Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS)

DOI: http://dx.doi.org/10.15439/2024F7286

Citation: Proceedings of the 19th Conference on Computer Science and Intelligence Systems (FedCSIS), M. Bolanowski, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 39, pages 707–712 (2024)

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Abstract. The detection and control of diseases constitute a primary objective of French viticultural research. In this paper, we present a bottom-up hierarchical approach for selecting spectral bands suitable for class discrimination of spectra acquired by Infrared spectroscopy. Our method entails evaluating neighboring bands using various similarity metrics, applying aggregation criteria, and ultimately identifying a limited number of the most relevant bands for the separation of classes. The bandwidths are limited within a range as is typically required for choosing existing optical filters or specifying colored filter arrays. Our approach facilitates the discovery of distinctive spectral bands associated with a disease of interest, enabling the customization of multispectral cameras to meet specific requirements. It was applied to spectra collected on vine leaves spanning a three-year period with the goal to identify the most discriminant bands for the detection of grapevine yellows. The results show that a limited number of bands are sufficient to identify this class of interest through a classifier based on Linear Discriminant Analysis.

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