Content area
High-throughput measurements of phenotypic parameters in plants generate substantial data, significantly improving agricultural production optimization and breeding efficiency. However, these measurements face several challenges, including environmental variability, sample heterogeneity, and complex data processing. This study presents a method applicable to stem and leaf segmentation and parameter extraction during the tomato seedling stage, utilizing three-dimensional point clouds. Focusing on tomato seedlings, data was captured using a depth camera to create point cloud models. The RANSAC, region-growing, and greedy projection triangulation algorithms were employed to extract phenotypic parameters such as plant height, stem thickness, leaf area, and leaf inclination angle. The results showed strong correlations, with coefficients of determination for manually measured parameters versus extracted 3D point cloud parameters being 0.920, 0.725, 0.905, and 0.917, respectively. The root-mean-square errors were 0.643, 0.168, 1.921, and 4.513, with absolute percentage errors of 3.804%, 5.052%, 5.509%, and 7.332%. These findings highlight a robust relationship between manual measurements and the extracted parameters, establishing a technical foundation for high-throughput automated phenotypic parameter extraction in tomato seedlings.
Details
Accuracy;
Leaf area;
Data processing;
Plant breeding;
Scanners;
Tomatoes;
Segmentation;
Corn;
Leaves;
Greedy algorithms;
Inclination angle;
Crops;
Parameter robustness;
Automation;
Heterogeneity;
Plants;
Efficiency;
Cameras;
Seedlings;
Image segmentation;
Lasers;
Stems;
Three dimensional models;
Triangulation;
Plant extracts;
Agricultural production;
Methods;
Errors;
Algorithms;
Spatial data;
Plant growth;
Human error;
Geometry
1 College of Information and Technology, Jilin Agricultural University, Changchun 130118, China;
2 State Key Laboratory of Luminescence and Application, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China;
3 Key Laboratory of Facility Vegetable of Jilin Province, Jilin Academy of Vegetable and Flower Sciences, Changchun 130119, China;