Exploring Different Architectures to Support Crop Farmers with a Mobile Application on Pesticide Control
Bibliographic record
Abstract
The MobiCrop app, which is a distributed mobile application has been proposed to aid crop farmers with timely decision making on the applicability of pesticides (i.e., which pesticide to apply, when, where, and how to apply them). Due to the vast amount of pesticide and crop data, the application is designed following the three-tier architecture technique which comprises the mobile devices, a cloud-hosted middleware, and cloud-based database. The idea is to enable the mobile device to retrieve the needed pesticide data from the back-end and when necessary, part of the data can be stored on the mobile through caching for offline accessibility. However, constantly updating the mobile cache through data polling is costly for the wireless bandwidth and energy usage on the mobile. Also, it is difficult to update the stale cache data when there is no wireless connectivity. Hence, this work explores three architectural designs of the MobiCrop app which are the: 1) the standalone (network independent), 2) distributed architecture through data offloading, and 3) distributed architecture through data partitioning.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".