3D terrestrial LiDAR for obtaining phenotypic information of cigar tobacco plants

The study of individual phenotypic information of cigar tobacco plants holds significant importance for enhancing mechanized production levels of cigar tobacco. It provides a foundational basis for the mechanization of field management, plant protection, and the design of harvesting machinery during the production process. Addressing the time-consuming, labour-intensive, inefficient, and highly subjective nature of traditional methods for extracting phenotypic information of cigar tobacco, this paper proposed a novel approach using terrestrial lidar scanning technology for the extraction of phenotypic information in field-grown cigar plants. By utilizing terrestrial lidar to acquire millimeter-precision three-dimensional point cloud data of individual cigar plants and conducting pre-processing of this point cloud data, the study employed a skeleton extraction algorithm based on Laplacian mesh contraction and topological refinement to construct a triangular mesh model of the leaves and a point cloud skeleton of the plant. Based on the triangular mesh of the leaves, this study extracted the leaf area, while the leaf length, leaf inclination angle, and petiole angle were derived from the plant’s skeletal point cloud. Additionally, the plant height was ascertained from the point cloud of the cigar tobacco plant. The experimental results, compared with manual field measurements, indicated that the Root Mean Square Error values for actual leaf length, leaf area, leaf inclination angle, petiole angle, and growth height were 1.659 cm, 8.374 cm², 2.371°, 2.73°, and 2.229 cm, respectively. The average absolute percentage errors for these measurements were 3.102 %, 0.782 %, 3.323 %, 4.148 %, and 1.194 %, respectively. This method provided an effective means of phenotypic information measurement to assist in the growth monitoring of mature cigar plants, mechanized plant protection, mechanized harvesting, and other projects that integrate agro-mechanics and agronomy.