Kim JaeYoung, Lee Chaewon, Park JiEun, Kim Nyunhee, Kim Song-Lim, Baek JeongHo, Chung Yong-Suk, Kim Kyunghwan
Gene Engineering Division, Department of Agricultural Biotechnology, National Institute of Agricultural Science, Jeonju-si 55365, Republic of Korea.
Crop Cultivation & Environment Research Division, National Institute of Crop Science, Suwon-si 16613, Republic of Korea.
Plants (Basel). 2023 Jun 15;12(12):2331. doi: 10.3390/plants12122331.
Drought is being annually exacerbated by recent global warming, leading to crucial damage of crop growth and final yields. Soybean, one of the most consumed crops worldwide, has also been affected in the process. The development of a resistant cultivar is required to solve this problem, which is considered the most efficient method for crop producers. To accelerate breeding cycles, genetic engineering and high-throughput phenotyping technologies have replaced conventional breeding methods. However, the current novel phenotyping method still needs to be optimized by species and varieties. Therefore, we aimed to assess the most appropriate and effective phenotypes for evaluating drought stress by applying a high-throughput image-based method on the nested association mapping (NAM) population of soybeans. The acquired image-based traits from the phenotyping platform were divided into three large categories-area, boundary, and color-and demonstrated an aspect for each characteristic. Analysis on categorized traits interpreted stress responses in morphological and physiological changes. The evaluation of drought stress regardless of varieties was possible by combining various image-based traits. We might suggest that a combination of image-based traits obtained using computer vision can be more efficient than using only one trait for the precision agriculture.
近年来的全球变暖每年都在加剧干旱，导致作物生长和最终产量受到严重损害。大豆是全球消费最多的作物之一，在这一过程中也受到了影响。培育抗性品种是解决这一问题的必要手段，这被认为是对作物生产者最有效的方法。为了加快育种周期，基因工程和高通量表型分析技术已经取代了传统育种方法。然而，目前的新型表型分析方法仍需根据物种和品种进行优化。因此，我们旨在通过对大豆嵌套关联作图（NAM）群体应用基于图像的高通量方法，评估用于评价干旱胁迫的最合适、最有效的表型。从表型分析平台获取的基于图像的性状分为三大类——面积、边界和颜色，并展示了每个特征的一个方面。对分类性状的分析解释了形态和生理变化中的胁迫反应。通过结合各种基于图像的性状，可以对不同品种的干旱胁迫进行评估。我们可能会提出，使用计算机视觉获得的基于图像的性状组合比仅使用一个性状在精准农业中更有效。
