Farming Techniques and Pest Management Strategies of Queen Pineapple Farmers in Camarines Norte: Basis for a Mobile-Based Pest Detection
Bibliographic record
Abstract
The importance of agriculture is obvious, from food security and for the farmers who are simply living for it.At the context of developing an AI-based system for pest detection, the researchers conducted an in-depth study on the farmers' farming techniques and their pest management strategy that would provide a baseline data for the development.This study aims to provide insights on the different farming techniques and pest management utilized by farmers on the queen pineapple propagation in the province of Camarines Norte.Using a quantitative descriptive method from four large queen pineapple sites in Camarines Norte with N=200 local farmers as participants of the study, a survey across the different respondents were conducted.Similarly, intensive literature review was provided along with some first-hand interview with the officials from the Department of Agriculture and other partner agency.Findings of the study revealed that there are various farming techniques and pest management followed by farmers while issues and concerns were lack of access to information, lack of infrastructures and machines for pre-and post-harvest, and lack of innovative farming and pest management control.Efforts were made to continually fund researchers and materials needed to boost QP production but the fast dissemination of new knowledge hinders the practice and implementation of new knowledge.It is highly recommended that a mobile-based application with validated pest and diseases library is needed by the farmers and utilizing new technology enablers such as AI to detect pest and provide proper recommendations could be useful to them.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".