The aim of this research is the early detection of plant diseases based on the combination of vegetation indices. We have seen that an individual index such as the most popular one, namely NDVI, does not discriminate adequately between healthy and diseased plants, e.g. Cercospora beticola, Erysiphe betae, and Uromyces betae. However, by combining vegetation indices, which are usually called features in classification, very reliable results can be achieved. We use Support Vector Machines for classification. By this we receive a classification accuracy of almost 95% for Cercospora beticola and Uromyces betae and still over 92% for Erysiphe betae. Depending on the different plant diseases we have found that different vegetation indices are important, too. Consequently, the question how to find the best index for every plant disease and the choice of the best subset arise. Both questions are not the same, because different indices contain similar information which can already be seen from the formula of the calculation of the vegetation index. These dependencies do not have to be linear. In order to identify optimal subsets of features for the different pathogens already at an early stage of infestation, we have found that entropy and mutual information are adequate concepts. Accordingly we use the minimum redundancy - maximum relevance (mRMR) criterion to evaluate the features. We have found that we need different indices and feature subsets of different sizes for different diseases.

Date Published: 18 September 2009
PDF: 10 pages
Proc. SPIE 7472, Remote Sensing for Agriculture, Ecosystems, and Hydrology XI, 747217 (18 September 2009); doi: 10.1117/12.830525

Published in SPIE Proceedings Vol. 7472:
Remote Sensing for Agriculture, Ecosystems, and Hydrology XI
Christopher M. U. Neale; Antonino Maltese, Editor(s)