Distributed mobile application for crop farmers
Bibliographic record
Abstract
In a recent research collaboration with the College of Agriculture at the University of Saskatchewan, Canada, we investigate the best ways that mobile devices can aid in applying pesticides. Farmers require different pesticides for different crops and at every stage in the development cycle of the crops. Our work presents MobiCrop, which is a mobile app that is deployed to aid farmers with timely decision making on the application of pesticides (i.e., which pesticide to apply, when, where, and how to apply them). MobiCrop is not a standalone mobile app but, a three-tier distributed application with a cloud-hosted backend middleware and a web server. This is to ensure consistent state of services in a single update to the many geographically dispersed mobile users (farmers). The deployment of the middleware layer is to aid mobile updates synchronization in an optimal time based on policies. We further implement a caching technique on the mobile device in order to facilitate seamless access to the data in situations where there is no connectivity. The work also details the preliminary results of the evaluation of MobiCrop which is promising.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".