  The classification of different grapevine varieties is a relevant phenotyping
task in Precision Viticulture since it enables estimating the growth of
vineyard rows dedicated to different varieties, among other applications
concerning the wine industry. This task can be performed with destructive
methods that require time-consuming tasks, including data collection and
analysis in the laboratory. However, Unmanned Aerial Vehicles (UAV) provide a
more efficient and less prohibitive approach to collecting hyperspectral data,
despite acquiring noisier data. Therefore, the first task is the processing of
these data to correct and downsample large amounts of data. In addition, the
hyperspectral signatures of grape varieties are very similar. In this work, a
Convolutional Neural Network (CNN) is proposed for classifying seventeen
varieties of red and white grape variants. Rather than classifying single
samples, these are processed together with their neighbourhood. Hence, the
extraction of spatial and spectral features is addressed with 1) a spatial
attention layer and 2) Inception blocks. The pipeline goes from processing to
dataset elaboration, finishing with the training phase. The fitted model is
evaluated in terms of response time, accuracy and data separability, and
compared with other state-of-the-art CNNs for classifying hyperspectral data.
Our network was proven to be much more lightweight with a reduced number of
input bands, a lower number of trainable weights and therefore, reduced
training time. Despite this, the evaluated metrics showed much better results
for our network (~99% overall accuracy), in comparison with previous works
barely achieving 81% OA.
