Agriculture applications contribution to improve precise pest management in China
Bibliographic record
Abstract
The rapid proliferation of agricultural applications (apps) in China's digital village initiative necessitates systematic evaluation of their functionality and accessibility. Regarding the agricultural pest control apps that can be searched in the Chinese market, this study collected and analyzed information using 18 variables, involving developers, languages, application systems, identified objects and functions. There were 158 apps that met the 11 mandatory features, and most of the applications were developed for Android and iOS systems. The functions, accuracy, response time and goals of agricultural apps are all important factors affecting the download and application of agricultural apps. Identification apps are in the initial stage, while comprehensive application apps are gradually increasing. Regional or National, even of crop-specific pest management apps are becoming mainstream. Case studies of prominent Chinese apps provide critical services such as disease diagnosis, pest control recommendations, and farm management solutions, leading to quantifiable benefits including reduced pesticide use, decreased crop losses, and increased farmer income in China. Agricultural applications accessible via smartphones have great potential in preventing crop losses and reducing pesticide use. The development of agricultural pest and disease control applications still has a long way to go, including precise assessment and potential risks during the implementation process. There is no doubt that against the backdrop of the continuous growth of the global population, these applications will facilitate the digital prevention and control of agricultural pests.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".