Remote supervision of autonomous agricultural machines: Concepts and feasibility
Bibliographic record
Abstract
The scientific literature provides a description of various models depicting autonomous agricultural machines working to complete typical field operations. Many of the models involve some form of automation interface that is used by the machine owner to supervise the operation of the machine from a remote location. The objective of this study was to interview experts in the design of autonomous agricultural machines (university researchers, entrepreneurs, and leaders in the agricultural machinery sector) to ascertain their opinions about future autonomous agricultural machines, particularly related to how such machines will be supervised by the machine’s owner. Of the four remote supervision concepts described by participants (within the field, close to the field, from the farm office, and outside the farm site), the close-to-the-field remote supervision concept was determined to be the most viable concept. Designers were divided on the idea of providing real-time live video on the automation interface, however, most of them believed that having live video would reassure the farmer that everything was going well. Desktop computer, tablet and phone were the main devices recommended as tools for remote supervision (i.e., the hardware on which to display the automation interface), with tablet perhaps being the preferred alternative.
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 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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".