Real-Time Framework for Sustainable IoT-Based Grain Drying Integrated Load, Temperature, and Energy Performance Monitoring
Bibliographic record
Abstract
This research develops an Internet of Things (IoT)-based real-time framework for integrated monitoring of load, temperature, and energy consumption performance in grain drying.Although IoT technology has transformed the agricultural sector, implementing low-cost sensors for precision monitoring in grain drying and the relationship between monitoring data and energy optimization still faces various challenges.The study integrated the DHT11 sensor and HX711 load cell with a closed-loop system and a mathematical model for estimating moisture content.The experimental method used comprehensive observation instruments, including continuous load measurement and monitoring at five strategic points of the DHT11 sensor and real-time power measurement.The results showed an increase in energy efficiency of 27.5% and reduced drying time by 20-30% compared to conventional farmers' methods in Merauke.In addition, the system can achieve an optimal humidity level (12-14%).The consistency of product quality reached 96.7%, and seed cracking cases reduced by 15.7%.The system ensures moisture accuracy, achieving an average error percentage of ±1.92% in moisture detection and an overall drying efficiency of 67.1%; using a blower improves on the results of previous studies, which only reached 64.8% using LPG.The framework provides theoretical contributions in the form of empirical validation of drying dynamics correlation models and energy optimization, as well as practical contributions in the form of sustainable drying solutions that are adaptable to various scales of operations and support improving global food security through improved post-harvest efficiency.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".