Design and Implementation of Smart Agriculture System Based on Wireless Sensors Networks
Bibliographic record
Abstract
The development of information and communication technology has provided the conditions for the advancement of smart agriculture. This article focuses on designing a smart agriculture system based on wireless sensor network technology for large-scale outdoor cultivation of grain crops, aiming to provide scientific guidance for increasing crop yield and applying scientific and technological advancements to modern agricultural production. The design of the smart agriculture system in this article is divided into two parts: the perception layer and the network layer. The perception layer includes data collection terminals and control terminals. The data collection terminals are used for acquiring and transmitting environmental parameters of crops, while the control terminals are used to regulate the environmental parameters of agricultural equipment. Both the data collection terminals and control terminals employ LoRa wireless communication technology to achieve long-range data transmission. The network layer consists of LoRa gateways, which connect to the network server via Ethernet and are responsible for uploading data from the data collection terminals and control terminals, as well as issuing control commands. Finally, the article conducted functional tests on the smart agriculture system, and the results indicated that all functions and performance of the system met the expected requirements. The system is capable of long-range data collection and transmission, meeting the demands of smart agriculture, and has promising application prospects.
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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 | 0.001 | 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".