Sun Jian, Ren Zhengwei, Cui Jiale, Tang Chen, Luo Tao, Yang Wanneng, Song Peng
National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, PR China.
Plant Phenomics. 2024 Aug 1;6:0213. doi: 10.34133/plantphenomics.0213. eCollection 2024.
Rice panicle traits serve as critical indicators of both yield potential and germplasm resource quality. However, traditional manual measurements of these traits, which typically involve threshing, are not only laborious and time-consuming but also prone to introducing measurement errors. This study introduces a high-throughput and nondestructive method, termed extraction of panicle traits (EOPT), along with the software Panicle Analyzer, which is designed to assess unshaped intact rice panicle traits, including the panicle grain number, grain length, grain width, and panicle length. To address the challenge of grain occlusion within an intact panicle, we define a panicle morphology index to quantify the occlusion levels among the rice grains within the panicle. By calibrating the grain number obtained directly from rice panicle images based on the panicle morphology index, we substantially improve the grain number detection accuracy. For measuring grain length and width, the EOPT selects rice grains using an intersection over union threshold of 0.8 and a confidence threshold of 0.7 during the grain detection process. The mean values of these grains were calculated to represent all the panicle grain lengths and widths. In addition, EOPT extracted the main path of the skeleton of the rice panicle using the Astar algorithm to determine panicle lengths. Validation on a dataset of 1,554 panicle images demonstrated the effectiveness of the proposed method, achieving 93.57% accuracy in panicle grain counting with a mean absolute percentage error of 6.62%. High accuracy rates were also recorded for grain length (96.83%) and panicle length (97.13%). Moreover, the utility of EOPT was confirmed across different years and scenes, both indoors and outdoors. A genome-wide association study was conducted, leveraging the phenotypic traits obtained via EOPT and genotypic data. This study identified single-nucleotide polymorphisms associated with grain length, width, number per panicle, and panicle length, further emphasizing the utility and potential of this method in advancing rice breeding.
水稻穗部性状是产量潜力和种质资源质量的关键指标。然而,传统的这些性状的人工测量通常需要脱粒,不仅费力、耗时,而且容易引入测量误差。本研究介绍了一种高通量、无损的方法,称为穗部性状提取(EOPT),以及穗部分析软件,该软件旨在评估不规则完整水稻穗部性状,包括穗粒数、粒长、粒宽和穗长。为了解决完整穗部内籽粒遮挡的挑战,我们定义了一个穗部形态指数来量化穗部内水稻籽粒之间的遮挡程度。通过基于穗部形态指数校准直接从水稻穗部图像获得的粒数,我们大幅提高了粒数检测精度。对于测量粒长和粒宽,EOPT在籽粒检测过程中使用0.8的交并比阈值和0.7的置信度阈值来选择水稻籽粒。计算这些籽粒的平均值以代表所有穗部籽粒的长度和宽度。此外,EOPT使用Astar算法提取水稻穗部骨架的主路径以确定穗长。在1554张穗部图像的数据集上进行的验证证明了该方法的有效性,穗粒计数准确率达到93.57%,平均绝对百分比误差为6.62%。粒长(96.83%)和穗长(97.13%)也记录到了较高的准确率。此外,EOPT在不同年份和室内外场景中的实用性得到了证实。利用通过EOPT获得的表型性状和基因型数据进行了全基因组关联研究。该研究确定了与粒长、粒宽、每穗粒数和穗长相关的单核苷酸多态性,进一步强调了该方法在推进水稻育种方面的实用性和潜力。