Greenhouse Climate Controller by Using of Internet of Things Technology and Fuzzy Logic
Bibliographic record
Abstract
The Internet of Things (IoT) is a new, ongoing revolution. It often uses wireless sensor network (WSN) technologies because these technologies are among the most important solutions for monitoring and controlling the systems. This article provides a model of a smart greenhouse. In this regard, the main contribution of this paper is an innovative implementation of a micro-climate controlled environment for optimal plant growth, based on loT technology and using a fuzzy logic controller. Using this system, we attempted to optimize the functionality of the system proposed by exploiting an Arduino UNO board for data acquisition and processing. The input variables are analog values captured by ZigBee wireless network sensors that are then processed using fuzzy logic control software with heating and extractor control signals. At the same time, all data were sent to the server through a Wi-Fi internet connection, which permitted remote monitoring and analysis of the data via a web browser with tablets, smartphones, and laptops. Results show that the choice of a fuzzy logic controller could promote a comfortable greenhouse micro-climate. Also, we showed the efficiency of our proposed solution for greenhouse climate remote monitoring anywhere via IoT technology.
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 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".