Masa: AI-Adaptive Mobile App for Sustainable Agriculture
Bibliographic record
Abstract
Mobile applications have made significant positive contributions to our daily lives in recent years. They have greatly improved practices in critical sectors such as healthcare, education, and agriculture etc. The agricultural sector is under pressure to meet the ever-increasing food demand while needing to deal with lack of agricultural resources (such as water and soil) and climate change issues. There is also a need to figure out how to attract and involve young people in agriculture in order to replace aging farmers. These issues necessitate the development of new, sustainable agricultural solutions. This is the demand that Masa app is attempting to meet. This paper presents the design and implementation of an AI-adaptive mobile app for sustainable agriculture. The app is divided into two sections: the market section which creates a platform where buyers are able to connect with the farmers directly and the resource center section where the power of AI is used to give guidance to the farmers. It also has learning resources where new entrants into the Agric sector can have first-hand walk-through guides in the Agric field containing all the information they need to venture into farming any product of choice. This will help mitigate some of the challenges faced by people who are new or venturing into the agricultural sector thereby encouraging more people to enter agriculture hence its sustainability. Initial feedback on the application from researchers of human-computer interaction domain show the application's promise in closing the gap in farming knowledge among general people while highlighting some limitations and suggestion that can be considered for improving the application.
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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 | 0.001 | 0.000 |
| 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) | 0.023 | 0.008 |
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".