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...
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| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
IEEE
2026
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| Subjects: | |
| 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 |
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