Remote Sensing-Based Estimation of Seedling Density in Nursery Gardens Using YOLOv4 Deep Learning Algorithm
Bibliographic record
Abstract
In nursery gardens, seedlings are traditionally densely planted, leading to large errors and questionable accuracy when employing standard sampling and probability statistical methods.Such conventional methods also prove labor-intensive.To address these challenges, a patrol platform equipped with a drone-mounted image acquisition system was developed.Remote sensing images, sourced from a nursery garden situated in the Linjiang Forestry Bureau of Jilin Province, China, served as the primary dataset.By leveraging the deep learning-based target detection capabilities of the YOLOv4 algorithm, seedlings within the nursery garden were meticulously surveyed, delineated, and enumerated.For the statistical evaluation of Pinus Koraiensis (Korean pine) seedlings, a precision of 91.85% was achieved using the YOLOv4 algorithm.Results suggest a notable robustness of the model in standard environments.Compared to traditional quadrat sampling and detection approaches, the methodology introduced here offers an intelligent, efficient, and precise mapping strategy for large-scale seedling surveys.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".