Classification of grapevine varieties using UAV hyperspectral imaging

• URL: http://arxiv.org/abs/2401.12851v1
• Date: Tue, 23 Jan 2024 15:35:50 GMT
• Title: Classification of grapevine varieties using UAV hyperspectral imaging
• Authors: Alfonso L\'opez, Carlos Javier Ogayar, Francisco Ram\'on Feito, Joaquim Jo\~ao Sousa
• Abstract summary: The classification of different grapevine varieties is a relevant phenotyping task in Precision Viticulture. Unmanned Aerial Vehicles (UAVs) provide a more efficient and less prohibitive approach to collecting hyperspectral data. In this work, a Convolutional Neural Network (CNN) is proposed for classifying seventeen varieties of red and white grape variants.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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