A semi‐automatic workflow for plot boundary extraction of irregularly sized and spaced field plots from UAV imagery
Bibliographic record
Abstract
Abstract Advances in high‐throughput platforms such as UAVs (unoccupied aerial vehicles) facilitate rapid image‐based phenotypic data acquisition. However, existing plot‐level data extraction methods are unreliable if field plots differ in size and spacing, as often occurs in early‐generation plant breeding trials. To overcome the limitations of conventional plot extraction techniques, a combinational approach with both field‐map information and image classification techniques can be used to optimize plot extraction. The objective of this study was to develop a plot boundary extraction workflow for irregularly sized and spaced field plots from UAV imagery using plot spacing data and vegetation index‐based classifiers. An herbicide screening experiment consisting of three replications of 780 lentil ( Lens culinaris Medik.) populations was foliar sprayed with saflufenacil. Aerial image acquisition was conducted during the peak vegetation stage using a RedEdge multispectral camera. A semi‐automatic workflow was compiled in eCognition software to extract lentil plot boundaries. Normalized difference vegetation index (NDVI) was calculated to locate the plots with vegetation and those with low NDVI or no vegetation, and pixel resizing based on plot size and orientation was used to draw the plot boundary. The extraction results showed a precise estimation of plot boundary for all the plots with a wide range of herbicide damage, including the plots with complete loss of vegetation. By using a simple convolutional filter (line filter), image thresholding, and pixel resizing, this approach avoided the use of complex algorithm‐based methodologies. Results suggest that this workflow can be extended to a wide range of phenotyping studies.
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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 | 0.001 | 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) | 0.001 | 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 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".