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Generating fuzzy rules by learning from olive tree transpiration measurement - An algorithm to automatize Granier sap flow data analysis

2014Journal article Open access
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dc.contributor.authorSiqueira, J.M.
dc.contributor.authorPaço, Teresa Afonso do
dc.contributor.authorSilvestre, José C.
dc.contributor.authorSantos, F.L.
dc.contributor.authorFalcão, A.O.
dc.contributor.authorPereira, L.S.
dc.date.accessioned2017-05-08T09:55:11Z
dc.date.available2017-05-08T09:55:11Z
dc.date.issued2014
dc.description.abstractThe present study aims at developing an intelligent system of automating data analysis and prediction embedded in a fuzzy logic algorithm (FAUSY) to capture the relationship between environmental variables and sap flow measurements (Granier method). Environmental thermal gradients often interfere with Granier sap flow measurements since this method uses heat as a tracer, thus introducing a bias in transpiration flux calculation. The FAUSY algorithm is applied to solve measurement problems and provides an approximate and yet effective way of finding the relationship between the environmental variables and the natural temperature gradient (NTG), which is too complex or too ill-defined for precise mathematical analysis. In the process, FAUSY extracts the relationships from a set of input–output environmental observations, thus general directions for algorithm-based machine learning in fuzzy systems are outlined. Through an iterative procedure, the algorithm plays with the learning or forecasting via a simulated model. After a series of error control iterations, the outcome of the algorithm may become highly refined and be able to evolve into a more formal structure of rules, facilitating the automation of Granier sap flow data analysis. The system presented herein simulates the occurrence of NTG with reasonable accuracy, with an average residual error of 2.53% for sap flux rate, when compared to data processing performed in the usual way. For practical applications, this is an acceptable margin of error given that FAUSY could correct NTG errors up to an average of 76% of the normal manual correction process. In this sense, FAUSY provides a powerful and flexible way of establishing the relationships between the environment and NTG occurrencespt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citation"Computers and Electronics in Agriculture". ISSN 0168-1699. 101 (2014) p. 1-10pt_PT
dc.identifier.doihttp://dx.doi.org/10.1016/j.compag.2013.11.013pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.5/13600
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.subjectfuzzy rulept_PT
dc.subjectmachine learningpt_PT
dc.subjectsap flow measurementpt_PT
dc.subjectplant transpirationpt_PT
dc.subjectGranier methodpt_PT
dc.titleGenerating fuzzy rules by learning from olive tree transpiration measurement - An algorithm to automatize Granier sap flow data analysispt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleComputers and Electronics in Agriculturept_PT
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT

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