Stored-grain Monitoring Utilizing Radio Wave Imaging
Bibliographic record
Abstract
Storage of large amounts of grain post-harvest is common during drying, distribution and preservation of crops. During storage, where grain is usually held in a large metallic container or bin, changes in temperature, moisture, and insect infestation can cause grain to spoil annual post-harvest crop losses are estimated up to 30% in some countries while Canadian losses of 2% exceed a billion dollars. Currently, locally-sensitive temperature and moisture sensors are a common way to monitor grain bins. Sensors are generally strung on heavy duty cables that can withstand the forces generated when unloading grain. This monitoring method provides a coarse sampling of the storage environment due to system cost and the fact that using many sensor cables would require significantly reinforcing the bin. Further, these cables are not suitable for monitoring stored crops that are dried by a combination of stirring and aeration. Over the past four years, I have developed multiple electromagnetic imaging based grain-monitoring systems with the goals of overcoming the deficiencies of existing sensor technology and allowing farmers and distributors a robust way to preserve our food stores and increase revenue. The proposed technology aims to produce global, quantitative images of grain properties throughout the bin from measurements taken by a few side-mounted antennas used to interrogate the bin contents. To develop this technology intensive research was put into the design of low profile, robust antennas as well as numerical analysis of the effects of different field distributions within conducting boundaries. Both electric and magnetic field sensitive antennas were built and tested in small lab-scale as well as full-scale grain bins to experimentally evaluate the performance of such imaging system. This thesis provides details on different system designs and analysis and describes the advantages and challenges associated with the techniques described.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".