Estimating economic benefit of sugar beet based on three-dimensional computer vision: a case study in Inner Mongolia, China

Selecting and breeding crop varieties with high economic benefits is of great significance for social stability and development. The economic benefit of crops is usually reflected by the purchase price. Traditional estimation of economic benefits using purchase price formula based on manual measured traits is time-consuming. Structure-from-Motion in conjunction with multi-view stereo (SFM-MVS) method could extract plant phenotypic traits and has the potential for the efficient and timely estimation of economic benefits for sugar beet. In this study, a framework was developed to obtain phenotypic traits in order to estimate the economic benefits of sugar beet with 207 genotypes based on the calculation of a non-linear formula and the partial least square regression (PLSR) model. The first part of the framework was the designing of a low-cost portable equipment that can be used to obtain multi-view images of taproot in order to facilitate its three-dimensional (3D) reconstruction based on SFM-MVS method. The following part was the development of an automated pipeline for estimating ten traits from the reconstructed 3D taproot. Good agreement was found between measured and estimated traits with R² >0.97. The PLSR model constructed using the data in 2018 was used to predict the data in 2019 with moderate performance (R² = 0.5). A new PLSR model built using 70 % of the data collected in 2018 and 2019 could predict the remaining 30 % of the data with a higher R² of 0.61. The model built with multi-years data had a higher accuracy in estimating phenotypic traits, which suggests that PLSR model can estimate beet economic benefit by using the SFM-MVS method with multi-year data. The current method is more efficient than the manual measurement and may provide a basis for selecting and cultivating sugar beet with high economic benefit.