Images Processing of Unmanned Aerial Vehicle (UAV) for Cannabis Identification
Bibliographic record
Abstract
This research purpose is to find new effective ways to search cannabis fields where there is an illegal plant in Indonesia.The remote sensing method was used, initial identification coordinates of the suspected area using satellite imagery, but due to limited image resolution it was not possible to analyze details in a small area, so it was continued by taking aerial photos using the Unmanned Aerial Vehicle (UAV) of Quantum Trinity F90+ on the coordinate point.The results of data processing aerial photos from UAV were analyzed using visual analytics, to obtain coordinate points that were positively suspected cannabis fields.For validation, the DJI Matrice 300 RTK drone is flown equipped with a camera that can zoom very well on suspected objects, so it can be ascertained whether the positive object on the coordinate point is cannabis or not.From UAV succeeded in finding 19 coordinate points suspected of cannabis fields, but after validation using a zoom camera drone, it was confirmed only 11 locations positive for cannabis fields.This research is very helpful for law enforcement officers who are tasked with destroying cannabis fields, ensuring the shortened time and discovery at the positive coordinate target point, when compared with the traditional methods they have used the whole time.
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".