Real-Time IoT-Blynk Application for Log Hive Weight Monitoring in Stingless Bees
Bibliographic record
Abstract
The paper aims to develop a weight-sensing system and monitoring platform to track the activity patterns of a stingless bee colony using a weight-sensing system that consists of an Arduino Mega, a NodeMCU ESP8266, and load cell as weight sensors with a Real-Time Clock (RTC) module.The system was designed with an improved weight sensor that includes triangle or delta positioning and placement for greater stability on the log hive.It also featured real-time mobile monitoring via the Blynk application, which sends weight measurements continuously via Wi-Fi.It shows that the weight fluctuations indicate that the stingless bees are most active in their foraging activities between 9:00 and 13:00, with occasional activity between 21:00 and 22:00, gathering food sources to produce honey or beebread.Consequently, analysing the daily pattern of weight measurements using a correlation analysis throughout a week enable beekeepers to observe their diverse foraging patterns.These patterns can be strongly linked to the movements of active bees, human interference, or environmental errors when the correlation value approaches one.The findings of this research can assist beekeepers in understanding routine activities by observing the foraging patterns of stingless bees, which could reveal valuable insights regarding bee health and honey production levels and help detect a decline in the bee colony.This can be achieved by utilising Internet of Things (IoT) technology to enhance hive management practices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".