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Reducing the use of pesticides by early visual detection of diseases in precision agriculture is important. Because of the color similarity between potato-plant diseases, narrow band hyper-spectral imaging is required. Payload constraints on unmanned aerial vehicles require reduction of spectral bands. Therefore, we present a methodology for per-patch classification combined with hyper-spectral band selection. In controlled experiments performed on a set of individual leaves, we measure the performance of five classifiers and three dimensionality-reduction methods with three patch sizes. With the best-performing classifier an error rate of 1.5% is achieved for distinguishing two important potato-plant diseases.


Klaas Dijkstra, Jaap van de Loosdrecht, Lambert Schomaker and Marco A. Wiering

Published at the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017

Post Author: Klaas Dijkstra

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