Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-05-15 Cooperative journals: 《智慧农业（中英文）》
Abstract： Crops have diverse structures and complex growth environments. RGB image data can reflect the texture and color features of plants accurately, while 3D data contains information about crop volume. The combination of RGB image and 3D point cloud data can achieve the extraction of two-dimensional and three-dimensional phenotypic parameters of crops, which is of great significance for the research of phenomics methods. In this study, potatoe plants were chosen as the research subject, and RGB cameras and laser scanners were used to collect 50 potato RGB images and 3D laser point cloud data. The segmentation accuracy of four deep learning semantic segmentation methods, OCRNet, UpNet, PaNet, and DeepLab v3+ , were compared and analyzed for the RGB images. OCRNet, which demonstrated higher accuracy, was used to perform semantic segmentation on top-view RGB images of potatoes. Mean shift clustering algorithm was optimized for laser point cloud data processing, and single-plant segmentation of laser point cloud data was completed. Stem and leaf segmentation of single-plant potato point cloud data were accurately performed using Euclidean clustering and K-Means clustering algorithms. In addition, a strategy was proposed to establish a one-to-one correspondence between RGB images and point clouds of single-plant potatoes using pot numbering. 8 2D phenotypic parameters and 10 3D phenotypic parameters, including maximum width, perimeter, area, plant height, volume, leaf length, and leaf width, etc., were extracted from RGB images and laser point clouds, respectively. Finally, the accuracy of three representative and easily measurable phenotypic parameters, leaf number, plant height, and maximum width were evaluated. The mean absolute percentage errors (MAPE) were 8.6%, 8.3% and 6.0%, respectively, while the root mean square errors (RMSE) were 1.371 pieces, 3.2 cm and 1.86 cm, respectively, and the determination coefficients (R2) were 0.93, 0.95 and 0.91, respectively. The research results indicated that the extracted phenotype parameters can accurately and efficiently reflect the growth status of potatoes. Combining the RGB image data of potatoes with three-dimensional laser point cloud data can fully exploit the advantages of the rich texture and color characteristics of RGB images and the volumetric information provided by three-dimensional point clouds, achieving non-destructive, efficient, and high-precision extraction of two-dimensional and three-dimensional phenotype parameters of potato plants. The achievements of this study could not only provide important technical support for the cultivation and breeding of potatoes but also provide strong support for phenotype-based research.
Subjects: Agriculture, Forestry,Livestock & Aquatic Products Science >> Other Disciplines of Agriculture, Forestry,Livestock & Aquatic Products Science submitted time 2023-02-17 Cooperative journals: 《智慧农业（中英文）》
Abstract： To cope with the challenges posed by the rapid growth of world population and global environmental changes, scholars should employ genetic and phenotypic analyses to breed crop varieties with improved responses to limited resource environments and soil conditions to increase crop yield and quality. Therefore, the efficient, accurate, and non-destructive measurement of crop phenotypic traits, and the dynamic quantification of phenotypic traits are urgently needed for crop phenotypic research, and breeding as well as for modern agricultural development. In this study, cotton plants were taken as research objects, and the multi-temporal point cloud data of cotton plants were collected by using three-dimensional laser scanning technology. The multitemporal point clouds of three cotton plants at four time points were collected. First, RANSAC algorithm was implemented for main stem extraction on the original point cloud data of cotton plants, then region growing based clustering was carried out for leaf segmentation. Plant height was estimated by calculating the end points of the segmented main stem. Leaf length and width measurements were conducted on the segmented leaf parts. In addition, the volume was also estimated through the convex hull of the original point cloud of plant cotton. Then, multi-temporal point clouds of plants were registered, and organ correspondence was constructed with the Hungarian method. Finally, dynamic quantification of phenotypic traits including plant volume, plant height, leaf length, leaf width, and leaf area were calculated and analyzed. The overall performance of the approaches achieved a matching rate through a series of experiments, and the traits extracted by using of point cloud showed high correlation with the manually measured ones. The relative error between plant height and manual measurement results did not exceed 1.0%. The estimated leaf length and width on point clouds were highly correlated with the manually measured ones, and the coefficient of determination was nearly 1.0. The proposed 3D phenotyping methodology can be introduced and used to other crops for phenotyping.
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