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

[1]  Georg Noga,et al.  UV-induced fluorescence spectra and lifetime determination for detection of leaf rust (Puccinia triticina) in susceptible and resistant wheat (Triticum aestivum) cultivars. , 2011, Functional plant biology : FPB.

[2]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[3]  Hong Zheng,et al.  Automatic sorting of Chinese jujube (Zizyphus jujuba Mill. cv. ‘hongxing’) using chlorophyll fluorescence and support vector machine , 2010 .

[4]  G. Noga,et al.  Quantum yield of non-regulated energy dissipation in PSII (Y(NO)) for early detection of leaf rust (Puccinia triticina) infection in susceptible and resistant wheat (Triticum aestivum L.) cultivars , 2010, Precision Agriculture.

[5]  L. Plümer,et al.  Original paper: Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance , 2010 .

[6]  Yubin Lan,et al.  Review: Development of soft computing and applications in agricultural and biological engineering , 2010 .

[7]  Jean-Michel Roger,et al.  Improved Discrimination between Monocotyledonous and Dicotyledonous Plants for Weed Control Based on the Blue-Green Region of Ultraviolet-Induced Fluorescence Spectra , 2010, Applied spectroscopy.

[8]  Lorenzo Bruzzone,et al.  Kernel methods for remote sensing data analysis , 2009 .

[9]  Panos M. Pardalos,et al.  A survey of data mining techniques applied to agriculture , 2009, Oper. Res..

[10]  Jeremy S. Smith,et al.  Image pattern classification for the identification of disease causing agents in plants , 2009 .

[11]  Jan Kuckenberg,et al.  Temporal and spatial changes of chlorophyll fluorescence as a basis for early and precise detection of leaf rust and powdery mildew infections in wheat leaves , 2009, Precision Agriculture.

[12]  J. Roger,et al.  Detection of powdery mildew in grapevine using remotely sensed UV‐induced fluorescence , 2008 .

[13]  W. Vermerris,et al.  The Role of Phenols in Plant Defense , 2008 .

[14]  C. Buschmann.  Variability and application of the chlorophyll fluorescence emission ratio red/far-red of leaves , 2007, Photosynthesis Research.

[15]  Ladislav Nedbal,et al.  Visualization of dynamics of plant-pathogen interaction by novel combination of chlorophyll fluorescence imaging and statistical analysis: differential effects of virulent and avirulent strains of P. syringae and of oxylipins on A. thaliana. , 2007, Journal of experimental botany.

[16]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[17]  Jean-Pierre Da Costa,et al.  Hyperspectral Image Analysis for Precision Viticulture , 2006, ICIAR.

[18]  Ingo Mierswa,et al.  YALE: rapid prototyping for complex data mining tasks , 2006, KDD '06.

[19]  S. Prasher,et al.  Application of support vector machine technology for weed and nitrogen stress detection in corn , 2006 .

[20]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[21]  R. Nicholson,et al.  Phenolic Compound Biochemistry , 2006 .

[22]  Paul M. Mather,et al.  Support vector machines for classification in remote sensing , 2005 .

[23]  D. Hagenbeek,et al.  Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. , 2004, Plant & cell physiology.

[24]  Erich-Christian Oerke,et al.  Safeguarding production-losses in major crops and the role of crop protection , 2004 .

[25]  Jason Weston,et al.  Gene Selection for Cancer Classification using Support Vector Machines , 2002, Machine Learning.

[26]  D. Moshou,et al.  The potential of optical canopy measurement for targeted control of field crop diseases. , 2003, Annual review of phytopathology.

[27]  Alexander J. Smola,et al.  Learning with Kernels: support vector machines, regularization, optimization, and beyond , 2001, Adaptive computation and machine learning series.

[28]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[29]  Lin Zhang,et al.  Fluorescence from rust fungi: a simple and effective method to monitor the dynamics of fungal growth in planta , 2001 .

[30]  Ismael Moya,et al.  Ultraviolet-induced fluorescence for plant monitoring: present state and prospects , 1999 .

[31]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[32]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[33]  W. Lüdeker,et al.  Detection of Fungal Infection of Plants by Laser-induced Fluorescence: An Attempt to Use Remote Sensing , 1996 .

[34]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[35]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[36]  R. Tyrrell Rockafellar,et al.  Lagrange Multipliers and Optimality , 1993, SIAM Rev..

[37]  H. Lichtenthaler,et al.  Fluorescence emission spectra of plant leaves and plant constituents , 1991, Radiation and environmental biophysics.

[38]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[39]  Karl-Rudolf Koch,et al.  Parameter estimation and hypothesis testing in linear models , 1988 .

[40]  Carl de Boor,et al.  A Practical Guide to Splines , 1978, Applied Mathematical Sciences.

[41]  G. F. Hughes,et al.  On the mean accuracy of statistical pattern recognizers , 1968, IEEE Trans. Inf. Theory.

[42]  H. Lilliefors.  On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .

[43]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .