Advancements In Precision Agriculture Technologies For Enhancing Crop Yields
Bibliographic record
Abstract
Precision agriculture, a contemporary farming method, harnesses advanced technologies to optimize resource utilization, elevate productivity, and diminish environmental impact. The incorporation of diverse technologies, encompassing hardware, software, and data analytics solutions, is pivotal in accurate decision-making across all agricultural phases. Key technologies such as satellite imagery, drones, sensors, and IoT devices play integral roles in this process. these technologies, enabling farmers to tailor interventions at a granular level and optimize resources like water, fertilizers, and pesticides to enhance yields, reduce costs, and minimize environmental impact. However, persistent challenges including data interoperability, accessibility, and adoption restrictions underscore the need for ongoing research and cooperation to fully unlock the potential of precision agriculture in addressing global food security and sustainability challenges. Progress in remote sensing, including satellite imaging and drones, is explored, alongside the integration of GPS and GIS technology for precise field mapping and monitoring. The present review explores precision agriculture technologies, including remote sensing with satellite imaging, drones, GPS, GIS, and sensor applications for real-time data. It also emphasizes transformative potential in reshaping farming practices, enhancing resource efficiency, and contributing to sustainability.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".