IoT-based Hydroponic Plant Monitoring System
Bibliographic record
Abstract
The Industrial Revolution 4.0 has an impact in the form of changes in various fields of human civilization, one of which is the agricultural sector. By applying IoT technology, hydroponic plants will effectively be accurate. IoT has room for improvement in the quality and quantity of agricultural production because it facilitates the automation of monitoring various processes with high precision This research uses the prototype method. Which uses the concept of direct monitoring and allows iterative changes to be made until the desired results are achieved. So this prototype method makes it possible to display the display directly. The microcontroller used is ESP32 which is connected to 3 sensors, namely the TDS sensor, DHT11 sensor and HC-SR04 sensor which are directly updated in the blynk application. In making the software program used is the Arduino IDE. Implementation of the tool is carried out on a floating raft installation. This iot-based hydroponic plant monitoring system has been successfully made and is able to monitor well. Because the system made is related to water, it is necessary to design a tool that is safer and has more protection so that it can’t only run well but also safer for users and a high level of durability.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".