Advancing agricultural time-series data analysis with a new preprocessing method - Nor Azhar, Ahmad; Mohamed Ariff, Ameedeen; Syafiq Fauzi, Kamarulzaman (IEEE, 2026)
Agricultural IoT time-series data are big, varied, and full of artefacts. If they aren't preprocessed with methods that are aware of the domain and work on the edge, they might cause problems with downstream analytics. This study introduces a streamlined pipeline that integrates algorithmic outlier correction, discrete wavelet denoising, adaptive z-score normalisation, principal component projection, and Seasonal and Trend decomposition using Locally Estimated Scatterplot Smoothing (LOESS)(STL)-style seasonality-trend decomposition to cleanse signals while maintaining the temporal structure essential for control and forecasting. The method is tested on field datasets that include Chinese Kale, Rock Melon, and Tomato (ambient and zone streams), as well as a synthetic control, and compared to three agriculture-adapted baselines (M1-M3). Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) are used to test accuracy, the Seasonality Index (SI) and period-Autocorrelation (AC) are used to measure integrity, and processing time and memory footprints that fit within farm-node limits are used to measure feasibility. Results show consistent error reductions relative to M1-M3 across crops, near-zero disturbance of seasonal amplitude and period-AC, and lower runtime, including an end-to-end 1.245885 -s pass on the control set versus substantially longer times for a statistical smoother baseline. These results show that preprocessing that is aligned with the domain can produce cleaner signals that are true to the structure at edge latency. This makes greenhouse and field monitoring more reliable and allows for better forecasting, scheduling, and actuation. The contribution bridges a practical gap between generic cleaning routines and farm-specific requirements, enhancing reliable and efficient analysis for modern digital agriculture.