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.

利用卷积神经网络对红白葡萄品种进行分类，通过空间注意力层和 Inception 块进行空间和光谱特征提取，相对于其他卷积神经网络在处理高光谱数据分类时，网络模型更轻量，并且表现出更好的准确性，和以前研究相比，准确率提高到 99％。