Multi Node Based Smart Monitoring System with Motor Dry Run Avoidance for Sustainable Agriculture
Bibliographic record
Abstract
Food is the major source for the existence of the mankind. In order to meet the food requirements for the ever increasing population, the quantity of the food production has to be increased maintaining the quality standards. Agriculture is one such major sector which provides the food for mankind. It not only provides food but also supplies raw materials for industries. Implementation of advanced technologies such as Internet of Things in agriculture helps in improving the production with limited resources. The key parameter for the sustainable agriculture is moisture content available for the crop from the soil. This can be supplied efficiently by controlling the irrigation process. In this system an intelligent agriculture monitoring system with multiple wireless monitoring sensor nodes are used at different locations to monitor the parameters such as temperature, moisture content in the soil, humidity and rainfall. The data from the various sensors is aggregated at each node and transmitted to the coordinator station using long range transceiver. The data received at the coordinator station is subjected to a rule based decision making process to efficiently control the irrigation process. Also motor protection against the dry run was implemented, which not only protects the motor from break down but also avoids the unwanted power consumption. All the live data is in turn uploaded to the cloud so that the user can have a track of the current status of his farm at any 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.005 | 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".