Automated Seedling Height Assessment for Tree Nurseries Using Point Cloud Processing
Bibliographic record
Abstract
This paper presents a prototype of an automated seedling height assessment system for tree nurseries. The proposed system can acquire and store real-time 3D point-cloud data of seedlings; and perform offline identification, measurement, and report generation of seedling heights with an overall system accuracy that meets a 5mm accuracy specification. Periodic growth information of seedlings allows quantifying effects of different factors on the overall seedling development process for research and production optimization purposes. However, current manual sampling approaches used at these facilities produce quite limited data samples, and the process is rather time-consuming and labor intensive for industrial scale operations. In contrast, the proposed system is capable of significantly increasing the measurement sample size, measurement resolution, and frequency of measurement by automating the seedling measurement process using a scanning laser profilometer and an application specific point-cloud processing algorithm. The performance of the proposed profilometry solution for point-cloud generation is compared with several other point-cloud generation methods such as a 3D structured light sensing, light intensity detection and ranging (LiDAR), stereovision, and photogrammetry. This comparison results demonstrate a superior performance of the laser-profilometer over other sensing solutions available for seedling height measurement. The proposed system is experimentally validated for its measurement accuracy and repeatability. The field-test of the measurement system was conducted at Centre for Agriculture and Forestry Development, Wooddale, Newfoundland and Labrador (NL), Canada, and the results demonstrate the practical applicability and technological readiness of the proposed system for field deployment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".