Use of Unfold PCA for on-line plant stress monitoring and sensor failure detection

Kris Villez, Kathy Steppe, Dirk J.W. De Pauw

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

Phytodiagnostic tools are aimed at the early detection of plant and/or crop stress conditions, with a view to assisting farmers to timely intervene in order to prevent or limit plant damage. Continuous monitoring of the variations in stem diameter has attracted a lot of attention in the assessment of the plant's response to stress conditions, but on-line data acquisition leads to large datasets. Unfold Principal Component Analysis (UPCA) is a powerful and conventional tool for intelligent handling of large datasets and on-line process monitoring of batch processes in the absence of exact process knowledge. Although the use of UPCA is already common practice for certain biotechnological applications, practical applications in plant science with respect to on-line plant stress monitoring are still lacking. This work describes for the first time the application of this technique to datasets of two different plant species (i.e. young apple trees and truss tomato plants) with a view to the development of an early warning system for stress detection in plant crops. To this end, UPCA modelling was applied in such a way that a single diurnal cycle corresponds to a single batch in the original methodology. For both plant species, a PCA model was calibrated using the data of the first days of the experiment of a fault-free control plant. The remaining data of the control plant were projected onto this model for validation. Projection of experimental data of a stressed plant onto the same PCA model allowed successful stress detection, days before the appearance of visible symptoms. It can be concluded that successful alarm generation is possible with UPCA.

Original languageEnglish
Pages (from-to)23-34
Number of pages12
JournalBiosystems Engineering
Volume103
Issue number1
DOIs
StatePublished - May 2009
Externally publishedYes

Funding

The authors wish to thank the Institute for Encouragement of Innovation by means of Science and Technology in Flanders (IWT) for the Ph.D. project funding of the first author, the Research Foundation – Flanders (FWO-Vlaanderen) for the Postdoctoral Fellow funding granted to the second author and the Special Research Fund (BOF) of Ghent University for the Postdoctoral Fellow funding granted to the third author.

FundersFunder number
FWO-Vlaanderen
Research Foundation – Flanders
Agentschap voor Innovatie door Wetenschap en Technologie
Universiteit Gent
Bijzonder Onderzoeksfonds UGent

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