Systematic Review on Capacity Building through Renewable Energy enabled IoT- Unmanned Aerial Vehicle for Smart Agroforestry
Bibliographic record
Abstract
Agroforestry (AF) aims to produce more ecologically diversified and socially beneficial goods from the land than traditional farming. Capacity building on AF will aid in the information and the production required from agro-forested farms to undergo the technical assistance provided by unmanned aerial vehicle (UAV), and Internet of things (IoT) powered by the Renewable Energy (RE) sources. The potential adoption of these technological advancements in applications has shown tangible results. The pace in the development of UAVs, IoT, and RE has indicated that their applications will make smart-AF very feasible. This paper connects the RE, UAV, and IoT for capacity building in the AF farms. The four phases regarding RE, UAV, and IoT on AF have been constructed and the interpretive ranking on PRISMA extended (IR-PRISMA) has been used to filter and rank the most dominant theme among RE, UAV, and IoT for AF practices. Subsequently, the interventions are categorized into appropriate themes and subthemes. Based on the themes and sub-themes gaps and interdependency are identified and implications for research are discussed. The distribution across the globe and the Direct and Partial Influence (DI and PI) have also been determined to enhance their technological feasibility and potential impact on the AF farmlands considering the crops grown, climatic methods, demographic distribution, and geographic location. The technological intervention will enhance agriculture 4.0 which highly depends on the RE, UAV, and IoT, especially for AF farms.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".