Mobile Technology in Agriculture: A Systematic Literature Review of Emerging Trends and Future Research Directions
Bibliographic record
Abstract
This literature review examines the current trends and future research directions in the use of mobile technology in agriculture from 2014 to 2024. In 2024, a search was conducted on the SCOPUS database, resulting in 2,570 documents published between 2014 and 2024. These documents were analyzed to identify the most frequently cited articles in the SCOPUS database. The search used keywords such as mobile technology, mobile applications, smartphones, and mobile devices in combination with keywords like agriculture, farming, and crop production. The analysis of publication trends revealed a significant increase in the number of documents and citations. Notably, India, the United States, and China emerged as the leading contributors in agricultural technology research. Most publications were conference papers and journal articles, with notable contributions from institutions such as Bina Nusantara University and the Sri Lanka Institute of Information Technology. Through keyword analysis, major research clusters focusing on crops, the Internet of Things (IoT), and mobile applications were identified. Key journals in this field, such as Communications in Computer and Information Science, Sensors (Switzerland), and Agriculture (Switzerland), cover these topics. This review aims to highlight the global, dynamic, and interdisciplinary nature of research in mobile agricultural technology. It underscores the increasing importance of this field and emphasizes the need for further exploration
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.026 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".