Solar-Powered Smart Agricultural Monitoring System Using Internet of Things Devices
Bibliographic record
Abstract
Advances in wireless communication technologies for monitoring and control systems have paved the way for a new method of farming known as smart farming. Smart farming can be achieved through the use of Precision Agriculture (PA) which involves using novel technology along with inch-scale devices to monitor crops and provide precise treatments when required. Smart PA is able to take traditional farming practices and apply technological advances from areas such as Wireless Sensor Networks (WSN) and the Internet of Things (IoT) to assist in increasing the output yield of a crop while improving efficiency and reducing the amount of stress placed on a farmer. In this paper, a solar-powered smart agricultural monitoring system with IoT devices is presented. Solar-powered prototype nodes were designed to measure environmental conditions in a field and report to a base station for data storage and further processing. Two prototypes were compared in identifying the advantages gained when using energy harvesting in a device. Using an experimental testbed, a proof of concept of how the system would function is presented. According to experimental results, using an energy harvesting device can provide an increased lifetime for a device by supplying power and recharging its battery.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".