8050063
关键词
imaging; sensing; image processing; coleoptile; root injury; seed; seedling traits; automated trait assessment
资金
- Agriculture and Food Research Initiative of the USDA National Institute of Food and Agriculture [1008828]
- USDA National Institute for Food and Agriculture, Hatch Project [1002864]
- NIFA [690384, 1002864] Funding Source: Federal RePORTER
Image-based evaluation of phenotypic traits has been applied for plant architecture, seed, canopy growth/vigor, and root characterization. However, such applications using computer vision have not been exploited for the purpose of assessing the coleoptile length and herbicide injury in seeds. In this study, high-throughput phenotyping using digital image analysis was applied to evaluate seed/seedling traits. Images of seeds or seedlings were acquired using a commercial digital camera and analyzed using custom-developed image processing algorithms. Results from two case studies demonstrated that it was possible to use image-based high-throughput phenotyping to assess seeds/seedlings. In the seedling evaluation study, using a color-based detection method, image-based and manual coleoptile length were positively and significantly correlated (p < 0.0001) with reasonable accuracy (r = 0.69-0.91). As well, while using a width-and-color-based detection method, the correlation coefficient was also significant (p < 0.0001, r = 0.89). The improvement of the germination protocol designed for imaging will increase the throughput and accuracy of coleoptile detection using image processing methods. In the herbicide study, using image-based features, differences between injured and uninjured seedlings can be detected. In the presence of the treatment differences, such a technique can be applied for non-biased symptom rating.
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