The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples
The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples
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DOI：
10.1186/s13007-020-00591-8
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发表时间：
2020-04-10
期刊：
影响因子：
5.1
通讯作者：
Noble, Scott D.
中科院分区：
文献类型：
--
作者：
Halcro, Keith;McNabb, Kaitlin;Noble, Scott D.
背景对种子视觉和形态特征的定量和定性评估缓慢且不精确，使用手持工具收集时可能会引入偏差。颜色、大小和形状特征可以从正确校准的种子图像中获得。需要新的自动化工具来提高数据采集效率，重点是开发研究工作流程。结果创建了由图像采集和分析软件 (phenoSEED) 支持的便携式成像系统 (BELT)，用于小种子光学分析。扁豆（Lens culinaris L.）表型被用作主要测试案例。将种子加载到系统中，样品中的所有种子都会自动单独成像，以在种子通过成像室时获取顶视图和侧视图。 Python 分析脚本应用了颜色校准并提取了种子颜色、大小和形状的可量化特征。提取扁豆种皮图案以进一步描述种皮。预计该设备的使用将消除操作员偏差，提高性状的获取率，并捕获历史上通过肉眼分析的性状的定性信息。结论与传统技术相比，提高的精度和更高的数据获取率将有助于提取更大的数据集并探索更多的研究问题。所提出的系统可作为学术和非商业用途的开源项目。
BackgroundQuantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools were requested to improve data acquisition efficacy with an emphasis on developing research workflows.ResultsA portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil (Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye.ConclusionsIncreased precision and higher rates of data acquisition compared to traditional techniques will help to extract larger datasets and explore more research questions. The system presented is available as an open-source project for academic and non-commercial use.