The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W8, 151–156, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W8-151-2019
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W8, 151–156, 2019
https://doi.org/10.5194/isprs-archives-XLII-3-W8-151-2019

  21 Aug 2019

21 Aug 2019

EVALUATION OF MACHINE LEARNING TECHNIQUES IN VINE LEAVES DISEASE DETECTION: A PRELIMINARY CASE STUDY ON FLAVESCENCE DORÉE

J. Hruška1, T. Adão1,2, L. Pádua1,2, N. Guimarães1, E. Peres1,2, R. Morais1,2, and J. J. Sousa1,2 J. Hruška et al.
  • 1University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal
  • 2INESC TEC Technology and Science, 4200-465 Porto, Portugal

Keywords: Disease detection, Flavescence Dorée, Hyperspectral data, Machine learning, Viticulture

Abstract. Vine culture is influenced by many factors, such as the weather, soil or topography, which are triggers to phytosanitary issues. Among them are some diseases, that are responsible for major economic losses that can, however, be managed with timely interventions in the field, viable of leading to effective results by preventing damage propagation. While not all symptoms might present a visible evidence, hyperspectral sensors can tackle this aspect with their ability for measuring hundreds of continuously sparse bands that range beyond the eye-perceptible spectrum. Having such research line in mind in this work, a hyperspectral sensor was applied to analyse the spectral status of vine leaves samples, collected in three chronologically distinct campaigns, while costly and destructive laboratory methods were used to track Flavescence Dorée (FD) in the same samples, for a ground truth information. Regarding data processing, machine learning approaches were used, in which several classifiers were selected to detect FD in vine leaves hyperspectral images. The goal was to evaluate and find most suitable classifier for this task.