Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine

Rumpf, Till; Römer, Christoph; Weis, Martin; Sökefeld, Markus; Gerhards, Roland; Plümer, Lutz

doi:10.1016/j.compag.2011.10.018

Cited by 68 publications

(33 citation statements)

References 19 publications

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“…The SVM has been widely applied in classification problems in recent years (Vapnick, 1998;Leslie et al, 2002;Tarabalka et al, 2010;Rumpf et al, 2012). The essence of SVM is to find a hyperplane, by which all of categories can be separated but the deviation is low.…”

Section: Support Vector Machinementioning

confidence: 99%

Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR

Gong

Shi

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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Section: Support Vector Machinementioning

confidence: 99%

Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR

Gong

Shi

et al. 2016

International Journal of Applied Earth Observation and Geoinfor

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“…Further loss in classification accuracy is due to influencing factors such as changing ambient light conditions in the field and the need of rectification or repeated calibration in the field (Peteinatos et al, 2014). Weed detection can also be done by means of image processing methods, using infrared, multispectral, or RGB cameras to extract plant properties (Andújar et al, 2011;Gerhards and Oebel, 2006;Romeo et al, 2013;Weis and Gerhards, 2007) employing color, shape, and texture features (Guijarro et al, 2011;McCarthy et al, 2010;Pérez et al, 2000;Rumpf et al, 2012;Tellaeche et al, 2011), either taken from a low distance above ground mounted on agricultural vehicles or by Unmanned Aerial Vehicles (UAV) based remote sensing Primicerio et al, 2012;Torres-Sánchez et al, 2013;Zhang and Kovacs, 2012).…”

Section: Methods For Site-specific Management and Mappingmentioning

confidence: 99%

Comparative classification analysis of post-harvest growth detection from terrestrial LiDAR point clouds in precision agriculture

Koenig

Höfle

Hämmerle

et al. 2015

ISPRS Journal of Photogrammetry and Remote Sensing

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“…The point cloud classification analysis included the classification of data within four channels and the comparative classification of the spatial parameter and four channels with that. Support vector machine (SVM), which is a popular machine learning method, has been widely applied for data classification and regression [43,44]. SVM has certain advantages, such as robustness and demanding small sample size of remote sensing data for training [45].…”

Section: System Evaluation Based On Point Cloud Classificationmentioning

confidence: 99%

Active 3D Imaging of Vegetation Based on Multi-Wavelength Fluorescence LiDAR

Zhao

Shi

Yang

et al. 2020

Sensors

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Comprehensive and accurate vegetation monitoring is required in forestry and agricultural applications. The optical remote sensing method could be a solution. However, the traditional light detection and ranging (LiDAR) scans a surface to create point clouds and provide only 3D-state information. Active laser-induced fluorescence (LIF) only measures the photosynthesis and biochemical status of vegetation and lacks information about spatial structures. In this work, we present a new Multi-Wavelength Fluorescence LiDAR (MWFL) system. The system extended the multi-channel fluorescence detection of LIF on the basis of the LiDAR scanning and ranging mechanism. Based on the principle prototype of the MWFL system, we carried out vegetation-monitoring experiments in the laboratory. The results showed that MWFL simultaneously acquires the 3D spatial structure and physiological states for precision vegetation monitoring. Laboratory experiments on interior scenes verified the system's performance. Fluorescence point cloud classification results were evaluated at four wavelengths and by comparing them with normal vectors, to assess the MWFL system capabilities. The overall classification accuracy and Kappa coefficient increased from 70.7% and 0.17 at the single wavelength to 88.9% and 0.75 at four wavelengths. The overall classification accuracy and Kappa coefficient improved from 76.2% and 0.29 at the normal vectors to 92.5% and 0.84 at the normal vectors with four wavelengths. The study demonstrated that active 3D fluorescence imaging of vegetation based on the MWFL system has a great application potential in the field of remote sensing detection and vegetation monitoring.

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Sequential support vector machine classification for small-grain weed species discrimination with special regard to Cirsium arvense and Galium aparine

Cited by 68 publications

References 19 publications

Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR

Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR

Comparative classification analysis of post-harvest growth detection from terrestrial LiDAR point clouds in precision agriculture

Active 3D Imaging of Vegetation Based on Multi-Wavelength Fluorescence LiDAR

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