Humidity Sensor Drift Detection and Correction Based on a Kalman Filter with an Artificial Neural Network for Commercial Cultivation of Tropical Orchids

Author:

Sirisanwannakul Kraithep,Siripool Nutchanon,Kongprawechnon Waree,Dangsakul Prachumpong,Lewlomphaisarl Udom,Sartsatit Seksun,Duangtanoo Thanika,Keerativittayanun Suthum,Suhaili Wida Susanty Haji,Owada Yasunori,Mya Khin Than,Ariffin Sharifah H. S.,Karnjana Jessada

Publisher

Springer International Publishing

Reference11 articles.

1. Department of International Trade Promotion.: The orchid world situation (2018). https://www.ditp.go.th/contents_attach/539560/539560.pdf. Accessed 27 June 2020

2. Du, X., Wang, J., Ji, P., Gan, K.: Design and implement of wireless measure and control system for greenhouse. In: Proceedings of the 30th Chinese Control Conference, pp. 4572–4575. IEEE (2011)

3. Griesel, S., Theel, M., Niemand, H., Lanzinger, E.: Acceptance test procedure for capacitive humidity sensors in saturated conditions. WMO CIMO TECO-2012, Brussels, Belgium, pp. 1–7 (2012)

4. Holmberg, M., Artursson, T.: Drift compensation, standards, and calibration methods. In: Handbook of Machine Olfaction: Electronic Nose Technology, pp. 325–346 (2002)

5. Kumar, D., Rajasegarar, S., Palaniswami, M.: Automatic sensor drift detection and correction using spatial Kriging and Kalman filtering. In: 2013 IEEE International Conference on Distributed Computing in Sensor Systems, pp. 183–190. IEEE (2013)

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2. Multi-Sensor Data Fusion for Detection of Sensor Drifts in a Landslide Monitoring System;2022 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON);2022-01-26

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