There is a constant push on agriculture to produce more food and other inputs for different industries. Precision agriculture is essential to meet these demands. The intake of this modern technology is rapidly increasing among large and medium-sized farms. However, small farms still struggle with their adaptation due to the expensive initial costs. A contribution in handling this challenge, this paper presents data gathering for testing an in-house made, cost-effective, multispectral camera to detect Flavescence dorée (FD). FD is a grapevine disease that, in the last few years, has become a major concern for grapevine producers across Europe. As a quarantine disease, mandatory control procedures, such as uprooting infected plants and removing all vineyard if the infection is higher than 20%, lead to an immense economic loss. Therefore, it is critical to detect each diseased plant promptly, thus reducing the expansion of Flavescence dorée. Data from two vineyards near Riva del Garda, Trentino, Italy, was acquired in 2022 using multispectral and hyperspectral cameras. The initial finding showed that there is a possibility to detect Flavescence dorée using Linear discriminant analysis (LDA) with hyperspectral data, obtaining an accuracy of 96.6 %. This result justifies future investigation on the use of multispectral images for Flavescence dorée detection.