  One of the major goals of tomorrow's agriculture is to increase agricultural
productivity but above all the quality of production while significantly
reducing the use of inputs. Meeting this goal is a real scientific and
technological challenge. Smart farming is among the promising approaches that
can lead to interesting solutions for vineyard management and reduce the
environmental impact. Automatic vine disease detection can increase efficiency
and flexibility in managing vineyard crops, while reducing the chemical inputs.
This is needed today more than ever, as the use of pesticides is coming under
increasing scrutiny and control. The goal is to map diseased areas in the
vineyard for fast and precise treatment, thus guaranteeing the maintenance of a
healthy state of the vine which is very important for yield management. To
tackle this problem, a method is proposed here for vine disease detection using
a deep learning segmentation approach on Unmanned Aerial Vehicle (UAV) images.
The method is based on the combination of the visible and infrared images
obtained from two different sensors. A new image registration method was
developed to align visible and infrared images, enabling fusion of the
information from the two sensors. A fully convolutional neural network approach
uses this information to classify each pixel according to different instances,
namely, shadow, ground, healthy and symptom. The proposed method achieved more
than 92% of detection at grapevine-level and 87% at leaf level, showing
promising perspectives for computer aided disease detection in vineyards.
