Plot extraction from aerial imagery: A precision agriculture approach
Bibliographic record
Abstract
Abstract The plant phenotyping community is adopting technological innovations in order to record phenotypic attributes more quickly and objectively. Low altitude aerial imaging is an appealing option for increasing throughput but there are still challenges in the image processing pipeline. One such challenge involves the assignment of a spatial reference to each plot entry in an experimental layout. Image‐based approaches are increasingly popular since plot boundaries are often, but not always, clearly visible in low altitude imagery. In addition, workflows that make geometric assumptions about plot layout also show promise. We outline an alternative approach to generate plot boundaries to overlay with aerial imagery. The proposed method involves high‐accuracy georeferencing (i.e., within a few cm) of imagery and planter activity, after which georeferencing of all plot entries is complete and only requires a few simple steps to convert logged spatial positions to polygons using open source geographic information systems (GIS) software. Compared with other approaches, the proposed method provides imagery that is precisely aligned over time and always aligns with plot boundaries, which are fixed and do not vary from image to image.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".