Quantitative analysis and planting optimization of multi-genotype sugar beet plant types based on 3D plant architecture

Abstract

The type of crops plays a critical role in determining the canopy light interception and is a decisive factor for yield. Thus, it is of significant importance to have a comprehensive understanding of the similarities and differences in plant type for crop improvement. In this study, the Structure-from-Motion in conjunction with multi-view stereo (SfM-MVS) method was employed to capture multi-angle images of 132 sugar beet varieties at two growth stages, from which three-dimensional(3D) point clouds were reconstructed for all individual sugar beets. Nine plant phenotypic traits were extracted based on the point clouds, and their correlations and heritability were calculated. An unsupervised machine learning approach was utilized to classify all varieties based on their plant type, and the characteristics of different types were statistically analyzed. Subsequently, a variety of different canopies were simulated, and a ray-tracing software was used to simulate light interception of the day. The results revealed that sugar beet plants could be roughly classified into five distinct types with significant differences of the structure. The coefficient of variation of phenotypic parameters for all varieties was 33.2 % in July and decreased to 26.7 % in August. The heritability similarly declined from 0.82 to 0.50, indicating that the structure of the sugar beet plants was exacerbated by environmental influences as the growing season progressed. The light interception results showed that intercropping with different plant types had different effects on light interception, with differences in light interception of up to 1000 W/h across the canopy in July, but this effect was not always favorable, and a decrease in the total amount of light interception also occurred in intercropping with different plant types compared to monocropping.