Remote supervision of autonomous agricultural sprayers: The farmer’s perspective
Bibliographic record
Abstract
The autonomous agricultural machine may soon be a reality.For it to be commercially viable, designers must also consider the design of the interface that will be used to remotely supervise the operation of the autonomous machine.Before the interface can be designed, however, it is necessary to understand the information required by the individual supervising the machine from a remote location Hence, the goal of this study was to determine the expectations of farmers regarding autonomous agricultural machines (specifically, an autonomous sprayer), and to determine the essential information that should be provided on the interface to enable the farmer to supervise the operation of an autonomous sprayer remotely.A questionnaire was developed and distributed to farmers and agriculture students attending western Canadian universities.In a follow-up study, experienced, operators were then asked to rank each parameter that was recommended from the survey using a five-point Likert scale.Findings indicated that sprayer operators are willing to accept an autonomous sprayer.However, to increase the ease of adoption, certain conditions need to be met.Parameters and features that were recommended varied in their level of importance and frequency of usage.These results will help designers minimize the chance of overcrowding the interface with irrelevant information.It will also assist designers with organization of information based on the needs of the farmer and in a way that will increase the situation awareness of the farmer.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".