Machine Learning on the estimation of Leaf Area Index

Yasamin Afrasiabian; Ali Mokhtari; Kang Yu

Machine Learning on the estimation of Leaf Area Index

Yasamin Afrasiabian
, Ali Mokhtari
, Kang Yu

Assistant Professorship of Precision Agriculture

Technical University of Munich

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The 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.

Original language	English
Title of host publication	Informatik in der Land-, Forst- und Ernahrungswirtschaft - Fokus
Subtitle of host publication	Kunstliche Intelligenz in der Agrar- und Ernahrungswirtschaft - Referate der 42. GIL-Jahrestagung
Editors	Markus Gandorfer, Christa Hoffmann, Nadja El Benni, Marianne Cockburn, Thomas Anken, Helga Floto
Publisher	Gesellschaft fur Informatik (GI)
Pages	21-26
Number of pages	6
ISBN (Electronic)	9783885797111
State	Published - 2022
Event	42. Jahrestagung 2022 der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft: Was bedeutet Kunstliche Intelligenz fur die Agrar- und Ernahrungswirtschaft, GIL 2022 - 42nd Annual Conference 2022 of the Society for Information Technology in Agriculture, Forestry and Food Industry: What does Artificial Intelligence mean for the Agricultural and Food industry, GIL 2022 - Ettenhausen, Switzerland Duration: 21 Feb 2022 → 22 Feb 2022

Publication series

Name	Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)
Volume	P-317
ISSN (Print)	1617-5468
ISSN (Electronic)	2944-7682

Conference

Conference	42. Jahrestagung 2022 der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft: Was bedeutet Kunstliche Intelligenz fur die Agrar- und Ernahrungswirtschaft, GIL 2022 - 42nd Annual Conference 2022 of the Society for Information Technology in Agriculture, Forestry and Food Industry: What does Artificial Intelligence mean for the Agricultural and Food industry, GIL 2022
Country/Territory	Switzerland
City	Ettenhausen
Period	21/02/22 → 22/02/22

Keywords

Leaf Area Index
Random Forest
Support Vector Machine
hyperspectral
multispectral
thermal

Cite this

Afrasiabian, Y., Mokhtari, A., & Yu, K. (2022). Machine Learning on the estimation of Leaf Area Index. In M. Gandorfer, C. Hoffmann, N. El Benni, M. Cockburn, T. Anken, & H. Floto (Eds.), Informatik in der Land-, Forst- und Ernahrungswirtschaft - Fokus: Kunstliche Intelligenz in der Agrar- und Ernahrungswirtschaft - Referate der 42. GIL-Jahrestagung (pp. 21-26). (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); Vol. P-317). Gesellschaft fur Informatik (GI).

Afrasiabian, Yasamin ; Mokhtari, Ali ; Yu, Kang. / Machine Learning on the estimation of Leaf Area Index. Informatik in der Land-, Forst- und Ernahrungswirtschaft - Fokus: Kunstliche Intelligenz in der Agrar- und Ernahrungswirtschaft - Referate der 42. GIL-Jahrestagung. editor / Markus Gandorfer ; Christa Hoffmann ; Nadja El Benni ; Marianne Cockburn ; Thomas Anken ; Helga Floto. Gesellschaft fur Informatik (GI), 2022. pp. 21-26 (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)).

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title = "Machine Learning on the estimation of Leaf Area Index",

abstract = "The 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.",

keywords = "Leaf Area Index, Random Forest, Support Vector Machine, hyperspectral, multispectral, thermal",

author = "Yasamin Afrasiabian and Ali Mokhtari and Kang Yu",

note = "Publisher Copyright: {\textcopyright} 2022 Gesellschaft fur Informatik (GI). All rights reserved.; 42. Jahrestagung 2022 der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft: Was bedeutet Kunstliche Intelligenz fur die Agrar- und Ernahrungswirtschaft, GIL 2022 - 42nd Annual Conference 2022 of the Society for Information Technology in Agriculture, Forestry and Food Industry: What does Artificial Intelligence mean for the Agricultural and Food industry, GIL 2022 ; Conference date: 21-02-2022 Through 22-02-2022",

