Application of navigation technology in agricultural machinery: A review and prospects
Bibliographic record
Abstract
With the rapid advancement of information technology, the intelligent and unmanned applications of agricultural machinery and equipment have become a central focus of current research. Navigation technology is central to achieving autonomous driving in agricultural machinery and plays a key role in advancing intelligent agriculture. However, although some studies have reviewed aspects of agricultural machinery navigation technologies, a comprehensive and systematic overview that clearly outlines the developmental trajectory of these technologies is still lacking. At the same time, there is an urgent need to break through traditional navigation frameworks to address the challenges posed by complex agricultural environments. Addressing this gap, this study provides a comprehensive overview of the evolution of navigation technologies in agricultural machinery, categorizing them into three stages: assisted navigation, autonomous navigation, and intelligent navigation, based on the level of autonomy in agricultural machinery. Special emphasis is placed on the brain-inspired navigation technology, which is an important branch of intelligent navigation and has attracted widespread attention as an emerging direction. It innovatively mimics the cognitive and learning abilities of the brain, demonstrating high adaptability and robustness to better handle uncertainty and complex environments. Importantly, this paper innovatively explores six potential applications of brain-inspired navigation technology in the agricultural field, highlighting its significant potential to enhance the intelligence of agricultural machinery. The review concludes by discussing current limitations and future research directions. The findings of this study aim to guide the development of more intelligent, adaptive, and resilient navigation systems, accelerating the transformation toward fully autonomous agricultural operations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".