Convolutional neural networks for the detection of esca disease complex in asymptomatic grapevine leaves

The Esca complex is a grapevine trunk disease that significantly threatens modern viticulture. The lack of effective control strategies and the intricacy of Esca disease manifestation render essential the identification of affected plants before symptoms become evident to the naked eye. This study applies Convolutional Neural Networks (CNNs) to distinguish, at the pixel level, between healthy, asymptomatic and symptomatic grapevine leaves of a Tempranillo red-berried cultivar using Hyperspectral imaging (HSI) in the 900–1700 nm spectral range. We show that a 1D CNN performs semantic image segmentation (SiS) with higher accuracy than PLS-DA, one of HSI data’s most widely used classification algorithms.