Lagrange multipliers and optimality
SIAM Review
Estimating attributes: analysis and extensions of RELIEF
ECML-94 Proceedings of the European conference on machine learning on Machine Learning
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
YALE: rapid prototyping for complex data mining tasks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Detection of powdery mildew in grapevine using remotely sensed UV-induced fluorescence
International Journal of Remote Sensing
Image pattern classification for the identification of disease causing agents in plants
Computers and Electronics in Agriculture
A study of cross-validation and bootstrap for accuracy estimation and model selection
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Application of support vector machine technology for weed and nitrogen stress detection in corn
Computers and Electronics in Agriculture
Review: Development of soft computing and applications in agricultural and biological engineering
Computers and Electronics in Agriculture
Computers and Electronics in Agriculture
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Hyperspectral image analysis for precision viticulture
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
On the mean accuracy of statistical pattern recognizers
IEEE Transactions on Information Theory
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Early recognition of pathogen infection is of great relevance in precision plant protection. Pre-symptomatic disease detection is of particular interest. By use of a laserfluoroscope, UV-light induced fluorescence data were collected from healthy and with leaf rust inoculated wheat leaves of the susceptible cultivar Ritmo 2-4days after inoculation under controlled conditions. In order to evaluate pathogen impact on fluorescence spectra 215 wavelengths in the range of 370-800nm were recorded. The medians of fluorescence signatures suggest that inoculated leaves may be separated from healthy ones, but high-frequency oscillations and individual reactions of leaves indicate that separability is difficult to achieve. The misbalance between the high number of measured wavelengths and the low number of training examples induces a high overfitting risk. For a pre-symptomatic pathogen identification a small number of robust features was desired which comprise most of the information relevant for the given classification task. Instead of choosing only the most relevant wavelengths, the coefficients of polynomials fitting the spectra were used for classification. They specify the global curve characteristics. Piecewise fitting by polynomials of fourth order led to high classification accuracy. Support Vector Machines were used for classification. Cross validation demonstrated that the achieved classification accuracy reached 93%. This result could be attained on the second day after inoculation, before any visible symptoms appeared. The described method is of general interest for pre-symptomatic pathogen detection based on fluorescence spectra.