year = "2022",

language = "English",

series = "Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)",

publisher = "Gesellschaft fur Informatik (GI)",

pages = "21--26",

editor = "Markus Gandorfer and Christa Hoffmann and \{El Benni\}, Nadja and Marianne Cockburn and Thomas Anken and Helga Floto",

booktitle = "Informatik in der Land-, Forst- und Ernahrungswirtschaft - Fokus",

}

Afrasiabian, Y, Mokhtari, A & Yu, K 2022, Machine Learning on the estimation of Leaf Area Index. in M Gandorfer, C Hoffmann, N El Benni, M Cockburn, T Anken & H Floto (eds), Informatik in der Land-, Forst- und Ernahrungswirtschaft - Fokus: Kunstliche Intelligenz in der Agrar- und Ernahrungswirtschaft - Referate der 42. GIL-Jahrestagung. Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI), vol. P-317, Gesellschaft fur Informatik (GI), pp. 21-26, 42. Jahrestagung 2022 der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft: Was bedeutet Kunstliche Intelligenz fur die Agrar- und Ernahrungswirtschaft, GIL 2022 - 42nd Annual Conference 2022 of the Society for Information Technology in Agriculture, Forestry and Food Industry: What does Artificial Intelligence mean for the Agricultural and Food industry, GIL 2022, Ettenhausen, Switzerland, 21/02/22.

Machine Learning on the estimation of Leaf Area Index. / Afrasiabian, Yasamin; Mokhtari, Ali; Yu, Kang.
Informatik in der Land-, Forst- und Ernahrungswirtschaft - Fokus: Kunstliche Intelligenz in der Agrar- und Ernahrungswirtschaft - Referate der 42. GIL-Jahrestagung. ed. / Markus Gandorfer; Christa Hoffmann; Nadja El Benni; Marianne Cockburn; Thomas Anken; Helga Floto. Gesellschaft fur Informatik (GI), 2022. p. 21-26 (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI); Vol. P-317).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Machine Learning on the estimation of Leaf Area Index

AU - Afrasiabian, Yasamin

AU - Mokhtari, Ali

AU - Yu, Kang

PY - 2022

Y1 - 2022

N2 - The 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.

AB - The 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.

KW - Leaf Area Index

KW - Random Forest

KW - Support Vector Machine

KW - hyperspectral

KW - multispectral

KW - thermal

UR - https://www.scopus.com/pages/publications/85128170919

M3 - Conference contribution

AN - SCOPUS:85128170919

T3 - Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)

SP - 21

EP - 26

BT - Informatik in der Land-, Forst- und Ernahrungswirtschaft - Fokus

A2 - Gandorfer, Markus

A2 - Hoffmann, Christa

A2 - El Benni, Nadja

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PB - Gesellschaft fur Informatik (GI)

T2 - 42. Jahrestagung 2022 der Gesellschaft fur Informatik in der Land-, Forst- und Ernahrungswirtschaft: Was bedeutet Kunstliche Intelligenz fur die Agrar- und Ernahrungswirtschaft, GIL 2022 - 42nd Annual Conference 2022 of the Society for Information Technology in Agriculture, Forestry and Food Industry: What does Artificial Intelligence mean for the Agricultural and Food industry, GIL 2022

Y2 - 21 February 2022 through 22 February 2022

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Afrasiabian Y, Mokhtari A, Yu K. Machine Learning on the estimation of Leaf Area Index. In Gandorfer M, Hoffmann C, El Benni N, Cockburn M, Anken T, Floto H, editors, Informatik in der Land-, Forst- und Ernahrungswirtschaft - Fokus: Kunstliche Intelligenz in der Agrar- und Ernahrungswirtschaft - Referate der 42. GIL-Jahrestagung. Gesellschaft fur Informatik (GI). 2022. p. 21-26. (Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI)).

Machine Learning on the estimation of Leaf Area Index

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Publication series

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Keywords

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