Table 1.

The classification accuracy achieved by models derived from hyperspectral data collected from a presymptomatic sugar beet infected with C. beticola.

Method	Resistant		Tolerant		Susceptible
	Mean accuracy	Standard accuracy	Mean accuracy	Standard accuracy	Mean accuracy	Standard accuracy
Single model	78.4%	1.4%	74.9%	2.50%	69.4%	2.9%
Multi model	99.9%	0.1%	99.6%	0.2%	98.5%	0.2%

The mean accuracy and associated standard deviation obtained from a fivefold cross validation of infected vs. non-infected leaf were based on single pixel spectra.(A 100% accuracy score is realized when each pixel is assigned to the correct class.) Two different methods have been used, a single model approach with one Neural Network and a multi-model approach, in which an ensemble of classifiers is used to predict the respective class for each pixels. The rate of successful classification was increased by using a multi-model approach.