Measuring Agricultural Area Using YOLO Object Detection and ArUco Markers
Bibliographic record
Abstract
This paper discusses the use of drones in image acquisition of agricultural land to detect the presence of disease and calculate the area of infected agriculture.Calculation of the area of infected and healthy areas will be calculated by combining the You Only Look Once (Yolo) object detection algorithm version 4 with the ArUco Marker reference image.The image resulting from the detection from the Yolo v4 algorithm will be used as a reference to be referenced using a reference image in the form of an AruCo Marker to convert it to area units to determine the area of the infected area and calculate the ratio between the area of the infected area and the area of the healthy area.The coordinate points at each corner are used as the first stage in converting pixels into area units.Measuring the infected area is necessary to localize the infection so that it does not spread to healthy plant areas.Apart from that, to anticipate the spread of infection which could result in crop failure.Evaluation of the calculation of the area of the detection area with the actual area resulted in an accuracy of 97.05%.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".