<!doctype html><html lang=en-us><head><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1"><meta http-equiv=X-UA-Compatible content="IE=edge"><meta name=generator content="Hugo Blox Builder 5.9.7"><link rel=stylesheet href=/css/vendor-bundle.min.26c458e6907dc03073573976b7f4044e.css media=print onload='this.media="all"'><link rel=stylesheet href=https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1.9.4/css/academicons.min.css integrity="sha512-IW0nhlW5MgNydsXJO40En2EoCkTTjZhI3yuODrZIc8cQ4h1XcF53PsqDHa09NqnkXuIe0Oiyyj171BqZFwISBw==" crossorigin=anonymous media=print onload='this.media="all"'><link rel=stylesheet href=/css/wowchemy.537f3239551c0a1e51610e26c483ab65.css><link rel=stylesheet href=/css/libs/chroma/github-light.min.css title=hl-light media=print onload='this.media="all"'><link rel=stylesheet href=/css/libs/chroma/dracula.min.css title=hl-dark media=print onload='this.media="all"' disabled><meta name=author content="Prof. Dr. Kang Yu"><meta name=description content="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."><link rel=alternate hreflang=en-us href=https://paglab.org/dl.gi.de/handle/20.500.12116/38373><link rel=canonical href=https://paglab.org/dl.gi.de/handle/20.500.12116/38373><link rel=manifest href=/manifest.webmanifest><link rel=icon type=image/png href=/media/icon_hu_6d3d749f91f4f799.png><link rel=apple-touch-icon type=image/png href=/media/icon_hu_fe7fd4d210ee4baa.png><meta name=theme-color content="#1565c0"><meta property="twitter:card" content="summary"><meta property="twitter:site" content="@GetResearchDev"><meta property="twitter:creator" content="@GetResearchDev"><meta property="twitter:image" content="https://paglab.org/media/logo_hu_32055a858e223df5.png"><meta property="og:type" content="article"><meta property="og:site_name" content="Precision Agriculture Lab"><meta property="og:url" content="https://paglab.org/dl.gi.de/handle/20.500.12116/38373"><meta property="og:title" content="Machine Learning on the estimation of Leaf Area Index | Precision Agriculture Lab"><meta property="og:description" content="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."><meta property="og:image" content="https://paglab.org/media/logo_hu_32055a858e223df5.png"><meta property="og:locale" content="en-us"><meta property="article:published_time" content="2022-01-01T00:00:00+00:00"><meta property="article:modified_time" content="2022-01-01T00:00:00+00:00"><script type=application/ld+json>{"@context":"https://schema.org","@type":"Article","mainEntityOfPage":{"@type":"WebPage","@id":"https://paglab.org/dl.gi.de/handle/20.500.12116/38373"},"headline":"Machine Learning on the estimation of Leaf Area Index","datePublished":"2022-01-01T00:00:00Z","dateModified":"2022-01-01T00:00:00Z","author":{"@type":"Person","name":"Yasamin Afrasiabian"},"publisher":{"@type":"Organization","name":"Precision Agriculture Lab","logo":{"@type":"ImageObject","url":"https://paglab.org/media/logo_hu_416f580d5df88753.png"}},"description":"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."}</script><title>Machine Learning on the estimation of Leaf Area Index | Precision Agriculture Lab</title></head><body id=top data-spy=scroll data-offset=70 data-target=#TableOfContents class=page-wrapper data-wc-page-id=ebaf2d4e367304778a8427ca159e162f><script src=/js/wowchemy-init.min.4fef3e534144e9903491f0cc6527eccd.js></script><div class="page-header header--fixed"><header><nav class="navbar navbar-expand-lg navbar-light compensate-for-scrollbar" id=navbar-main><div class=container-xl><div class="d-none d-lg-inline-flex"><a class=navbar-brand href=/><img src=/media/logo_hu_43f54c113cf5fe64.png alt="Precision Agriculture Lab"></a></div><button type=button class=navbar-toggler data-toggle=collapse data-target=#navbar-content aria-controls=navbar-content aria-expanded=false aria-label="Toggle navigation">
