A Wi-Fi Positioning System for Material Transport in Greenhouses
Bibliographic record
Abstract
In greenhouse farming, lots of materials need to be transported to the greenhouse in many phases.However, the current transport method is too costly and time-consuming to meet the material demand of modern greenhouses.To solve the problem, this paper presents a novel positioning system based on Wi-Fi for material transport in greenhouses.Firstly, the base station (BS) nodes were selected and deployed according to the signal attenuation model.Next, the STM32F103RE microcontroller and ESP8266 chip were adopted to design low-power positioning node and communication node.After that, a positioning algorithm was formulated based on received signal strength indication (RSSI) ranging and maximum likelihood estimation (MLE).Finally, the initial positioning system was verified through simulation and experiments, and then the vehicle posture was corrected with grayscale sensors and cross marks.After the correction, our Wi-Fi positioning system can position the targets in greenhouses accurately, enabling the unmanned vehicle to transport the materials required for sowing, fertilizing, picking, etc.Our research results provide a good reference for the design of indoor positioning systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".