Over the last few years, several Convolutional Neural Networks for object detection have
been proposed, characterised by different accuracy and speed. In viticulture, yield …

The potential of precision viticulture has been highlighted since the first studies performed in
the context of viticulture, but especially in the last decade there have been excellent results …

Defining the most profitable remote sensing platforms is a difficult decision-making process,
as it requires agronomic and economic considerations. In this paper, the price and …

Yield assessment is a highly relevant task for the wine industry. The goal of this work was to
develop a new algorithm for early yield prediction in different grapevine varieties using …

Addressing the intrinsic variability within vineyards is a key factor to perform precision
viticulture management. To this aim, new and more reliable methods for vineyard monitoring …

Vegetation indices (VI), especially the normalised difference vegetation index (NDVI), are
used to determine management units (MU) and to explain quantity and quality of vineyard …

Precision viticulture benefits from the accurate detection of vineyard vegetation from remote
sensing, without a priori knowledge of vine locations. Vineyard detection enables efficient …

Over the last few years, several versions of the machine learning algorithm, YOLO, have
been developed, improving its performance. In this study, the last official version of YOLO …

The digital techniques, spreading across the agriculture sector, allow access to helpful
information from fields, crops, and routine operations. Additionally, artificial intelligence is …

Spatial variability represents a challenge for viticulture. Variable-rate fertilization is a
promising technology to manage different vigor and yield zones within the. In this study, on …