<span><i class="fas fa-bars"></i></span></button><div class="navbar-brand-mobile-wrapper d-inline-flex d-lg-none"><a class=navbar-brand href=/><img src=/media/logo_hu_43f54c113cf5fe64.png alt="Precision Agriculture Lab"></a></div><div class="navbar-collapse main-menu-item collapse justify-content-end" id=navbar-content><ul class="navbar-nav d-md-inline-flex"><li class=nav-item><a class=nav-link href=/tour><span>Tour</span></a></li><li class=nav-item><a class=nav-link href=/post><span>News</span></a></li><li class=nav-item><a class=nav-link href=/people><span>People</span></a></li><li class=nav-item><a class=nav-link href=/event><span>Events</span></a></li><li class=nav-item><a class=nav-link href=/publication><span>Publications</span></a></li><li class=nav-item><a class=nav-link href=/data><span>Data</span></a></li><li class=nav-item><a class=nav-link href=/project><span>Projects</span></a></li><li class=nav-item><a class=nav-link href=/contact><span>Contact</span></a></li></ul></div><ul class="nav-icons navbar-nav flex-row ml-auto d-flex pl-md-2"></ul></div></nav></header></div><div class=page-body><div class=pub><div class="article-container pt-3"><h1>Machine Learning on the estimation of Leaf Area Index</h1><div class=article-metadata><div><span><a href=/author/yasamin-afrasiabian/>Yasamin Afrasiabian</a></span>, <span><a href=/author/ali-mokhtari/>Ali Mokhtari</a></span>, <span><a href=/author/prof.-dr.-kang-yu/>Prof. Dr. Kang Yu</a></span></div><span class=article-date>January, 2022</span></div></div><div class=article-container><h3>Abstract</h3><p class=pub-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.</p><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Type</div><div class="col-12 col-md-9"><a href=/publication/#conference-papers>Conference Papers</a></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=row><div class=col-md-1></div><div class=col-md-10><div class=row><div class="col-12 col-md-3 pub-row-heading">Publication</div><div class="col-12 col-md-9"><ol start=42><li>{GIL}-{Jahrestagung}, {Künstliche} {Intelligenz} in der {Agrar}- und {Ernährungswirtschaft}</li></ol></div></div></div><div class=col-md-1></div></div><div class="d-md-none space-below"></div><div class=space-below></div><div class=article-style><p>Yasamin Afrasiabian, Ali Mokhtari, & Kang Yu (2022). Machine Learning on the estimation of Leaf Area Index. <em>42. {GIL}-{Jahrestagung}, {Künstliche} {Intelligenz} in der {Agrar}- und {Ernährungswirtschaft}</em>: 21–26.</p></div><div class=share-box><ul class=share><li><a href="https://twitter.com/intent/tweet?url=https%3A%2F%2Fpaglab.org%2Fdl.gi.de%2Fhandle%2F20.500.12116%2F38373&text=Machine+Learning+on+the+estimation+of+Leaf+Area+Index" target=_blank rel=noopener class=share-btn-twitter aria-label=twitter><i class="fab fa-twitter"></i></a></li><li><a href="https://www.facebook.com/sharer.php?u=https%3A%2F%2Fpaglab.org%2Fdl.gi.de%2Fhandle%2F20.500.12116%2F38373&t=Machine+Learning+on+the+estimation+of+Leaf+Area+Index" target=_blank rel=noopener class=share-btn-facebook aria-label=facebook><i class="fab fa-facebook"></i></a></li><li><a href="mailto:?subject=Machine%20Learning%20on%20the%20estimation%20of%20Leaf%20Area%20Index&body=https%3A%2F%2Fpaglab.org%2Fdl.gi.de%2Fhandle%2F20.500.12116%2F38373" target=_blank rel=noopener class=share-btn-email aria-label=envelope><i class="fas