Supplementary Data for Trustworthy Acquisition-Layer Preprocessing in Agricultural IoT Time-Series: Screening Records, Evidence Mapping, and Technical Synthesis

This supplementary dataset supports a systematic literature review on trustworthy acquisition-layer preprocessing for agricultural Internet of Things (Agri-IoT) time-series data. The review examines how previous studies address sensor-stream quality problems such as noise, outliers, missing data, sensor faults, drift, event preservation, data reduction, and resource-constrained edge processing. The dataset contains screening records, database search counts, deduplication summaries, publication-year distributions, source-title distributions, methodological classifications, technical-basis mappings, paper-to-section mappings, review-matrix tables, Mermaid figure source files, and working citation/reference files. The evidence base covers studies from 2018 to 2025 and includes a final unique synthesis pool of 294 studies after duplicate and overlap removal. The supplementary files are intended to improve transparency, traceability, and reproducibility of the review process. They show how selected studies were mapped to the manuscript sections, technical themes, data-quality problems, method families, edge/MCU/TinyML feasibility, and acquisition-layer trustworthiness contributions. The package includes CSV files, Excel workbooks, Mermaid figure scripts, reference support files, metadata documentation, and checksum information. Technical-basis categories are overlapping because one study may contribute to more than one theme, while methodological categories use one primary method per study.