IoT-Based Cattle Pen Monitoring and Mobile Application Interface for WS Farm: Enhancing Livestock Management Through Real-Time Data
Bibliographic record
Abstract
The development of beef cattle farming in Indonesia has significant potential, but is faced with various challenges, including monitoring optimal housing environments, limited monitoring technology, and automatic device control processes.Like WS Farm which is in Subang Regency.The condition of the WS Farm cage is an open cage, the condition of this cage is greatly influenced by the conditions of the external environment.This can affect the health of the cows in the cage.Therefore, through smart cages, cage managers can monitor the temperature, humidity and smell of the cage via a mobile device-based application.This system can monitor temperature, humidity and ammonia gas levels in the cow cage in real-time.Through the automatic fan and light control feature, the system is expected to be able to maintain the temperature of the pen according to the needs of the cows.This system was created using DHT 21 and MQ 135 sensors and Arduino as the microcontroller, while the storage process uses Firebase Realtime Database and to access the interface using an Android application.Based on the results of field testing, this system shows data measurement can be done continuously and data transmission can be done in real time.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".