Sugar beet is an economically significant crop, and the homozygosity of paired monogerm cytoplasmic male sterile (CMS) and maintainer lines directly influences the number of hybrid combinations that can be created. This study aimed to evaluate the genetic variation within monogerm sugar beet germplasm resources to establish a foundation for advancements in sugar beet breeding and the development of hybrid female parent lines. This study analyzed the genetic diversity of 86 distinct monogerm germplasm resources, including 38 paired monogerm CMS and maintainer lines, 5 individual maintainer lines, and 5 externally introduced sterile lines. The analysis employed 26 pairs of SSR primers and 35 pairs of InDel primers across nine sugar beet chromosomes. Several genetic parameters, and analyses such as structural analysis, genetic diversity analysis, and principal coordinate analysis, were used to evaluate the samples. The results indicated that these strains could be classified into two groups: Group I and Group II. Group I was further divided into three subgroups. Further, 18 pairs of original CMS and maintainer lines were successfully clustered, confirming that their nuclei had achieved homozygosity, making them suitable for use in the development of binary sterile lines. However, 20 other pairs still require further backcrossing to achieve homozygosity. The analysis of molecular variance (AMOVA) revealed that most of the genetic variation occurred within individuals, with relatively low genetic differentiation between groups. Significant genetic differentiation was observed between Subgroups 2 and 3, and between Subgroups 1 and 3. The results suggest that additional monogerm sterile and maintainer lines from these subgroups should be selected to configure binary sterile lines. This study offers a theoretical foundation for developing new sugar beet germplasm resources and cultivating hybrid mother plants.

Remote sensing has become an important tool for crop monitoring and precision agriculture, yet its applications in sugar beet production remain fragmented across sensing platforms, target traits and modelling strategies. This review synthesises the development, current applications and future directions of remote sensing in sugar beet production, with particular attention to the transition from crop monitoring to precision management. A structured search was conducted in Scopus and the Web of Science Core Collection for publications from 2003 to 2025, and 181 relevant peer-reviewed articles were retained for thematic analysis. The literature shows a clear increase in sugar beet remote sensing studies, particularly after 2015, coinciding with the availability of Sentinel-2 imagery and, from 2016 onwards, the growing use of unmanned aerial vehicle-based sensing. It also indicates a gradual shift from crop mapping and canopy monitoring towards disease detection, weed mapping, yield prediction and management-oriented applications. Current studies demonstrate the value of satellite, unmanned aerial vehicle and proximal sensing for retrieving canopy traits, assessing biotic stresses, estimating root yield and supporting field-scale management. However, sugar beet presents specific challenges because its economic value depends not only on canopy development or root biomass, but also on sucrose concentration, recoverable sugar yield, and processing quality. These quality-related traits remain less studied and are difficult to infer directly from canopy observations. Modelling approaches have evolved from vegetation-index-based empirical models towards machine learning, deep learning, multi-temporal analysis, data fusion and crop model assimilation, but issues of model transferability, ground-truth availability and operational decision support remain unresolved. Future research should strengthen multi-source observations, external validation, quality-oriented prediction and decision-support workflows to promote robust, scalable and economically meaningful remote sensing applications in sugar beet production.