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.

The detection and control of diseases is a primary objective of French viticultural research. In this context, we have collected Near Infra-Red (NIR) spectra on vine leaves over different acquisition times, three years from 2021 to 2023, with the aim of selecting the discriminating spectral bands of yellowing of the vine compared to healthy plants, confounding symptoms, and other diseases of vines: rolling of leaves and esca. We also want these bands to be insensitive with respect to the acquisition time. To achieve this, we adapt the Correlation Explanation (CorEx) algorithm so that it can select suitable bands from NIR spectra under the constraint of insensitivity versus the acquisition times (CorEx-BS). Our two-steps method consists firstly in searching for a set of bands that best explain the correlations between the wavelengths, as measured by the multivariate mutual information, and secondly to bind these bands with the labels and the acquisition time to get the bands relevant to the labels. This approach facilitates the discovery of distinctive spectral bands associated with a class of interest, yellowing of the vine in our application, which are robust regarding the different acquisition times of spectra, the years in our case. The results in terms of Davies-Bouldin index (DB) and Calinski-Harabasz Index (CH) show that our method outperforms other classical bands clustering selection techniques. Index Terms-Correlation Explanation, Band clustering, Band selection, Classification, Grapevine Flavescence Dorée