fa-envelope"></i></a></li><li><a href="https://www.linkedin.com/shareArticle?url=https%3A%2F%2Fpaglab.org%2Fdl.gi.de%2Fhandle%2F20.500.12116%2F38373&title=Machine+Learning+on+the+estimation+of+Leaf+Area+Index" target=_blank rel=noopener class=share-btn-linkedin aria-label=linkedin-in><i class="fab fa-linkedin-in"></i></a></li><li><a href="whatsapp://send?text=Machine+Learning+on+the+estimation+of+Leaf+Area+Index%20https%3A%2F%2Fpaglab.org%2Fdl.gi.de%2Fhandle%2F20.500.12116%2F38373" target=_blank rel=noopener class=share-btn-whatsapp aria-label=whatsapp><i class="fab fa-whatsapp"></i></a></li><li><a href="https://service.weibo.com/share/share.php?url=https%3A%2F%2Fpaglab.org%2Fdl.gi.de%2Fhandle%2F20.500.12116%2F38373&title=Machine+Learning+on+the+estimation+of+Leaf+Area+Index" target=_blank rel=noopener class=share-btn-weibo aria-label=weibo><i class="fab fa-weibo"></i></a></li></ul></div><div class="media author-card content-widget-hr"><a href=/author/yasamin-afrasiabian/><img class="avatar mr-3 avatar-circle" src=/author/yasamin-afrasiabian/avatar_hu_2d1ec659c116dda4.jpeg alt="Yasamin Afrasiabian"></a><div class=media-body><h5 class=card-title><a href=/author/yasamin-afrasiabian/>Yasamin Afrasiabian</a></h5><h6 class=card-subtitle>PhD student</h6><p class=card-text>My research interests include remote sensing, particularly hyperspectral UAV and satellite imaging, and machine-learning methods for ecosystem-biodiversity characterisation, precision agriculture, and hydrological analysis.</p><ul class=network-icon aria-hidden=true><li><a href=/yasamin.afrasiabian@tum.de><i class="fas fa-envelope"></i></a></li><li><a href=https://orcid.org/0009-0003-6672-4095 target=_blank rel=noopener><i class="fab fa-orcid"></i></a></li><li><a href="https://scholar.google.com/citations?hl=en&user=xpDr418AAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/ target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://twitter.com/ target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li></ul></div></div><div class="media author-card content-widget-hr"><a href=/author/prof.-dr.-kang-yu/><img class="avatar mr-3 avatar-circle" src=/author/prof.-dr.-kang-yu/avatar_hu_3e776bb6562f294b.jpg alt="Prof. Dr. Kang Yu"></a><div class=media-body><h5 class=card-title><a href=/author/prof.-dr.-kang-yu/>Prof. Dr. Kang Yu</a></h5><h6 class=card-subtitle>Professor of Precision Agriculture</h6><p class=card-text>My research interests include precision crop farming, hyperspectral remote sensing, and AI in agriculture.</p><ul class=network-icon aria-hidden=true><li><a href=https://www.pa.wzw.tum.de target=_blank rel=noopener><i class="fas fa-globe"></i></a></li><li><a href=https://orcid.org/0000-0002-0686-6783 target=_blank rel=noopener><i class="fab fa-orcid"></i></a></li><li><a href="https://scholar.google.co.uk/citations?user=atIbrIsAAAAJ" target=_blank rel=noopener><i class="ai ai-google-scholar"></i></a></li><li><a href=https://github.com/kang-yu target=_blank rel=noopener><i class="fab fa-github"></i></a></li><li><a href=https://twitter.com/PrecisionAgLab target=_blank rel=noopener><i class="fab fa-twitter"></i></a></li><li><a href=https://www.researchgate.net/profile/Kang_Yu target=_blank rel=noopener><i class="ai ai-researchgate"></i></a></li><li><a href=https://www.linkedin.com/in/kang-yu/ target=_blank rel=noopener><i class="fab fa-linkedin"></i></a></li></ul></div></div></div></div></div><div class=page-footer><div class=container><footer class=site-footer><p class="powered-by copyright-license-text">© 2025 Precision Agriculture Lab. 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