Developing a new IoT network topology for effective Greenhouse Monitoring and Control
Bibliographic record
Abstract
Greenhouses provide a controlled environment that supports optimal plant growth by controlling key environmental conditions. However, traditional greenhouse systems face challenges such as real-time data collection and monitoring of accurate environmental parameters. IoT is becoming a critical tool in addressing these concerns and optimizing environmental monitoring in greenhouses. This paper presents an IoT-based Wireless Sensor Network (WSN) for smart greenhouse monitoring. The system is implemented at the K.C. Irving Environmental Science Centre in Wolfville, NS. It integrates Crossbow Iris Motes with sensors for temperature, humidity, and light to collect and transmit real-time environmental data. The system uses a Raspberry Pi as a processing hub to clean and modify data before it is sent to InfluxDB, which acts as a central server for accessing and storing the data. Grafana is used next for data visualization and analytics. Additionally, we discuss the potential for integrating Artificial Intelligence (AI) into the system. This includes predictive analytics to support decision-making and automation, as well as anomaly detection to ensure smooth system operation. Our system addresses key limitations of existing systems by improving scalability and real-time responsiveness. It also lays the groundwork for future project iterations through the integration of expanded sensor networks and AI. This work contributes to the advancement of smart agriculture by using IoT to enable energy-efficient, data-driven greenhouse management that supports plant growth and health.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".