Automation of Agricultural Machines: the farmers perspective
Bibliographic record
Abstract
As world population increases every year, so does the demand for quality and affordable food. Mobile agricultural machines like tractors, planters, combines, and sprayers have played a major role in ensuring that there is adequate food for this growing population. As a result, these machines have undergone several modifications to increase their productivity and safety. Today’s farmers make use of current technology to perform various farm operations. Some of these technologies include GPS, autosteer, and variable rate technology. With the advancements made so far, efforts are still being made to further improve their efficiency and operability. Agricultural machine manufacturers/researchers are currently working toward full automation, meaning that these machines would neither have an operator in their cabin nor would they require human involvement to navigate and control their operations. But, is this what farmers desire? Is the farming community willing to fully embrace a driverless tractor, or do farmers prefer to remain in the operator’s seat? The goals of this study were to gain an understanding of farmers’ satisfaction regarding the various modifications that were made on previous/current agricultural machines and to determine the prevailing perception of farmers towards full automation of agricultural machines. To achieve these objectives, a survey was developed and distributed to practicing farmers and university agriculture students in Alberta, Manitoba, and Saskatchewan. Findings from the study provided valuable information that will foster decision making during future modification of agricultural machines. Likewise, it will help manufacturers design machines that focus on the needs of the farming community.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".