MoundCount: A detection-based approach for automatic counting of planting microsites on UAV images
Bibliographic record
Abstract
Planting by mounding is a commonly used forestry technique that improves soil quality and ensures optimal tree growth conditions. During planting operations, one of the main planning steps is to estimate the number of mechanically created mounds in each planting block. Traditional counting methods involve manual field surveys or human photo-interpretation of UAV images, which are generally subject to errors and time-consuming. In this work, we propose a new approach to count mounds on UAV orthomosaics. Our framework is designed to estimate the required number of seedlings for a given planting block, based on a visual detection approach and a global estimation module. Firstly, a deep local detection model is applied on local patches to recognize and count visible mounds. Then, an estimation model, based on global features is used to predict the final number of plant seedling required for a given plantation block. To evaluate the proposed framework in real-world conditions, we constructed a large UAV dataset, including 18 UAV orthomosaics, comprising 111,000 mounds. We have conducted extensive experiments in our dataset, including a comparison with the state-of-the-art counting methods, as well as an analysis of Human-Level Performance (HLP) in identifying and annotating mounds. The experimental results show that our model reaches the best performance in terms of MAE and MSE, by comparison to state-of-the-art automatic counting mehtods.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".