0206 Comparison of high definition Zenmuse X3 and X5 video cameras onboard unmanned aerial vehicles for future use in precision ranching
Bibliographic record
Abstract
The use of unmanned aerial vehicles (UAVs) in agriculture to increase the efficiency of management is a new and rapidly advancing field. Being able to locate and identify cattle with UAVs would enable producers to better utilize spatiotemporal data from range livestock to better manage both livestock herds and range. UAVs provide a method of capturing aerial video observation data of cattle on extensive range that is more flexible, affordable, and safer for the pilot than traditional aircraft. In this study we used common industry cattle ear tags of two sizes (large tags = 7.5 × 5 cm, small tags = 5.5 × 3 cm) of varying colors as well as back tags (23 × 14 cm, used in behavioral studies) to assess the visual acuity of two commercially available aerial high definition cameras. The aim of this project was to assess the capabilities and limitations of these aerial cameras specifically designed for UAV use, with the goal of determining their practical use in the field as tools for observing and identifying cattle. A set of images from each camera was obtained using the DJI Inspire 1 Pro flight platform (Da-Jiang Innovations Science and Technology Co. Ltd., Shenzhen, China). Images were captured at an initial height of 5 m and progressing upward at 5 m intervals to 80 m above ground level (ABL). An onboard GPS module on the UAV was used to monitor and record the height of the aircraft at each interval. Recorded images were then assessed on a computer monitor to determine whether identification of numbering and lettering on the tags was possible. Through qualitative visual assessment of these aerial photographs, it was determined that the capabilities of the Zenmuse X5 were significantly superior than that of the Zenmuse X3 and will therefore be of greater use in future identification of animals using UAVs. We concluded that identification by cattle ear tags using a UAV is likely not a practical application due to the maximum ABL that is required to identify numbering and lettering on tags (15 m using the Zenmuse X5); however, identification of numbering on back tags to identify individual animals was possible up to an ABL of 70 m using the Zenmuse X5 camera.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".