Sugar beet is one of the main crops for sugar production in the world, and originated from the western and southern coasts of Europe. Selecting and breeding of varieties of sugar beet based on plant phenotyping are the key factors for the development of sugar beet industry on a large-scale cultivation. In China, sugar beet was widely planted in arid and semi-arid regions, particularly for poverty alleviation of farmers living in border areas and ethnic minority areas. The type of beet root with great different genotypes directly determines the sugar yield and mechanization efficiency in modern agriculture. The traditional classification of beet root type depends mainly on manual separation, and thereby greatly limits industry production and breeding of the sugar beet due to heavy workload and relatively large errors. In order to meet the requirements of high-throughput analysis, a three-dimensional (3D) phenotyping technique with multi-view images was recently developed to facilitate the classification of fruit and vegetable with high accuracy and efficiency. In this study, the beet roots with 207 genotypes were selected as experimental materials. Multi-view images were obtained by moving mobile phone around beet root. Three-dimensional point clouds were reconstructed in 3DF Zephyr Aerial software, which can restore position and direction from a dataset of multi-views images to extract for the matching feature points between each pair of images. After the postprocessing of the matching images, including noise reduction, rotating and segment, the detailed features of beet root shape, color, and texture can be achieved in the 3D point cloud. Ten phenotypic parameters can be used to clarify the morphological characteristics of beet roots, the maximum diameter, root length, convex hull volume, top projection area, compactness, convex index, convex angle, distal root end ratio, proximal root end ratio and root taper index. There was a good agreement between the measured maximum diameter and root length, with coefficient of determination R2 > 0.95. The K-medoids clustering algorithm with high stability was selected to classify the beet root into four groups. Group 1, namely as cone beet root, indicates that the maximum root diameter located at the middle of the root body. Group 2, namely as hammer beet root, shows the shortest body of root, the smallest root head ration while larger root tail ration. Group 3, namely as wedge beet root, has the maximum diameter of root body close to the root head, whereas, the width of root from head to tail gradually decreased. Group 4, namely as long wedge beet root, has longer root body than that in group 3, wider root head and smaller root tail. The reduction rate of root body from head to tail was the greatest. Based on the combination of phenotypic traits and experts' knowledge, Group 1 (cone beet root) and Group 3 (wedge beet root) were recommended due to their high sugar yield, medium root length and moderate proportion. After adjusting the categories by the experts as the true values, five prediction models were established to discriminate beet root type, including linear discrimination, random forest, support vector machine, decision tree, and naive Bayes. The results showed that the prediction accuracies of the five models were above 70.0%, where accuracy of random forest reached 81.4%. These results demonstrated that 3D point cloud reconstructed by multi-view image sequences can be used for the identification of beet root shape, and thereby to effectively improve the yield prediction of sugar beet and the selection of high-quality beet varieties. Since 207 genotypes have been selected for the classification of root types during this time, much more genotypes at different environments can be expected to enrich the 3D phenotyping library, and thereby further improve the accuracy of classification. This finding can provide a potential practical basis for the beet root type screening and breeding. © 2020, Editorial Department of the Transactions of the Chinese Society of Agricultural Engineering. All right reserved.
