The evaluation of the genetic quality of a seed lot is crucial for the quality control process in its production and commercialization, as well as in the identification of superior genotypes and the verification of the correct crossing in plant breeding programmes. Current techniques, based on the identification of seed morphological characteristics, require skilled analysts, while biochemical methods are time-consuming and costly. The application of spectral imaging analysis, which combines digital imaging with spectroscopy, is gaining ground as a fast, accurate and non-destructive method. The success of this technique is closely linked to chemometric techniques, which use statistical and mathematical tools in data processing. The aim of the work was to evaluate the main procedures in terms of spectral image analysis and chemometric procedures applied in seed phenotyping and its practical application. A systematic review was conducted using the PRISMA methodology, in which a total of 1304 articles were identified and screened to the inclusion of 44 articles pertaining to the scope. It was concluded that spectral image analysis has a high ability to classify seeds of different genotypes (93.33%) in a range of situations: between cultivars; hybrids and progenitors; and hybrids and lines, as well as in the separation of coated seeds. Accurate classification can be obtained by different strategies, such as the choice of the equipment type, the spectrum range and extra features, guided by the characteristics of the species, as well as in the choice of algorithms and dimensionality reduction procedures for the optimization of models when there is a large amount of data. Despite the fact that the practical application of this technique in seed phenotyping still needs to be developed for use in laboratories with large volumes of analyses, lots, genotypes and harvests. Research has been accelerated to overcome the practical challenges of this method, as seen in works using model update algorithms, online classification systems, and real-time classification maps. Thus, there are strong indications that the application of multispectral image analysis will reach the routine of seed analysis laboratories.

种子批次遗传质量的评估对于其生产和商业化的质量控制过程以及植物育种计划中优良基因型的鉴定和正确杂交的验证至关重要。目前的技术基于种子形态特征的鉴定，需要熟练的分析人员，而生化方法既耗时又昂贵。光谱成像分析将数字成像与光谱学相结合，作为一种快速、准确和非破坏性的方法正在得到普及。这项技术的成功与化学计量技术密切相关，化学计量技术在数据处理中使用统计和数学工具。这项工作的目的是评估种子表型分析中应用的光谱图像分析和化学计量学程序的主要程序及其实际应用。使用 PRISMA 方法进行系统审查，共识别和筛选了 1304 篇文章，纳入了与该范围相关的 44 篇文章。结论是，光谱图像分析在多种情况下对不同基因型的种子（93.33%）具有很高的分类能力：品种之间；品种之间；杂交种和祖先；以及杂交种和品系，以及包衣种子的分离。以物种特征为指导，通过不同的策略，如选择设备类型、光谱范围和额外特征等，可以得到准确的分类，以及当存在大量数据时优化模型的算法和降维程序的选择。尽管事实上，该技术在种子表型分析中的实际应用仍然需要开发用于具有大量分析、批次、基因型和收获的实验室。正如使用模型更新算法、在线分类系统和实时分类图的工作中所看到的那样，人们正在加速研究以克服这种方法的实际挑战。因此，有强有力的迹象表明多光谱图像分析的应用将进入种子分析实验室的常规。尽管事实上，该技术在种子表型分析中的实际应用仍然需要开发用于具有大量分析、批次、基因型和收获的实验室。正如使用模型更新算法、在线分类系统和实时分类图的工作中所看到的那样，人们正在加速研究以克服这种方法的实际挑战。因此，有强有力的迹象表明多光谱图像分析的应用将进入种子分析实验室的常规。尽管事实上，该技术在种子表型分析中的实际应用仍然需要开发用于具有大量分析、批次、基因型和收获的实验室。正如使用模型更新算法、在线分类系统和实时分类图的工作中所看到的那样，人们正在加速研究以克服这种方法的实际挑战。因此，有强有力的迹象表明多光谱图像分析的应用将进入种子分析实验室的常规。