Digital agriculture has evolved significantly over the last few years due to the technological developments in automation and computational intelligence applied to the agricultural sector, including vineyards which are a relevant crop in the Mediterranean region. In this paper, a study of semantic segmentation for vine detection in real-world vineyards is presented by exploring state-of-the-art deep segmentation networks and conventional unsupervised methods. Camera data was collected on vineyards using an Unmanned Aerial System (UAS) equipped with a dual imaging sensor payload, namely a high-resolution color camera and a five-band multispectral and thermal camera. Extensive experiments of the segmentation networks and unsupervised methods have been performed on multimodal datasets representing three distinct vineyards located in the central region of Portugal. The reported results indicate that the best segmentation performances are obtained with deep networks, while traditional (non-deep) approaches using the NIR band shown competitive results. The results also show that multimodality slightly improves the performance of vine segmentation but the NIR spectrum alone generally is sufficient on most of the datasets. The code and dataset are publicly available on \url{https://github.com/Cybonic/DL_vineyard_segmentation_study.git

中文翻译：

---

多光谱葡萄园分割：一种深度学习方法

由于应用于农业部门（包括作为地中海地区相关作物的葡萄园）的自动化和计算智能技术的发展，数字农业在过去几年中取得了重大进展。在本文中，通过探索最先进的深度分割网络和传统的无监督方法，对现实世界葡萄园中葡萄藤检测的语义分割进行了研究。使用配备双成像传感器有效载荷的无人机系统 (UAS) 在葡萄园收集相机数据，即高分辨率彩色相机和五波段多光谱和热成像相机。已经在代表位于葡萄牙中部地区的三个不同葡萄园的多模式数据集上进行了分割网络和无监督方法的广泛实验。报告的结果表明，最好的分割性能是通过深度网络获得的，而使用 NIR 波段的传统（非深度）方法显示出具有竞争力的结果。结果还表明，多模态略微提高了葡萄藤分割的性能，但在大多数数据集上，仅 NIR 光谱通常就足够了。代码和数据集在 \url{https://github.com/Cybonic/DL_vineyard_segmentation_study.git 上公开可用 而使用 NIR 波段的传统（非深度）方法显示出具有竞争力的结果。结果还表明，多模态略微提高了葡萄藤分割的性能，但在大多数数据集上，仅 NIR 光谱通常就足够了。代码和数据集在 \url{https://github.com/Cybonic/DL_vineyard_segmentation_study.git 上公开可用 而使用 NIR 波段的传统（非深度）方法显示出具有竞争力的结果。结果还表明，多模态略微提高了葡萄藤分割的性能，但在大多数数据集上，仅 NIR 光谱通常就足够了。代码和数据集在 \url{https://github.com/Cybonic/DL_vineyard_segmentation_study.git 上公开可用