Automatic Plant Watering Based on IoT-Based Light Intensity (Case Study: STMIK Kaputama Plantation)
Bibliographic record
Abstract
Kangkung is one of the most popular vegetable commodities that requires sufficient water availability to support optimal growth. Manual watering often causes problems, such as water deficiency that leads to wilting or excessive watering that increases the risk of root rot. These issues are further influenced by environmental factors such as light intensity and soil moisture, which strongly affect the plant’s water requirements. This study aims to design and implement an automatic watering system based on the Internet of Things (IoT) to address these problems, with a case study in the STMIK Kaputama Garden. The system employs an LDR sensor to detect light intensity and an FC-28 soil moisture sensor as the main parameters. A NodeMCU ESP32 microcontroller acts as the controller, processing sensor data in real-time, operating the water pump via a relay module, and connecting to the Blynk application for remote monitoring and control through a smartphone. Experimental results show that the pump activates when light intensity exceeds 700 lux and soil moisture is below 40%, and automatically stops when soil moisture reaches 65%. The system has proven effective in maintaining soil moisture according to plant needs, conserving water, and simplifying plant care. Therefore, this research provides a practical and efficient solution to support modern technology-based agriculture.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".