Recognition of field-grown tobacco plant type characteristics based on three-dimensional point cloud and ensemble learning
To develop an efficient method for quantifying tobacco plant types in the field, the three-dimensional (3D) point clouds of individual plant of five tobacco cultivars were reconstructed based on multi-view image sequences using the structure from motion method. According to the plant type characteri...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Zhejiang University Press
2022-06-01
|
| Series: | 浙江大学学报. 农业与生命科学版 |
| Subjects: | |
| Online Access: | https://www.academax.com/doi/10.3785/j.issn.1008-9209.2021.05.173 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | To develop an efficient method for quantifying tobacco plant types in the field, the three-dimensional (3D) point clouds of individual plant of five tobacco cultivars were reconstructed based on multi-view image sequences using the structure from motion method. According to the plant type characteristic indexes commonly used, ten phenotypic parameters such as plant height, top width, bottom width, and maximum width of leaf layer were automatically extracted based on the 3D point cloud of tobacco plant, and the calculation accuracy was evaluated based on the plant height and maximum width of leaf layer measured manually in situ in the field. The results indicated the coefficients of determination (R<sup>2</sup>) of the plant height and maximum width of leaf layer extracted from the 3D point cloud were all greater than 0.97, and the root mean square errors were 3.0, 3.1 cm, respectively. Meanwhile, the extracted phenotypic parameters of tobacco plants were analyzed by different methods. The results of intergroup correlation analysis showed that 16 pairs of traits were extremely significant positive correlations, while one pair of traits was extremely significant negative correlation. The results of one-way multivariate analysis of variance showed that there were highly significant differences among the plant types. The first three principal components were extracted by principal component analysis, and their cumulative contribution rate to the overall variance was 81.6%. The accuracy of plant type discrimination was 93.7% using Stacking ensemble learning method, which was significantly higher than those using random forest, support vector machine and naive Bayesian. This study can provide a method basis for phenotypic characteristics and plant type recognition of field-grown tobacco plants. |
|---|---|
| ISSN: | 1008-9209 2097-5155 |