A Multifunctional UAV System for Precision Agriculture and Environmental Monitoring
Bibliographic record
Abstract
Today, precision agriculture as a modern approach to farming has been aided by advanced technology for optimizing crop management and resource utilization. A multifunctional UAV system for real-time environmental monitoring in agriculture landscapes is presented here. This drone has a Pixhawk flight controller and a variety of environmental sensors from which air quality using the MQ-135, soil moisture, temperature using DHT22, water pollution, and LIDAR-based distance sensors (VL53L0X) would be applied. Moreover, it has an SD card-associated black box for safe data storage, allows solar charging so it can have a much longer mission time, and permits autonomous water landing when necessary for better survivability in emergency situations. The UAV can provide accurate environmental data which could help farmers or environmental agencies in decision making with regards to irrigation, fertilization, or pollution control. The efficiency of capturing accurate sensors data is verified through test results that also show the solar charging-enabled extended mission endurance and miss emergency landings without damage to critical components. The cashless data integrity is ensured with the black box mechanism. The system is also far superior to conventional systems based on ground monitoring in terms of scalability, flexibility, and reduced cost. In the future, research initiatives will focus on developing machine learning algorithms that enable improved navigation and decision-making in a more autonomous fashion, with the real-time capability for dynamic field management. This UAV-based precision agriculture platform thus assures sustainable agriculture by reducing wastages, improving productivity, and preventing environmental degradation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".