Multispectral Vineyard Segmentation: A Deep Learning approach.

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 this https URL