Grid Cell Detection of Dandelion Weed Centers via Convolutional Neural Network
Bibliographic record
Abstract
This paper presents a novel method for detecting dandelion weed (Taraxacum officinale) plant centers in perennial ryegrass using partial information gathered only from plant leaves. A primitive region proposal method generates proposals from original birds-eye view images of whole dandelion weeds in grass. The proposals containing dandelion weed leaves are taken and plant centers are labeled with a point based on the novel concept of the most “prominent” leaf. The samples are divided into a grid of cells and the cell containing the labeled point is considered the truth cell. A radial map and its inverse are generated based on the spatial location of the cells w.r.t. the truth cell. A fully convolutional network is trained to detect the positive truth cell using novel loss functions based on these maps. Using a relatively small dataset, the loss functions with the terms that compute regression loss on the maps yield significantly better model performance than those without. In addition, some errors are simply the result of the center of an alternate “prominent” leaf being automatically detected. Further, the comparison results with segmentation models reveal some advantages in detecting only plant centers as opposed to training computationally costly inference models.
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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 | 0.001 |
| 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".