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.

翻译：精准葡萄栽培中不同葡萄品种的分类是一项重要的表型分析任务，因为它有助于估算专用于不同品种的葡萄园行生长情况，并涉及葡萄酒行业的其他应用。这一任务可通过破坏性方法实现，但需要在实验室中完成数据采集和分析等耗时工作。然而，无人机提供了一种更高效且成本更低的途径来采集高光谱数据，尽管获取的数据噪声较大。因此，首要任务是对这些数据进行处理，以校正和降采样大量数据。此外，葡萄品种的高光谱特征非常相似。本研究提出了一种卷积神经网络（CNN），用于对十七个红葡萄和白葡萄变种进行分类。该方法并非对单个样本进行分类，而是结合其邻域信息进行处理。因此，通过1）空间注意力层和2）Inception模块来实现空间和光谱特征的提取。整个流程从数据处理到数据集构建，最终完成训练阶段。拟合模型在响应时间、准确性和数据可分性方面进行了评估，并与用于高光谱数据分类的其他先进CNN进行了比较。实验证明，我们的网络具有更低的输入波段数量、更少的可训练权重，因此训练时间更短，整体更为轻量。尽管如此，评估指标显示我们的网络结果远优于此前仅能达到约81%总体准确率的研究，实现了约99%的总体准确率。