Advancing agricultural time-series data analysis with a new preprocessing method

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 out...

Full description

Bibliographic Details

Main Authors:	Nor Azhar, Ahmad, Mohamed Ariff, Ameedeen, Syafiq Fauzi, Kamarulzaman
Format:	Conference or Workshop Item
Language:	en
Published:	IEEE 2026
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software S Agriculture (General)
Online Access:	https://umpir.ump.edu.my/id/eprint/47682/1/Advancing_Agricultural_Time-Series_Data_Analysis_with_a_New_Preprocessing_Method.pdf https://umpir.ump.edu.my/id/eprint/47682/ https://doi.org/10.1109/AGRETA68375.2025.11474186
Tags:	Add Tag No Tags, Be the first to tag this record!