Sustainable management and agriculture resource technology system using remote sensing descriptors and IoT
Bibliographic record
Abstract
The agricultural sector is a paramount arena of research pertaining to both global and Indian context. Despite India's agricultural dominance, productivity has consistently declined due to various factors, leading to economic and farmer losses. This study introduces a dual-approach research deployment aimed at enhancing agricultural management efficiency. The first approach involves utilization of remote sensing descriptors to identify moisture deficit areas using vegetation-moisture indices, land surface temperature and slope-elevation profiles. The descriptors were computed using a ten-year period from 2015–2024 using Sentinel-2 data. The second approach involves the deployment of soil moisture sensors integrated with microcontrollers and IoT infrastructure for real-time monitoring. Soil moisture levels were monitored at real-time during morning, afternoon and evening time-periods with specific thresholds identified for urgent watering and optimum moisture conditions. Data was collected and tested at moisture deficit areas identified by remote sensing descriptors at South and North Roorkee, achieving 97.24 % precision score in real-time alerts. The system developed and portrayed in this research is termed as “SMARTS” (Sustainable Management and Agriculture Resource Technology System) which is scalable and flexible for being deployed at any geographical location offering a robust foundation for future sustainable farming.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".