Calculation Method of Phenotypic Traits for Tomato Canopy in Greenhouse Based on the Extraction of Branch Skeleton

Automatic acquisition of phenotypic traits in tomato plants is important for tomato variety selection and scientific cultivation. Because of time-consuming and labor-intensive traditional manual measurements, the lack of complete structural information in two-dimensional (2D) images, and the complex structure of the plants, it is difficult to automatically obtain the phenotypic traits of the tomato canopy. Thus, a method for calculating the phenotypic traits of tomato canopy in greenhouse was proposed based on the extraction of the branch skeleton. First, a top-view-based acquisition platform was built to obtain the point cloud data of the tomato canopy, and the improved K-means algorithm was used to segment the three-dimensional (3D) point cloud of branches. Second, the Laplace algorithm was used to extract the canopy branch skeleton structure. Branch and leaf point cloud separation was performed using branch local skeleton vectors and internal features. In addition, the DBSCAN clustering algorithm was applied to recognize individual leaf organs. Finally, phenotypic traits including mean leaf inclination, digital biomass, and light penetration depth of tomato canopies were calculated separately based on the morphological structure of the 3D point cloud. The experimental results show that the detection accuracies of branches and leaves were above 88% and 93%, respectively, and the coefficients of determination between the calculated and measured values of mean leaf inclination, digital biomass, and light penetration depth were 0.9419, 0.9612, and 0.9093, respectively. The research results can provide an effective quantitative basis and technical support for variety selection and scientific cultivation of the tomato plant.