3D Annotation and deep learning for cotton plant part segmentation and architectural trait extraction
Bibliographic record
Abstract
Abstract Background Plant architecture can influence crop yield and quality. Manual extraction of architectural traits is, however, time-consuming, tedious, and error prone. The trait estimation from 3D data allows for highly accurate results with the availability of depth information. The goal of this study was to allow 3D annotation and apply 3D deep learning model using both point and voxel representations of the 3D data to segment cotton plant parts and derive important architectural traits. Results The Point Voxel Convolutional Neural Network (PVCNN) combining both point- and voxel-based representations of data shows less time consumption and better segmentation performance than point-based networks. The segmented plants were postprocessed using correction algorithms for the main stem and branch. From the postprocessed results, seven architectural traits were extracted including main stem height, main stem diameter, number of branches, number of nodes, branch inclination angle, branch diameter and number of bolls. Results indicate that the best mIoU (89.12%) and accuracy (96.19%) with average inference time of 0.88 seconds were achieved through PVCNN, compared to Pointnet and Pointnet++. On the seven derived architectural traits from segmented parts, an R 2 value of more than 0.8 and mean absolute percentage error of less than 10% were attained. Conclusion This plant part segmentation method based on 3D deep learning enables effective and efficient architectural trait measurement from point clouds, which could be useful to advance plant breeding programs and characterization of in-season developmental traits.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".