Abstract
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.
Highlights
With current technological advancements, the application of image analysis is anticipated to grow for practical applications in evaluating crop traits in an accurate and high-throughput manner
One-on-one comparison of coleoptile comparison resulted in r of 0.69 in and
0.77 in the first and second experiment, of coleoptile length comparison resulted in r of
Summary
Computer vision with conventional RGB cameras or cell phones is highly affordable and beneficial in evaluating several phenotypic traits in a quantitative and high-throughput manner.Some of the many applications that have been explored in literature towards phenotyping include: plant architecture [1,2], seed characterization [3,4], canopy growth/vigor [5,6,7,8,9,10,11], and root characterization [12,13,14]. Computer vision with conventional RGB cameras or cell phones is highly affordable and beneficial in evaluating several phenotypic traits in a quantitative and high-throughput manner. Digital image analysis in a controlled environment offers high resolution, high throughput, and the precise evaluation of crop traits of interest comparable with human data collection [15]. With current technological advancements (camera resolution, processing speed, storage, availability of image processing tools, etc.), the application of image analysis is anticipated to grow for practical applications in evaluating crop traits in an accurate and high-throughput manner. Two such applications for digital image-based trait assessment have been evaluated.
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