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Machine Learning on the estimation of Leaf Area Index

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dc.contributor.authorAfrasiabian, Yasamin
dc.contributor.authorMokhtari, Ali
dc.contributor.authorYu, Kang
dc.contributor.editorGandorfer, Markus
dc.contributor.editorHoffmann, Christa
dc.contributor.editorEl Benni, Nadja
dc.contributor.editorCockburn, Marianne
dc.contributor.editorAnken, Thomas
dc.contributor.editorFloto, Helga
dc.date.accessioned2022-02-24T13:34:34Z
dc.date.available2022-02-24T13:34:34Z
dc.date.issued2022
dc.description.abstractThe Leaf Area Index (LAI) is an important indicator in agriculture that can be considered a reliable plant growth parameter. The objective of this study is to make use of two different machine learning algorithms including Support Vector Machine (SVM), and Random Forest (RF) to improve the estimation of leaf area index using multispectral, thermal, and hyperspectral data. The results showed that RF was the best model to improve the accuracy of the LAI estimation compared to the simple linear regression (previous study) and SVM (R2 = 0.91 for RF and R2 = 0.87 for SVM). To evaluate the effects of spectral portions on LAI estimation without calculating the spectral indices, (SI) we inputted each pair of spectral bands for training and testing both RF and SVM. It was found that the best correlation was lower compared to use SIs. However, R2 variations were more homogeneous across the whole spectrum, which suggests that even by using multispectral broadband bands in RF and SVM, a good correlation will be achieved.en
dc.identifier.isbn978-3-88579-711-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/38373
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartof42. GIL-Jahrestagung, Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-317
dc.subjectLeaf Area Index
dc.subjecthyperspectral
dc.subjectmultispectral
dc.subjectRandom Forest
dc.subjectSupport Vector Machine
dc.subjectthermal
dc.titleMachine Learning on the estimation of Leaf Area Indexen
dc.typeText/Conference Paper
gi.citation.endPage26
gi.citation.publisherPlaceBonn
gi.citation.startPage21
gi.conference.date21.-22. Februar 2022
gi.conference.locationTänikon, Online

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P317 - 42. GIL-Jahrestagung 2022 - Fokus: Künstliche Intelligenz in der Agrar- und Ernährungswirtschaft