A Prototype Wireless Sensor Network for Precision Agriculture
Bibliographic record
Abstract
Wireless Sensor Networks (WSNs) have become the ideal candidate to provide effective and economically viable solutions for a large variety of applications ranging from health monitoring, scientific data collection, environmental monitoring to military operations. In this paper, we present a proof-of-concept WSN to collect soil moisture content, which is one of the most fundamental data required for precision agriculture. Leveraging existing off-the-shelf hardware (MicaZ motes, MDA300CA data acquisition board, and EC-5 soil moisture sensors), the dominant open source embedded operating systems software (TinyOS 2.1.1), and MViz, we have built a prototype WSN to collect soil moisture. We present the detailed design and implementation of MDA300CA driver for TinyOS 2.1.1. Note that our architecture is general, so it is easy to integrate the driver of other sensor probes, thus collecting more types of data for different research purposes. Limited battery supply is a major concern when utilizing WSNs to build realistic applications. Therefore, we utilize solar panels and rechargeable battery to address this challenging problem. We illustrate our detailed design to package WSN nodes for outdoor development and present collected data to validate our design. Specifically, using sand soils with different water content, we demonstrate the result of our system. With enough details for researchers to understand and thus improve our design, our system could be a good starting point for their own research.
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 | 0.001 |
| 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) | 0.007 | 0.002 |
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".