Smart garden with intruder detection system
Bibliographic record
Abstract
IoT contains of devices associated with the internet and communicate with each other. Considering the present scenario, people are more interested towards home gardening. Sometimes they forget to water the plants because of their monotonous work lifestyle, and also cannot guard them which eventually affects the growth of the plants. To overcome this situation a Smart gardening system using IoT is being developed. The system can detect the soil moisture level based on which it will inform the user whether to turn on the motor or not and also detects unauthorized entry into the garden. The IoT-based device is linked to the motor, which can be monitored and controlled by using a smartphone. It consists of a soil moisture sensor that can predict the soil’s moisture level and a PIR sensor that detects any objects entering the range and a BMP280 sensor which detects the humidity, temperature and atmospheric pressure of the air and sends the result to the user through a smartphone application named Kodular app. It transfers the soil moisture data and motor status to the firebase which displays it in the app. It is an efficient way of sensing the necessity of the plant and watering it to maintain the soil moistness. Further it also helps in guarding the garden which keeps it away from birds and animals. Which thereby maintains the plant’s health.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.013 | 0.006 |
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".