Sugar beet is one of the main crops for sugar production in the world. With the increasing demand for sugar, more desirable sugar beet genotypes need to be cultivated through plant breeding programs. Precise plant phenotyping in the field still remains challenge. In this study, structure from motion (SFM) approach was used to reconstruct a three-dimensional (3D) model for sugar beets from 20 genotypes at three growth stages in the field. An automatic data processing pipeline was developed to process point clouds of sugar beet including preprocessing, coordinates correction, filtering and segmentation of point cloud of individual plant. Phenotypic traits were also automatically extracted regarding plant height, maximum canopy area, convex hull volume, total leaf area and individual leaf length. Total leaf area and convex hull volume were adopted to explore the relationship with biomass. The results showed that high correlations between measured and estimated values with R² > 0.8. Statistical analyses between biomass and extracted traits proved that both convex hull volume and total leaf area can predict biomass well. The proposed pipeline can estimate sugar beet traits precisely in the field and provide a basis for sugar beet breeding.

Root rot as a result of Salvia miltiorrhiza is a common root disease caused by Fusarium spp., which has become one of the main diseases affecting the production of S. miltiorrhiza. Currently, several hypovirulence-related mycoviruses have been identified in many phytopathogenic fungi, including Fusarium spp., which show potential as biological controls. In this study, we report a new mycovirus, Fusarium oxysporum partitivirus 1 (FoPV1), isolated from F. oxysporum strain FCR51, which is a causal agent of S. miltiorrhiza dry rot. The FoPV1 genome contains two double-stranded RNA segments (dsRNA1 and dsRNA2). The size of dsRNA1 is 1773 bp, and it encodes a putative RNA-dependent RNA polymerase (RdRp). The dsRNA2 is 1570 bp in length, encoding a putative capsid protein (CP). Multiple sequence alignments and phylogenetic analyses based on the amino acid sequences of the RdRp and the CP proteins indicated that FoPV1 appears to be a new member of the family Partitiviridae that is related to members of the genus Gammapartitivirus. Pathogenicity assay showed that FoPV1 confers hypervirulence to its host, F. oxysporum. This is the first report of a partitivirus infecting F. oxysporum and the first hypovirulence-related mycovirus from the causal agent of S. miltiorrhiza dry rot.

Accurate segmentation of individual leaves of sugar beet plants is of great significance for obtaining the leaf-related phenotypic data. This paper developed a method to segment the point clouds of sugar beet plants to obtain high-quality segmentation results of individual leaves. Firstly, we used the SFM algorithm to reconstruct the 3D point clouds from multi-view 2D images and obtained the sugar beet plant point clouds after preprocessing. We then segmented them using the multiscale tensor voting method (MSTVM)-based region-growing algorithm, resulting in independent leaves and overlapping leaves. Finally, we used the surface boundary filter (SBF) method to segment overlapping leaves and obtained all leaves of the whole plant. Segmentation results of plants with different complexities of leaf arrangement were evaluated using the manually segmented leaf point clouds as benchmarks. Our results suggested that the proposed method can effectively segment the 3D point cloud of individual leaves for field grown sugar beet plants. The leaf length and leaf area of the segmented leaf point clouds were calculated and compared with observations. The calculated leaf length and leaf area were highly correlated with the observations with R² (0.80–0.82). It was concluded that the MSTVM-based region-growing algorithm combined with SBF can be used as a basic segmentation step for high-throughput plant phenotypic data extraction of field sugar beet plants.