Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines

Römer, Christoph; Bürling, Kathrin; Hunsche, Maurício; Rumpf, Till; Noga, Georg; Plümer, Lutz

doi:10.1016/j.compag.2011.09.011

Cited by 84 publications

(40 citation statements)

References 28 publications

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“…In this study, two different supervised classification methods were used for data analysis, the LDA and the non-linear SVM methods. Both classification methods have shown good results for detecting plant diseases at small scale [14,21,67,68] but to our knowledge have not been used previously for the detection of plant diseases at large scale, such as the one of this study. For the whole dataset, LDA reached an overall accuracy of 59.0% and a κ of 0.487 while SVM showed a higher overall accuracy, 79.2%, and a slightly higher κ, 0.495.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, Rumpf et al [21] used this approach to discriminate between healthy sugar beet leaves from that infected with various foliar pathogens that included Cercospora beticola, Uromyces beate and Erysiphe betae at early stages of pathogenesis based on hyperspectral data. Similarly, Römer et al [68] detected wheat leaf rust at a pre-symptomatic stage using UV-light induced fluorescence data analysed by SVM classification methods. Nevertheless, in our study, although SVM reached the highest overall accuracy, LDA classified olive trees better at the initial and low VW severity levels with accuracies of 71.4% and 75.0%, respectively, in comparison with the 14.3% and 40.6% obtained by SVM.…”

Section: Discussionmentioning

confidence: 99%

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Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas

2015

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Abstract:Automatic methods for an early detection of plant diseases (i.e., visible symptoms at early stages of disease development) using remote sensing are critical for precision crop protection. Verticillium wilt (VW) of olive caused by Verticillium dahliae can be controlled only if detected at early stages of development. Linear discriminant analysis (LDA) and support vector machine (SVM) classification methods were applied to classify V. dahliae severity using remote sensing at large scale. High-resolution thermal and hyperspectral imagery were acquired with a manned platform which flew a 3000-ha commercial olive area. LDA reached an overall accuracy of 59.0% and a κ of 0.487 while SVM obtained a higher overall accuracy, 79.2% with a similar κ, 0.495. However, LDA better classified trees at initial and low severity levels, reaching accuracies of 71.4 and 75.0%, respectively, in comparison with the 14.3% and 40.6% obtained by SVM. Normalized canopy temperature, chlorophyll fluorescence, structural, xanthophyll, chlorophyll, carotenoid and disease indices were found to be the best indicators for early and advanced stage infection by VW. These results demonstrate that the methods developed in other studies at orchard scale are valid for flights in large areas comprising several olive orchards differing in soil and crop management characteristics.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas

2015

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“…In using SVMs, an upper bound is minimized by the principle of structural risk minimization (SRM) whereas in using ANNs, traditional empirical risk minimization (ERM) is employed [30]. Recently, SVMs have been applied to estimate soil moisture [31], leaf area index, leaf chlorophyll density [32] and leaf infections [33].…”

Section: Introductionmentioning

confidence: 99%

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Shi

Yang

et al. 2016

Remote Sensing

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Abstract:Nitrogen is an essential nutrient element in crop photosynthesis and yield improvement. Thus, it is urgent and important to accurately estimate the leaf nitrogen contents (LNC) of crops for precision nitrogen management. Based on the correlation between LNC and reflectance spectra, the hyperspectral LiDAR (HSL) system can determine three-dimensional structural parameters and biochemical changes of crops. Thereby, HSL technology has been widely used to monitor the LNC of crops at leaf and canopy levels. In addition, the laser-induced fluorescence (LIF) of chlorophyll, related to the histological structure and physiological conditions of green plants, can also be utilized to detect nutrient stress in crops. In this study, four regression algorithms, support vector machines (SVMs), partial least squares (PLS) and two artificial neural networks (ANNs), back propagation NNs (BP-NNs) and radial basic function NNs (RBF-NNs), were selected to estimate rice LNC in booting and heading stages based on reflectance and LIF spectra. These four regression algorithms were used for 36 input variables, including the reflectance spectral variables on 32 wavelengths and four peaks of the LIF spectra. A feature weight algorithm was proposed to select different band combinations for the LNC retrieval models. The determination coefficient (R 2 ) and the root mean square error (RMSE) of the retrieval models were utilized to compare their abilities of estimating the rice LNC. The experimental results demonstrate that (I) these four regression methods are useful for estimating rice LNC in the order of RBF-NNs > SVMs > BP-NNs > PLS; (II) The LIF data in two forms, including peaks and indices, display potential in rice LNC retrieval, especially when using the PLS regression (PLSR) model for the relationship of rice LNC with spectral variables. The feature weighting algorithm is an effective and necessary method to determine appropriate band combinations for rice LNC estimation.

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“…Although both regression models aim to predict crop performance traits, the univariate approach only uses a limited set of SRI (Gonzalez-Dugo et al 2015), whereas the multivariate approach utilises the entire spectrum to model plant response (Kipp et al 2014). Some authors show that, in comparison to univariate approaches, multivariate approaches were able to provide better results in detection of early stages of biotic stress (Römer et al 2011), or in the predict nitrogen and water content (Kusnierek and Korsaeth 2015). Another approach, based on radiative transfer models, seems promising for field phenotyping, as this takes into account biochemical and structural properties of the leaf and canopy (Thorp et al 2015).…”

Section: Data Analysis and Interpretationmentioning

confidence: 99%

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

Virlet

Sabermanesh

Sadeghi‐Tehran

et al. 2017

Functional Plant Biol.

319

210

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Abstract. Current approaches to field phenotyping are laborious or permit the use of only a few sensors at a time. In an effort to overcome this, a fully automated robotic field phenotyping platform with a dedicated sensor array that may be accurately positioned in three dimensions and mounted on fixed rails has been established, to facilitate continual and high-throughput monitoring of crop performance. Employed sensors comprise of high-resolution visible, chlorophyll fluorescence and thermal infrared cameras, two hyperspectral imagers and dual 3D laser scanners. The sensor array facilitates specific growth measurements and identification of key growth stages with dense temporal and spectral resolution. Together, this platform produces a detailed description of canopy development across the crops entire lifecycle, with a high-degree of accuracy and reproducibility.

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Robust fitting of fluorescence spectra for pre-symptomatic wheat leaf rust detection with Support Vector Machines

Cited by 84 publications

References 28 publications

Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas

Early Detection and Quantification of Verticillium Wilt in Olive Using Hyperspectral and Thermal Imagery over Large Areas

Using Different Regression Methods to Estimate Leaf Nitrogen Content in Rice by Fusing Hyperspectral LiDAR Data and Laser-Induced Chlorophyll Fluorescence Data

Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring

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